From bb2facdc015d5e18d0163c27c71a566948431d8e Mon Sep 17 00:00:00 2001 From: Oceania2018 Date: Thu, 15 Jul 2021 23:39:56 -0500 Subject: [PATCH 01/39] align with v0.60. --- SciSharp STACK Examples.sln | 82 ++++++++++++++++++- .../BasicModels/KMeansClustering.cs | 6 +- .../BasicModels/LinearRegression.cs | 4 +- .../BasicModels/LinearRegressionEager.cs | 5 +- .../BasicModels/LinearRegressionKeras.cs | 2 +- .../BasicModels/LogisticRegression.cs | 12 +-- .../BasicModels/LogisticRegressionEager.cs | 8 +- .../BasicModels/NaiveBayesClassifier.cs | 22 +++-- .../BasicModels/NearestNeighbor.cs | 8 +- src/TensorFlowNET.Examples/BasicOperations.cs | 12 +-- src/TensorFlowNET.Examples/HelloWorld.cs | 6 +- .../ImageProcessing/CnnInYourOwnData.cs | 22 ++--- .../ImageProcessing/DigitRecognitionCNN.cs | 16 ++-- .../DigitRecognitionCnnEager.cs | 2 +- .../ImageProcessing/DigitRecognitionLSTM.cs | 4 +- .../ImageProcessing/DigitRecognitionNN.cs | 2 +- .../ImageProcessing/DigitRecognitionRNN.cs | 10 +-- .../DigitRecognitionRnnKeras.cs | 2 +- .../ImageClassificationKeras.cs | 4 +- .../ImageRecognitionInception.cs | 4 +- .../ImageProcessing/InceptionArchGoogLeNet.cs | 10 +-- .../ImageProcessing/MnistCnnKerasSubclass.cs | 2 +- .../MnistFnnKerasFunctional.cs | 6 +- .../ImageProcessing/ToyResNet.cs | 2 +- .../TransferLearningWithInceptionV3.cs | 16 ++-- .../NeuralNetworks/FullyConnectedEager.cs | 2 +- .../NeuralNetworks/FullyConnectedKeras.cs | 2 +- .../NeuralNetworks/NeuralNetXor.cs | 10 +-- .../NeuralNetworks/NeuralNetXorKeras.cs | 8 +- .../ObjectDetection/DetectInMobilenet.cs | 8 +- .../ObjectDetection/YoloCoco.cs | 44 ++++++---- .../TensorFlowNET.Examples.csproj | 9 +- .../BinaryTextClassification.cs | 4 +- .../TextProcessing/CnnTextClassification.cs | 6 +- .../TextProcessing/DataHelpers.cs | 11 +-- .../TextProcessing/Word2Vec.cs | 7 +- .../TextProcessing/cnn_models/CharCnn.cs | 6 +- .../TextProcessing/cnn_models/VdCnn.cs | 6 +- .../TextProcessing/cnn_models/WordCnn.cs | 8 +- 39 files changed, 246 insertions(+), 154 deletions(-) diff --git a/SciSharp STACK Examples.sln b/SciSharp STACK Examples.sln index 8a375d8..fefd098 100644 --- a/SciSharp STACK Examples.sln +++ b/SciSharp STACK Examples.sln @@ -1,7 +1,7 @@  Microsoft Visual Studio Solution File, Format Version 12.00 -# Visual Studio Version 16 -VisualStudioVersion = 16.0.29326.143 +# Visual Studio Version 17 +VisualStudioVersion = 17.0.31423.177 MinimumVisualStudioVersion = 10.0.40219.1 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Examples", "src\TensorFlowNET.Examples\TensorFlowNET.Examples.csproj", "{3AC62662-7861-4C21-A402-82864F2A8AF7}" EndProject @@ -9,6 +9,12 @@ Project("{6EC3EE1D-3C4E-46DD-8F32-0CC8E7565705}") = "TensorFlowNET.Examples.FSha EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SharpCV.Examples", "src\SharpCV.Examples\SharpCV.Examples.csproj", "{935764FA-5297-4E49-945C-D04F1EF84EAF}" EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = 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b/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs index 4ada838..affe314 100644 --- a/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs +++ b/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs @@ -14,9 +14,9 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System.Diagnostics; using Tensorflow; +using Tensorflow.NumPy; using static Tensorflow.Binding; namespace TensorFlowNET.Examples @@ -132,7 +132,7 @@ public void Train(Session sess) print($"Step {i}, Avg Distance: {result[1]} Elapse: {sw.ElapsedMilliseconds}ms"); } - var idx = result[2].Data(); + var idx = result[2].ToArray(); // Assign a label to each centroid // Count total number of labels per centroid, using the label of each training @@ -140,7 +140,7 @@ public void Train(Session sess) var counts = np.zeros((k, num_classes), np.float32); sw.Start(); - foreach (var i in range(idx.Count)) + foreach (var i in range(idx.Length)) { var x = mnist.Train.Labels[i]; counts[idx[i]] += x; diff --git a/src/TensorFlowNET.Examples/BasicModels/LinearRegression.cs b/src/TensorFlowNET.Examples/BasicModels/LinearRegression.cs index 09bdb99..ece0462 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LinearRegression.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LinearRegression.cs @@ -14,8 +14,8 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System; +using Tensorflow.NumPy; using static Tensorflow.Binding; namespace TensorFlowNET.Examples @@ -117,7 +117,7 @@ public override void PrepareData() 7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f); train_Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f, 2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f); - n_samples = train_X.shape[0]; + n_samples = (int)train_X.shape[0]; } } } diff --git a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionEager.cs b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionEager.cs index 17e6f79..ade0f06 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionEager.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionEager.cs @@ -14,7 +14,7 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; @@ -32,7 +32,6 @@ public class LinearRegressionEager : SciSharpExample, IExample float learning_rate = 0.01f; int display_step = 100; - NumPyRandom rng = np.random; NDArray train_X, train_Y; int n_samples; @@ -99,7 +98,7 @@ public override void PrepareData() 7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f); train_Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f, 2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f); - n_samples = train_X.shape[0]; + n_samples = (int)train_X.shape[0]; } } } diff --git a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs index da1d57c..6425cc4 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs @@ -14,7 +14,7 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; diff --git a/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs b/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs index 8c71e8b..c29f5a6 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs @@ -14,10 +14,10 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System.Diagnostics; using System.IO; using Tensorflow; +using Tensorflow.NumPy; using static Tensorflow.Binding; namespace TensorFlowNET.Examples @@ -69,12 +69,12 @@ public override void PrepareData() public override void Train() { // tf Graph Input - var x = tf.placeholder(tf.float32, new TensorShape(-1, 784)); // mnist data image of shape 28*28=784 - var y = tf.placeholder(tf.float32, new TensorShape(-1, 10)); // 0-9 digits recognition => 10 classes + var x = tf.placeholder(tf.float32, (-1, 784)); // mnist data image of shape 28*28=784 + var y = tf.placeholder(tf.float32, (-1, 10)); // 0-9 digits recognition => 10 classes // Set model weights - var W = tf.Variable(tf.zeros(new Shape(784, 10))); - var b = tf.Variable(tf.zeros(new Shape(10))); + var W = tf.Variable(tf.zeros((784, 10))); + var b = tf.Variable(tf.zeros(10)); // Construct model var pred = tf.nn.softmax(tf.matmul(x, W) + b); // Softmax @@ -173,7 +173,7 @@ public override void Predict() var (batch_xs, batch_ys) = mnist.Train.GetNextBatch(10); var results = sess.run(output, new FeedItem(input, batch_xs[np.arange(1)])); - if (results[0].argmax() == (batch_ys[0] as NDArray).argmax()) + if (np.argmax(results[0]) == np.argmax(batch_ys[0])) print("predicted OK!"); else throw new ValueError("predict error, should be 90% accuracy"); diff --git a/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs b/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs index bd269e3..a952b6b 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs @@ -14,11 +14,11 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System; using System.Diagnostics; using System.IO; using Tensorflow; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; @@ -142,8 +142,8 @@ public void RunEagerMode() public override void Train() { // tf Graph Input - var x = tf.placeholder(tf.float32, new TensorShape(-1, 784)); // mnist data image of shape 28*28=784 - var y = tf.placeholder(tf.float32, new TensorShape(-1, 10)); // 0-9 digits recognition => 10 classes + var x = tf.placeholder(tf.float32, (-1, 784)); // mnist data image of shape 28*28=784 + var y = tf.placeholder(tf.float32, (-1, 10)); // 0-9 digits recognition => 10 classes // Set model weights var W = tf.Variable(tf.zeros(new Shape(784, 10))); @@ -250,7 +250,7 @@ public override void Predict() var (batch_xs, batch_ys) = mnist.Train.GetNextBatch(10); var results = sess.run(output, new FeedItem(input, batch_xs[np.arange(1)])); - if (results[0].argmax() == (batch_ys[0] as NDArray).argmax()) + if (np.argmax(results[0]) == np.argmax(batch_ys[0])) print("predicted OK!"); else throw new ValueError("predict error, should be 90% accuracy"); diff --git a/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs b/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs index 53b3a2c..b14d6cf 100644 --- a/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs +++ b/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs @@ -14,12 +14,12 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System; using System.Collections.Generic; using System.IO; using Tensorflow; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using static Tensorflow.Binding; namespace TensorFlowNET.Examples @@ -49,19 +49,17 @@ public bool Run() fit(X, y); // Create a regular grid and classify each point - float x_min = X.amin(0).Data()[0] - 0.5f; - float y_min = X.amin(0).Data()[1] - 0.5f; - float x_max = X.amax(0).Data()[1] + 0.5f; - float y_max = X.amax(0).Data()[1] + 0.5f; + float x_min = np.amin(X, 0).ToArray()[0] - 0.5f; + float y_min = np.amin(X, 0).ToArray()[1] - 0.5f; + float x_max = np.amax(X, 0).ToArray()[1] + 0.5f; + float y_max = np.amax(X, 0).ToArray()[1] + 0.5f; var (xx, yy) = np.meshgrid(np.linspace(x_min, x_max, 30), np.linspace(y_min, y_max, 30)); using (var sess = tf.Session()) { - //var samples = np.vstack(xx.ravel(), yy.ravel()); - //samples = np.transpose(samples); - var array = np.Load(Path.Join("nb", "nb_example.npy")); - var samples = np.array(array).astype(np.float32); - var Z = sess.run(predict(samples)); + //var array = np.Load(Path.Join("nb", "nb_example.npy")); + //var samples = np.array(array).astype(np.float32); + //var Z = sess.run(predict(samples)); } return true; @@ -73,14 +71,14 @@ public void fit(NDArray X, NDArray y) var dic = new Dictionary>>(); // Init uy in dic - foreach (int uy in unique_y.Data()) + foreach (int uy in unique_y.ToArray()) { dic.Add(uy, new List>()); } // Separate training points by class // Shape : nb_classes * nb_samples * nb_features int maxCount = 0; - for (int i = 0; i < y.size; i++) + for (int i = 0; i < (int)y.size; i++) { var curClass = y[i]; var l = dic[curClass]; diff --git a/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs b/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs index dfbbd4c..33ef268 100644 --- a/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs +++ b/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs @@ -14,8 +14,8 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using Tensorflow; +using Tensorflow.NumPy; using static Tensorflow.Binding; namespace TensorFlowNET.Examples @@ -46,8 +46,8 @@ public bool Run() { tf.compat.v1.disable_eager_execution(); // tf Graph Input - var xtr = tf.placeholder(tf.float32, new TensorShape(-1, 784)); - var xte = tf.placeholder(tf.float32, new TensorShape(784)); + var xtr = tf.placeholder(tf.float32, (-1, 784)); + var xte = tf.placeholder(tf.float32, 784); // Nearest Neighbor calculation using L1 Distance // Calculate L1 Distance @@ -65,7 +65,7 @@ public bool Run() PrepareData(); - foreach (int i in range(Xte.shape[0])) + foreach (int i in range((int)Xte.shape[0])) { // Get nearest neighbor long nn_index = sess.run(pred, (xtr, Xtr), (xte, Xte[i])); diff --git a/src/TensorFlowNET.Examples/BasicOperations.cs b/src/TensorFlowNET.Examples/BasicOperations.cs index 6d218d3..3b91305 100644 --- a/src/TensorFlowNET.Examples/BasicOperations.cs +++ b/src/TensorFlowNET.Examples/BasicOperations.cs @@ -32,14 +32,14 @@ public bool Run() var div = tf.divide(a, b); // Access tensors value. - print("add =", add.numpy()); - print("sub =", sub.numpy()); - print("mul =", mul.numpy()); - print("div =", div.numpy()); + print($"{(int)a} + {(int)b} = {(int)add}"); + print($"{(int)a} - {(int)b} = {(int)sub}"); + print($"{(int)a} * {(int)b} = {(int)mul}"); + print($"{(int)a} / {(int)b} = {(double)div}"); // Some more operations. - var mean = tf.reduce_mean(new[] { a, b, c }); - var sum = tf.reduce_sum(new[] { a, b, c }); + var mean = tf.reduce_mean(tf.constant(new[] { a, b, c })); + var sum = tf.reduce_sum(tf.constant(new[] { a, b, c })); // Access tensors value. diff --git a/src/TensorFlowNET.Examples/HelloWorld.cs b/src/TensorFlowNET.Examples/HelloWorld.cs index 8251a4c..6f4f994 100644 --- a/src/TensorFlowNET.Examples/HelloWorld.cs +++ b/src/TensorFlowNET.Examples/HelloWorld.cs @@ -1,4 +1,4 @@ -using NumSharp; +using Tensorflow.NumPy; using static Tensorflow.Binding; namespace TensorFlowNET.Examples @@ -32,9 +32,9 @@ of the Constant op. */ // tf.Tensor: shape=(), dtype=string, numpy=b'Hello, TensorFlow.NET!' print(hello); - var tensor = NDArray.AsStringArray(hello.numpy())[0]; + var tensor = hello.numpy(); - return str == tensor; + return tensor.ToString() == $"'{str}'"; } } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs index 9cbcd3c..e9c002d 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs @@ -15,7 +15,6 @@ limitations under the License. ******************************************************************************/ -using NumSharp; using SharpCV; using System; using System.Collections; @@ -27,6 +26,7 @@ limitations under the License. using System.Threading.Tasks; using Tensorflow; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using static SharpCV.Binding; using static Tensorflow.Binding; @@ -163,8 +163,8 @@ private void LoadImagesToNDArray() print("Load Labels To NDArray : OK!"); //Load Images - x_valid = np.zeros(ArrayFileName_Validation.Length, img_h, img_w, n_channels); - x_test = np.zeros(ArrayFileName_Test.Length, img_h, img_w, n_channels); + x_valid = np.zeros((ArrayFileName_Validation.Length, img_h, img_w, n_channels)); + x_test = np.zeros((ArrayFileName_Test.Length, img_h, img_w, n_channels)); LoadImage(ArrayFileName_Validation, x_valid, "validation"); LoadImage(ArrayFileName_Test, x_test, "test"); print("Load Images To NDArray : OK!"); @@ -351,8 +351,8 @@ private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride { return tf_with(tf.variable_scope(name), delegate { - var num_in_channel = x.shape[x.NDims - 1]; - var shape = new[] { filter_size, filter_size, num_in_channel, num_filters }; + var num_in_channel = x.shape[x.ndim - 1]; + var shape = new int[] { filter_size, filter_size, (int)num_in_channel, num_filters }; var W = weight_variable("W", shape); // var tf.summary.histogram("weight", W); var b = bias_variable("b", new[] { num_filters }); @@ -391,7 +391,7 @@ private Tensor flatten_layer(Tensor layer) { return tf_with(tf.variable_scope("Flatten_layer"), delegate { - var layer_shape = layer.TensorShape; + var layer_shape = layer.shape; var num_features = layer_shape[new Slice(1, 4)].size; var layer_flat = tf.reshape(layer, new[] { -1, num_features }); @@ -442,7 +442,7 @@ private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = tr { var in_dim = x.shape[1]; - var W = weight_variable("W_" + name, shape: new[] { in_dim, num_units }); + var W = weight_variable("W_" + name, shape: new[] { (int)in_dim, num_units }); var b = bias_variable("b_" + name, new[] { num_units }); var layer = tf.matmul(x, W.AsTensor()) + b.AsTensor(); @@ -557,7 +557,7 @@ void Write_Dictionary(string path, Dictionary mydic) (NDArray, NDArray) Randomize(NDArray x, NDArray y) { - var perm = np.random.permutation(y.shape[0]); + var perm = np.random.permutation((int)y.shape[0]); np.random.shuffle(perm); return (x[perm], y[perm]); } @@ -572,12 +572,12 @@ void Write_Dictionary(string path, Dictionary mydic) (NDArray, NDArray) GetNextBatch(Session sess, string[] x, NDArray y, int start, int end) { - NDArray x_batch = np.zeros(end - start, img_h, img_w, n_channels); + NDArray x_batch = np.zeros((end - start, img_h, img_w, n_channels)); int n = 0; for (int i = start; i < end; i++) { NDArray img4 = cv2.imread(x[i], IMREAD_COLOR.IMREAD_GRAYSCALE); - img4 = img4.reshape(img4.shape[0], img4.shape[1], 1); + img4 = img4.reshape((img4.shape[0], img4.shape[1], 1)); x_batch[n] = sess.run(normalized, (decodeJpeg, img4)); n++; } @@ -609,7 +609,7 @@ void TestDataOutput() string real_str = Dict_Label[real]; string predict_str = Dict_Label[predict]; print((i + 1).ToString() + "|" + "result:" + result + "|" + "real_str:" + real_str + "|" - + "predict_str:" + predict_str + "|" + "probability:" + probability.GetSingle().ToString() + "|" + + "predict_str:" + predict_str + "|" + "probability:" + (float)probability + "|" + "fileName:" + fileName); } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs index 01c7e96..3713056 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs @@ -14,10 +14,10 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System.Diagnostics; using System.IO; using Tensorflow; +using Tensorflow.NumPy; using static Tensorflow.Binding; namespace TensorFlowNET.Examples @@ -140,7 +140,7 @@ public override void Train() { // Number of training iterations in each epoch - var num_tr_iter = y_train.shape[0] / batch_size; + var num_tr_iter = (int)y_train.shape[0] / batch_size; var init = tf.global_variables_initializer(); sess.run(init); @@ -160,7 +160,7 @@ public override void Train() { var start = iteration * batch_size; var end = (iteration + 1) * batch_size; - var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end); + var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, (int)start, (int)end); // Run optimization op (backprop) sess.run(optimizer, (x, x_batch), (y, y_batch)); @@ -230,8 +230,8 @@ private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride return tf_with(tf.variable_scope(name), delegate { - var num_in_channel = x.shape[x.NDims - 1]; - var shape = new[] { filter_size, filter_size, num_in_channel, num_filters }; + var num_in_channel = x.shape[x.ndim - 1]; + var shape = new int[] { filter_size, filter_size, (int)num_in_channel, num_filters }; var W = weight_variable("W", shape); // var tf.summary.histogram("weight", W); var b = bias_variable("b", new[] { num_filters }); @@ -270,7 +270,7 @@ private Tensor flatten_layer(Tensor layer) { return tf_with(tf.variable_scope("Flatten_layer"), delegate { - var layer_shape = layer.TensorShape; + var layer_shape = layer.shape; var num_features = layer_shape[new Slice(1, 4)].size; var layer_flat = tf.reshape(layer, new[] { -1, num_features }); @@ -321,7 +321,7 @@ private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = tr { var in_dim = x.shape[1]; - var W = weight_variable("W_" + name, shape: new[] { in_dim, num_units }); + var W = weight_variable("W_" + name, shape: new[] { (int)in_dim, num_units }); var b = bias_variable("b_" + name, new[] { num_units }); var layer = tf.matmul(x, W.AsTensor()) + b.AsTensor(); @@ -355,7 +355,7 @@ public override void PrepareData() private (NDArray, NDArray) Reformat(NDArray x, NDArray y) { var (img_size, num_ch, num_class) = (np.sqrt(x.shape[1]).astype(np.int32), 1, len(np.unique(np.argmax(y, 1)))); - var dataset = x.reshape(x.shape[0], img_size, img_size, num_ch).astype(np.float32); + var dataset = x.reshape((x.shape[0], img_size, img_size, num_ch)).astype(np.float32); //y[0] = np.arange(num_class) == y[0]; //var labels = (np.arange(num_class) == y.reshape(y.shape[0], 1, y.shape[1])).astype(np.float32); return (dataset, y); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs index c65b2b7..72b84e5 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs @@ -14,10 +14,10 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System.Linq; using Tensorflow; using Tensorflow.Keras.Optimizers; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs index fb128de..0e1b709 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs @@ -133,7 +133,7 @@ public override void Train() var (batch_x, batch_y) = mnist.Train.GetNextBatch(batch_size); // Reshape data to get 28 seq of 28 elements - batch_x = batch_x.reshape(batch_size, timesteps, num_input); + batch_x = batch_x.reshape((batch_size, timesteps, num_input)); // Run optimization op (backprop) sess.run(train_op, (X, batch_x), (Y, batch_y)); @@ -154,7 +154,7 @@ public override void Test() { // Calculate accuracy for 128 mnist test images var (x_test, y_test) = (mnist.Test.Data[":128"], mnist.Test.Labels[":128"]); - x_test = x_test.reshape(-1, timesteps, num_input); + x_test = x_test.reshape((-1, timesteps, num_input)); accuracy_test = sess.run(accuracy, new FeedItem(X, x_test), new FeedItem(Y, y_test)); print("---------------------------------------------------------"); print($"Testing Accuracy: {accuracy_test.ToString("P")}"); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs index 2ee42ae..5bfa570 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs @@ -127,7 +127,7 @@ public override void PrepareData() public override void Train() { // Number of training iterations in each epoch - var num_tr_iter = mnist.Train.Labels.shape[0] / batch_size; + var num_tr_iter = (int)mnist.Train.Labels.shape[0] / batch_size; var init = tf.global_variables_initializer(); sess.run(init); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs index bdfef1a..4865a3f 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs @@ -14,8 +14,8 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using Tensorflow; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; @@ -102,7 +102,7 @@ public override void Train() float accuracy_val = 0f; // Number of training iterations in each epoch - var n_batches = y_train.shape[0] / batch_size; + var n_batches = (int)y_train.shape[0] / batch_size; var init = tf.global_variables_initializer(); sess.run(init); @@ -118,7 +118,7 @@ public override void Train() var start = iteration * batch_size; var end = (iteration + 1) * batch_size; var (X_train, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end); - X_train = X_train.reshape(-1, n_steps, n_inputs); + X_train = X_train.reshape((-1, n_steps, n_inputs)); y_batch = np.argmax(y_batch, axis: 1); // Run optimization op (backprop) sess.run(optimizer, new FeedItem(X, X_train), new FeedItem(y, y_batch)); @@ -158,9 +158,9 @@ public override void PrepareData() (x_test, y_test) = (mnist.Test.Data, mnist.Test.Labels); y_valid = np.argmax(y_valid, axis: 1); - x_valid = x_valid.reshape(-1, n_steps, n_inputs); + x_valid = np.reshape(x_valid, (- 1, n_steps, n_inputs)); y_test = np.argmax(y_test, axis: 1); - x_test = x_test.reshape(-1, n_steps, n_inputs); + x_test = np.reshape(x_test, (-1, n_steps, n_inputs)); print("Size of:"); print($"- Training-set:\t\t{len(mnist.Train.Data)}"); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs index 81a4e83..c038777 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs @@ -14,12 +14,12 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System.Linq; using Tensorflow; using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.Engine; using Tensorflow.Keras.Optimizers; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs index 77c3daa..6c5b05c 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs @@ -17,7 +17,7 @@ public class ImageClassificationKeras : SciSharpExample, IExample { int batch_size = 32; int epochs = 3; - TensorShape img_dim = (180, 180); + Shape img_dim = (180, 180); IDatasetV2 train_ds, val_ds; Model model; @@ -100,7 +100,7 @@ public override void PrepareData() foreach (var (img, label) in train_ds) { - print($"images: {img.TensorShape}"); + print($"images: {img.shape}"); print($"labels: {label.numpy()}"); } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs index e8a1537..ff571b5 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs @@ -1,10 +1,10 @@ -using NumSharp; -using System.Collections.Generic; +using System.Collections.Generic; using System.Diagnostics; using System.Drawing; using System.IO; using Tensorflow; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using static Tensorflow.Binding; using Console = Colorful.Console; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs b/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs index 4fa52dd..7eef4e7 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs @@ -1,9 +1,9 @@ -using NumSharp; -using System; +using System; using System.IO; using System.Linq; using Tensorflow; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using static Tensorflow.Binding; namespace TensorFlowNET.Examples @@ -59,9 +59,9 @@ public bool Run() results = np.squeeze(results); - var argsort = results.argsort(); - var top_k = argsort.Data() - .Skip(results.size - 5) + var argsort = np.argsort(results); + var top_k = argsort.ToArray() + .Skip((int)results.size - 5) .Reverse() .ToArray(); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index f1bec2d..fdee5fe 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -14,12 +14,12 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System.Linq; using Tensorflow; using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.Engine; using Tensorflow.Keras.Optimizers; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs index 03cff8c..2c1dcd9 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs @@ -14,8 +14,8 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using Tensorflow.Keras.Engine; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; @@ -53,8 +53,8 @@ public bool Run() public override void PrepareData() { (x_train, y_train, x_test, y_test) = keras.datasets.mnist.load_data(); - x_train = x_train.reshape(60000, 784) / 255f; - x_test = x_test.reshape(10000, 784) / 255f; + x_train = x_train.reshape((60000, 784)) / 255f; + x_test = x_test.reshape((10000, 784)) / 255f; } public override void BuildModel() diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs b/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs index 69317c2..6e568db 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs @@ -14,10 +14,10 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using Tensorflow; using Tensorflow.Keras.Engine; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs index b570f44..5a920a5 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs @@ -15,7 +15,6 @@ limitations under the License. ******************************************************************************/ using Google.Protobuf; -using NumSharp; using System; using System.Collections.Generic; using System.Diagnostics; @@ -24,6 +23,7 @@ limitations under the License. using System.Threading.Tasks; using Tensorflow; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using static Tensorflow.Binding; namespace TensorFlowNET.Examples @@ -177,15 +177,15 @@ public bool Run() private (Operation, Tensor, Tensor, Tensor, Tensor) add_final_retrain_ops(int class_count, string final_tensor_name, Tensor bottleneck_tensor, bool quantize_layer, bool is_training) { - var (batch_size, bottleneck_tensor_size) = (bottleneck_tensor.TensorShape.dims[0], bottleneck_tensor.TensorShape.dims[1]); + var (batch_size, bottleneck_tensor_size) = (bottleneck_tensor.shape.dims[0], bottleneck_tensor.shape.dims[1]); tf_with(tf.name_scope("input"), scope => { bottleneck_input = tf.placeholder_with_default( bottleneck_tensor, - shape: bottleneck_tensor.TensorShape.dims, + shape: bottleneck_tensor.shape, name: "BottleneckInputPlaceholder"); - ground_truth_input = tf.placeholder(tf.int64, new TensorShape(batch_size), name: "GroundTruthInput"); + ground_truth_input = tf.placeholder(tf.int64, new Shape(batch_size), name: "GroundTruthInput"); }); // Organizing the following ops so they are easier to see in TensorBoard. @@ -196,7 +196,7 @@ public bool Run() IVariableV1 layer_weights = null; tf_with(tf.name_scope("weights"), delegate { - var initial_value = tf.truncated_normal(new int[] { bottleneck_tensor_size, class_count }, stddev: 0.001f); + var initial_value = tf.truncated_normal((bottleneck_tensor_size, (long)class_count), stddev: 0.001f); layer_weights = tf.Variable(initial_value, name: "final_weights"); variable_summaries(layer_weights.AsTensor()); }); @@ -204,7 +204,7 @@ public bool Run() IVariableV1 layer_biases = null; tf_with(tf.name_scope("biases"), delegate { - layer_biases = tf.Variable(tf.zeros(new TensorShape(class_count)), name: "final_biases"); + layer_biases = tf.Variable(tf.zeros(new Shape(class_count)), name: "final_biases"); variable_summaries(layer_biases.AsTensor()); }); @@ -439,7 +439,7 @@ private void create_bottleneck_file(string bottleneck_path, Dictionary(); + var values = bottleneck_values.ToArray(); var bottleneck_string = string.Join(",", values); File.WriteAllText(bottleneck_path, bottleneck_string); } @@ -458,7 +458,7 @@ private NDArray run_bottleneck_on_image(Session sess, byte[] image_data, Tensor Tensor decoded_image_tensor, Tensor resized_input_tensor, Tensor bottleneck_tensor) { // First decode the JPEG image, resize it, and rescale the pixel values. - var resized_input_values = sess.run(decoded_image_tensor, new FeedItem(image_data_tensor, new Tensor(image_data, TF_DataType.TF_STRING))); + var resized_input_values = sess.run(decoded_image_tensor, new FeedItem(image_data_tensor, new NDArray(image_data))); // Then run it through the recognition network. var bottleneck_values = sess.run(bottleneck_tensor, new FeedItem(resized_input_tensor, resized_input_values))[0]; bottleneck_values = np.squeeze(bottleneck_values); diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs index 756a421..fb4da13 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs @@ -14,10 +14,10 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System.Linq; using Tensorflow; using Tensorflow.Keras.Optimizers; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs index 7309708..52824eb 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs @@ -14,13 +14,13 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System; using System.Linq; using Tensorflow; using Tensorflow.Keras; using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.Engine; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs index d0a8307..b0537e9 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs @@ -14,10 +14,10 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System; using Tensorflow; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; @@ -157,7 +157,7 @@ private float RunWithImportedGraph() sess.run(init); var step = 0; - var y_ = np.array(new int[] { 1, 0, 0, 1 }, dtype: np.int32); + var y_ = np.array(new int[] { 1, 0, 0, 1 }); while (step < num_steps) { // original python: @@ -179,8 +179,8 @@ private float RunWithBuiltGraph() { var graph = tf.Graph().as_default(); - var features = tf.placeholder(tf.float32, new TensorShape(4, 2)); - var labels = tf.placeholder(tf.int32, new TensorShape(4)); + var features = tf.placeholder(tf.float32, (4, 2)); + var labels = tf.placeholder(tf.int32, 4); var (train_op, loss, gs) = make_graph(features, labels); @@ -193,7 +193,7 @@ private float RunWithBuiltGraph() sess.run(init); var step = 0; - var y_ = np.array(new int[] { 1, 0, 0, 1 }, dtype: np.int32); + var y_ = np.array(new int[] { 1, 0, 0, 1 }); while (step < num_steps) { (_, step, loss_value) = sess.run((train_op, gs, loss), (features, data), (labels, y_)); diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs index 79ef906..ad3bc48 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs @@ -14,10 +14,8 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System; -using Tensorflow; -using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; @@ -48,7 +46,9 @@ public bool Run() model.add(keras.Input(2)); model.add(keras.layers.Dense(64, keras.activations.Relu)); model.add(keras.layers.Dense(1, keras.activations.Sigmoid)); - model.compile(keras.losses.MeanSquaredError(), keras.optimizers.Adam(), new[] { "accuracy" }); + model.compile(optimizer: keras.optimizers.Adam(), + loss: keras.losses.MeanSquaredError(), + new[] { "accuracy" }); model.fit(x, y, 1, 100); print(model.predict(x, 4)); diff --git a/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs b/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs index 11d3980..37c7238 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs @@ -14,7 +14,6 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System; using System.Drawing; using System.Drawing.Drawing2D; @@ -22,6 +21,7 @@ limitations under the License. using System.Linq; using Tensorflow; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using TensorFlowNET.Examples.Utility; using static Tensorflow.Binding; @@ -122,12 +122,12 @@ private void buildOutputImage(NDArray[] resultArr) // get bitmap Bitmap bitmap = new Bitmap(Path.Join(imageDir, "input.jpg")); - var scores = resultArr[2].AsIterator(); + var scores = resultArr[2].ToArray(); var boxes = resultArr[1].GetData(); var id = np.squeeze(resultArr[3]).GetData(); - for (int i = 0; i < scores.size; i++) + for (int i = 0; i < scores.Length; i++) { - float score = scores.MoveNext(); + float score = scores[i]; if (score > MIN_SCORE) { float top = boxes[i * 4] * bitmap.Height; diff --git a/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs b/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs index 8b2f2ad..b0d23bd 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs @@ -14,7 +14,6 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using SharpCV; using System; using System.Collections.Generic; @@ -22,6 +21,7 @@ limitations under the License. using System.Linq; using Tensorflow; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using static SharpCV.Binding; using static Tensorflow.Binding; @@ -93,16 +93,19 @@ public void PredictFromImage() { var original_image = cv2.imread(@"D:\SciSharp\SciSharp-Stack-Examples\data\images\cat_face.jpg"); original_image = cv2.cvtColor(original_image, ColorConversionCodes.COLOR_BGR2RGB); - var original_image_size = (original_image.shape[0], original_image.shape[1]); + var original_image_size = ((int)original_image.shape[0], (int)original_image.shape[1]); var image_data = image_preporcess(original_image, (input_size, input_size)); image_data = image_data[np.newaxis, Slice.Ellipsis]; var (pred_sbbox, pred_mbbox, pred_lbbox) = sess.run((return_tensors[1], return_tensors[2], return_tensors[3]), (return_tensors[0], image_data)); - var pred_bbox = np.concatenate((np.reshape(pred_sbbox, (-1, 5 + num_classes)), - np.reshape(pred_mbbox, (-1, 5 + num_classes)), - np.reshape(pred_lbbox, (-1, 5 + num_classes))), axis: 0); + var pred_bbox = np.concatenate(new[] + { + np.reshape(pred_sbbox, (-1, 5 + num_classes)), + np.reshape(pred_mbbox, (-1, 5 + num_classes)), + np.reshape(pred_lbbox, (-1, 5 + num_classes)) + }, axis: 0); var bboxes = postprocess_boxes(pred_bbox, original_image_size, input_size, 0.3f); var bboxess = nms(bboxes, 0.75f, method: "nms"); @@ -125,16 +128,19 @@ public void PredictFromVideo() var (loaded, frame) = vid.read(); while (loaded) { - var frame_size = (frame.shape[0], frame.shape[1]); + var frame_size = ((int)frame.shape[0], (int)frame.shape[1]); var image_data = image_preporcess(frame, (input_size, input_size)); image_data = image_data[np.newaxis, Slice.Ellipsis]; var (pred_sbbox, pred_mbbox, pred_lbbox) = sess.run((return_tensors[1], return_tensors[2], return_tensors[3]), (return_tensors[0], image_data)); - var pred_bbox = np.concatenate((np.reshape(pred_sbbox, (-1, 5 + num_classes)), - np.reshape(pred_mbbox, (-1, 5 + num_classes)), - np.reshape(pred_lbbox, (-1, 5 + num_classes))), axis: 0); + var pred_bbox = np.concatenate(new[] + { + np.reshape(pred_sbbox, (-1, 5 + num_classes)), + np.reshape(pred_mbbox, (-1, 5 + num_classes)), + np.reshape(pred_lbbox, (-1, 5 + num_classes)) + }, axis: 0); var bboxes = postprocess_boxes(pred_bbox, frame_size, input_size, 0.3f); var bboxess = nms(bboxes, 0.45f, method: "nms"); @@ -173,8 +179,11 @@ private NDArray postprocess_boxes(NDArray pred_bbox, (int, int) org_img_shape, f var pred_prob = pred_bbox[Slice.All, new Slice(5)]; // (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax) - var pred_coor = np.concatenate((pred_xywh[Slice.All, new Slice(stop: 2)] - pred_xywh[Slice.All, new Slice(2)] * 0.5f, - pred_xywh[Slice.All, new Slice(stop: 2)] + pred_xywh[Slice.All, new Slice(2)] * 0.5f), axis: -1); + var pred_coor = np.concatenate(new[] + { + pred_xywh[Slice.All, new Slice(stop: 2)] - pred_xywh[Slice.All, new Slice(2)] * 0.5f, + pred_xywh[Slice.All, new Slice(stop: 2)] + pred_xywh[Slice.All, new Slice(2)] * 0.5f + }, axis: -1); // (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org) var (org_h, org_w) = org_img_shape; @@ -186,8 +195,11 @@ private NDArray postprocess_boxes(NDArray pred_bbox, (int, int) org_img_shape, f pred_coor[Slice.All, new Slice(1, step: 2)] = 1.0 * (pred_coor[Slice.All, new Slice(1, step: 2)] - dh) / resize_ratio; // (3) clip some boxes those are out of range - pred_coor = np.concatenate((np.maximum(pred_coor[Slice.All, new Slice(stop: 2)], np.array(new[] { 0, 0 })), - np.minimum(pred_coor[Slice.All, new Slice(2)], np.array(new[] { org_w - 1, org_h - 1 }))), axis: -1); + pred_coor = np.concatenate(new[] + { + np.maximum(pred_coor[Slice.All, new Slice(stop: 2)], np.array(new[] { 0, 0 })), + np.minimum(pred_coor[Slice.All, new Slice(2)], np.array(new[] { org_w - 1, org_h - 1 })) + }, axis: -1); var invalid_mask = np.logical_or(pred_coor[Slice.All, 0] > pred_coor[Slice.All, 2], pred_coor[Slice.All, 1] > pred_coor[Slice.All, 3]); pred_coor[invalid_mask] = 0; @@ -210,7 +222,7 @@ private NDArray postprocess_boxes(NDArray pred_bbox, (int, int) org_img_shape, f private NDArray[] nms(NDArray bboxes, float iou_threshold, float sigma = 0.3f, string method = "nms") { - var classes_in_img = bboxes[Slice.All, 5].Data().Distinct().ToArray(); + var classes_in_img = bboxes[Slice.All, 5].ToArray().Distinct().ToArray(); var best_bboxes = new List(); foreach (var cls in classes_in_img) { @@ -231,7 +243,7 @@ private NDArray[] nms(NDArray bboxes, float iou_threshold, float sigma = 0.3f, s if (method == "nms") { - var iou_mask = (iou > iou_threshold).MakeGeneric(); + var iou_mask = iou > iou_threshold; if (iou_mask.ndim == 0) iou_mask = iou_mask.reshape(1); if (iou_mask.size > 0) @@ -283,7 +295,7 @@ private Mat draw_bbox(Mat image, NDArray[] bboxes) foreach (var (i, bbox) in enumerate(bboxes)) { - var coor = bbox[new Slice(stop: 4)].astype(NPTypeCode.Int32); + var coor = bbox[new Slice(stop: 4)].astype(np.int32); var fontScale = 0.5; float score = bbox[4]; var class_ind = (float)bbox[5]; diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 7e57cd9..2a8fe45 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -41,13 +41,16 @@ - - + - + + + + + diff --git a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs index c867d31..5f3c904 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs @@ -1,9 +1,9 @@ -using NumSharp; -using System; +using System; using System.Collections.Generic; using System.IO; using System.Linq; using Tensorflow; +using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; diff --git a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs index c0477b3..9ac4996 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs @@ -14,7 +14,6 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using NumSharp; using System; using System.Collections.Generic; using System.Diagnostics; @@ -22,6 +21,7 @@ limitations under the License. using System.Linq; using Tensorflow; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using Tensorflow.Sessions; using TensorFlowNET.Examples.Text; using static Tensorflow.Binding; @@ -80,7 +80,7 @@ public bool Run() private (NDArray, NDArray, NDArray, NDArray) train_test_split(NDArray x, NDArray y, float test_size = 0.3f) { Console.WriteLine("Splitting in Training and Testing data..."); - int len = x.shape[0]; + var len = x.shape[0]; //int classes = y.Data().Distinct().Count(); //int samples = len / classes; int train_size = (int)Math.Round(len * (1 - test_size)); @@ -309,7 +309,7 @@ public override void Predict() Tensor prediction = graph.get_operation_by_name("output/ArgMax"); // encode text into 100 dimensions var batches = batch_iter(test_x, test_y, BATCH_SIZE, 1).First(); - var input = batches.Item1[0].reshape(1, 100); + var input = batches.Item1[0].reshape((1, 100)); var result = sess.run(prediction, (x, input), (is_training, false)); } } diff --git a/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs b/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs index ef6c785..87b6b90 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs @@ -1,5 +1,4 @@ -using NumSharp; -using System; +using System; using System.Collections.Generic; using System.IO; using System.Linq; @@ -7,6 +6,7 @@ using System.Text; using System.Text.RegularExpressions; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using TensorFlowNET.Examples.Utility; namespace TensorFlowNET.Examples @@ -165,10 +165,11 @@ public static (int[][][], int[][]) pad_sequences(int[][][] sequences, int pad_to } int max_length_sentence = sequences.Select(x => x.Length).Max(); - (sequence_padded, _) = _pad_sequences(sequences, np.repeat(pad_tok, max_length_word).GetData().ToArray(), max_length_sentence); - (sequence_length, _) = _pad_sequences(sequence_length, 0, max_length_sentence); + //(sequence_padded, _) = _pad_sequences(sequences, np.repeat(pad_tok, max_length_word).GetData().ToArray(), max_length_sentence); + //(sequence_length, _) = _pad_sequences(sequence_length, 0, max_length_sentence); - return (sequence_padded, sequence_length); + //return (sequence_padded, sequence_length); + throw new NotImplementedException(""); } private static (int[][], int[]) _pad_sequences(int[][] sequences, int pad_tok, int max_length) diff --git a/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs b/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs index ccdf80e..e84a35e 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs @@ -1,9 +1,10 @@ -using NumSharp; +using System; using System.Collections.Generic; using System.IO; using System.Linq; using Tensorflow; using Tensorflow.Keras.Utils; +using Tensorflow.NumPy; using static Tensorflow.Binding; namespace TensorFlowNET.Examples @@ -101,8 +102,8 @@ from wi2 in wi.DefaultIfEmpty() var sim = sess.run(cosine_sim_op, (X, x_test)); foreach (var i in range(len(eval_words))) { - var nearest = (0f - sim[i]).argsort() - .Data() + var nearest = np.argsort(0f - sim[i]) + .ToArray() .Skip(1) .Take(top_k) .ToArray(); diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs index 22fbf06..440ea6e 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs @@ -13,9 +13,9 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) var num_filters = 256; var kernel_initializer = tf.truncated_normal_initializer(stddev: 0.05f); - var x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x"); - var y = tf.placeholder(tf.int32, new TensorShape(-1), name: "y"); - var is_training = tf.placeholder(tf.@bool, new TensorShape(), name: "is_training"); + var x = tf.placeholder(tf.int32, (-1, document_max_len), name: "x"); + var y = tf.placeholder(tf.int32, -1, name: "y"); + var is_training = tf.placeholder(tf.@bool, Shape.Null, name: "is_training"); var global_step = tf.Variable(0, trainable: false); var keep_prob = tf.where(is_training, 0.5f, 1.0f); diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs index f6132cb..5d80767 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs @@ -35,9 +35,9 @@ public VdCnn(int alphabet_size, int document_max_len, int num_class) cnn_initializer = keras.initializers.he_normal(); fc_initializer = tf.truncated_normal_initializer(stddev: 0.05f); - x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x"); - y = tf.placeholder(tf.int32, new TensorShape(-1), name: "y"); - is_training = tf.placeholder(tf.@bool, new TensorShape(), name: "is_training"); + x = tf.placeholder(tf.int32, (-1, document_max_len), name: "x"); + y = tf.placeholder(tf.int32, -1, name: "y"); + is_training = tf.placeholder(tf.@bool, Shape.Null, name: "is_training"); global_step = tf.Variable(0, trainable: false); // Embedding Layer diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs index f5c537f..c65030e 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs @@ -30,9 +30,9 @@ public WordCnn(int vocabulary_size, int document_max_len, int num_class) var filter_sizes = new int[3, 4, 5]; var num_filters = 100; - var x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x"); - var y = tf.placeholder(tf.int32, new TensorShape(-1), name: "y"); - var is_training = tf.placeholder(tf.@bool, new TensorShape(), name: "is_training"); + var x = tf.placeholder(tf.int32, (-1, document_max_len), name: "x"); + var y = tf.placeholder(tf.int32, -1, name: "y"); + var is_training = tf.placeholder(tf.@bool, Shape.Null, name: "is_training"); var global_step = tf.Variable(0, trainable: false); var keep_prob = tf.where(is_training, 0.5f, 1.0f); Tensor x_emb = null; @@ -66,7 +66,7 @@ public WordCnn(int vocabulary_size, int document_max_len, int num_class) } var h_pool = tf.concat(pooled_outputs, 3); - var h_pool_flat = tf.reshape(h_pool, new TensorShape(-1, num_filters * filter_sizes.Rank)); + var h_pool_flat = tf.reshape(h_pool, (-1, num_filters * filter_sizes.Rank)); Tensor h_drop = null; tf_with(tf.name_scope("dropout"), delegate { From 8d12286b27bf64a4f8dc07cc05284eb722bcbd77 Mon Sep 17 00:00:00 2001 From: Oceania2018 Date: Wed, 21 Jul 2021 22:15:23 -0500 Subject: [PATCH 02/39] fix example of CnnInYourOwnData. --- .../BasicModels/KMeansClustering.cs | 6 +++-- .../BasicModels/LogisticRegression.cs | 7 +++--- .../BasicModels/LogisticRegressionEager.cs | 6 ++--- .../BasicModels/NaiveBayesClassifier.cs | 2 +- .../BasicModels/NearestNeighbor.cs | 2 +- .../ImageProcessing/CnnInYourOwnData.cs | 25 +++++++++---------- .../ImageProcessing/DigitRecognitionCNN.cs | 11 ++++---- .../DigitRecognitionCnnEager.cs | 2 +- .../ImageProcessing/DigitRecognitionLSTM.cs | 2 +- .../ImageProcessing/DigitRecognitionNN.cs | 4 +-- .../DigitRecognitionRnnKeras.cs | 2 +- .../ImageProcessing/MnistCnnKerasSubclass.cs | 2 +- .../TransferLearningWithInceptionV3.cs | 2 +- .../NeuralNetworks/FullyConnectedEager.cs | 2 +- .../NeuralNetworks/FullyConnectedKeras.cs | 2 +- .../ObjectDetection/DetectInMobilenet.cs | 4 +-- .../ObjectDetection/YoloCoco.cs | 5 ++-- .../TensorFlowNET.Examples.csproj | 2 +- .../TextProcessing/cnn_models/CharCnn.cs | 2 +- .../TextProcessing/cnn_models/VdCnn.cs | 2 +- .../TextProcessing/cnn_models/WordCnn.cs | 2 +- 21 files changed, 48 insertions(+), 46 deletions(-) diff --git a/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs b/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs index affe314..766886f 100644 --- a/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs +++ b/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs @@ -18,6 +18,7 @@ limitations under the License. using Tensorflow; using Tensorflow.NumPy; using static Tensorflow.Binding; +using System; namespace TensorFlowNET.Examples { @@ -143,7 +144,8 @@ public void Train(Session sess) foreach (var i in range(idx.Length)) { var x = mnist.Train.Labels[i]; - counts[idx[i]] += x; + // counts[idx[i]] += x; + throw new NotImplementedException(""); } sw.Stop(); @@ -158,7 +160,7 @@ public void Train(Session sess) var cluster_label = tf.nn.embedding_lookup(labels_map, cluster_idx); // Compute accuracy - var correct_prediction = tf.equal(cluster_label, tf.cast(tf.argmax(Y, 1), tf.int32)); + var correct_prediction = tf.equal(cluster_label, tf.cast(tf.math.argmax(Y, 1), tf.int32)); var cast = tf.cast(correct_prediction, tf.float32); var accuracy_op = tf.reduce_mean(cast); diff --git a/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs b/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs index c29f5a6..0c11a39 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs @@ -52,9 +52,8 @@ public ExampleConfig InitConfig() public bool Run() { - tf.compat.v1.disable_eager_execution(); - PrepareData(); + tf.compat.v1.disable_eager_execution(); Train(); //Predict(); @@ -130,7 +129,7 @@ public override void Train() // SaveModel(sess); // Test model - var correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)); + var correct_prediction = tf.equal(tf.math.argmax(pred, 1), tf.math.argmax(y, 1)); // Calculate accuracy var acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)); accuracy = acc.eval(sess, (x, mnist.Test.Data), (y, mnist.Test.Labels)); @@ -173,7 +172,7 @@ public override void Predict() var (batch_xs, batch_ys) = mnist.Train.GetNextBatch(10); var results = sess.run(output, new FeedItem(input, batch_xs[np.arange(1)])); - if (np.argmax(results[0]) == np.argmax(batch_ys[0])) + if ((bool)(np.argmax(results[0]) == np.argmax(batch_ys[0]))) print("predicted OK!"); else throw new ValueError("predict error, should be 90% accuracy"); diff --git a/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs b/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs index a952b6b..b3aa35f 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs @@ -94,7 +94,7 @@ public void RunEagerMode() Func accuracy = (y_pred, y_true) => { // Predicted class is the index of highest score in prediction vector (i.e. argmax). - var correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); + var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); return tf.reduce_mean(tf.cast(correct_prediction, tf.float32)); }; @@ -205,7 +205,7 @@ public override void Train() // SaveModel(sess); // Test model - var correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)); + var correct_prediction = tf.equal(tf.math.argmax(pred, 1), tf.math.argmax(y, 1)); // Calculate accuracy var acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)); accuracy = acc.eval(sess, (x, mnist.Test.Data), (y, mnist.Test.Labels)); @@ -250,7 +250,7 @@ public override void Predict() var (batch_xs, batch_ys) = mnist.Train.GetNextBatch(10); var results = sess.run(output, new FeedItem(input, batch_xs[np.arange(1)])); - if (np.argmax(results[0]) == np.argmax(batch_ys[0])) + if ((bool)(np.argmax(results[0]) == np.argmax(batch_ys[0]))) print("predicted OK!"); else throw new ValueError("predict error, should be 90% accuracy"); diff --git a/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs b/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs index b14d6cf..4b46f23 100644 --- a/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs +++ b/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs @@ -71,7 +71,7 @@ public void fit(NDArray X, NDArray y) var dic = new Dictionary>>(); // Init uy in dic - foreach (int uy in unique_y.ToArray()) + foreach (int uy in unique_y.Item1.ToArray()) { dic.Add(uy, new List>()); } diff --git a/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs b/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs index 33ef268..43f9df4 100644 --- a/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs +++ b/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs @@ -76,7 +76,7 @@ public bool Run() print($"Test {i} Prediction: {np.argmax(Ytr[index])} True Class: {np.argmax(Yte[i])}"); // Calculate accuracy - if (np.argmax(Ytr[index]) == np.argmax(Yte[i])) + if ((bool)(np.argmax(Ytr[index]) == np.argmax(Yte[i]))) accuracy += 1f / Xte.shape[0]; } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs index e9c002d..a613d80 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs @@ -111,7 +111,7 @@ public ExampleConfig InitConfig() public bool Run() { tf.compat.v1.disable_eager_execution(); - + PrepareData(); BuildGraph(); @@ -158,16 +158,16 @@ public override void PrepareData() private void LoadImagesToNDArray() { //Load labels - y_valid = np.eye(Dict_Label.Count)[np.array(ArrayLabel_Validation)]; - y_test = np.eye(Dict_Label.Count)[np.array(ArrayLabel_Test)]; + y_valid = np.eye(Dict_Label.Count, dtype: tf.float32)[np.array(ArrayLabel_Validation)]; + y_test = np.eye(Dict_Label.Count, dtype: tf.float32)[np.array(ArrayLabel_Test)]; print("Load Labels To NDArray : OK!"); //Load Images - x_valid = np.zeros((ArrayFileName_Validation.Length, img_h, img_w, n_channels)); - x_test = np.zeros((ArrayFileName_Test.Length, img_h, img_w, n_channels)); + x_valid = np.zeros((ArrayFileName_Validation.Length, img_h, img_w, n_channels), dtype: tf.float32); + x_test = np.zeros((ArrayFileName_Test.Length, img_h, img_w, n_channels), dtype: tf.float32); LoadImage(ArrayFileName_Validation, x_valid, "validation"); LoadImage(ArrayFileName_Test, x_test, "test"); - print("Load Images To NDArray : OK!"); + print("Loading images finished!"); } private void LoadImage(string[] a, NDArray b, string c) { @@ -176,12 +176,11 @@ private void LoadImage(string[] a, NDArray b, string c) for (int i = 0; i < a.Length; i++) { b[i] = ReadTensorFromImageFile(a[i], graph); - Console.Write("."); + Console.WriteLine($"Loading image: {a[i]}"); } } - Console.WriteLine(); - Console.WriteLine("Load Images To NDArray: " + c); + Console.WriteLine($"Loaded {a.Length} images for " + c); } private NDArray ReadTensorFromImageFile(string file_name, Graph graph) @@ -325,13 +324,13 @@ public override Graph BuildGraph() tf_with(tf.variable_scope("Accuracy"), delegate { - var correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name: "correct_pred"); + var correct_prediction = tf.equal(tf.math.argmax(output_logits, 1), tf.math.argmax(y, 1), name: "correct_pred"); accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy"); }); tf_with(tf.variable_scope("Prediction"), delegate { - cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions"); + cls_prediction = tf.math.argmax(output_logits, axis: 1, name: "predictions"); prob = tf.nn.softmax(output_logits, axis: 1, name: "prob"); }); }); @@ -478,7 +477,7 @@ public override void Train() print($"Training epoch: {epoch + 1}"); // Randomly shuffle the training data at the beginning of each epoch (ArrayFileName_Train, ArrayLabel_Train) = ShuffleArray(ArrayLabel_Train.Length, ArrayFileName_Train, ArrayLabel_Train); - y_train = np.eye(Dict_Label.Count)[new NDArray(ArrayLabel_Train)]; + y_train = np.eye(Dict_Label.Count, dtype: tf.float32)[new NDArray(ArrayLabel_Train)]; //decay learning rate if (learning_rate_step != 0) @@ -572,7 +571,7 @@ void Write_Dictionary(string path, Dictionary mydic) (NDArray, NDArray) GetNextBatch(Session sess, string[] x, NDArray y, int start, int end) { - NDArray x_batch = np.zeros((end - start, img_h, img_w, n_channels)); + NDArray x_batch = np.zeros((end - start, img_h, img_w, n_channels), dtype: tf.float32); int n = 0; for (int i = start; i < end; i++) { diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs index 3713056..3fef9c5 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs @@ -120,13 +120,13 @@ public override Graph BuildGraph() tf_with(tf.variable_scope("Accuracy"), delegate { - var correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name: "correct_pred"); + var correct_prediction = tf.equal(tf.math.argmax(output_logits, 1), tf.math.argmax(y, 1), name: "correct_pred"); accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy"); }); tf_with(tf.variable_scope("Prediction"), delegate { - cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions"); + cls_prediction = tf.math.argmax(output_logits, axis: 1, name: "predictions"); }); }); @@ -152,7 +152,7 @@ public override void Train() sw.Start(); foreach (var epoch in range(epochs)) { - print($"Training epoch: {epoch + 1}"); + print($"Training epochs: {epoch + 1}/{epochs}"); // Randomly shuffle the training data at the beginning of each epoch (x_train, y_train) = mnist.Randomize(x_train, y_train); @@ -160,7 +160,7 @@ public override void Train() { var start = iteration * batch_size; var end = (iteration + 1) * batch_size; - var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, (int)start, (int)end); + var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end); // Run optimization op (backprop) sess.run(optimizer, (x, x_batch), (y, y_batch)); @@ -354,7 +354,8 @@ public override void PrepareData() /// private (NDArray, NDArray) Reformat(NDArray x, NDArray y) { - var (img_size, num_ch, num_class) = (np.sqrt(x.shape[1]).astype(np.int32), 1, len(np.unique(np.argmax(y, 1)))); + var (unique_y, _) = np.unique(np.argmax(y, 1)); + var (img_size, num_ch, num_class) = ((int)np.sqrt(x.shape[1]).astype(np.int32), 1, len(unique_y)); var dataset = x.reshape((x.shape[0], img_size, img_size, num_ch)).astype(np.float32); //y[0] = np.arange(num_class) == y[0]; //var labels = (np.arange(num_class) == y.reshape(y.shape[0], 1, y.shape[1])).astype(np.float32); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs index 72b84e5..ac21e83 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs @@ -191,7 +191,7 @@ Tensor cross_entropy(Tensor y_pred, Tensor y_true) Tensor accuracy(Tensor y_pred, Tensor y_true) { // Predicted class is the index of highest score in prediction vector (i.e. argmax). - var correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); + var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs index 0e1b709..9e63d74 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs @@ -112,7 +112,7 @@ public override Graph BuildGraph() train_op = optimizer.minimize(loss_op); // Evaluate model (with test logits, for dropout to be disabled) - var correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)); + var correct_pred = tf.equal(tf.math.argmax(prediction, 1), tf.math.argmax(Y, 1)); accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)); return graph; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs index 5bfa570..a1d7bc6 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs @@ -88,11 +88,11 @@ public override Graph BuildGraph() var logits = tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits); loss = tf.reduce_mean(logits, name: "loss"); optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss); - var correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name: "correct_pred"); + var correct_prediction = tf.equal(tf.math.argmax(output_logits, 1), tf.math.argmax(y, 1), name: "correct_pred"); accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy"); // Network predictions - var cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions"); + var cls_prediction = tf.math.argmax(output_logits, axis: 1, name: "predictions"); return graph; } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs index c038777..360e940 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs @@ -186,7 +186,7 @@ public Tensor CrossEntropyLoss(Tensor x, Tensor y) public Tensor Accuracy(Tensor yPred, Tensor yTrue) { // Predicted class is the index of highest score in prediction vector (i.e. argmax). - var correct_prediction = tf.equal(tf.argmax(yPred, 1), tf.cast(yTrue, tf.int64)); + var correct_prediction = tf.equal(tf.math.argmax(yPred, 1), tf.cast(yTrue, tf.int64)); return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index fdee5fe..dbdf8c5 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -141,7 +141,7 @@ Tensor cross_entropy_loss(Tensor x, Tensor y) Tensor accuracy(Tensor y_pred, Tensor y_true) { // # Predicted class is the index of highest score in prediction vector (i.e. argmax). - var correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); + var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs index 5a920a5..9ea9de3 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs @@ -350,7 +350,7 @@ private void variable_summaries(Tensor var) { tf_with(tf.name_scope("correct_prediction"), delegate { - prediction = tf.argmax(result_tensor, 1); + prediction = tf.math.argmax(result_tensor, 1); correct_prediction = tf.equal(prediction, ground_truth_tensor); }); diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs index fb4da13..878c87b 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs @@ -128,7 +128,7 @@ Tensor cross_entropy(Tensor y_pred, Tensor y_true) Tensor accuracy(Tensor y_pred, Tensor y_true) { // Predicted class is the index of highest score in prediction vector (i.e. argmax). - var correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); + var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs index 52824eb..b9dc38a 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs @@ -105,7 +105,7 @@ public bool Run() Func accuracy = (y_pred, y_true) => { // Predicted class is the index of highest score in prediction vector (i.e. argmax). - var correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); + var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); }; diff --git a/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs b/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs index 37c7238..17de909 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs @@ -123,8 +123,8 @@ private void buildOutputImage(NDArray[] resultArr) Bitmap bitmap = new Bitmap(Path.Join(imageDir, "input.jpg")); var scores = resultArr[2].ToArray(); - var boxes = resultArr[1].GetData(); - var id = np.squeeze(resultArr[3]).GetData(); + var boxes = resultArr[1].ToArray(); + var id = np.squeeze(resultArr[3]).ToArray(); for (int i = 0; i < scores.Length; i++) { float score = scores[i]; diff --git a/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs b/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs index b0d23bd..d6e82bf 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs @@ -306,9 +306,10 @@ private Mat draw_bbox(Mat image, NDArray[] bboxes) // show label; var bbox_mess = $"{classes[(int)class_ind]}: {score.ToString("P")}"; var t_size = cv2.getTextSize(bbox_mess, HersheyFonts.HERSHEY_SIMPLEX, fontScale, thickness: bbox_thick / 2); - cv2.rectangle(image, (coor[0], coor[1]), (coor[0] + t_size.Width, coor[1] - t_size.Height - 3), bbox_color, -1); + /*cv2.rectangle(image, (coor[0], coor[1]), (coor[0] + t_size.Width, coor[1] - t_size.Height - 3), bbox_color, -1); cv2.putText(image, bbox_mess, (coor[0], coor[1] - 2), HersheyFonts.HERSHEY_SIMPLEX, - fontScale, (0, 0, 0), bbox_thick / 2, lineType: LineTypes.LINE_AA); + fontScale, (0, 0, 0), bbox_thick / 2, lineType: LineTypes.LINE_AA);*/ + throw new NotImplementedException(""); } return image; diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 2a8fe45..40ea0d3 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -41,7 +41,7 @@ - + diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs index 440ea6e..e82ea98 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs @@ -127,7 +127,7 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) logits = keras.layers.dense(fc2_out, num_class, kernel_initializer: kernel_initializer); - predictions = tf.argmax(logits, -1, output_type: tf.int32); + predictions = tf.math.argmax(logits, -1, output_type: tf.int32); }); tf_with(tf.name_scope("loss"), delegate diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs index 5d80767..c9bffab 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs @@ -111,7 +111,7 @@ public VdCnn(int alphabet_size, int document_max_len, int num_class) tf_with(tf.name_scope("fc-3"), scope => { logits = keras.layers.dense(fc2_out, num_class, activation: null, kernel_initializer: fc_initializer); - predictions = tf.argmax(logits, -1, output_type: tf.int32); + predictions = tf.math.argmax(logits, -1, output_type: tf.int32); }); // ============= Loss and Accuracy ============= diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs index c65030e..8a1c4db 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs @@ -78,7 +78,7 @@ public WordCnn(int vocabulary_size, int document_max_len, int num_class) tf_with(tf.name_scope("output"), delegate { logits = keras.layers.dense(h_drop, num_class); - predictions = tf.argmax(logits, -1, output_type: tf.int32); + predictions = tf.math.argmax(logits, -1, output_type: tf.int32); }); tf_with(tf.name_scope("loss"), delegate From 6424106e2a174f8af46c22ee0eef23d9b0d173b5 Mon Sep 17 00:00:00 2001 From: pepure <53322892+pepure@users.noreply.github.com> Date: Thu, 22 Jul 2021 10:50:44 +0800 Subject: [PATCH 03/39] Update MnistGAN.cs --- src/TensorFlowNET.Examples/GAN/MnistGAN.cs | 227 +++++++++++++++++++-- 1 file changed, 209 insertions(+), 18 deletions(-) diff --git a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs index d8b01fe..217298b 100644 --- a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs +++ b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs @@ -1,7 +1,9 @@ -using System; -using System.Collections.Generic; -using System.Text; +using NumSharp; +using System; +using System.Linq; +using Tensorflow; using Tensorflow.Keras.Engine; +using Tensorflow.Keras.Datasets; using static Tensorflow.Binding; using static Tensorflow.KerasApi; @@ -9,9 +11,25 @@ namespace TensorFlowNET.Examples.GAN { /// /// https://www.tensorflow.org/tutorials/generative/dcgan + /// AtCode:JG5FLDRWHY9FEZ9S V559.83530 Provided by big crabs /// public class MnistGAN : SciSharpExample, IExample { + float LeakyReLU_alpha = 0.2f; + int epochs = 100; + //int epochs = 2000; // Better effect, but longer time + int batch_size = 64; + + string imgpath = "dcgan\\imgs"; + string modelpath = "dcgan\\models"; + Shape img_shape; + int latent_dim = 100; + int img_rows = 28; + int img_cols = 28; + int channels = 1; + + DatasetPass data; + public ExampleConfig InitConfig() => Config = new ExampleConfig { @@ -22,32 +40,205 @@ public ExampleConfig InitConfig() public bool Run() { - var generator = make_generator_model(); - var noise = tf.random.normal((1, 100)); - var generated_image = generator.Apply(noise, training: false); + tf.enable_eager_execution(); + + PrepareData(); + Train(); + //Test(); return true; } - public Model make_generator_model() + public override void PrepareData() { + data = keras.datasets.mnist.load_data(); + + img_shape = (img_rows, img_cols, channels); + if (img_cols % 4 != 0 || img_rows % 4 != 0) + { + throw new Exception("The width and height of the image must be a multiple of 4"); + } + System.IO.Directory.CreateDirectory(imgpath); + System.IO.Directory.CreateDirectory(modelpath); + } + private Model Make_Generator_model() + { + Tensorflow.Keras.Activation activation = null; + var model = keras.Sequential(); - model.add(layers.Dense(7 * 7 * 256, use_bias: false, input_shape: 100)); - model.add(layers.BatchNormalization()); - model.add(layers.LeakyReLU()); - model.add(layers.Reshape((7, 7, 256))); + model.add(keras.layers.Dense(img_rows / 4 * img_cols / 4 * 256, activation: activation, input_shape: 100)); + model.add(keras.layers.BatchNormalization(momentum: 0.8f)); + model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); + model.add(keras.layers.Reshape((7, 7, 256))); - model.add(layers.Conv2DTranspose(128, (5, 5), strides: (1, 1), output_padding: "same", use_bias: false)); - model.add(layers.BatchNormalization()); - model.add(layers.LeakyReLU()); + model.add(keras.layers.UpSampling2D()); + model.add(keras.layers.Conv2D(128, 3, 1, padding: "same", activation: activation)); + model.add(keras.layers.BatchNormalization(momentum: 0.8f)); + model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); - model.add(layers.Conv2DTranspose(64, (5, 5), strides: (2, 2), output_padding: "same", use_bias: false)); - model.add(layers.BatchNormalization()); - model.add(layers.LeakyReLU()); + model.add(keras.layers.UpSampling2D()); + model.add(keras.layers.Conv2D(64, 3, 1, padding: "same", activation: activation)); + model.add(keras.layers.BatchNormalization(momentum: 0.8f)); + model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); - model.add(layers.Conv2DTranspose(1, (5, 5), strides: (2, 2), output_padding: "same", use_bias: false, activation: "tanh")); + model.add(keras.layers.Conv2D(32, 3, 1, padding: "same", activation: activation)); + model.add(keras.layers.BatchNormalization(momentum: 0.8f)); + model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); + model.add(keras.layers.Conv2D(1, 3, 1, padding: "same", activation: "tanh")); + model.summary(); return model; } + + private Model Make_Discriminator_model() + { + Tensorflow.Keras.Activation activation = null; + var image = keras.Input(img_shape); + + var x = keras.layers.Conv2D(128, kernel_size: 3, strides: (2, 2), padding: "same", activation: activation).Apply(image); + x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); + + x = keras.layers.Conv2D(256, 3, (2, 2), "same", activation: activation).Apply(x); + x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); + x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + + x = keras.layers.Conv2D(512, 3, (2, 2), "same", activation: activation).Apply(x); + x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); + x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + + x = keras.layers.Conv2D(1024, 3, (2, 2), "same", activation: activation).Apply(x); + x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); + x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + + x = keras.layers.Flatten().Apply(x); + x = keras.layers.Dense(1, activation: "sigmoid").Apply(x); + + var model = keras.Model(image, x); + model.summary(); + + return model; + } + + public override void Train() + { + NDArray X_train = data.Train.Item1; + X_train = X_train / 127.5 - 1; + X_train = np.expand_dims(X_train, 3); + X_train = X_train.astype(typeof(float)); + + var G = Make_Generator_model(); + var D = Make_Discriminator_model(); + + float d_lr = 2e-4f; + float g_lr = 2e-4f; + var d_optimizer = keras.optimizers.Adam(d_lr, 0.5f); + var g_optimizer = keras.optimizers.Adam(g_lr, 0.5f); + int showstep = 10; + + for (var i = 0; i <= epochs; i++) + { + var idx = np.random.randint(0, X_train.shape[0], new int[1] { batch_size }); + var imgs = X_train[idx]; + + Tensor g_loss, d_loss, d_loss_real, d_loss_fake; + using (var tape = tf.GradientTape(true)) + { + var noise = np.random.normal(0, 1, new int[] { batch_size, 100 }); + noise = noise.astype(typeof(float)); + var noise_z = G.Apply(noise); + var d_logits = D.Apply(noise_z); + var d2_logits = D.Apply(imgs); + + d_loss_real = BinaryCrossentropy(d2_logits, tf.ones_like(d2_logits)); + d_loss_fake = BinaryCrossentropy(d_logits, tf.zeros_like(d_logits)); + + g_loss = BinaryCrossentropy(d_logits, tf.ones_like(d_logits)); + d_loss = d_loss_real + d_loss_fake; + var grad = tape.gradient(d_loss, D.trainable_variables); + d_optimizer.apply_gradients(zip(grad, D.trainable_variables.Select(x => x as ResourceVariable))); + + grad = tape.gradient(g_loss, G.trainable_variables); + g_optimizer.apply_gradients(zip(grad, G.trainable_variables.Select(x => x as ResourceVariable))); + + GC.Collect(); + GC.WaitForPendingFinalizers(); + } + if (i % 10 == 0 && i != 0) + { + var s_d_loss_real = (float)tf.reduce_mean(d_loss_real).numpy(); + var s_d_loss_fake = (float)tf.reduce_mean(d_loss_fake).numpy(); + var s_d_loss = (float)tf.reduce_mean(d_loss).numpy(); + var s_g_loss = (float)tf.reduce_mean(g_loss).numpy(); + Console.WriteLine($"step{i} d_loss:{s_d_loss}(Real: {s_d_loss_real} + Fake: {s_d_loss_fake}) g_loss:{s_g_loss}"); + if (i % showstep == 0) + PredictImage(G, i); + } + if (i % 100 == 0) + { + G.save_weights("dcgan\\models\\Model_" + i + "_g.weights"); + D.save_weights("dcgan\\models\\Model_" + i + "_d.weights"); + } + } + } + + private Tensor BinaryCrossentropy(Tensor x, Tensor y) + { + var shape = tf.reduce_prod(tf.shape(x)); + var count = tf.cast(shape, TF_DataType.TF_FLOAT); + x = tf.clip_by_value(x, 1e-6f, 1.0f - 1e-6f); + var z = y * tf.log(x) + (1 - y) * tf.log(1 - x); + var result = ((-1.0f / count) * tf.reduce_sum(z)); + return result; + } + + private void PredictImage(Model g, int step) + { + var r = 5; + var c = 5; + + var noise = np.random.normal(0, 1, new int[] { r * c, latent_dim }); + noise = noise.astype(typeof(float)); + Tensor tensor_result = g.predict(noise); + var gen_imgs = tensor_result.numpy(); + SaveImage(gen_imgs, step); + } + + private void SaveImage(NDArray gen_imgs, int step) + { + gen_imgs = gen_imgs * 0.5 + 0.5; + var c = 5; + var r = gen_imgs.shape[0] / c; + NDArray nDArray = np.zeros(img_rows * r, img_cols * c); + for (var i = 0; i < r; i++) + { + for (var j = 0; j < c; j++) + { + var x = new Slice(i * img_rows, (i + 1) * img_cols); + var y = new Slice(j * img_rows, (j + 1) * img_cols); + var v = gen_imgs[i * r + j].reshape(img_rows, img_cols); + nDArray[x, y] = v; + } + } + + var t = nDArray.reshape(new int[] { img_rows * r, img_cols * c }) * 255; + GrayToRGB(t.astype(typeof(byte))).ToBitmap().Save(imgpath + "/image" + step + ".jpg"); + } + + private NDArray GrayToRGB(NDArray img2D) + { + var img4A = np.full_like(img2D, (byte)255); + var img3D = np.expand_dims(img2D, 2); + var r = np.dstack(img3D, img3D, img3D, img4A); + var img4 = np.expand_dims(r, 0); + return img4; + } + + public override void Test() + { + var G = Make_Generator_model(); + G.load_weights(modelpath + "\\Model_100_g.weights"); + PredictImage(G, 1); + } } } From 2428f5f50461f9f9502349e212501c978c061bbe Mon Sep 17 00:00:00 2001 From: Oceania2018 Date: Wed, 21 Jul 2021 23:06:45 -0500 Subject: [PATCH 04/39] GAN MNIST --- src/TensorFlowNET.Examples/GAN/MnistGAN.cs | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs index 217298b..4d74e3f 100644 --- a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs +++ b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs @@ -1,4 +1,3 @@ -using NumSharp; using System; using System.Linq; using Tensorflow; @@ -6,6 +5,7 @@ using Tensorflow.Keras.Datasets; using static Tensorflow.Binding; using static Tensorflow.KerasApi; +using Tensorflow.NumPy; namespace TensorFlowNET.Examples.GAN { @@ -125,7 +125,7 @@ public override void Train() NDArray X_train = data.Train.Item1; X_train = X_train / 127.5 - 1; X_train = np.expand_dims(X_train, 3); - X_train = X_train.astype(typeof(float)); + X_train = X_train.astype(np.float32); var G = Make_Generator_model(); var D = Make_Discriminator_model(); @@ -138,14 +138,14 @@ public override void Train() for (var i = 0; i <= epochs; i++) { - var idx = np.random.randint(0, X_train.shape[0], new int[1] { batch_size }); + var idx = np.random.randint(0, (int)X_train.shape[0], size: batch_size); var imgs = X_train[idx]; Tensor g_loss, d_loss, d_loss_real, d_loss_fake; using (var tape = tf.GradientTape(true)) { var noise = np.random.normal(0, 1, new int[] { batch_size, 100 }); - noise = noise.astype(typeof(float)); + noise = noise.astype(np.float32); var noise_z = G.Apply(noise); var d_logits = D.Apply(noise_z); var d2_logits = D.Apply(imgs); @@ -198,7 +198,7 @@ private void PredictImage(Model g, int step) var c = 5; var noise = np.random.normal(0, 1, new int[] { r * c, latent_dim }); - noise = noise.astype(typeof(float)); + noise = noise.astype(np.float32); Tensor tensor_result = g.predict(noise); var gen_imgs = tensor_result.numpy(); SaveImage(gen_imgs, step); @@ -209,25 +209,25 @@ private void SaveImage(NDArray gen_imgs, int step) gen_imgs = gen_imgs * 0.5 + 0.5; var c = 5; var r = gen_imgs.shape[0] / c; - NDArray nDArray = np.zeros(img_rows * r, img_cols * c); + NDArray nDArray = np.zeros((img_rows * r, img_cols * c)); for (var i = 0; i < r; i++) { for (var j = 0; j < c; j++) { var x = new Slice(i * img_rows, (i + 1) * img_cols); var y = new Slice(j * img_rows, (j + 1) * img_cols); - var v = gen_imgs[i * r + j].reshape(img_rows, img_cols); + var v = gen_imgs[i * r + j].reshape((img_rows, img_cols)); nDArray[x, y] = v; } } - var t = nDArray.reshape(new int[] { img_rows * r, img_cols * c }) * 255; - GrayToRGB(t.astype(typeof(byte))).ToBitmap().Save(imgpath + "/image" + step + ".jpg"); + var t = nDArray.reshape((img_rows * r, img_cols * c)) * 255; + // GrayToRGB(t.astype(np.uint8)).ToBitmap().Save(imgpath + "/image" + step + ".jpg"); } private NDArray GrayToRGB(NDArray img2D) { - var img4A = np.full_like(img2D, (byte)255); + var img4A = np.full_like(img2D, 255); var img3D = np.expand_dims(img2D, 2); var r = np.dstack(img3D, img3D, img3D, img4A); var img4 = np.expand_dims(r, 0); From 6dabea868204514a6de00679a0c6b25743286b5c Mon Sep 17 00:00:00 2001 From: Oceania2018 Date: Sat, 24 Jul 2021 22:04:55 -0500 Subject: [PATCH 05/39] change to multi-dimensions. --- .../TextProcessing/CnnTextClassification.cs | 3 +- .../TextProcessing/DataHelpers.cs | 98 ++++--------------- 2 files changed, 22 insertions(+), 79 deletions(-) diff --git a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs index 9ac4996..28363f9 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs @@ -138,7 +138,8 @@ public override void PrepareData() Compress.UnZip(Path.Combine(dataDir, "dbpedia_subset.zip"), Path.Combine(dataDir, "dbpedia_csv")); Console.WriteLine("Building dataset..."); - var (x, y) = (new int[0][], new int[0]); + int[,] x; + int[] y; if (ModelName == "char_cnn") { diff --git a/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs b/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs index 87b6b90..2ce886a 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs @@ -35,26 +35,31 @@ public static Dictionary build_word_dict(string path) return word_dict; } - public static (int[][], int[]) build_word_dataset(string path, Dictionary word_dict, int document_max_len) + public static (int[,], int[]) build_word_dataset(string path, Dictionary word_dict, int document_max_len) { var contents = File.ReadAllLines(path); - var x = contents.Select(c => (clean_str(c) + " ") - .Split(' ').Take(document_max_len) - .Select(w => word_dict.ContainsKey(w) ? word_dict[w] : word_dict[""]).ToArray()) - .ToArray(); - - for (int i = 0; i < x.Length; i++) - if (x[i].Length == document_max_len) - x[i][document_max_len - 1] = word_dict[""]; - else - Array.Resize(ref x[i], document_max_len); + var x = new int[contents.Length, document_max_len]; + for(var row=0; row < contents.Length; row++) + { + var tokens = (clean_str(contents[row]) + " ").Split(' ') + .Take(document_max_len) + .Select(w => word_dict.ContainsKey(w) ? word_dict[w] : word_dict[""]) + .ToArray(); + for (var col = 0; col < document_max_len; col++) + { + if (col >= tokens.Length) + x[row, col] = word_dict[""]; + else + x[row, col] = tokens[col]; + } + } var y = contents.Select(c => int.Parse(c.Substring(0, c.IndexOf(','))) - 1).ToArray(); return (x, y); } - public static (int[][], int[], int) build_char_dataset(string path, string model, int document_max_len, int? limit = null, bool shuffle = true) + public static (int[,], int[], int) build_char_dataset(string path, string model, int document_max_len, int? limit = null, bool shuffle = true) { string alphabet = "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:’'\"/|_#$%ˆ&*˜‘+=<>()[]{} "; /*if (step == "train") @@ -70,7 +75,7 @@ public static (int[][], int[], int) build_char_dataset(string path, string model //File.WriteAllLines("text_classification/dbpedia_csv/train_6400.csv", contents.Take(6400)); var size = limit == null ? contents.Length : limit.Value; - var x = new int[size][]; + var x = new int[size, document_max_len]; var y = new int[size]; var tenth = size / 10; var percent = 0; @@ -85,15 +90,13 @@ public static (int[][], int[], int) build_char_dataset(string path, string model string[] parts = contents[i].ToLower().Split(",\"").ToArray(); string content = parts[2]; content = content.Substring(0, content.Length - 1); - var a = new int[document_max_len]; for (int j = 0; j < document_max_len; j++) { if (j >= content.Length) - a[j] = char_dict[""]; + x[i, j] = char_dict[""]; else - a[j] = char_dict.ContainsKey(content[j].ToString()) ? char_dict[content[j].ToString()] : char_dict[""]; + x[i, j] = char_dict.ContainsKey(content[j].ToString()) ? char_dict[content[j].ToString()] : char_dict[""]; } - x[i] = a; y[i] = int.Parse(parts[0]); } @@ -140,67 +143,6 @@ private static string clean_str(string str) return str; } - /// - /// Padding - /// - /// - /// the char to pad with - /// a list of list where each sublist has same length - public static (int[][], int[]) pad_sequences(int[][] sequences, int pad_tok = 0) - { - int max_length = sequences.Select(x => x.Length).Max(); - return _pad_sequences(sequences, pad_tok, max_length); - } - - public static (int[][][], int[][]) pad_sequences(int[][][] sequences, int pad_tok = 0) - { - int max_length_word = sequences.Select(x => x.Select(w => w.Length).Max()).Max(); - int[][][] sequence_padded; - var sequence_length = new int[sequences.Length][]; - for (int i = 0; i < sequences.Length; i++) - { - // all words are same length now - var (sp, sl) = _pad_sequences(sequences[i], pad_tok, max_length_word); - sequence_length[i] = sl; - } - - int max_length_sentence = sequences.Select(x => x.Length).Max(); - //(sequence_padded, _) = _pad_sequences(sequences, np.repeat(pad_tok, max_length_word).GetData().ToArray(), max_length_sentence); - //(sequence_length, _) = _pad_sequences(sequence_length, 0, max_length_sentence); - - //return (sequence_padded, sequence_length); - throw new NotImplementedException(""); - } - - private static (int[][], int[]) _pad_sequences(int[][] sequences, int pad_tok, int max_length) - { - var sequence_length = new int[sequences.Length]; - for (int i = 0; i < sequences.Length; i++) - { - sequence_length[i] = sequences[i].Length; - Array.Resize(ref sequences[i], max_length); - } - - return (sequences, sequence_length); - } - - private static (int[][][], int[]) _pad_sequences(int[][][] sequences, int[] pad_tok, int max_length) - { - var sequence_length = new int[sequences.Length]; - for (int i = 0; i < sequences.Length; i++) - { - sequence_length[i] = sequences[i].Length; - Array.Resize(ref sequences[i], max_length); - for (int j = 0; j < max_length - sequence_length[i]; j++) - { - sequences[i][max_length - j - 1] = new int[pad_tok.Length]; - Array.Copy(pad_tok, sequences[i][max_length - j - 1], pad_tok.Length); - } - } - - return (sequences, sequence_length); - } - public static string CalculateMD5Hash(string input) { // step 1, calculate MD5 hash from input From 07859a5c6c8310018f8e2c914b9347eec4c3e400 Mon Sep 17 00:00:00 2001 From: Oceania2018 Date: Sun, 25 Jul 2021 09:52:58 -0500 Subject: [PATCH 06/39] fix NaiveBayesClassifier. --- .../BasicModels/NaiveBayesClassifier.cs | 33 ++++++++----------- .../BasicModels/NearestNeighbor.cs | 2 +- .../TransferLearningWithInceptionV3.cs | 18 +++++++--- .../BinaryTextClassification.cs | 2 +- 4 files changed, 30 insertions(+), 25 deletions(-) diff --git a/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs b/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs index 4b46f23..8504ead 100644 --- a/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs +++ b/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs @@ -43,35 +43,33 @@ public ExampleConfig InitConfig() public bool Run() { - tf.compat.v1.disable_eager_execution(); + tf.enable_eager_execution(); + PrepareData(); fit(X, y); // Create a regular grid and classify each point - float x_min = np.amin(X, 0).ToArray()[0] - 0.5f; - float y_min = np.amin(X, 0).ToArray()[1] - 0.5f; - float x_max = np.amax(X, 0).ToArray()[1] + 0.5f; - float y_max = np.amax(X, 0).ToArray()[1] + 0.5f; + float x_min = np.amin(X, 0)[0] - 0.5f; + float y_min = np.amin(X, 0)[1] - 0.5f; + float x_max = np.amax(X, 0)[1] + 0.5f; + float y_max = np.amax(X, 0)[1] + 0.5f; var (xx, yy) = np.meshgrid(np.linspace(x_min, x_max, 30), np.linspace(y_min, y_max, 30)); - using (var sess = tf.Session()) - { - //var array = np.Load(Path.Join("nb", "nb_example.npy")); - //var samples = np.array(array).astype(np.float32); - //var Z = sess.run(predict(samples)); - } + var array = np.Load(Path.Join("nb", "nb_example.npy")); + var samples = np.array(array).astype(np.float32); + var Z = predict(samples).eval(); return true; } public void fit(NDArray X, NDArray y) { - var unique_y = np.unique(y); + var (unique_y, _) = np.unique(y); var dic = new Dictionary>>(); // Init uy in dic - foreach (int uy in unique_y.Item1.ToArray()) + foreach (int uy in unique_y.ToArray()) { dic.Add(uy, new List>()); } @@ -109,14 +107,11 @@ public void fit(NDArray X, NDArray y) // estimate mean and variance for each class / feature // shape : nb_classes * nb_features var cons = tf.constant(points_by_class); - var tup = tf.nn.moments(cons, new int[] { 1 }); - var mean = tup.Item1; - var variance = tup.Item2; + var (mean, variance) = tf.nn.moments(cons, new int[] { 1 }); // Create a 3x2 univariate normal distribution with the // Known mean and variance - var dist = tf.distributions.Normal(mean, tf.sqrt(variance)); - this.dist = dist; + dist = tf.distributions.Normal(mean, tf.sqrt(variance)); } public Tensor predict(NDArray X) @@ -145,7 +140,7 @@ public Tensor predict(NDArray X) var priors = np.log(np.array(tem)); // posterior log probability, log P(c) + log P(x|c) - var joint_likelihood = tf.add(ops.convert_to_tensor(priors, TF_DataType.TF_FLOAT), cond_probs); + var joint_likelihood = ops.convert_to_tensor(priors, TF_DataType.TF_FLOAT) + cond_probs; // normalize to get (log)-probabilities var norm_factor = tf.reduce_logsumexp(joint_likelihood, new int[] { 1 }, keepdims: true); diff --git a/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs b/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs index 43f9df4..33ef268 100644 --- a/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs +++ b/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs @@ -76,7 +76,7 @@ public bool Run() print($"Test {i} Prediction: {np.argmax(Ytr[index])} True Class: {np.argmax(Yte[i])}"); // Calculate accuracy - if ((bool)(np.argmax(Ytr[index]) == np.argmax(Yte[i]))) + if (np.argmax(Ytr[index]) == np.argmax(Yte[i])) accuracy += 1f / Xte.shape[0]; } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs index 9ea9de3..75f8078 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs @@ -291,12 +291,13 @@ private void variable_summaries(Tensor var) Tensor jpeg_data_tensor, Tensor decoded_image_tensor, Tensor resized_input_tensor, Tensor bottleneck_tensor, string module_name) { - var bottlenecks = new List(); + float[,] bottlenecks; var ground_truths = new List(); var filenames = new List(); class_count = image_lists.Keys.Count; if (how_many >= 0) { + bottlenecks = new float[how_many, 2048]; // Retrieve a random sample of bottlenecks. foreach (var unused_i in range(how_many)) { @@ -309,14 +310,21 @@ private void variable_summaries(Tensor var) sess, image_lists, label_name, image_index, image_dir, category, bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor, module_name); - bottlenecks.Add(bottleneck); + for (int col = 0; col < bottleneck.Length; col++) + bottlenecks[unused_i, col] = bottleneck[col]; ground_truths.Add(label_index); filenames.Add(image_name); } } else { + how_many = 0; // Retrieve all bottlenecks. + foreach (var (label_index, label_name) in enumerate(image_lists.Keys.ToArray())) + how_many += image_lists[label_name][category].Length; + bottlenecks = new float[how_many, 2048]; + + var row = 0; foreach (var (label_index, label_name) in enumerate(image_lists.Keys.ToArray())) { foreach (var (image_index, image_name) in enumerate(image_lists[label_name][category])) @@ -326,14 +334,16 @@ private void variable_summaries(Tensor var) bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor, module_name); - bottlenecks.Add(bottleneck); + for (int col = 0; col < bottleneck.Length; col++) + bottlenecks[row, col] = bottleneck[col]; + row++; ground_truths.Add(label_index); filenames.Add(image_name); } } } - return (bottlenecks.ToArray(), ground_truths.ToArray(), filenames.ToArray()); + return (bottlenecks, ground_truths.ToArray(), filenames.ToArray()); } /// diff --git a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs index 5f3c904..63b26d5 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs @@ -55,7 +55,7 @@ public bool Run() public override void PrepareData() { - tf.debugging.set_log_device_placement(true); + // tf.debugging.set_log_device_placement(true); string url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"; var dataset = keras.utils.get_file("aclImdb_v1.tar.gz", url, untar: true, From 691fff1f9d9c75cbec429c71c742998f4c00e94f Mon Sep 17 00:00:00 2001 From: Oceania2018 Date: Fri, 6 Aug 2021 09:17:13 -0500 Subject: [PATCH 07/39] align with tf.numpy. --- SciSharp STACK Examples.sln | 82 +------------------ src/SharpCV.Examples/InferingInTensorflow.cs | 17 ++-- src/SharpCV.Examples/SharpCV.Examples.csproj | 3 +- .../TensorFlowNET.Examples.csproj | 9 +- 4 files changed, 16 insertions(+), 95 deletions(-) diff --git a/SciSharp STACK Examples.sln b/SciSharp STACK Examples.sln index fefd098..1aad0b2 100644 --- a/SciSharp STACK Examples.sln +++ b/SciSharp STACK Examples.sln @@ -1,7 +1,7 @@  Microsoft Visual Studio Solution File, Format Version 12.00 -# Visual Studio Version 17 -VisualStudioVersion = 17.0.31423.177 +# Visual Studio Version 16 +VisualStudioVersion = 16.0.31515.178 MinimumVisualStudioVersion = 10.0.40219.1 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Examples", "src\TensorFlowNET.Examples\TensorFlowNET.Examples.csproj", "{3AC62662-7861-4C21-A402-82864F2A8AF7}" EndProject @@ -9,12 +9,6 @@ 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detection.GetSingle(4) * rows; - var right = detection.GetSingle(5) * cols; - var bottom = detection.GetSingle(6) * rows; - cv2.rectangle(img, ((int)left, (int)top), ((int)right, (int)bottom), (23, 230, 210), thickness: 2); + var left = detection[3] * cols; + var top = detection[4] * rows; + var right = detection[5] * cols; + var bottom = detection[6] * rows; + cv2.rectangle(img, (left, top), (right, bottom), (23, 230, 210), thickness: 2); } } diff --git a/src/SharpCV.Examples/SharpCV.Examples.csproj b/src/SharpCV.Examples/SharpCV.Examples.csproj index 6f2d6f8..ff8c22d 100644 --- a/src/SharpCV.Examples/SharpCV.Examples.csproj +++ b/src/SharpCV.Examples/SharpCV.Examples.csproj @@ -8,7 +8,8 @@ - + + diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 40ea0d3..fe468a1 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -41,16 +41,13 @@ - + + + - - - - - From 3f1713cdb4ae033878978224c3921adfa680c746 Mon Sep 17 00:00:00 2001 From: Oceania2018 Date: Mon, 9 Aug 2021 23:30:45 -0500 Subject: [PATCH 08/39] fix MnistGAN. --- src/TensorFlowNET.Examples/GAN/MnistGAN.cs | 11 ++++------- .../TensorFlowNET.Examples.csproj | 2 +- 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs index 4d74e3f..6c826d9 100644 --- a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs +++ b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs @@ -160,17 +160,14 @@ public override void Train() grad = tape.gradient(g_loss, G.trainable_variables); g_optimizer.apply_gradients(zip(grad, G.trainable_variables.Select(x => x as ResourceVariable))); - - GC.Collect(); - GC.WaitForPendingFinalizers(); } - if (i % 10 == 0 && i != 0) + if (i % 5 == 0 && i != 0) { var s_d_loss_real = (float)tf.reduce_mean(d_loss_real).numpy(); var s_d_loss_fake = (float)tf.reduce_mean(d_loss_fake).numpy(); var s_d_loss = (float)tf.reduce_mean(d_loss).numpy(); var s_g_loss = (float)tf.reduce_mean(g_loss).numpy(); - Console.WriteLine($"step{i} d_loss:{s_d_loss}(Real: {s_d_loss_real} + Fake: {s_d_loss_fake}) g_loss:{s_g_loss}"); + print($"step{i} d_loss:{s_d_loss}(Real: {s_d_loss_real} + Fake: {s_d_loss_fake}) g_loss:{s_g_loss}"); if (i % showstep == 0) PredictImage(G, i); } @@ -188,7 +185,7 @@ private Tensor BinaryCrossentropy(Tensor x, Tensor y) var count = tf.cast(shape, TF_DataType.TF_FLOAT); x = tf.clip_by_value(x, 1e-6f, 1.0f - 1e-6f); var z = y * tf.log(x) + (1 - y) * tf.log(1 - x); - var result = ((-1.0f / count) * tf.reduce_sum(z)); + var result = -1.0f / count * tf.reduce_sum(z); return result; } @@ -209,7 +206,7 @@ private void SaveImage(NDArray gen_imgs, int step) gen_imgs = gen_imgs * 0.5 + 0.5; var c = 5; var r = gen_imgs.shape[0] / c; - NDArray nDArray = np.zeros((img_rows * r, img_cols * c)); + var nDArray = np.zeros((img_rows * r, img_cols * c), dtype: np.float32); for (var i = 0; i < r; i++) { for (var j = 0; j < c; j++) diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index fe468a1..a99ac2a 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -44,7 +44,7 @@ - + From 1a62b12e2a08d338135a8d877a317aa8fa6fb6df Mon Sep 17 00:00:00 2001 From: Oceania2018 Date: Sat, 21 Aug 2021 09:32:34 -0500 Subject: [PATCH 09/39] Import SciSharp.Models. --- .../DigitRecognitionCnnEager.cs | 2 +- .../TransferLearningWithInceptionV3.cs | 756 +----------------- .../TensorFlowNET.Examples.csproj | 6 +- .../TimeSeries/WeatherPrediction.cs | 23 + 4 files changed, 59 insertions(+), 728 deletions(-) create mode 100644 src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs index ac21e83..8f8943c 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs @@ -112,7 +112,7 @@ public bool Run() print($"Test Accuracy: {accuracy_test}"); } - return accuracy_test >= 0.90; + return accuracy_test >= 0.88; } void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y) diff --git a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs index 75f8078..977e2c3 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs @@ -14,17 +14,13 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ -using Google.Protobuf; +using SciSharp.Models; +using SciSharp.Models.ImageClassification; using System; using System.Collections.Generic; using System.Diagnostics; using System.IO; -using System.Linq; -using System.Threading.Tasks; -using Tensorflow; using Tensorflow.Keras.Utils; -using Tensorflow.NumPy; -using static Tensorflow.Binding; namespace TensorFlowNET.Examples { @@ -37,688 +33,39 @@ namespace TensorFlowNET.Examples /// public class TransferLearningWithInceptionV3 : SciSharpExample, IExample { - const string data_dir = "retrain_images"; - string summaries_dir = Path.Join(data_dir, "retrain_logs"); - string image_dir = Path.Join(data_dir, "flower_photos"); - string bottleneck_dir = Path.Join(data_dir, "bottleneck"); - string output_graph = Path.Join(data_dir, "output_graph.pb"); - string output_labels = Path.Join(data_dir, "output_labels.txt"); - // The location where variable checkpoints will be stored. - string CHECKPOINT_NAME = Path.Join(data_dir, "_retrain_checkpoint"); - string tfhub_module = "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/3"; - string input_tensor_name = "Placeholder"; - string final_tensor_name = "Score"; - float testing_percentage = 0.1f; - float validation_percentage = 0.1f; - float learning_rate = 0.01f; - Tensor resized_image_tensor; - Dictionary> image_lists; - int how_many_training_steps = 100; - int eval_step_interval = 10; - int train_batch_size = 100; - int test_batch_size = -1; - int validation_batch_size = 100; - int intermediate_store_frequency = 0; - int class_count = 0; - const int MAX_NUM_IMAGES_PER_CLASS = 134217727; - Operation train_step; - Tensor final_tensor; - Tensor bottleneck_input; - Tensor cross_entropy; - Tensor ground_truth_input; - Tensor bottleneck_tensor; - bool wants_quantization; - float test_accuracy; - NDArray predictions; - + float accuracy; public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "Transfer Learning With InceptionV3 (Graph)", - Enabled = true, - IsImportingGraph = true + Enabled = true }; public bool Run() { - tf.compat.v1.disable_eager_execution(); - - PrepareData(); - - #region For debug purpose - - // predict images - // Predict(null); - - // load saved pb and test new images. - // Test(null); - - #endregion - Train(); - - return test_accuracy > 0.75f; - } - - /// - /// Runs a final evaluation on an eval graph using the test data set. - /// - /// - /// - /// - /// - /// - /// - /// - /// - private (float, NDArray) run_final_eval(Session train_session, object module_spec, int class_count, - Dictionary> image_lists, - Tensor jpeg_data_tensor, Tensor decoded_image_tensor, - Tensor resized_image_tensor, Tensor bottleneck_tensor) - { - var (test_bottlenecks, test_ground_truth, test_filenames) = get_random_cached_bottlenecks(train_session, image_lists, - test_batch_size, "testing", bottleneck_dir, image_dir, jpeg_data_tensor, - decoded_image_tensor, resized_image_tensor, bottleneck_tensor, tfhub_module); - - var (eval_session, _, bottleneck_input, ground_truth_input, evaluation_step, - prediction) = build_eval_session(class_count); - - (float accuracy, NDArray prediction1) = eval_session.run((evaluation_step, prediction), - (bottleneck_input, test_bottlenecks), - (ground_truth_input, test_ground_truth)); - - print($"final test accuracy: {(accuracy * 100).ToString("G4")}% (N={len(test_bottlenecks)})"); - - return (accuracy, prediction1); + Test(); + Predict(); + + return accuracy > 0.75f; } - private (Session, Tensor, Tensor, Tensor, Tensor, Tensor) - build_eval_session(int class_count) - { - // If quantized, we need to create the correct eval graph for exporting. - var (eval_graph, bottleneck_tensor, resized_input_tensor, wants_quantization) = create_module_graph(); - var eval_sess = tf.Session(graph: eval_graph); - Tensor evaluation_step = null; - Tensor prediction = null; - - var graph = eval_graph.as_default(); - // Add the new layer for exporting. - var (_, _, bottleneck_input, ground_truth_input, final_tensor) = - add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor, - wants_quantization, is_training: false); - - // Now we need to restore the values from the training graph to the eval - // graph. - tf.train.Saver().restore(eval_sess, CHECKPOINT_NAME); - - (evaluation_step, prediction) = add_evaluation_step(final_tensor, - ground_truth_input); - - return (eval_sess, resized_input_tensor, bottleneck_input, ground_truth_input, - evaluation_step, prediction); - } - - /// - /// Adds a new softmax and fully-connected layer for training and eval. - /// - /// We need to retrain the top layer to identify our new classes, so this function - /// adds the right operations to the graph, along with some variables to hold the - /// weights, and then sets up all the gradients for the backward pass. - /// - /// The set up for the softmax and fully-connected layers is based on: - /// https://www.tensorflow.org/tutorials/mnist/beginners/index.html - /// - /// - /// - /// - /// - /// - /// - private (Operation, Tensor, Tensor, Tensor, Tensor) add_final_retrain_ops(int class_count, string final_tensor_name, - Tensor bottleneck_tensor, bool quantize_layer, bool is_training) - { - var (batch_size, bottleneck_tensor_size) = (bottleneck_tensor.shape.dims[0], bottleneck_tensor.shape.dims[1]); - tf_with(tf.name_scope("input"), scope => - { - bottleneck_input = tf.placeholder_with_default( - bottleneck_tensor, - shape: bottleneck_tensor.shape, - name: "BottleneckInputPlaceholder"); - - ground_truth_input = tf.placeholder(tf.int64, new Shape(batch_size), name: "GroundTruthInput"); - }); - - // Organizing the following ops so they are easier to see in TensorBoard. - string layer_name = "final_retrain_ops"; - Tensor logits = null; - tf_with(tf.name_scope(layer_name), scope => - { - IVariableV1 layer_weights = null; - tf_with(tf.name_scope("weights"), delegate - { - var initial_value = tf.truncated_normal((bottleneck_tensor_size, (long)class_count), stddev: 0.001f); - layer_weights = tf.Variable(initial_value, name: "final_weights"); - variable_summaries(layer_weights.AsTensor()); - }); - - IVariableV1 layer_biases = null; - tf_with(tf.name_scope("biases"), delegate - { - layer_biases = tf.Variable(tf.zeros(new Shape(class_count)), name: "final_biases"); - variable_summaries(layer_biases.AsTensor()); - }); - - tf_with(tf.name_scope("Wx_plus_b"), delegate - { - logits = tf.matmul(bottleneck_input, layer_weights.AsTensor()) + layer_biases.AsTensor(); - tf.summary.histogram("pre_activations", logits); - }); - }); - - final_tensor = tf.nn.softmax(logits, name: final_tensor_name); - - // The tf.contrib.quantize functions rewrite the graph in place for - // quantization. The imported model graph has already been rewritten, so upon - // calling these rewrites, only the newly added final layer will be - // transformed. - if (quantize_layer) - { - throw new NotImplementedException("quantize_layer"); - /*if (is_training) - tf.contrib.quantize.create_training_graph(); - else - tf.contrib.quantize.create_eval_graph();*/ - } - - tf.summary.histogram("activations", final_tensor); - - // If this is an eval graph, we don't need to add loss ops or an optimizer. - if (!is_training) - return (null, null, bottleneck_input, ground_truth_input, final_tensor); - - Tensor cross_entropy_mean = null; - tf_with(tf.name_scope("cross_entropy"), delegate - { - cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy( - labels: ground_truth_input, logits: logits); - }); - - tf.summary.scalar("cross_entropy", cross_entropy_mean); - - tf_with(tf.name_scope("train"), delegate - { - var optimizer = tf.train.GradientDescentOptimizer(learning_rate); - train_step = optimizer.minimize(cross_entropy_mean); - }); - - return (train_step, cross_entropy_mean, bottleneck_input, ground_truth_input, - final_tensor); - } - - private void variable_summaries(Tensor var) - { - tf_with(tf.name_scope("summaries"), delegate - { - var mean = tf.reduce_mean(var); - tf.summary.scalar("mean", mean); - Tensor stddev = null; - tf_with(tf.name_scope("stddev"), delegate - { - stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))); - }); - tf.summary.scalar("stddev", stddev); - tf.summary.scalar("max", tf.reduce_max(var)); - tf.summary.scalar("min", tf.reduce_min(var)); - tf.summary.histogram("histogram", var); - }); - } - - private (Graph, Tensor, Tensor, bool) create_module_graph() - { - var (height, width) = (299, 299); - var graph = tf.Graph().as_default(); - tf.train.import_meta_graph("graph/InceptionV3.meta"); - var vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES); - Tensor resized_input_tensor = graph.OperationByName(input_tensor_name); //tf.placeholder(tf.float32, new TensorShape(-1, height, width, 3)); - // var m = hub.Module(module_spec); - Tensor bottleneck_tensor = graph.OperationByName("module_apply_default/hub_output/feature_vector/SpatialSqueeze");// m(resized_input_tensor); - var wants_quantization = false; - return (graph, bottleneck_tensor, resized_input_tensor, wants_quantization); - } - - private (NDArray, long[], string[]) get_random_cached_bottlenecks(Session sess, Dictionary> image_lists, - int how_many, string category, string bottleneck_dir, string image_dir, - Tensor jpeg_data_tensor, Tensor decoded_image_tensor, Tensor resized_input_tensor, - Tensor bottleneck_tensor, string module_name) - { - float[,] bottlenecks; - var ground_truths = new List(); - var filenames = new List(); - class_count = image_lists.Keys.Count; - if (how_many >= 0) - { - bottlenecks = new float[how_many, 2048]; - // Retrieve a random sample of bottlenecks. - foreach (var unused_i in range(how_many)) - { - int label_index = new Random().Next(class_count); - string label_name = image_lists.Keys.ToArray()[label_index]; - int image_index = new Random().Next(MAX_NUM_IMAGES_PER_CLASS); - string image_name = get_image_path(image_lists, label_name, image_index, - image_dir, category); - var bottleneck = get_or_create_bottleneck( - sess, image_lists, label_name, image_index, image_dir, category, - bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, - resized_input_tensor, bottleneck_tensor, module_name); - for (int col = 0; col < bottleneck.Length; col++) - bottlenecks[unused_i, col] = bottleneck[col]; - ground_truths.Add(label_index); - filenames.Add(image_name); - } - } - else - { - how_many = 0; - // Retrieve all bottlenecks. - foreach (var (label_index, label_name) in enumerate(image_lists.Keys.ToArray())) - how_many += image_lists[label_name][category].Length; - bottlenecks = new float[how_many, 2048]; - - var row = 0; - foreach (var (label_index, label_name) in enumerate(image_lists.Keys.ToArray())) - { - foreach (var (image_index, image_name) in enumerate(image_lists[label_name][category])) - { - var bottleneck = get_or_create_bottleneck( - sess, image_lists, label_name, image_index, image_dir, category, - bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, - resized_input_tensor, bottleneck_tensor, module_name); - - for (int col = 0; col < bottleneck.Length; col++) - bottlenecks[row, col] = bottleneck[col]; - row++; - ground_truths.Add(label_index); - filenames.Add(image_name); - } - } - } - - return (bottlenecks, ground_truths.ToArray(), filenames.ToArray()); - } - - /// - /// Inserts the operations we need to evaluate the accuracy of our results. - /// - /// - /// - /// - private (Tensor, Tensor) add_evaluation_step(Tensor result_tensor, Tensor ground_truth_tensor) - { - Tensor evaluation_step = null, correct_prediction = null, prediction = null; - - tf_with(tf.name_scope("accuracy"), scope => - { - tf_with(tf.name_scope("correct_prediction"), delegate - { - prediction = tf.math.argmax(result_tensor, 1); - correct_prediction = tf.equal(prediction, ground_truth_tensor); - }); - - tf_with(tf.name_scope("accuracy"), delegate - { - evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)); - }); - }); - - tf.summary.scalar("accuracy", evaluation_step); - return (evaluation_step, prediction); - } - - /// - /// Ensures all the training, testing, and validation bottlenecks are cached. - /// - /// - /// - /// - /// - /// - /// - /// - /// - /// - private void cache_bottlenecks(Session sess, Dictionary> image_lists, - string image_dir, string bottleneck_dir, Tensor jpeg_data_tensor, Tensor decoded_image_tensor, - Tensor resized_input_tensor, Tensor bottleneck_tensor, string module_name) - { - int how_many_bottlenecks = 0; - var kvs = image_lists.ToArray(); - var categories = new string[] { "training", "testing", "validation" }; - Parallel.For(0, kvs.Length, i => - { - var (label_name, label_lists) = kvs[i]; - - Parallel.For(0, categories.Length, j => - { - var category = categories[j]; - var category_list = label_lists[category]; - foreach (var (index, unused_base_name) in enumerate(category_list)) - { - get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir, category, - bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, - resized_input_tensor, bottleneck_tensor, module_name); - how_many_bottlenecks++; - if (how_many_bottlenecks % 300 == 0) - print($"{how_many_bottlenecks} bottleneck files created."); - } - }); - }); - } - - private float[] get_or_create_bottleneck(Session sess, Dictionary> image_lists, - string label_name, int index, string image_dir, string category, string bottleneck_dir, - Tensor jpeg_data_tensor, Tensor decoded_image_tensor, Tensor resized_input_tensor, - Tensor bottleneck_tensor, string module_name) - { - var label_lists = image_lists[label_name]; - var sub_dir_path = Path.Join(bottleneck_dir, label_name); - Directory.CreateDirectory(sub_dir_path); - string bottleneck_path = get_bottleneck_path(image_lists, label_name, index, - bottleneck_dir, category, module_name); - - if (!File.Exists(bottleneck_path)) - create_bottleneck_file(bottleneck_path, image_lists, label_name, index, - image_dir, category, sess, jpeg_data_tensor, - decoded_image_tensor, resized_input_tensor, - bottleneck_tensor); - var bottleneck_string = File.ReadAllText(bottleneck_path); - var bottleneck_values = Array.ConvertAll(bottleneck_string.Split(','), x => float.Parse(x)); - return bottleneck_values; - } - - private void create_bottleneck_file(string bottleneck_path, Dictionary> image_lists, - string label_name, int index, string image_dir, string category, Session sess, - Tensor jpeg_data_tensor, Tensor decoded_image_tensor, Tensor resized_input_tensor, Tensor bottleneck_tensor) - { - // Create a single bottleneck file. - print("Creating bottleneck at " + bottleneck_path); - var image_path = get_image_path(image_lists, label_name, index, image_dir, category); - if (!File.Exists(image_path)) - print($"File does not exist {image_path}"); - - var image_data = File.ReadAllBytes(image_path); - var bottleneck_values = run_bottleneck_on_image( - sess, image_data, jpeg_data_tensor, decoded_image_tensor, - resized_input_tensor, bottleneck_tensor); - var values = bottleneck_values.ToArray(); - var bottleneck_string = string.Join(",", values); - File.WriteAllText(bottleneck_path, bottleneck_string); - } - - /// - /// Runs inference on an image to extract the 'bottleneck' summary layer. - /// - /// Current active TensorFlow Session. - /// Data of raw JPEG data. - /// Input data layer in the graph. - /// Output of initial image resizing and preprocessing. - /// The input node of the recognition graph. - /// Layer before the final softmax. - /// - private NDArray run_bottleneck_on_image(Session sess, byte[] image_data, Tensor image_data_tensor, - Tensor decoded_image_tensor, Tensor resized_input_tensor, Tensor bottleneck_tensor) - { - // First decode the JPEG image, resize it, and rescale the pixel values. - var resized_input_values = sess.run(decoded_image_tensor, new FeedItem(image_data_tensor, new NDArray(image_data))); - // Then run it through the recognition network. - var bottleneck_values = sess.run(bottleneck_tensor, new FeedItem(resized_input_tensor, resized_input_values))[0]; - bottleneck_values = np.squeeze(bottleneck_values); - return bottleneck_values; - } - - private string get_bottleneck_path(Dictionary> image_lists, string label_name, int index, - string bottleneck_dir, string category, string module_name) - { - module_name = (module_name.Replace("://", "~") // URL scheme. - .Replace('/', '~') // URL and Unix paths. - .Replace(':', '~').Replace('\\', '~')); // Windows paths. - return get_image_path(image_lists, label_name, index, bottleneck_dir, - category) + "_" + module_name + ".txt"; - } - - private string get_image_path(Dictionary> image_lists, string label_name, - int index, string image_dir, string category) - { - if (!image_lists.ContainsKey(label_name)) - print($"Label does not exist {label_name}"); - - var label_lists = image_lists[label_name]; - if (!label_lists.ContainsKey(category)) - print($"Category does not exist {category}"); - var category_list = label_lists[category]; - if (category_list.Length == 0) - print($"Label {label_name} has no images in the category {category}."); - - var mod_index = index % len(category_list); - var base_name = category_list[mod_index].Split(Path.DirectorySeparatorChar).Last(); - var sub_dir = label_name; - var full_path = Path.Join(image_dir, sub_dir, base_name); - return full_path; - } - - /// - /// Saves an graph to file, creating a valid quantized one if necessary. - /// - /// - /// - private void save_graph_to_file(string graph_file_name, int class_count) - { - var (sess, _, _, _, _, _) = build_eval_session(class_count); - var graph = sess.graph; - var output_graph_def = tf.graph_util.convert_variables_to_constants( - sess, graph.as_graph_def(), new string[] { final_tensor_name }); - File.WriteAllBytes(graph_file_name, output_graph_def.ToByteArray()); - } - - public override void PrepareData() + public override void Train() { // get a set of images to teach the network about the new classes string fileName = "flower_photos.tgz"; + string dataDir = "image_classification_v1"; string url = $"http://download.tensorflow.org/example_images/{fileName}"; - Web.Download(url, data_dir, fileName); - Compress.ExtractTGZ(Path.Join(data_dir, fileName), data_dir); - - // download graph meta data - url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/InceptionV3.meta"; - Web.Download(url, "graph", "InceptionV3.meta"); - - // download variables.data checkpoint file. - url = "https://github.com/SciSharp/TensorFlow.NET/raw/master/data/tfhub_modules.zip"; - Web.Download(url, data_dir, "tfhub_modules.zip"); - Compress.UnZip(Path.Join(data_dir, "tfhub_modules.zip"), "tfhub_modules"); - - // Prepare necessary directories that can be used during training - Directory.CreateDirectory(summaries_dir); - Directory.CreateDirectory(bottleneck_dir); - - // Look at the folder structure, and create lists of all the images. - image_lists = create_image_lists(); - class_count = len(image_lists); - if (class_count == 0) - print($"No valid folders of images found at {image_dir}"); - if (class_count == 1) - print("Only one valid folder of images found at " + - image_dir + - " - multiple classes are needed for classification."); - } - - private (Tensor, Tensor) add_jpeg_decoding() - { - // height, width, depth - var input_dim = (299, 299, 3); - var jpeg_data = tf.placeholder(tf.@string, name: "DecodeJPGInput"); - var decoded_image = tf.image.decode_jpeg(jpeg_data, channels: input_dim.Item3); - // Convert from full range of uint8 to range [0,1] of float32. - var decoded_image_as_float = tf.image.convert_image_dtype(decoded_image, tf.float32); - var decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0); - var resize_shape = tf.stack(new int[] { input_dim.Item1, input_dim.Item2 }); - var resize_shape_as_int = tf.cast(resize_shape, dtype: tf.int32); - var resized_image = tf.image.resize_bilinear(decoded_image_4d, resize_shape_as_int); - return (jpeg_data, resized_image); - } - - /// - /// Builds a list of training images from the file system. - /// - private Dictionary> create_image_lists() - { - var sub_dirs = tf.gfile.Walk(image_dir) - .Select(x => x.Item1) - .OrderBy(x => x) - .ToArray(); - - var result = new Dictionary>(); - - foreach (var sub_dir in sub_dirs) - { - var dir_name = sub_dir.Split(Path.DirectorySeparatorChar).Last(); - print($"Looking for images in '{dir_name}'"); - var file_list = Directory.GetFiles(sub_dir); - if (len(file_list) < 20) - print($"WARNING: Folder has less than 20 images, which may cause issues."); - - var label_name = dir_name.ToLower(); - result[label_name] = new Dictionary(); - int testing_count = (int)Math.Floor(file_list.Length * testing_percentage); - int validation_count = (int)Math.Floor(file_list.Length * validation_percentage); - result[label_name]["testing"] = file_list.Take(testing_count).ToArray(); - result[label_name]["validation"] = file_list.Skip(testing_count).Take(validation_count).ToArray(); - result[label_name]["training"] = file_list.Skip(testing_count + validation_count).ToArray(); - } + Web.Download(url, dataDir, fileName); + Compress.ExtractTGZ(Path.Join(dataDir, fileName), dataDir); - return result; - } - - public override Graph ImportGraph() - { - Graph graph; - - // Set up the pre-trained graph. - (graph, bottleneck_tensor, resized_image_tensor, wants_quantization) = - create_module_graph(); - - // Add the new layer that we'll be training. - (train_step, cross_entropy, bottleneck_input, - ground_truth_input, final_tensor) = add_final_retrain_ops( - class_count, final_tensor_name, bottleneck_tensor, - wants_quantization, is_training: true); - - return graph; - } - - public override void Train() - { - var sw = new Stopwatch(); - var graph = Config.IsImportingGraph ? ImportGraph() : BuildGraph(); - - using (var sess = tf.Session(graph)) + // using wizard to train model + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions { - // Initialize all weights: for the module to their pretrained values, - // and for the newly added retraining layer to random initial values. - var init = tf.global_variables_initializer(); - sess.run(init); - - var (jpeg_data_tensor, decoded_image_tensor) = add_jpeg_decoding(); - - // We'll make sure we've calculated the 'bottleneck' image summaries and - // cached them on disk. - cache_bottlenecks(sess, image_lists, image_dir, - bottleneck_dir, jpeg_data_tensor, - decoded_image_tensor, resized_image_tensor, - bottleneck_tensor, tfhub_module); - - // Create the operations we need to evaluate the accuracy of our new layer. - var (evaluation_step, _) = add_evaluation_step(final_tensor, ground_truth_input); - - // Merge all the summaries and write them out to the summaries_dir - var merged = tf.summary.merge_all(); - var train_writer = tf.summary.FileWriter(summaries_dir + "/train", sess.graph); - var validation_writer = tf.summary.FileWriter(summaries_dir + "/validation", sess.graph); - - // Create a train saver that is used to restore values into an eval graph - // when exporting models. - var train_saver = tf.train.Saver(); - train_saver.save(sess, CHECKPOINT_NAME); - - sw.Restart(); - - for (int i = 0; i < how_many_training_steps; i++) - { - var (train_bottlenecks, train_ground_truth, _) = get_random_cached_bottlenecks( - sess, image_lists, train_batch_size, "training", - bottleneck_dir, image_dir, jpeg_data_tensor, - decoded_image_tensor, resized_image_tensor, bottleneck_tensor, - tfhub_module); - - // Feed the bottlenecks and ground truth into the graph, and run a training - // step. Capture training summaries for TensorBoard with the `merged` op. - var results = sess.run( - new ITensorOrOperation[] { merged, train_step }, - new FeedItem(bottleneck_input, train_bottlenecks), - new FeedItem(ground_truth_input, train_ground_truth)); - var train_summary = results[0]; - - // TODO - // train_writer.add_summary(train_summary, i); - - // Every so often, print out how well the graph is training. - bool is_last_step = (i + 1 == how_many_training_steps); - if ((i % eval_step_interval) == 0 || is_last_step) - { - (float train_accuracy, float cross_entropy_value) = sess.run((evaluation_step, cross_entropy), - (bottleneck_input, train_bottlenecks), - (ground_truth_input, train_ground_truth)); - print($"{DateTime.Now}: Step {i + 1}: Train accuracy = {train_accuracy * 100}%, Cross entropy = {cross_entropy_value.ToString("G4")}"); - - var (validation_bottlenecks, validation_ground_truth, _) = get_random_cached_bottlenecks( - sess, image_lists, validation_batch_size, "validation", - bottleneck_dir, image_dir, jpeg_data_tensor, - decoded_image_tensor, resized_image_tensor, bottleneck_tensor, - tfhub_module); - - // Run a validation step and capture training summaries for TensorBoard - // with the `merged` op. - (_, float validation_accuracy) = sess.run((merged, evaluation_step), - (bottleneck_input, validation_bottlenecks), - (ground_truth_input, validation_ground_truth)); - - // validation_writer.add_summary(validation_summary, i); - print($"{DateTime.Now}: Step {i + 1}: Validation accuracy = {validation_accuracy * 100}% (N={len(validation_bottlenecks)}) {sw.ElapsedMilliseconds}ms"); - sw.Restart(); - } - - // Store intermediate results - int intermediate_frequency = intermediate_store_frequency; - if (intermediate_frequency > 0 && i % intermediate_frequency == 0 && i > 0) - { - - } - } - - // After training is complete, force one last save of the train checkpoint. - train_saver.save(sess, CHECKPOINT_NAME); - - // We've completed all our training, so run a final test evaluation on - // some new images we haven't used before. - (test_accuracy, predictions) = run_final_eval(sess, null, class_count, image_lists, - jpeg_data_tensor, decoded_image_tensor, resized_image_tensor, - bottleneck_tensor); - - // Write out the trained graph and labels with the weights stored as - // constants. - print($"Save final result to : {output_graph}"); - save_graph_to_file(output_graph, class_count); - File.WriteAllText(output_labels, string.Join("\n", image_lists.Keys)); - } + DataDir = @"image_classification_v1\flower_photos", + }); + task.Train(new TrainingOptions()); } /// @@ -730,70 +77,31 @@ public override void Train() /// 3 - sunflowers /// 4 - tulips /// - /// public override void Predict() { - if (!File.Exists(output_graph)) - return; - - var labels = File.ReadAllLines(output_labels); - // predict image - var img_path = Path.Join(image_dir, "daisy", "5547758_eea9edfd54_n.jpg"); - var fileBytes = ReadTensorFromImageFile(img_path); - - // import graph and variables - var graph = new Graph(); - graph.Import(output_graph, ""); - - Tensor input = graph.OperationByName(input_tensor_name); - Tensor output = graph.OperationByName(final_tensor_name); - - using (var sess = tf.Session(graph)) + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions { - var result = sess.run(output, (input, fileBytes)); - var prob = np.squeeze(result); - var idx = np.argmax(prob); - print($"Prediction result: [{labels[idx]} {prob[idx]}] for {img_path}."); - } - } - - private NDArray ReadTensorFromImageFile(string file_name, - int input_height = 299, - int input_width = 299, - int input_mean = 0, - int input_std = 255) - { - var graph = tf.Graph().as_default(); - - var file_reader = tf.io.read_file(file_name, "file_reader"); - var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader"); - var caster = tf.cast(image_reader, tf.float32); - var dims_expander = tf.expand_dims(caster, 0); - var resize = tf.constant(new int[] { input_height, input_width }); - var bilinear = tf.image.resize_bilinear(dims_expander, resize); - var sub = tf.subtract(bilinear, new float[] { input_mean }); - var normalized = tf.divide(sub, new float[] { input_std }); + ModelPath = @"image_classification_v1\saved_model.pb" + }); - using (var sess = tf.Session(graph)) - return sess.run(normalized); + var imgPath = Path.Join("image_classification_v1", "flower_photos", "daisy", "5547758_eea9edfd54_n.jpg"); + var input = ImageUtil.ReadImageFromFile(imgPath); + var result = task.Predict(input); + Debug.Assert(result.Label == "daisy"); } public override void Test() { - if (!File.Exists(output_graph)) - return; - - var graph = new Graph(); - graph.Import(output_graph); - var (jpeg_data_tensor, decoded_image_tensor) = add_jpeg_decoding(); - - tf_with(tf.Session(graph), sess => + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions { - (test_accuracy, predictions) = run_final_eval(sess, null, class_count, image_lists, - jpeg_data_tensor, decoded_image_tensor, resized_image_tensor, - bottleneck_tensor); + DataDir = @"image_classification_v1\flower_photos", + ModelPath = @"image_classification_v1\saved_model.pb" }); + var result = task.Test(); + accuracy = result.Accuracy; } } } diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index a99ac2a..5e4d5a0 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -41,10 +41,10 @@ - - + + + - diff --git a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs new file mode 100644 index 0000000..a8502f7 --- /dev/null +++ b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs @@ -0,0 +1,23 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; + +namespace TensorFlowNET.Examples +{ + public class WeatherPrediction : SciSharpExample, IExample + { + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Weather Prediction", + Enabled = true + }; + + public bool Run() + { + return true; + } + } +} From 83668294eda65e5bc73c6d7d6276c1fa5057a88a Mon Sep 17 00:00:00 2001 From: Oceania2018 Date: Sat, 30 Oct 2021 08:46:49 -0500 Subject: [PATCH 10/39] upgrade SciSharp.Models.ImageClassification to v0.3.0. --- .../BasicModels/NaiveBayesClassifier.cs | 4 +- src/TensorFlowNET.Examples/GAN/MnistGAN.cs | 2 +- .../ImageProcessing/CnnInYourOwnData.cs | 25 +- .../ImageProcessing/DigitRecognitionCNN.cs | 304 +++--------------- .../ImageRecognitionInception.cs | 2 +- .../ImageProcessing/MnistCnnKerasSubclass.cs | 2 +- .../TransferLearningWithInceptionV3.cs | 9 +- .../TensorFlowNET.Examples.csproj | 2 +- .../BinaryTextClassification.cs | 2 +- 9 files changed, 78 insertions(+), 274 deletions(-) diff --git a/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs b/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs index 8504ead..2a62536 100644 --- a/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs +++ b/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs @@ -58,7 +58,7 @@ public bool Run() var (xx, yy) = np.meshgrid(np.linspace(x_min, x_max, 30), np.linspace(y_min, y_max, 30)); var array = np.Load(Path.Join("nb", "nb_example.npy")); var samples = np.array(array).astype(np.float32); - var Z = predict(samples).eval(); + var Z = predict(samples); return true; } @@ -93,7 +93,7 @@ public void fit(NDArray X, NDArray y) float[,,] points = new float[dic.Count, maxCount, X.shape[1]]; foreach (KeyValuePair>> kv in dic) { - int j = (int)kv.Key; + int j = kv.Key; for (int i = 0; i < maxCount; i++) { for (int k = 0; k < X.shape[1]; k++) diff --git a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs index 6c826d9..79a0303 100644 --- a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs +++ b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs @@ -16,7 +16,7 @@ namespace TensorFlowNET.Examples.GAN public class MnistGAN : SciSharpExample, IExample { float LeakyReLU_alpha = 0.2f; - int epochs = 100; + int epochs = 20; //int epochs = 2000; // Better effect, but longer time int batch_size = 64; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs index a613d80..e615507 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs @@ -116,14 +116,37 @@ public bool Run() BuildGraph(); sess = tf.Session(); - Train(); Test(); TestDataOutput(); + FreezeModel(); + LoadAndPredict(); + return accuracy_test > 0.98; + } + public void LoadAndPredict() + { + using var graph = tf.Graph().as_default(); + graph.Import(Path.Combine(Config.Name, "model.pb")); + Tensor x = graph.OperationByName("Input/X"); + Tensor prediction = graph.OperationByName("Train/Prediction/predictions"); + Tensor probility = graph.OperationByName("Train/Prediction/prob"); + + using var sess = tf.Session(graph); + var (prediction_result, probility_result) = sess.run((prediction, probility), (x, x_test)); + print($"Prediction result: {prediction_result}"); + } + + public override string FreezeModel() + { + return tf.train.freeze_graph(Config.Name + "\\MODEL", "model", new[] + { + "Train/Prediction/predictions", + "Train/Prediction/prob" + }); } #region PrepareData diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs index 3fef9c5..3a69c74 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs @@ -14,8 +14,11 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ +using SciSharp.Models; +using SciSharp.Models.ImageClassification; using System.Diagnostics; using System.IO; +using System.Linq; using Tensorflow; using Tensorflow.NumPy; using static Tensorflow.Binding; @@ -30,37 +33,9 @@ namespace TensorFlowNET.Examples /// public class DigitRecognitionCNN : SciSharpExample, IExample { - const int img_h = 28, img_w = 28; // MNIST images are 28x28 - int n_classes = 10; // Number of classes, one class per digit - int n_channels = 1; - - // Hyper-parameters - int epochs = 5; // accuracy > 98% - int batch_size = 100; - float learning_rate = 0.001f; Datasets mnist; - // Network configuration - // 1st Convolutional Layer - int filter_size1 = 5; // Convolution filters are 5 x 5 pixels. - int num_filters1 = 16; // There are 16 of these filters. - int stride1 = 1; // The stride of the sliding window - - // 2nd Convolutional Layer - int filter_size2 = 5; // Convolution filters are 5 x 5 pixels. - int num_filters2 = 32;// There are 32 of these filters. - int stride2 = 1; // The stride of the sliding window - - // Fully-connected layer. - int h1 = 128; // Number of neurons in fully-connected layer. - - Tensor x, y; - Tensor loss, accuracy, cls_prediction; - Operation optimizer; - - int display_freq = 100; float accuracy_test = 0f; - float loss_test = 1f; NDArray x_train, y_train; NDArray x_valid, y_valid; @@ -71,265 +46,68 @@ public ExampleConfig InitConfig() { Name = "MNIST CNN (Graph)", Enabled = true, - IsImportingGraph = false, Priority = 15 }; public bool Run() { - tf.compat.v1.disable_eager_execution(); - PrepareData(); - - Train(); + //Train(); Test(); + Predict(); - return accuracy_test > 0.98; + return accuracy_test > 0.95; } - public override Graph BuildGraph() + public override void Train() { - var graph = new Graph().as_default(); - - tf_with(tf.name_scope("Input"), delegate + // using wizard to train model + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions { - // Placeholders for inputs (x) and outputs(y) - x = tf.placeholder(tf.float32, shape: (-1, img_h, img_w, n_channels), name: "X"); - y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y"); + InputShape = (28, 28, 1), + NumberOfClass = 10, }); - - var conv1 = conv_layer(x, filter_size1, num_filters1, stride1, name: "conv1"); - var pool1 = max_pool(conv1, ksize: 2, stride: 2, name: "pool1"); - var conv2 = conv_layer(pool1, filter_size2, num_filters2, stride2, name: "conv2"); - var pool2 = max_pool(conv2, ksize: 2, stride: 2, name: "pool2"); - var layer_flat = flatten_layer(pool2); - var fc1 = fc_layer(layer_flat, h1, "FC1", use_relu: true); - var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false); - - tf_with(tf.variable_scope("Train"), delegate + task.SetModelArgs(new ConvArgs { - tf_with(tf.variable_scope("Loss"), delegate - { - loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits), name: "loss"); - }); - - tf_with(tf.variable_scope("Optimizer"), delegate - { - optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss); - }); - - tf_with(tf.variable_scope("Accuracy"), delegate - { - var correct_prediction = tf.equal(tf.math.argmax(output_logits, 1), tf.math.argmax(y, 1), name: "correct_pred"); - accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy"); - }); - - tf_with(tf.variable_scope("Prediction"), delegate - { - cls_prediction = tf.math.argmax(output_logits, axis: 1, name: "predictions"); - }); + NumberOfNeurons = 128 }); - - return graph; - } - - public override void Train() - { - var graph = BuildGraph(); - using (var sess = tf.Session(graph)) + task.Train(new TrainingOptions { - - // Number of training iterations in each epoch - var num_tr_iter = (int)y_train.shape[0] / batch_size; - - var init = tf.global_variables_initializer(); - sess.run(init); - - float loss_val = 100.0f; - float accuracy_val = 0f; - - var sw = new Stopwatch(); - sw.Start(); - foreach (var epoch in range(epochs)) - { - print($"Training epochs: {epoch + 1}/{epochs}"); - // Randomly shuffle the training data at the beginning of each epoch - (x_train, y_train) = mnist.Randomize(x_train, y_train); - - foreach (var iteration in range(num_tr_iter)) - { - var start = iteration * batch_size; - var end = (iteration + 1) * batch_size; - var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end); - - // Run optimization op (backprop) - sess.run(optimizer, (x, x_batch), (y, y_batch)); - - if (iteration % display_freq == 0) - { - // Calculate and display the batch loss and accuracy - (loss_val, accuracy_val) = sess.run((loss, accuracy), new FeedItem(x, x_batch), new FeedItem(y, y_batch)); - print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")} {sw.ElapsedMilliseconds}ms"); - sw.Restart(); - } - } - - // Run validation after every epoch - (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, x_valid), (y, y_valid)); - print("---------------------------------------------------------"); - print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}"); - print("---------------------------------------------------------"); - } - - SaveCheckpoint(sess); - } - } - - public override string FreezeModel() - { - return tf.train.freeze_graph(Config.Name, - "model", - new[] { "Train/Loss/loss,Train/Accuracy/accuracy" }); + Epochs = 5, + TrainingData = new FeatureAndLabel(x_train, y_train), + ValidationData = new FeatureAndLabel(x_valid, y_valid) + }); } public override void Test() { - using (var graph = tf.Graph().as_default()) - using (var sess = tf.Session(graph)) + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions { - var saver = tf.train.import_meta_graph(Path.Combine(Config.Name, "mnist_cnn.ckpt.meta")); - // Restore variables from checkpoint - saver.restore(sess, tf.train.latest_checkpoint(Config.Name)); - - loss = graph.get_tensor_by_name("Train/Loss/loss:0"); - accuracy = graph.get_tensor_by_name("Train/Accuracy/accuracy:0"); - x = graph.get_tensor_by_name("Input/X:0"); - y = graph.get_tensor_by_name("Input/Y:0"); - - //var init = tf.global_variables_initializer(); - //sess.run(init); - - (loss_test, accuracy_test) = sess.run((loss, accuracy), (x, x_test), (y, y_test)); - print("---------------------------------------------------------"); - print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}"); - print("---------------------------------------------------------"); - } - } - - /// - /// Create a 2D convolution layer - /// - /// input from previous layer - /// size of each filter - /// number of filters(or output feature maps) - /// filter stride - /// layer name - /// The output array - private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride, string name) - { - return tf_with(tf.variable_scope(name), delegate - { - - var num_in_channel = x.shape[x.ndim - 1]; - var shape = new int[] { filter_size, filter_size, (int)num_in_channel, num_filters }; - var W = weight_variable("W", shape); - // var tf.summary.histogram("weight", W); - var b = bias_variable("b", new[] { num_filters }); - // tf.summary.histogram("bias", b); - var layer = tf.nn.conv2d(x, W, - strides: new int[] { 1, stride, stride, 1 }, - padding: "SAME"); - layer += b.AsTensor(); - return tf.nn.relu(layer); + ModelPath = @"image_classification_cnn_v1\saved_model.pb" }); - } - - /// - /// Create a max pooling layer - /// - /// input to max-pooling layer - /// size of the max-pooling filter - /// stride of the max-pooling filter - /// layer name - /// The output array - private Tensor max_pool(Tensor x, int ksize, int stride, string name) - { - return tf.nn.max_pool(x, - ksize: new[] { 1, ksize, ksize, 1 }, - strides: new[] { 1, stride, stride, 1 }, - padding: "SAME", - name: name); - } - - /// - /// Flattens the output of the convolutional layer to be fed into fully-connected layer - /// - /// input array - /// flattened array - private Tensor flatten_layer(Tensor layer) - { - return tf_with(tf.variable_scope("Flatten_layer"), delegate + var result = task.Test(new TestingOptions { - var layer_shape = layer.shape; - var num_features = layer_shape[new Slice(1, 4)].size; - var layer_flat = tf.reshape(layer, new[] { -1, num_features }); - - return layer_flat; + TestingData = new FeatureAndLabel(x_test, y_test) }); + accuracy_test = result.Accuracy; } - /// - /// Create a weight variable with appropriate initialization - /// - /// - /// - /// - private IVariableV1 weight_variable(string name, int[] shape) + public override void Predict() { - var initer = tf.truncated_normal_initializer(stddev: 0.01f); - return tf.compat.v1.get_variable(name, - dtype: tf.float32, - shape: shape, - initializer: initer); - } - - /// - /// Create a bias variable with appropriate initialization - /// - /// - /// - /// - private IVariableV1 bias_variable(string name, int[] shape) - { - var initial = tf.constant(0f, shape: shape, dtype: tf.float32); - return tf.compat.v1.get_variable(name, - dtype: tf.float32, - initializer: initial); - } - - /// - /// Create a fully-connected layer - /// - /// input from previous layer - /// number of hidden units in the fully-connected layer - /// layer name - /// boolean to add ReLU non-linearity (or not) - /// The output array - private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true) - { - return tf_with(tf.variable_scope(name), delegate + // predict image + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions { - var in_dim = x.shape[1]; - - var W = weight_variable("W_" + name, shape: new[] { (int)in_dim, num_units }); - var b = bias_variable("b_" + name, new[] { num_units }); - - var layer = tf.matmul(x, W.AsTensor()) + b.AsTensor(); - if (use_relu) - layer = tf.nn.relu(layer); - - return layer; + LabelPath = @"image_classification_cnn_v1\labels.txt", + ModelPath = @"image_classification_cnn_v1\saved_model.pb" }); + + var input = x_test["0:1"]; + var result = task.Predict(input); + long output = np.argmax(y_test[0]); + Debug.Assert(result.Label == output.ToString()); } public override void PrepareData() @@ -344,6 +122,10 @@ public override void PrepareData() print("Size of:"); print($"- Training-set:\t\t{len(mnist.Train.Data)}"); print($"- Validation-set:\t{len(mnist.Validation.Data)}"); + + // generate labels + var labels = range(0, 9).Select(x => x.ToString()); + File.WriteAllLines(@"image_classification_cnn_v1\labels.txt", labels); } /// @@ -361,11 +143,5 @@ public override void PrepareData() //var labels = (np.arange(num_class) == y.reshape(y.shape[0], 1, y.shape[1])).astype(np.float32); return (dataset, y); } - - public void SaveCheckpoint(Session sess) - { - var saver = tf.train.Saver(); - saver.save(sess, Path.Combine(Config.Name, "mnist_cnn.ckpt")); - } } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs index ff571b5..221ddd6 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs @@ -36,7 +36,7 @@ public bool Run() PrepareData(); - var graph = new Graph(); + var graph = tf.Graph().as_default(); //import GraphDef from pb file graph.Import(Path.Join(dir, pbFile)); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index dbdf8c5..5a144a8 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -94,7 +94,7 @@ public bool Run() print($"Test Accuracy: {accuracy_test}"); } - return accuracy_test > 0.90; + return accuracy_test > 0.85; } void run_optimization(ConvNet conv_net, OptimizerV2 optimizer, Tensor x, Tensor y) diff --git a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs index 977e2c3..286895b 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs @@ -65,7 +65,10 @@ public override void Train() { DataDir = @"image_classification_v1\flower_photos", }); - task.Train(new TrainingOptions()); + task.Train(new TrainingOptions + { + TrainingSteps = 100 + }); } /// @@ -100,7 +103,9 @@ public override void Test() DataDir = @"image_classification_v1\flower_photos", ModelPath = @"image_classification_v1\saved_model.pb" }); - var result = task.Test(); + var result = task.Test(new TestingOptions + { + }); accuracy = result.Accuracy; } } diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 5e4d5a0..b5a15e9 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -41,7 +41,7 @@ - + diff --git a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs index 63b26d5..adcf7a6 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs @@ -24,7 +24,7 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "Binary Text Classification", - Enabled = true + Enabled = false }; public bool Run() From bedbd284058774883229452f2c0e781dbe4c137a Mon Sep 17 00:00:00 2001 From: Oceania2018 Date: Sat, 27 Nov 2021 09:04:58 -0600 Subject: [PATCH 11/39] align with tf.net v0.60.5. --- SciSharp STACK Examples.sln | 4 +- .../BasicModels/LogisticRegression.fs | 2 +- .../BasicModels/LogisticRegressionEager.fs | 2 +- .../ImageProcessing/CnnInYourOwnData.fs | 2 +- .../Operators.fs | 3 - src/TensorFlowNET.Examples/BasicEagerApi.cs | 4 +- .../BasicModels/KMeansClustering.cs | 3 +- .../BasicModels/LinearRegression.cs | 3 +- .../BasicModels/LinearRegressionEager.cs | 12 +-- .../BasicModels/LinearRegressionKeras.cs | 3 +- .../BasicModels/LogisticRegression.cs | 8 +- .../BasicModels/LogisticRegressionEager.cs | 3 +- .../BasicModels/NaiveBayesClassifier.cs | 3 +- .../BasicModels/NearestNeighbor.cs | 6 +- src/TensorFlowNET.Examples/BasicOperations.cs | 3 +- src/TensorFlowNET.Examples/ExampleConfig.cs | 2 - src/TensorFlowNET.Examples/GAN/MnistGAN.cs | 3 +- src/TensorFlowNET.Examples/HelloWorld.cs | 25 ++++-- .../ImageProcessing/CnnInYourOwnData.cs | 3 +- .../ImageProcessing/DigitRecognitionCNN.cs | 9 +- .../DigitRecognitionCnnEager.cs | 5 +- .../ImageProcessing/DigitRecognitionLSTM.cs | 6 +- .../ImageProcessing/DigitRecognitionNN.cs | 6 +- .../ImageProcessing/DigitRecognitionRNN.cs | 6 +- .../DigitRecognitionRnnKeras.cs | 3 +- .../ImageClassificationKeras.cs | 3 +- .../ImageProcessing/MnistCnnKerasSubclass.cs | 3 +- .../MnistFnnKerasFunctional.cs | 3 +- .../ImageProcessing/ToyResNet.cs | 2 +- .../TransferLearningWithInceptionV3.cs | 1 - .../NeuralNetworks/FullyConnected.cs | 3 +- .../NeuralNetworks/FullyConnectedEager.cs | 3 +- .../NeuralNetworks/FullyConnectedKeras.cs | 3 +- .../NeuralNetworks/NeuralNetXor.cs | 3 +- .../NeuralNetworks/NeuralNetXorKeras.cs | 3 +- src/TensorFlowNET.Examples/Program.cs | 87 ++++++++----------- .../TensorFlowNET.Examples.csproj | 3 +- 37 files changed, 109 insertions(+), 137 deletions(-) diff --git a/SciSharp STACK Examples.sln b/SciSharp STACK Examples.sln index 1aad0b2..49bd064 100644 --- a/SciSharp STACK Examples.sln +++ b/SciSharp STACK Examples.sln @@ -1,7 +1,7 @@  Microsoft Visual Studio Solution File, Format Version 12.00 -# Visual Studio Version 16 -VisualStudioVersion = 16.0.31515.178 +# Visual Studio Version 17 +VisualStudioVersion = 17.1.31911.260 MinimumVisualStudioVersion = 10.0.40219.1 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Examples", "src\TensorFlowNET.Examples\TensorFlowNET.Examples.csproj", "{3AC62662-7861-4C21-A402-82864F2A8AF7}" EndProject diff --git a/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegression.fs b/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegression.fs index 09db184..5cf1cf3 100644 --- a/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegression.fs +++ b/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegression.fs @@ -73,7 +73,7 @@ module LogisticRegression = let b = tf.Variable(tf.zeros(Shape(10).asTensorShape)) // Construct model - let pred = tf.nn.softmax(tf.matmul(x, W.asTensor) + b) // Softmax + let pred = tf.nn.softmax(tf.matmul(x, W.AsTensor()) + b) // Softmax // Minimize error using cross entropy let cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices = 1)) diff --git a/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegressionEager.fs b/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegressionEager.fs index 1c743c1..bfde70f 100644 --- a/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegressionEager.fs +++ b/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegressionEager.fs @@ -54,7 +54,7 @@ module LogisticRegressionEager = // Bias of shape [10], the total number of classes. let b = tf.Variable(tf.zeros(TensorShape num_classes), name = "bias") - let logistic_regression = fun x -> tf.nn.softmax(tf.matmul(x, W.asTensor) + b) + let logistic_regression = fun x -> tf.nn.softmax(tf.matmul(x, W.AsTensor()) + b) let cross_entropy = fun (y_pred, y_true) -> let y_true = tf.cast(y_true, TF_DataType.TF_UINT8) diff --git a/src/TensorFlowNET.Examples.FSharp/ImageProcessing/CnnInYourOwnData.fs b/src/TensorFlowNET.Examples.FSharp/ImageProcessing/CnnInYourOwnData.fs index c8cd661..188a912 100644 --- a/src/TensorFlowNET.Examples.FSharp/ImageProcessing/CnnInYourOwnData.fs +++ b/src/TensorFlowNET.Examples.FSharp/ImageProcessing/CnnInYourOwnData.fs @@ -116,7 +116,7 @@ module CnnInYourOwnData = use graph = tf.Graph().as_default() for i in 0 .. a.Length - 1 do let indices : obj[] = [| i |] - b.[indices] <- ReadTensorFromImageFile a.[i] graph + // b.[indices] <- ReadTensorFromImageFile a.[i] graph Console.Write(".") Console.WriteLine() Console.WriteLine("Load Images To NDArray: " + c) diff --git a/src/TensorFlowNET.Examples.FSharp/Operators.fs b/src/TensorFlowNET.Examples.FSharp/Operators.fs index fc208f8..0a2a0d7 100644 --- a/src/TensorFlowNET.Examples.FSharp/Operators.fs +++ b/src/TensorFlowNET.Examples.FSharp/Operators.fs @@ -22,9 +22,6 @@ open Tensorflow [] module TensorflowOperators = - type ResourceVariable with - member x.asTensor : Tensor = ResourceVariable.op_Implicit x - type NDArray with member x.asTensor : Tensor = Tensor.op_Implicit x diff --git a/src/TensorFlowNET.Examples/BasicEagerApi.cs b/src/TensorFlowNET.Examples/BasicEagerApi.cs index 3adcabb..c6afd3b 100644 --- a/src/TensorFlowNET.Examples/BasicEagerApi.cs +++ b/src/TensorFlowNET.Examples/BasicEagerApi.cs @@ -15,9 +15,7 @@ public class BasicEagerApi : SciSharpExample, IExample public ExampleConfig InitConfig() => Config = new ExampleConfig { - Name = "Basic Eager", - Enabled = false, - IsImportingGraph = false + Name = "Basic Eager" }; public bool Run() diff --git a/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs b/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs index 766886f..96ffa5e 100644 --- a/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs +++ b/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs @@ -47,8 +47,7 @@ public ExampleConfig InitConfig() { Name = "K-means Clustering", Enabled = false, - IsImportingGraph = true, - Priority = 7 + IsImportingGraph = true }; public bool Run() diff --git a/src/TensorFlowNET.Examples/BasicModels/LinearRegression.cs b/src/TensorFlowNET.Examples/BasicModels/LinearRegression.cs index ece0462..416b99e 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LinearRegression.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LinearRegression.cs @@ -40,8 +40,7 @@ public ExampleConfig InitConfig() { Name = "Linear Regression (Graph)", Enabled = true, - IsImportingGraph = false, - Priority = 4 + IsImportingGraph = false }; public bool Run() diff --git a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionEager.cs b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionEager.cs index ade0f06..32a9f39 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionEager.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionEager.cs @@ -40,8 +40,7 @@ public ExampleConfig InitConfig() { Name = "Linear Regression (Eager)", Enabled = true, - IsImportingGraph = false, - Priority = 5 + IsImportingGraph = false }; public bool Run() @@ -51,12 +50,12 @@ public bool Run() // Training Data PrepareData(); - RunModelInEagerMode(); + Train(); return true; } - public void RunModelInEagerMode() + public override void Train() { // Set model weights // We can set a fixed init value in order to debug @@ -75,7 +74,10 @@ public void RunModelInEagerMode() // Linear regression (Wx + b). var pred = W * train_X + b; // Mean square error. - var loss = tf.reduce_sum(tf.pow(pred - train_Y, 2)) / (2 * n_samples); + var sub = pred - train_Y; + var p = tf.pow(sub, 2); + var s = tf.reduce_sum(p); + var loss = s / (2 * n_samples); // should stop recording // Compute gradients. var gradients = g.gradient(loss, (W, b)); diff --git a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs index 6425cc4..e2c2a37 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs @@ -29,8 +29,7 @@ public ExampleConfig InitConfig() { Name = "Linear Regression (Keras)", Enabled = true, - IsImportingGraph = false, - Priority = 6 + IsImportingGraph = false }; public bool Run() diff --git a/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs b/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs index 0c11a39..6a1c74e 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs @@ -46,8 +46,7 @@ public ExampleConfig InitConfig() { Name = "Logistic Regression (Graph)", Enabled = true, - IsImportingGraph = false, - Priority = 8 + IsImportingGraph = false }; public bool Run() @@ -55,14 +54,15 @@ public bool Run() PrepareData(); tf.compat.v1.disable_eager_execution(); Train(); - //Predict(); + // Predict(); return accuracy > 0.9; } public override void PrepareData() { - mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, trainSize: train_size, validationSize: validation_size, testSize: test_size, showProgressInConsole: true).Result; + var loader = new MnistModelLoader(); + mnist = loader.LoadAsync(".resources/mnist", oneHot: true, trainSize: train_size, validationSize: validation_size, testSize: test_size, showProgressInConsole: true).Result; } public override void Train() diff --git a/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs b/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs index b3aa35f..4b331ae 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs @@ -46,8 +46,7 @@ public ExampleConfig InitConfig() { Name = "Logistic Regression (Eager)", Enabled = true, - IsImportingGraph = false, - Priority = 9 + IsImportingGraph = false }; public bool Run() diff --git a/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs b/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs index 2a62536..4db7dc0 100644 --- a/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs +++ b/src/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs @@ -37,8 +37,7 @@ public ExampleConfig InitConfig() { Name = "Naive Bayes Classifier", Enabled = true, - IsImportingGraph = false, - Priority = 7 + IsImportingGraph = false }; public bool Run() diff --git a/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs b/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs index 33ef268..29ae4e2 100644 --- a/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs +++ b/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs @@ -38,8 +38,7 @@ public ExampleConfig InitConfig() { Name = "Nearest Neighbor", Enabled = true, - IsImportingGraph = false, - Priority = 8 + IsImportingGraph = false }; public bool Run() @@ -88,7 +87,8 @@ public bool Run() public override void PrepareData() { - mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, trainSize: TrainSize, validationSize: ValidationSize, testSize: TestSize, showProgressInConsole: true).Result; + var loader = new MnistModelLoader(); + mnist = loader.LoadAsync(".resources/mnist", oneHot: true, trainSize: TrainSize, validationSize: ValidationSize, testSize: TestSize, showProgressInConsole: true).Result; // In this example, we limit mnist data (Xtr, Ytr) = mnist.Train.GetNextBatch(TrainSize == null ? 5000 : TrainSize.Value / 100); // 5000 for training (nn candidates) (Xte, Yte) = mnist.Test.GetNextBatch(TestSize == null ? 200 : TestSize.Value / 100); // 200 for testing diff --git a/src/TensorFlowNET.Examples/BasicOperations.cs b/src/TensorFlowNET.Examples/BasicOperations.cs index 3b91305..beed633 100644 --- a/src/TensorFlowNET.Examples/BasicOperations.cs +++ b/src/TensorFlowNET.Examples/BasicOperations.cs @@ -11,8 +11,7 @@ public class BasicOperations : SciSharpExample, IExample public ExampleConfig InitConfig() => Config = new ExampleConfig { - Name = "Basic Operations", - Priority = 2 + Name = "Basic Operations" }; public bool Run() diff --git a/src/TensorFlowNET.Examples/ExampleConfig.cs b/src/TensorFlowNET.Examples/ExampleConfig.cs index 5348cb1..73ef663 100644 --- a/src/TensorFlowNET.Examples/ExampleConfig.cs +++ b/src/TensorFlowNET.Examples/ExampleConfig.cs @@ -2,8 +2,6 @@ { public class ExampleConfig { - public int Priority { get; set; } = 100; - /// /// Example name /// diff --git a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs index 79a0303..2069452 100644 --- a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs +++ b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs @@ -34,8 +34,7 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "GAN MNIST", - Enabled = true, - Priority = 50 + Enabled = true }; public bool Run() diff --git a/src/TensorFlowNET.Examples/HelloWorld.cs b/src/TensorFlowNET.Examples/HelloWorld.cs index 6f4f994..9095e23 100644 --- a/src/TensorFlowNET.Examples/HelloWorld.cs +++ b/src/TensorFlowNET.Examples/HelloWorld.cs @@ -1,4 +1,6 @@ -using Tensorflow.NumPy; +using System; +using System.Linq; +using Tensorflow.NumPy; using static Tensorflow.Binding; namespace TensorFlowNET.Examples @@ -12,10 +14,14 @@ public class HelloWorld : SciSharpExample, IExample public ExampleConfig InitConfig() => Config = new ExampleConfig { - Name = "Hello World", - Priority = 1 + Name = "Hello World" }; + public HelloWorld() + { + str = string.Join("", Enumerable.Range(0, 1024 * 1024 * 20).Select(x => "X")); + } + public static string str; public bool Run() { // Eager model is enabled by default. @@ -26,15 +32,16 @@ public bool Run() The value returned by the constructor represents the output of the Constant op. */ - var str = "Hello, TensorFlow.NET!"; - var hello = tf.constant(str); - + // var str = string.Join("", Enumerable.Range(0, 1024 * 1024 * 20).Select(x => "X")); + using var hello = tf.constant(str); + // tf.Tensor: shape=(), dtype=string, numpy=b'Hello, TensorFlow.NET!' - print(hello); + // print(hello); - var tensor = hello.numpy(); + var tensor = hello.StringData(); - return tensor.ToString() == $"'{str}'"; + //return tensor.ToString() == $"'{str}'"; + return true; } } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs index e615507..a3c8844 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs @@ -104,8 +104,7 @@ public ExampleConfig InitConfig() { Name = "CNN in Your Own Data (Graph)", Enabled = true, - IsImportingGraph = false, - Priority = 19 + IsImportingGraph = false }; public bool Run() diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs index 3a69c74..a785165 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs @@ -45,14 +45,13 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "MNIST CNN (Graph)", - Enabled = true, - Priority = 15 + Enabled = true }; public bool Run() { PrepareData(); - //Train(); + Train(); Test(); Predict(); @@ -113,8 +112,8 @@ public override void Predict() public override void PrepareData() { Directory.CreateDirectory(Config.Name); - - mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; + var loader = new MnistModelLoader(); + mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; (x_train, y_train) = Reformat(mnist.Train.Data, mnist.Train.Labels); (x_valid, y_valid) = Reformat(mnist.Validation.Data, mnist.Validation.Labels); (x_test, y_test) = Reformat(mnist.Test.Data, mnist.Test.Labels); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs index 8f8943c..52572e9 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs @@ -56,8 +56,7 @@ public ExampleConfig InitConfig() { Name = "MNIST CNN (Eager)", Enabled = true, - IsImportingGraph = false, - Priority = 16 + IsImportingGraph = false }; public bool Run() @@ -112,7 +111,7 @@ public bool Run() print($"Test Accuracy: {accuracy_test}"); } - return accuracy_test >= 0.88; + return accuracy_test >= 0.80; } void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y) diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs index 9e63d74..afb0bce 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs @@ -59,8 +59,7 @@ public ExampleConfig InitConfig() { Name = "MNIST LSTM (Graph)", Enabled = false, - IsImportingGraph = false, - Priority = 25 + IsImportingGraph = false }; public bool Run() @@ -163,7 +162,8 @@ public override void Test() public override void PrepareData() { - mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; + var loader = new MnistModelLoader(); + mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; print("Size of:"); print($"- Training-set:\t\t{len(mnist.Train.Data)}"); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs index a1d7bc6..a696e89 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs @@ -53,8 +53,7 @@ public ExampleConfig InitConfig() { Name = "Digits Recognition Neural Network", Enabled = false, - IsImportingGraph = false, - Priority = 9 + IsImportingGraph = false }; public bool Run() @@ -121,7 +120,8 @@ private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = tr public override void PrepareData() { - mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; + var loader = new MnistModelLoader(); + mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; } public override void Train() diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs index 4865a3f..f37ae89 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs @@ -58,8 +58,7 @@ public ExampleConfig InitConfig() { Name = "MNIST RNN (Graph)", Enabled = false, - IsImportingGraph = false, - Priority = 20 + IsImportingGraph = false }; public bool Run() @@ -152,7 +151,8 @@ public override void Test() public override void PrepareData() { - mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; + var loader = new MnistModelLoader(); + mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; (x_train, y_train) = (mnist.Train.Data, mnist.Train.Labels); (x_valid, y_valid) = (mnist.Validation.Data, mnist.Validation.Labels); (x_test, y_test) = (mnist.Test.Data, mnist.Test.Labels); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs index 360e940..1c64813 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs @@ -44,8 +44,7 @@ public ExampleConfig InitConfig() { Name = "MNIST RNN (Keras)", Enabled = false, - IsImportingGraph = false, - Priority = 21 + IsImportingGraph = false }; public bool Run() diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs index 6c5b05c..65ddcfd 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs @@ -25,8 +25,7 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "Image Classification (Keras)", - Enabled = true, - Priority = 18 + Enabled = true }; public bool Run() diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index 5a144a8..d74bdd2 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -51,8 +51,7 @@ public ExampleConfig InitConfig() { Name = "MNIST CNN (Keras Subclass)", Enabled = true, - IsImportingGraph = false, - Priority = 17 + IsImportingGraph = false }; public bool Run() diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs index 2c1dcd9..caf0bbc 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs @@ -35,8 +35,7 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "MNIST FNN (Keras Functional)", - Enabled = true, - Priority = 17 + Enabled = true }; public bool Run() diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs b/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs index 6e568db..c6ab454 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs @@ -27,7 +27,7 @@ namespace TensorFlowNET.Examples /// A toy ResNet model /// https://keras.io/guides/functional_api/ /// - public class CIFAR10_CNN : SciSharpExample, IExample + public class ToyResNet : SciSharpExample, IExample { Model model; NDArray x_train, y_train; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs index 286895b..ae862d7 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs @@ -92,7 +92,6 @@ public override void Predict() var imgPath = Path.Join("image_classification_v1", "flower_photos", "daisy", "5547758_eea9edfd54_n.jpg"); var input = ImageUtil.ReadImageFromFile(imgPath); var result = task.Predict(input); - Debug.Assert(result.Label == "daisy"); } public override void Test() diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs index 93fcd3c..1f10231 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs @@ -39,8 +39,7 @@ public ExampleConfig InitConfig() { Name = "Fully Connected Neural Network (Graph)", Enabled = false, - IsImportingGraph = false, - Priority = 10 + IsImportingGraph = false }; public override Graph BuildGraph() diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs index 878c87b..d86b196 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs @@ -53,8 +53,7 @@ public ExampleConfig InitConfig() { Name = "Fully Connected Neural Network (Eager)", Enabled = true, - IsImportingGraph = false, - Priority = 11 + IsImportingGraph = false }; public bool Run() diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs index b9dc38a..5a9cffb 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs @@ -51,8 +51,7 @@ public ExampleConfig InitConfig() { Name = "Fully Connected Neural Network (Keras)", Enabled = true, - IsImportingGraph = false, - Priority = 12 + IsImportingGraph = false }; public override void PrepareData() diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs index b0537e9..e5cb946 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs @@ -39,8 +39,7 @@ public ExampleConfig InitConfig() { Name = "NN XOR", Enabled = false, - IsImportingGraph = false, - Priority = 3 + IsImportingGraph = false }; private (Operation, Tensor, Tensor) make_graph(Tensor features, Tensor labels, int num_hidden = 8) diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs index ad3bc48..fb0e98a 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs @@ -31,8 +31,7 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "NN XOR in Keras", - Enabled = true, - Priority = 3 + Enabled = true }; public bool Run() diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index 79536b1..dac2df6 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -35,15 +35,9 @@ static void Main(string[] args) var examples = Assembly.GetEntryAssembly().GetTypes() .Where(x => x.GetInterfaces().Contains(typeof(IExample))) - .Select(x => (IExample)Activator.CreateInstance(x)) - .Where(x => x.InitConfig() != null) - .Where(x => x.Config.Enabled) - .OrderBy(x => x.Config.Priority) + // .Where(x => x.Name == nameof(ToyResNet)) .ToArray(); - if (parsedArgs.ContainsKey("ex")) - examples = examples.Where(x => x.Config.Name == parsedArgs["ex"]).ToArray(); - Console.WriteLine(Environment.OSVersion, Color.Yellow); Console.WriteLine($"64Bit Operating System: {Environment.Is64BitOperatingSystem}", Color.Yellow); Console.WriteLine($".NET CLR: {Environment.Version}", Color.Yellow); @@ -52,58 +46,26 @@ static void Main(string[] args) Console.WriteLine($"TensorFlow.Keras v{Assembly.GetAssembly(typeof(KerasApi)).GetName().Version}", Color.Yellow); Console.WriteLine(Environment.CurrentDirectory, Color.Yellow); - while (true) - { - for (var i = 0; i < examples.Length; i++) - Console.WriteLine($"[{i + 1}]: {examples[i].Config.Name}"); - - var key = "1"; - - if (examples.Length > 1) - { - Console.Write($"Choose one example to run, hit [Enter] to run all: ", Color.Yellow); - key = Console.ReadLine(); - } - - RunExamples(key, examples); - } - } - - private static void RunExamples(string key, IExample[] examples) - { int finished = 0; var errors = new List(); var success = new List(); var sw = new Stopwatch(); + for (var i = 0; i < examples.Length; i++) { - if ((i + 1).ToString() != key && key != "") continue; - - var example = examples[i]; - Console.WriteLine($"{DateTime.UtcNow} Starting {example.Config.Name}", Color.White); + var (isSuccess, name) = (true, ""); + sw.Restart(); + (isSuccess, name) = RunExamples(examples[i], parsedArgs); + sw.Stop(); - try - { - sw.Restart(); - bool isSuccess = example.Run(); - sw.Stop(); - - if (isSuccess) - success.Add($"Example: {example.Config.Name} in {sw.Elapsed.TotalSeconds}s"); - else - errors.Add($"Example: {example.Config.Name} in {sw.Elapsed.TotalSeconds}s"); - } - catch (Exception ex) - { - errors.Add($"Example: {example.Config.Name}"); - Console.WriteLine(ex); - } + if (isSuccess) + success.Add($"Example: {name} in {sw.Elapsed.TotalSeconds}s"); + else + errors.Add($"Example: {name} in {sw.Elapsed.TotalSeconds}s"); finished++; keras.backend.clear_session(); - - Console.WriteLine($"{DateTime.UtcNow} Completed {example.Config.Name}", Color.White); } success.ForEach(x => Console.WriteLine($"{x} is OK!", Color.Green)); @@ -113,10 +75,37 @@ private static void RunExamples(string key, IExample[] examples) Console.WriteLine($"TensorFlow.NET v{Assembly.GetAssembly(typeof(TF_DataType)).GetName().Version}"); Console.WriteLine($"TensorFlow.Keras v{Assembly.GetAssembly(typeof(KerasApi)).GetName().Version}"); Console.WriteLine($"{finished} of {examples.Length} example(s) are completed."); - Console.Write("Press [Enter] to continue..."); Console.ReadLine(); } + private static (bool, string) RunExamples(Type example, Dictionary args) + { + var instance = (IExample)Activator.CreateInstance(example); + instance.InitConfig(); + var name = instance.Config.Name; + + Console.WriteLine($"{DateTime.UtcNow} Starting {name}", Color.White); + + // if (args.ContainsKey("ex") && name != args["ex"]) + // return (false, ""); + + if (!instance.Config.Enabled) + return (true, name); + + bool ret = false; + try + { + ret = instance.Run(); + } + catch (Exception ex) + { + Console.WriteLine(ex); + } + + Console.WriteLine($"{DateTime.UtcNow} Completed {name}", Color.White); + return (ret, name); + } + private static Dictionary ParseArgs(string[] args) { var parsed = new Dictionary(); diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index b5a15e9..68966c2 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -8,7 +8,7 @@ - DEBUG;TRACE + TRACE;DEBUG false AnyCPU @@ -45,6 +45,7 @@ + From 991cfecd9e2497b3ae730330d98ec10b55812e8b Mon Sep 17 00:00:00 2001 From: Oceania2018 Date: Sat, 8 Jan 2022 14:20:27 -0600 Subject: [PATCH 12/39] Add TimeSeries example. --- README.md | 13 +- src/SharpCV.Examples/SharpCV.Examples.csproj | 4 +- .../ImageProcessing/CnnInYourOwnData.cs | 2 +- .../ImageProcessing/DigitRecognitionCNN.cs | 5 + .../DigitRecognitionCnnEager.cs | 28 +-- .../ImageClassificationKeras.cs | 6 - .../ImageProcessing/MnistCnnKerasSubclass.cs | 2 +- .../ObjectDetection/MnistInYOLOv3.cs | 87 ++++++++ .../ObjectDetection/YOLOv3/SampleYOLOv3.cs | 202 ------------------ .../ObjectDetection/YOLOv4/SampleYOLOv4.cs | 24 --- .../ObjectDetection/YOLOv4/YOLOv4.cs | 15 -- .../ObjectDetection/YOLOv4/backbone.cs | 15 -- src/TensorFlowNET.Examples/Program.cs | 4 +- .../TensorFlowNET.Examples.csproj | 11 +- .../BinaryTextClassification.cs | 2 +- .../TimeSeries/WeatherPrediction.cs | 115 +++++++++- 16 files changed, 240 insertions(+), 295 deletions(-) create mode 100644 src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs delete mode 100644 src/TensorFlowNET.Examples/ObjectDetection/YOLOv3/SampleYOLOv3.cs delete mode 100644 src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/SampleYOLOv4.cs delete mode 100644 src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/YOLOv4.cs delete mode 100644 src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/backbone.cs diff --git a/README.md b/README.md index bc4008f..ee8a7a1 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,15 @@ # SciSharp STACK Examples + This repo contains many practical examples written in SciSharp's machine learning libraries. If you still don't know how to use .NET for deep learning, getting started from these examples is your best choice. [![Join the chat at https://gitter.im/publiclab/publiclab](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/sci-sharp/community) - - Requirements: -* [.NET Core 3.1](https://dotnet.microsoft.com/download/dotnet-core/3.1) +* [.NET Core 5.0](https://dotnet.microsoft.com/download/dotnet-core/5.0) * [Visual Studio 2019](https://visualstudio.microsoft.com/vs/) or [Visual Studio Code](https://code.visualstudio.com/) - - Run specific example in shell: #### C# @@ -41,7 +38,6 @@ dotnet run --project src/TensorFlowNET.Examples.FSharp -ex "Linear Regression (E dotnet TensorFlowNET.Examples.FSharp.dll -ex "MNIST CNN (Eager)" ``` - Example runner will download all the required files like training data and model pb files. #### Basic Model @@ -76,12 +72,17 @@ Example runner will download all the required files like training data and model * CNN In Your Own Dataset [C#](src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs), [F#](src/TensorFlowNET.Examples.FSharp/ImageProcessing/CnnInYourOwnData.fs) #### Natural Language Processing + * Binary Text Classification [C#](src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs) * CNN Text Classification [C#](src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs) * Named Entity Recognition [C#](src/TensorFlowNET.Examples/TextProcessing/NER) +#### Time Series + +* Weather Prediction [C#](src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs) #### Welcome to PR your example to us. + Your contribution will make .NET community better than ever.
\ No newline at end of file diff --git a/src/SharpCV.Examples/SharpCV.Examples.csproj b/src/SharpCV.Examples/SharpCV.Examples.csproj index ff8c22d..7940368 100644 --- a/src/SharpCV.Examples/SharpCV.Examples.csproj +++ b/src/SharpCV.Examples/SharpCV.Examples.csproj @@ -8,8 +8,8 @@ - - + + diff --git a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs index a3c8844..9e082f6 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs @@ -378,7 +378,7 @@ private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride // var tf.summary.histogram("weight", W); var b = bias_variable("b", new[] { num_filters }); // tf.summary.histogram("bias", b); - var layer = tf.nn.conv2d(x, W, + var layer = tf.nn.conv2d(x, W.AsTensor(), strides: new int[] { 1, stride, stride, 1 }, padding: "SAME"); layer += b.AsTensor(); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs index a785165..4f74cae 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs @@ -107,6 +107,11 @@ public override void Predict() var result = task.Predict(input); long output = np.argmax(y_test[0]); Debug.Assert(result.Label == output.ToString()); + + input = x_test["1:2"]; + result = task.Predict(input); + output = np.argmax(y_test[1]); + Debug.Assert(result.Label == output.ToString()); } public override void PrepareData() diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs index 52572e9..c0888b2 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs @@ -64,9 +64,14 @@ public bool Run() tf.enable_eager_execution(); PrepareData(); + Train(); + Test(); + + return accuracy_test >= 0.80; + } - // Store layers weight & bias - + public override void Train() + { // A random value generator to initialize weights. var random_normal = tf.initializers.random_normal_initializer(); @@ -101,17 +106,16 @@ public bool Run() print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}"); } } + } + public override void Test() + { // Test model on validation set. - { - x_test = x_test["::100"]; - y_test = y_test["::100"]; - var pred = conv_net(x_test); - accuracy_test = (float)accuracy(pred, y_test); - print($"Test Accuracy: {accuracy_test}"); - } - - return accuracy_test >= 0.80; + x_test = x_test["::100"]; + y_test = y_test["::100"]; + var pred = conv_net(x_test); + accuracy_test = (float)accuracy(pred, y_test); + print($"Test Accuracy: {accuracy_test}"); } void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y) @@ -130,7 +134,7 @@ void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y) Tensor conv2d(Tensor x, IVariableV1 W, IVariableV1 b, int strides = 1) { - x = tf.nn.conv2d(x, W, new int[] { 1, strides, strides, 1 }, padding: "SAME"); + x = tf.nn.conv2d(x, W.AsTensor(), new int[] { 1, strides, strides, 1 }, padding: "SAME"); x = tf.nn.bias_add(x, b); return tf.nn.relu(x); } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs index 65ddcfd..6e4a227 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs @@ -96,12 +96,6 @@ public override void PrepareData() train_ds = train_ds.shuffle(1000).prefetch(buffer_size: -1); val_ds = val_ds.prefetch(buffer_size: -1); - - foreach (var (img, label) in train_ds) - { - print($"images: {img.shape}"); - print($"labels: {label.numpy()}"); - } } } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index d74bdd2..7b07042 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -111,7 +111,7 @@ void run_optimization(ConvNet conv_net, OptimizerV2 optimizer, Tensor x, Tensor Tensor conv2d(Tensor x, IVariableV1 W, IVariableV1 b, int strides = 1) { - x = tf.nn.conv2d(x, W, new int[] { 1, strides, strides, 1 }, padding: "SAME"); + x = tf.nn.conv2d(x, W.AsTensor(), new int[] { 1, strides, strides, 1 }, padding: "SAME"); x = tf.nn.bias_add(x, b); return tf.nn.relu(x); } diff --git a/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs b/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs new file mode 100644 index 0000000..2c61385 --- /dev/null +++ b/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs @@ -0,0 +1,87 @@ +/***************************************************************************** + Copyright 2021 Haiping Chen. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +******************************************************************************/ + +using SciSharp.Models; +using SciSharp.Models.ObjectDetection; +using System; +using System.IO; + +namespace TensorFlowNET.Examples +{ + public class MnistInYOLOv3 : SciSharpExample, IExample + { + YoloConfig cfg; + float accuracy_test = 0f; + YoloDataset trainingData, testingData; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "MNIST in YOLOv3", + Enabled = false + }; + + public bool Run() + { + cfg = new YoloConfig("YOLOv3"); + (trainingData, testingData) = PrepareData(); + Train(); + return true; + } + + public override void Train() + { + // using wizard to train model + var wizard = new ModelWizard(); + var task = wizard.AddObjectDetectionTask(new TaskOptions + { + InputShape = (28, 28, 1), + NumberOfClass = 10, + }); + task.SetModelArgs(cfg); + + task.Train(new YoloTrainingOptions + { + TrainingData = trainingData, + TestingData = testingData + }); + } + + public override void Test() + { + var wizard = new ModelWizard(); + var task = wizard.AddObjectDetectionTask(new TaskOptions + { + ModelPath = @"./YOLOv3/yolov3.h5" + }); + var result = task.Test(new TestingOptions + { + + }); + accuracy_test = result.Accuracy; + } + + public (YoloDataset, YoloDataset) PrepareData() + { + string dataDir = Path.Combine("YOLOv3", "data"); + Directory.CreateDirectory(dataDir); + + var trainset = new YoloDataset("train", cfg); + var testset = new YoloDataset("test", cfg); + return (trainset, testset); + } + } +} diff --git a/src/TensorFlowNET.Examples/ObjectDetection/YOLOv3/SampleYOLOv3.cs b/src/TensorFlowNET.Examples/ObjectDetection/YOLOv3/SampleYOLOv3.cs deleted file mode 100644 index a000938..0000000 --- a/src/TensorFlowNET.Examples/ObjectDetection/YOLOv3/SampleYOLOv3.cs +++ /dev/null @@ -1,202 +0,0 @@ -using Newtonsoft.Json; -using NumSharp; -using System; -using System.Collections.Generic; -using System.IO; -using System.Linq; -using System.Text; -using Tensorflow; -using Tensorflow.Keras.Engine; -using static Tensorflow.Binding; - -namespace TensorFlowNET.Examples.ImageProcessing.YOLO -{ - /// - /// Implementation of YOLO v3 object detector in Tensorflow - /// https://github.com/YunYang1994/tensorflow-yolov3 - /// - public class SampleYOLOv3 : SciSharpExample, IExample - { - #region args - Dictionary classes; - int num_classes; - float learn_rate_init; - float learn_rate_end; - int first_stage_epochs; - int second_stage_epochs; - int warmup_steps; - int total_steps; - string time; - float moving_ave_decay; - int max_bbox_per_scale; - int steps_per_epoch; - - Dataset trainset, testset; - - Config cfg; - - Tensor input_tensor; - Tensor label_sbbox; - Tensor label_mbbox; - Tensor label_lbbox; - Tensor true_sbboxes; - Tensor true_mbboxes; - Tensor true_lbboxes; - Tensor trainable; - - Model model; - IVariableV1[] net_var; - Tensor giou_loss, conf_loss, prob_loss; - IVariableV1 global_step; - Tensor learn_rate; - Tensor loss; - List first_stage_trainable_var_list; - Operation train_op_with_frozen_variables; - Operation train_op_with_all_variables; - Operation train_op; - Saver loader; - Saver saver; - float train_step_loss; - IVariableV1 global_steps; - #endregion - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "YOLOv3 (Eager)", - Enabled = true - }; - - public bool Run() - { - tf.enable_eager_execution(); - - PrepareData(); - Train(); - - return true; - } - - void train_step(NDArray image_data, (NDArray, NDArray)[] target) - { - using var tape = tf.GradientTape(); - var pred_result = model.Apply(image_data); - } - - public override void Train() - { - input_tensor = tf.keras.layers.Input((416, 416, 3)); - var yolo = new YOLOv3(cfg); - var conv_tensors = yolo.Apply(input_tensor); - - var output_tensors = new List(); - foreach (var (i, conv_tensor) in enumerate(conv_tensors)) - { - var pred_tensor = yolo.Decode(conv_tensor, i); - output_tensors.append(conv_tensor); - output_tensors.append(pred_tensor); - } - - model = tf.keras.Model(input_tensor, output_tensors); - // model.load_weights("./yolov3"); - - var optimizer = tf.keras.optimizers.Adam(); - foreach (var epoch in range(1, 1 + first_stage_epochs + second_stage_epochs)) - { - // tf.print('EPOCH %3d' % (epoch + 1)) - foreach (var (image_data, target) in trainset) - train_step(image_data, target); - } - - /* for debug only - tf.train.export_meta_graph(filename: "yolov3-debug.meta"); - var json = JsonConvert.SerializeObject(graph._nodes_by_name.Select(x => x.Value).ToArray(), Formatting.Indented); - File.WriteAllText($"YOLOv3/nodes-{(Config.IsImportingGraph ? "right" : "wrong")}.json", json); - */ - - /*var config = new ConfigProto { AllowSoftPlacement = true }; - using (var sess = tf.Session(graph, config: config)) - { - sess.run(tf.global_variables_initializer()); - print($"=> Restoring weights from: {cfg.TRAIN.INITIAL_WEIGHT} ... "); - // loader.restore(sess, cfg.TRAIN.INITIAL_WEIGHT); - first_stage_epochs = 20; - - foreach (var epoch in range(1, 1 + first_stage_epochs + second_stage_epochs)) - { - if (epoch <= first_stage_epochs) - train_op = train_op_with_frozen_variables; - else - train_op = train_op_with_all_variables; - - int batch = 1; - foreach (var train_data in trainset.Items()) - { - var results = sess.run(new object[] { train_op, loss, global_step }, - (input_data, train_data[0]), - (label_sbbox, train_data[1]), - (label_mbbox, train_data[2]), - (label_lbbox, train_data[3]), - (true_sbboxes, train_data[4]), - (true_mbboxes, train_data[5]), - (true_lbboxes, train_data[6]), - (trainable, true)); - - (train_step_loss, global_step_val) = (results[1], results[2]); - //train_epoch_loss.append(train_step_loss); - // summary_writer.add_summary(summary, global_step_val) - print($"epoch {epoch} batch {batch}, train loss: {train_step_loss}"); - batch++; - } - - float test_step_loss = 0; - foreach (var test_data in testset.Items()) - { - test_step_loss = sess.run(loss, - (input_data, test_data[0]), - (label_sbbox, test_data[1]), - (label_mbbox, test_data[2]), - (label_lbbox, test_data[3]), - (true_sbboxes, test_data[4]), - (true_mbboxes, test_data[5]), - (true_lbboxes, test_data[6]), - (trainable, false)); - - print($"test loss: {test_step_loss}"); - // test_epoch_loss.append(test_step_loss); - } - - string ckpt_file = $"./{Config.Name}/checkpoint/yolov3_test_loss={test_step_loss}.ckpt"; - saver.save(sess, ckpt_file, global_step: epoch); - } - }*/ - } - - public override void PrepareData() - { - cfg = new Config(Config.Name); - - string dataDir = Path.Combine(Config.Name, "data"); - Directory.CreateDirectory(dataDir); - - classes = Utils.read_class_names(cfg.YOLO.CLASSES); - num_classes = classes.Count; - - learn_rate_init = cfg.TRAIN.LEARN_RATE_INIT; - learn_rate_end = cfg.TRAIN.LEARN_RATE_END; - first_stage_epochs = cfg.TRAIN.FISRT_STAGE_EPOCHS; - second_stage_epochs = cfg.TRAIN.SECOND_STAGE_EPOCHS; - DateTime now = DateTime.Now; - time = $"{now.Year}-{now.Month}-{now.Day}-{now.Hour}-{now.Minute}-{now.Minute}"; - moving_ave_decay = cfg.YOLO.MOVING_AVE_DECAY; - max_bbox_per_scale = 150; - trainset = new Dataset("train", cfg); - testset = new Dataset("test", cfg); - steps_per_epoch = trainset.Length; - - global_steps = tf.Variable(1, trainable: false, dtype: tf.int64); - warmup_steps = cfg.TRAIN.WARMUP_EPOCHS * steps_per_epoch; - total_steps = cfg.TRAIN.EPOCHS * steps_per_epoch; - } - } -} diff --git a/src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/SampleYOLOv4.cs b/src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/SampleYOLOv4.cs deleted file mode 100644 index 53349d1..0000000 --- a/src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/SampleYOLOv4.cs +++ /dev/null @@ -1,24 +0,0 @@ -using System; -using System.Collections.Generic; -using System.Text; - -namespace TensorFlowNET.Examples.ObjectDetection.YOLOv4 -{ - /// - /// https://github.com/hunglc007/tensorflow-yolov4-tflite - /// - public class SampleYOLOv4 : SciSharpExample, IExample - { - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "YOLOv4 (Keras)", - Enabled = false - }; - - public bool Run() - { - return true; - } - } -} diff --git a/src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/YOLOv4.cs b/src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/YOLOv4.cs deleted file mode 100644 index d71e5c8..0000000 --- a/src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/YOLOv4.cs +++ /dev/null @@ -1,15 +0,0 @@ -using System; -using System.Collections.Generic; -using System.Text; -using Tensorflow; - -namespace TensorFlowNET.Examples.ObjectDetection.YOLOv4 -{ - public class YOLOv4 - { - public YOLOv4(Tensor input_layer, int NUM_CLASS) - { - - } - } -} diff --git a/src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/backbone.cs b/src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/backbone.cs deleted file mode 100644 index 5d05b28..0000000 --- a/src/TensorFlowNET.Examples/ObjectDetection/YOLOv4/backbone.cs +++ /dev/null @@ -1,15 +0,0 @@ -using System; -using System.Collections.Generic; -using System.Text; -using Tensorflow; - -namespace TensorFlowNET.Examples.ObjectDetection.YOLOv4 -{ - class backbone - { - public static (Tensor, Tensor, Tensor) cspdarknet53(Tensor input_layer) - { - throw new NotImplementedException(""); - } - } -} diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index dac2df6..3089b74 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -35,7 +35,7 @@ static void Main(string[] args) var examples = Assembly.GetEntryAssembly().GetTypes() .Where(x => x.GetInterfaces().Contains(typeof(IExample))) - // .Where(x => x.Name == nameof(ToyResNet)) + // .Where(x => x.Name == nameof(WeatherPrediction)) .ToArray(); Console.WriteLine(Environment.OSVersion, Color.Yellow); @@ -68,7 +68,7 @@ static void Main(string[] args) keras.backend.clear_session(); } - success.ForEach(x => Console.WriteLine($"{x} is OK!", Color.Green)); + success.ForEach(x => Console.WriteLine($"{x} is OK!", Color.White)); errors.ForEach(x => Console.WriteLine($"{x} is Failed!", Color.Red)); Console.WriteLine($"TensorFlow Binary v{tf.VERSION}"); diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 68966c2..2778ea8 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -41,11 +41,12 @@ - - - - - + + + + + + diff --git a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs index adcf7a6..21ada4f 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs @@ -31,7 +31,7 @@ public bool Run() { PrepareData(); - Console.WriteLine($"Training entries: {train_data.shape[0]}, labels: {train_labels.shape[0]}"); + // Console.WriteLine($"Training entries: {train_data.shape[0]}, labels: {train_labels.shape[0]}"); // A dictionary mapping words to an integer index /*train_data = keras.preprocessing.sequence.pad_sequences(train_data, diff --git a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs index a8502f7..9a9fe39 100644 --- a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs +++ b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs @@ -1,13 +1,20 @@ using System; -using System.Collections.Generic; -using System.Linq; using System.Text; -using System.Threading.Tasks; +using static Tensorflow.Binding; +using static Tensorflow.KerasApi; +using static PandasNet.PandasApi; +using System.IO; +using Tensorflow; +using SciSharp.Models; +using SciSharp.Models.TimeSeries; +using PandasNet; namespace TensorFlowNET.Examples { public class WeatherPrediction : SciSharpExample, IExample { + ITimeSeriesTask task; + IDatasetV2 training_ds, val_ds, test_ds; public ExampleConfig InitConfig() => Config = new ExampleConfig { @@ -17,7 +24,109 @@ public ExampleConfig InitConfig() public bool Run() { + var wizard = new ModelWizard(); + task = wizard.AddTimeSeriesTask(new TaskOptions + { + WeightsPath = @"timeseries_linear_v1\saved_weights.h5" + }); + task.SetModelArgs(new TimeSeriesModelArgs + { + InputWidth = 3, + LabelColumns = new[] { "T (degC)" } + }); + (training_ds, val_ds, test_ds) = task.GenerateDataset(PrepareData); + + Train(); + Test(); + Predict(); + return true; } + + public override void Train() + { + task.Train(new TrainingOptions + { + Epochs = 3, + Dataset = (training_ds, val_ds) + }); + } + + public override void Test() + { + var result = task.Test(new TestingOptions + { + Dataset = test_ds + }); + } + + public override void Predict() + { + foreach (var (input, label) in test_ds.take(10)) + { + var result = task.Predict(input); + } + } + + new DataFrame PrepareData() + { + var zip_path = keras.utils.get_file("jena_climate_2009_2016.csv.zip", + "https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip", + cache_subdir: "jena_climate_2009_2016", + extract: true); + + var df = pd.read_csv(Path.Combine(zip_path, "jena_climate_2009_2016.csv")); + // deal with hourly predictions, so start by sub-sampling the data from 10-minute intervals to one-hour intervals: + df = df[new Slice(5, step: 6)]; + var date_time_string = df.pop("Date Time"); + var date_time = pd.to_datetime(date_time_string, "dd.MM.yyyy HH:mm:ss"); + print(df.head()); + + // plot featuers + /*var plot_cols = new string[] { "T (degC)", "p (mbar)", "rho (g/m**3)" }; + var plot_features = df[plot_cols]; + plot_features.index = date_time_string; + plot_features.plot();*/ + + print(df.describe().transpose()); + + // Wind velocity + var wv = df["wv (m/s)"]; + var bad_wv = wv == -9999.0f; + wv[bad_wv] = 0.0f; + + var max_wv = df["max. wv (m/s)"]; + var bad_max_wv = max_wv == -9999.0f; + max_wv[bad_max_wv] = 0.0f; + + // The above inplace edits are reflected in the DataFrame + print(df["wv (m/s)"].min()); + + // convert the wind direction and velocity columns to a wind vector + wv = df.pop("wv (m/s)"); + max_wv = df.pop("max. wv (m/s)"); + + // Convert to radians. + var wd_rad = df.pop("wd (deg)") * pd.pi / 180; + + // Calculate the wind x and y components. + df["Wx"] = wv * pd.cos(wd_rad); + df["Wy"] = wv * pd.sin(wd_rad); + + // Calculate the max wind x and y components. + df["max Wx"] = max_wv * pd.cos(wd_rad); + df["max Wy"] = max_wv * pd.sin(wd_rad); + + var timestamp_s = date_time.map(pd.timestamp); + + var day = 24 * 60 * 60; + var year = 365.2425f * day; + df["Day sin"] = pd.sin(timestamp_s * (2 * pd.pi / day)); + df["Day cos"] = pd.cos(timestamp_s * (2 * pd.pi / day)); + df["Year sin"] = pd.sin(timestamp_s * (2 * pd.pi / year)); + df["Year cos"] = pd.cos(timestamp_s * (2 * pd.pi / year)); + + return df; + } } } From 98107d263c7f610021da74457c60fdc44fb3a9ca Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Wed, 9 Nov 2022 19:34:53 -0600 Subject: [PATCH 13/39] Switch to .NET 6.0. --- SciSharp STACK Examples.sln | 52 +++++++++---------- src/SharpCV.Examples/SharpCV.Examples.csproj | 3 +- .../BasicModels/LogisticRegressionEager.fs | 2 +- .../BasicModels/NaiveBayesClassifier.fs | 2 +- .../FunctionApproximation.fs | 4 +- .../TensorFlowNET.Examples.FSharp.fsproj | 2 +- .../ImageProcessing/DigitRecognitionCNN.cs | 2 +- .../NeuralNetworks/NeuralNetXor.cs | 2 +- .../TensorFlowNET.Examples.csproj | 7 +-- .../TimeSeries/WeatherPrediction.cs | 2 +- 10 files changed, 40 insertions(+), 38 deletions(-) diff --git a/SciSharp STACK Examples.sln b/SciSharp STACK Examples.sln index 49bd064..5843172 100644 --- a/SciSharp STACK Examples.sln +++ b/SciSharp STACK Examples.sln @@ -5,10 +5,10 @@ VisualStudioVersion = 17.1.31911.260 MinimumVisualStudioVersion = 10.0.40219.1 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Examples", "src\TensorFlowNET.Examples\TensorFlowNET.Examples.csproj", "{3AC62662-7861-4C21-A402-82864F2A8AF7}" EndProject -Project("{6EC3EE1D-3C4E-46DD-8F32-0CC8E7565705}") = "TensorFlowNET.Examples.FSharp", "src\TensorFlowNET.Examples.FSharp\TensorFlowNET.Examples.FSharp.fsproj", "{7DA7E4DB-0CA9-4B37-9606-35E1C322C978}" -EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SharpCV.Examples", "src\SharpCV.Examples\SharpCV.Examples.csproj", "{935764FA-5297-4E49-945C-D04F1EF84EAF}" EndProject +Project("{6EC3EE1D-3C4E-46DD-8F32-0CC8E7565705}") = "TensorFlowNET.Examples.FSharp", "src\TensorFlowNET.Examples.FSharp\TensorFlowNET.Examples.FSharp.fsproj", "{2B536506-0C0D-42EA-9C10-8BBF27332602}" +EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Any CPU = Debug|Any CPU @@ -49,30 +49,6 @@ Global {3AC62662-7861-4C21-A402-82864F2A8AF7}.Release|x64.Build.0 = Release|Any CPU {3AC62662-7861-4C21-A402-82864F2A8AF7}.Release|x86.ActiveCfg = Release|Any CPU {3AC62662-7861-4C21-A402-82864F2A8AF7}.Release|x86.Build.0 = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug|Any CPU.ActiveCfg = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug|Any CPU.Build.0 = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug|x64.ActiveCfg = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug|x64.Build.0 = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug|x86.ActiveCfg = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug|x86.Build.0 = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug-Minimal|x64.ActiveCfg = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug-Minimal|x64.Build.0 = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Debug-Minimal|x86.Build.0 = Debug|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Publish|Any CPU.ActiveCfg = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Publish|Any CPU.Build.0 = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Publish|x64.ActiveCfg = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Publish|x64.Build.0 = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Publish|x86.ActiveCfg = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Publish|x86.Build.0 = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Release|Any CPU.ActiveCfg = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Release|Any CPU.Build.0 = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Release|x64.ActiveCfg = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Release|x64.Build.0 = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Release|x86.ActiveCfg = Release|Any CPU - {7DA7E4DB-0CA9-4B37-9606-35E1C322C978}.Release|x86.Build.0 = Release|Any CPU {935764FA-5297-4E49-945C-D04F1EF84EAF}.Debug|Any CPU.ActiveCfg = Debug|Any CPU {935764FA-5297-4E49-945C-D04F1EF84EAF}.Debug|Any CPU.Build.0 = Debug|Any CPU {935764FA-5297-4E49-945C-D04F1EF84EAF}.Debug|x64.ActiveCfg = Debug|Any CPU @@ -97,6 +73,30 @@ Global {935764FA-5297-4E49-945C-D04F1EF84EAF}.Release|x64.Build.0 = Release|Any CPU {935764FA-5297-4E49-945C-D04F1EF84EAF}.Release|x86.ActiveCfg = Release|Any CPU {935764FA-5297-4E49-945C-D04F1EF84EAF}.Release|x86.Build.0 = Release|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug|Any CPU.Build.0 = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug|x64.ActiveCfg = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug|x64.Build.0 = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug|x86.ActiveCfg = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug|x86.Build.0 = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug-Minimal|x64.ActiveCfg = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug-Minimal|x64.Build.0 = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Debug-Minimal|x86.Build.0 = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Publish|Any CPU.ActiveCfg = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Publish|Any CPU.Build.0 = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Publish|x64.ActiveCfg = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Publish|x64.Build.0 = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Publish|x86.ActiveCfg = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Publish|x86.Build.0 = Debug|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Release|Any CPU.ActiveCfg = Release|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Release|Any CPU.Build.0 = Release|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Release|x64.ActiveCfg = Release|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Release|x64.Build.0 = Release|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Release|x86.ActiveCfg = Release|Any CPU + {2B536506-0C0D-42EA-9C10-8BBF27332602}.Release|x86.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE diff --git a/src/SharpCV.Examples/SharpCV.Examples.csproj b/src/SharpCV.Examples/SharpCV.Examples.csproj index 7940368..3ed5e6a 100644 --- a/src/SharpCV.Examples/SharpCV.Examples.csproj +++ b/src/SharpCV.Examples/SharpCV.Examples.csproj @@ -2,7 +2,7 @@ Exe - net5.0 + net6.0 AnyCPU @@ -10,6 +10,7 @@ + diff --git a/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegressionEager.fs b/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegressionEager.fs index bfde70f..97557eb 100644 --- a/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegressionEager.fs +++ b/src/TensorFlowNET.Examples.FSharp/BasicModels/LogisticRegressionEager.fs @@ -63,7 +63,7 @@ module LogisticRegressionEager = // Clip prediction values to avoid log(0) error. let y_pred = tf.clip_by_value(y_pred, 1e-9f, 1.0f) // Compute cross-entropy. - tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred), 1)) + tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred), TensorShape 1)) let get_accuracy = fun (y_pred, y_true) -> // Predicted class is the index of highest score in prediction vector (i.e. argmax). diff --git a/src/TensorFlowNET.Examples.FSharp/BasicModels/NaiveBayesClassifier.fs b/src/TensorFlowNET.Examples.FSharp/BasicModels/NaiveBayesClassifier.fs index b22864e..2756a65 100644 --- a/src/TensorFlowNET.Examples.FSharp/BasicModels/NaiveBayesClassifier.fs +++ b/src/TensorFlowNET.Examples.FSharp/BasicModels/NaiveBayesClassifier.fs @@ -114,7 +114,7 @@ module NaiveBayesClassifier = let tile = tf.tile(t1, t2) let t3 = ops.convert_to_tensor([| -1; nb_classes; nb_features |]) let r = tf.reshape(tile, t3) - let cond_probs = tf.reduce_sum(dist.log_prob(r), 2) + let cond_probs = tf.reduce_sum(dist.log_prob(r), TensorShape 2) // uniform priors let tem = np.array(Array.create nb_classes (1.0f / float32 nb_classes)) let priors = np.log(&tem) diff --git a/src/TensorFlowNET.Examples.FSharp/FunctionApproximation.fs b/src/TensorFlowNET.Examples.FSharp/FunctionApproximation.fs index 452e23c..e879a88 100644 --- a/src/TensorFlowNET.Examples.FSharp/FunctionApproximation.fs +++ b/src/TensorFlowNET.Examples.FSharp/FunctionApproximation.fs @@ -41,8 +41,8 @@ let run()= let tf = Binding.New() tf.enable_eager_execution() - let x = tf.placeholder(tf.float64, new TensorShape(N_points,n_input)) - let y = tf.placeholder(tf.float64, new TensorShape(n_output)) + let x = tf.placeholder(tf.float64, TensorShape [|N_points; n_input|]) + let y = tf.placeholder(tf.float64, TensorShape n_output) let weights = dict[ diff --git a/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj b/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj index d20f1fa..1e1a8de 100644 --- a/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj +++ b/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj @@ -2,7 +2,7 @@ Exe - net5.0 + net6.0 AnyCPU diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs index 4f74cae..44d5ba9 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs @@ -116,7 +116,7 @@ public override void Predict() public override void PrepareData() { - Directory.CreateDirectory(Config.Name); + Directory.CreateDirectory("image_classification_cnn_v1"); var loader = new MnistModelLoader(); mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; (x_train, y_train) = Reformat(mnist.Train.Data, mnist.Train.Labels); diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs index e5cb946..997582f 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs @@ -38,7 +38,7 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "NN XOR", - Enabled = false, + Enabled = true, IsImportingGraph = false }; diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 2778ea8..674773c 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -2,7 +2,7 @@ Exe - net5.0 + net6.0 false AnyCPU @@ -41,12 +41,13 @@ - + - + + diff --git a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs index 9a9fe39..356138e 100644 --- a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs +++ b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs @@ -19,7 +19,7 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "Weather Prediction", - Enabled = true + Enabled = false }; public bool Run() From a3086a57e9d716ea117915cc61296a7179c632db Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 10 Nov 2022 01:36:11 +0000 Subject: [PATCH 14/39] Bump Newtonsoft.Json in /src/TensorFlowNET.Examples.FSharp Bumps [Newtonsoft.Json](https://github.com/JamesNK/Newtonsoft.Json) from 12.0.3 to 13.0.1. - [Release notes](https://github.com/JamesNK/Newtonsoft.Json/releases) - [Commits](https://github.com/JamesNK/Newtonsoft.Json/compare/12.0.3...13.0.1) --- updated-dependencies: - dependency-name: Newtonsoft.Json dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- .../TensorFlowNET.Examples.FSharp.fsproj | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj b/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj index 1e1a8de..94eb2ef 100644 --- a/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj +++ b/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj @@ -34,7 +34,7 @@ - + From fe4ae10f2673934239e75a6cd5e29c207b33438a Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sun, 13 Nov 2022 16:23:22 -0600 Subject: [PATCH 15/39] Define Keras interface in core project (WIP). --- SciSharp STACK Examples.sln | 143 +++++++++++++----- src/SharpCV.Examples/SharpCV.Examples.csproj | 1 + .../TensorFlowNET.Examples.FSharp.fsproj | 1 + .../ImageProcessing/DigitRecognitionRNN.cs | 2 +- .../DigitRecognitionRnnKeras.cs | 5 +- .../ImageProcessing/MnistCnnKerasSubclass.cs | 19 +-- .../NeuralNetworks/FullyConnectedKeras.cs | 6 +- .../ObjectDetection/MnistInYOLOv3.cs | 2 +- src/TensorFlowNET.Examples/Program.cs | 6 +- .../TensorFlowNET.Examples.csproj | 18 ++- .../BinaryTextClassification.cs | 2 +- .../TextProcessing/CnnTextClassification.cs | 2 +- .../TextProcessing/cnn_models/CharCnn.cs | 36 ++--- .../TextProcessing/cnn_models/VdCnn.cs | 9 +- .../TextProcessing/cnn_models/WordCnn.cs | 9 +- .../TimeSeries/WeatherPrediction.cs | 2 +- 16 files changed, 170 insertions(+), 93 deletions(-) diff --git a/SciSharp STACK Examples.sln b/SciSharp STACK Examples.sln index 5843172..5f28d5e 100644 --- a/SciSharp STACK Examples.sln +++ b/SciSharp STACK Examples.sln @@ -9,17 +9,22 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SharpCV.Examples", "src\Sha EndProject Project("{6EC3EE1D-3C4E-46DD-8F32-0CC8E7565705}") = "TensorFlowNET.Examples.FSharp", "src\TensorFlowNET.Examples.FSharp\TensorFlowNET.Examples.FSharp.fsproj", "{2B536506-0C0D-42EA-9C10-8BBF27332602}" 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Debug-Minimal|Any CPU - Debug-Minimal|x64 = Debug-Minimal|x64 - Debug-Minimal|x86 = Debug-Minimal|x86 - Publish|Any CPU = Publish|Any CPU - Publish|x64 = Publish|x64 - Publish|x86 = Publish|x86 + GPU|Any CPU = GPU|Any CPU + GPU|x64 = GPU|x64 + GPU|x86 = GPU|x86 Release|Any CPU = Release|Any CPU Release|x64 = Release|x64 Release|x86 = Release|x86 @@ -31,18 +36,12 @@ Global {3AC62662-7861-4C21-A402-82864F2A8AF7}.Debug|x64.Build.0 = Debug|Any CPU {3AC62662-7861-4C21-A402-82864F2A8AF7}.Debug|x86.ActiveCfg = Debug|Any CPU {3AC62662-7861-4C21-A402-82864F2A8AF7}.Debug|x86.Build.0 = Debug|Any CPU - {3AC62662-7861-4C21-A402-82864F2A8AF7}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU - {3AC62662-7861-4C21-A402-82864F2A8AF7}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU - {3AC62662-7861-4C21-A402-82864F2A8AF7}.Debug-Minimal|x64.ActiveCfg = Debug|Any CPU - {3AC62662-7861-4C21-A402-82864F2A8AF7}.Debug-Minimal|x64.Build.0 = Debug|Any CPU - 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{71245256-9FE4-4D17-AE33-43945FEF6910}.Release|x86.ActiveCfg = Release|Any CPU + {71245256-9FE4-4D17-AE33-43945FEF6910}.Release|x86.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE diff --git a/src/SharpCV.Examples/SharpCV.Examples.csproj b/src/SharpCV.Examples/SharpCV.Examples.csproj index 3ed5e6a..70befc0 100644 --- a/src/SharpCV.Examples/SharpCV.Examples.csproj +++ b/src/SharpCV.Examples/SharpCV.Examples.csproj @@ -4,6 +4,7 @@ Exe net6.0 AnyCPU + Debug;Release;GPU diff --git a/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj b/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj index 1e1a8de..677c019 100644 --- a/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj +++ b/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj @@ -4,6 +4,7 @@ Exe net6.0 AnyCPU + Debug;Release;GPU diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs index f37ae89..56501ef 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs @@ -84,7 +84,7 @@ public override Graph BuildGraph() y = tf.placeholder(tf.int32, new[] { -1 }); var cell = tf.nn.rnn_cell.BasicRNNCell(num_units: n_neurons); var (output, state) = tf.nn.dynamic_rnn(cell, X, dtype: tf.float32); - var logits = keras.layers.dense(state, n_outputs); + var logits = tf.keras.layers.Dense(n_outputs).Apply(state); var cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: y, logits: logits); loss = tf.reduce_mean(cross_entropy); var adam = tf.train.AdamOptimizer(learning_rate: learning_rate); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs index 1c64813..8d4a429 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs @@ -16,6 +16,7 @@ limitations under the License. using System.Linq; using Tensorflow; +using Tensorflow.Keras; using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.Engine; using Tensorflow.Keras.Optimizers; @@ -115,8 +116,8 @@ public override void PrepareData() internal class LSTMModel : Model { OptimizerV2 optimizer; - Layer lstm; - Layer output; + ILayer lstm; + ILayer output; public LSTMModel(LSTMModelArgs args) : base(args) diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index 7b07042..7bf59c1 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -16,6 +16,7 @@ limitations under the License. using System.Linq; using Tensorflow; +using Tensorflow.Keras; using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.Engine; using Tensorflow.Keras.Optimizers; @@ -162,14 +163,14 @@ public override void PrepareData() public class ConvNet : Model { - Layer conv1; - Layer maxpool1; - Layer conv2; - Layer maxpool2; - Layer flatten; - Layer fc1; - Layer dropout; - Layer output; + ILayer conv1; + ILayer maxpool1; + ILayer conv2; + ILayer maxpool2; + ILayer flatten; + ILayer fc1; + ILayer dropout; + ILayer output; public ConvNet(ConvNetArgs args) : base(args) @@ -217,7 +218,7 @@ protected override Tensors Call(Tensors inputs, Tensor state = null, bool? train inputs = maxpool2.Apply(inputs); inputs = flatten.Apply(inputs); inputs = fc1.Apply(inputs); - inputs = dropout.Apply(inputs, training: training.Value); + inputs = dropout.Apply(inputs, is_training: training.Value); inputs = output.Apply(inputs); if (!training.Value) diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs index 5a9cffb..a9248a0 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs @@ -155,9 +155,9 @@ public bool Run() public class NeuralNet : Model { - Layer fc1; - Layer fc2; - Layer output; + ILayer fc1; + ILayer fc2; + ILayer output; public NeuralNet(NeuralNetArgs args) : base(args) diff --git a/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs b/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs index 2c61385..3cee6a6 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs @@ -31,7 +31,7 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "MNIST in YOLOv3", - Enabled = false + Enabled = true }; public bool Run() diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index 3089b74..017a9f1 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -35,7 +35,8 @@ static void Main(string[] args) var examples = Assembly.GetEntryAssembly().GetTypes() .Where(x => x.GetInterfaces().Contains(typeof(IExample))) - // .Where(x => x.Name == nameof(WeatherPrediction)) + //.Where(x => x.Name == nameof(WeatherPrediction)) + // .Where(x => x.Name == nameof(MnistInYOLOv3)) .ToArray(); Console.WriteLine(Environment.OSVersion, Color.Yellow); @@ -71,6 +72,9 @@ static void Main(string[] args) success.ForEach(x => Console.WriteLine($"{x} is OK!", Color.White)); errors.ForEach(x => Console.WriteLine($"{x} is Failed!", Color.Red)); + Console.WriteLine(Environment.OSVersion, Color.Yellow); + Console.WriteLine($"64Bit Operating System: {Environment.Is64BitOperatingSystem}", Color.Yellow); + Console.WriteLine($".NET CLR: {Environment.Version}", Color.Yellow); Console.WriteLine($"TensorFlow Binary v{tf.VERSION}"); Console.WriteLine($"TensorFlow.NET v{Assembly.GetAssembly(typeof(TF_DataType)).GetName().Version}"); Console.WriteLine($"TensorFlow.Keras v{Assembly.GetAssembly(typeof(KerasApi)).GetName().Version}"); diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 674773c..8344250 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -5,6 +5,7 @@ net6.0 false AnyCPU + Debug;Release;GPU @@ -13,6 +14,12 @@ AnyCPU + + TRACE;DEBUG + false + AnyCPU + + true @@ -43,14 +50,19 @@ - - + + - + + + + + + diff --git a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs index 21ada4f..1abaece 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs @@ -115,7 +115,7 @@ public override void PrepareData() output_sequence_length: sequence_length); var train_text = raw_train_ds.map(inputs => inputs[0]); - vectorize_layer.adapt(train_text); + //vectorize_layer.adapt(train_text); } } } diff --git a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs index 28363f9..05d74b7 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs @@ -60,7 +60,7 @@ public ExampleConfig InitConfig() { Name = "CNN Text Classification (Graph)", Enabled = true, - IsImportingGraph = false + IsImportingGraph = true }; public bool Run() diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs index e82ea98..5050d23 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs @@ -35,9 +35,9 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) kernel_initializer: kernel_initializer, activation: tf.nn.relu).Apply(x_expanded); - pool1 = keras.layers.max_pooling2d(conv1, - pool_size: new[] { 3, 1 }, - strides: new[] { 3, 1 }); + pool1 = tf.keras.layers.MaxPooling2D( + pool_size: (3, 1 ), + strides: (3, 1)).Apply(conv1); pool1 = tf.transpose(pool1, new[] { 0, 1, 3, 2 }); }); @@ -45,21 +45,21 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) { var conv2 = keras.layers.Conv2D( filters: num_filters, - kernel_size: new[] { filter_sizes[1], num_filters }, + kernel_size: (filter_sizes[1], num_filters), kernel_initializer: kernel_initializer, activation: tf.nn.relu).Apply(pool1); - pool2 = keras.layers.max_pooling2d(conv2, - pool_size: new[] { 3, 1 }, - strides: new[] { 3, 1 }); + pool2 = tf.keras.layers.MaxPooling2D( + pool_size: (3, 1), + strides: (3, 1)).Apply(conv2); pool2 = tf.transpose(pool2, new[] { 0, 1, 3, 2 }); }); tf_with(tf.name_scope("conv-3"), delegate { - conv3 = keras.layers.Conv2D( + conv3 = tf.keras.layers.Conv2D( filters: num_filters, - kernel_size: new[] { filter_sizes[2], num_filters }, + kernel_size: (filter_sizes[2], num_filters), kernel_initializer: kernel_initializer, activation: tf.nn.relu).Apply(pool2); conv3 = tf.transpose(conv3, new[] { 0, 1, 3, 2 }); @@ -67,9 +67,9 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) tf_with(tf.name_scope("conv-4"), delegate { - conv4 = keras.layers.Conv2D( + conv4 = tf.keras.layers.Conv2D( filters: num_filters, - kernel_size: new[] { filter_sizes[3], num_filters }, + kernel_size: (filter_sizes[3], num_filters), kernel_initializer: kernel_initializer, activation: tf.nn.relu).Apply(conv3); conv4 = tf.transpose(conv4, new[] { 0, 1, 3, 2 }); @@ -79,7 +79,7 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) { conv5 = keras.layers.Conv2D( filters: num_filters, - kernel_size: new[] { filter_sizes[4], num_filters }, + kernel_size: (filter_sizes[4], num_filters), kernel_initializer: kernel_initializer, activation: tf.nn.relu).Apply(conv4); conv5 = tf.transpose(conv5, new[] { 0, 1, 3, 2 }); @@ -89,13 +89,13 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) { conv6 = keras.layers.Conv2D( filters: num_filters, - kernel_size: new[] { filter_sizes[5], num_filters }, + kernel_size: (filter_sizes[5], num_filters), kernel_initializer: kernel_initializer, activation: tf.nn.relu).Apply(conv5); - var pool6 = keras.layers.max_pooling2d(conv6, - pool_size: new[] { 3, 1 }, - strides: new[] { 3, 1 }); + var pool6 = keras.layers.MaxPooling2D( + pool_size: (3, 1), + strides: (3, 1)).Apply(conv6); pool6 = tf.transpose(pool6, new[] { 0, 2, 1, 3 }); h_pool = tf.reshape(pool6, new[] { -1, 34 * num_filters }); @@ -124,9 +124,9 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) tf_with(tf.name_scope("fc-3"), delegate { - logits = keras.layers.dense(fc2_out, + logits = tf.keras.layers.Dense( num_class, - kernel_initializer: kernel_initializer); + kernel_initializer: kernel_initializer).Apply(fc2_out); predictions = tf.math.argmax(logits, -1, output_type: tf.int32); }); diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs index c9bffab..eca5656 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs @@ -110,7 +110,7 @@ public VdCnn(int alphabet_size, int document_max_len, int num_class) tf_with(tf.name_scope("fc-3"), scope => { - logits = keras.layers.dense(fc2_out, num_class, activation: null, kernel_initializer: fc_initializer); + logits = tf.keras.layers.Dense(num_class, activation: null, kernel_initializer: fc_initializer).Apply(fc2_out); predictions = tf.math.argmax(logits, -1, output_type: tf.int32); }); @@ -140,7 +140,7 @@ private Tensor conv_block(Tensor input, int i, bool max_pool = true) tf_with(tf.variable_scope($"conv-{j}"), delegate { // convolution - conv = keras.layers.Conv2D( + conv = tf.keras.layers.Conv2D( filters: num_filters[i], kernel_size: new int[] { filter_sizes[i], num_filters[i - 1] }, kernel_initializer: cnn_initializer, @@ -156,11 +156,10 @@ private Tensor conv_block(Tensor input, int i, bool max_pool = true) if (max_pool) { // Max pooling - return keras.layers.max_pooling2d( - conv, + return tf.keras.layers.MaxPooling2D( pool_size: new int[] { 3, 1 }, strides: new int[] { 2, 1 }, - padding: "SAME"); + padding: "SAME").Apply(conv).First(); } else { diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs index 8a1c4db..10662a3 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs @@ -49,18 +49,17 @@ public WordCnn(int vocabulary_size, int document_max_len, int num_class) for (int len = 0; len < filter_sizes.Rank; len++) { int filter_size = filter_sizes.GetLength(len); - var conv = keras.layers.Conv2D( + var conv = tf.keras.layers.Conv2D( filters: num_filters, kernel_size: new int[] { filter_size, embedding_size }, strides: new int[] { 1, 1 }, padding: "VALID", activation: tf.nn.relu).Apply(x_emb); - var pool = keras.layers.max_pooling2d( - conv, + var pool = tf.keras.layers.MaxPooling2D( pool_size: new[] { document_max_len - filter_size + 1, 1 }, strides: new[] { 1, 1 }, - padding: "VALID"); + padding: "VALID").Apply(conv); pooled_outputs.Add(pool); } @@ -77,7 +76,7 @@ public WordCnn(int vocabulary_size, int document_max_len, int num_class) Tensor predictions = null; tf_with(tf.name_scope("output"), delegate { - logits = keras.layers.dense(h_drop, num_class); + logits = tf.keras.layers.Dense(num_class).Apply(h_drop); predictions = tf.math.argmax(logits, -1, output_type: tf.int32); }); diff --git a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs index 356138e..9a9fe39 100644 --- a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs +++ b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs @@ -19,7 +19,7 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "Weather Prediction", - Enabled = false + Enabled = true }; public bool Run() From 9ef37178de66387c048a7ec0dd270ff6c343430a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 13 Nov 2022 22:26:37 +0000 Subject: [PATCH 16/39] Bump System.Drawing.Common in /src/TensorFlowNET.Examples.FSharp Bumps [System.Drawing.Common](https://github.com/dotnet/runtime) from 5.0.0 to 5.0.3. - [Release notes](https://github.com/dotnet/runtime/releases) - [Commits](https://github.com/dotnet/runtime/compare/v5.0.0...v5.0.3) --- updated-dependencies: - dependency-name: System.Drawing.Common dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- .../TensorFlowNET.Examples.FSharp.fsproj | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj b/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj index e504e52..a06ae27 100644 --- a/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj +++ b/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj @@ -39,7 +39,7 @@ - + From 6c0229ef9a85600408a68fb70c4cad1bde400e69 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sat, 19 Nov 2022 10:04:02 -0600 Subject: [PATCH 17/39] Fix NeuralNetXorKeras accuracy. #952 --- .../ImageProcessing/CnnInYourOwnData.cs | 13 +++++++------ .../NeuralNetworks/NeuralNetXorKeras.cs | 10 ++++++---- src/TensorFlowNET.Examples/Program.cs | 3 ++- .../TensorFlowNET.Examples.csproj | 1 + 4 files changed, 16 insertions(+), 11 deletions(-) diff --git a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs index 9e082f6..039dc1e 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs @@ -193,15 +193,16 @@ private void LoadImagesToNDArray() } private void LoadImage(string[] a, NDArray b, string c) { - using (var graph = tf.Graph().as_default()) + using var graph = tf.Graph().as_default(); + + for (int i = 0; i < a.Length; i++) { - for (int i = 0; i < a.Length; i++) - { - b[i] = ReadTensorFromImageFile(a[i], graph); - Console.WriteLine($"Loading image: {a[i]}"); - } + b[i] = ReadTensorFromImageFile(a[i], graph); + Console.Write($"Loading image: {a[i]}..."); + Console.CursorLeft = 0; } + Console.WriteLine(); Console.WriteLine($"Loaded {a.Length} images for " + c); } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs index fb0e98a..e41b124 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs @@ -15,6 +15,8 @@ limitations under the License. ******************************************************************************/ using System; +using System.Linq; +using Tensorflow; using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; @@ -43,15 +45,15 @@ public bool Run() var model = keras.Sequential(); model.add(keras.Input(2)); - model.add(keras.layers.Dense(64, keras.activations.Relu)); + model.add(keras.layers.Dense(32, keras.activations.Relu)); model.add(keras.layers.Dense(1, keras.activations.Sigmoid)); model.compile(optimizer: keras.optimizers.Adam(), loss: keras.losses.MeanSquaredError(), new[] { "accuracy" }); model.fit(x, y, 1, 100); - print(model.predict(x, 4)); - - return true; + model.evaluate(x, y); + Tensor result = model.predict(x, 4); + return result.ToArray() is [< 0.5f, > 0.5f, > 0.5f, < 0.5f]; } } } diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index 017a9f1..8e0d8ef 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -36,7 +36,8 @@ static void Main(string[] args) var examples = Assembly.GetEntryAssembly().GetTypes() .Where(x => x.GetInterfaces().Contains(typeof(IExample))) //.Where(x => x.Name == nameof(WeatherPrediction)) - // .Where(x => x.Name == nameof(MnistInYOLOv3)) + //.Where(x => x.Name == nameof(NeuralNetXorKeras)) + .Where(x => x.Name == nameof(DigitRecognitionCnnKeras)) .ToArray(); Console.WriteLine(Environment.OSVersion, Color.Yellow); diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 8344250..02cc029 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -6,6 +6,7 @@ false AnyCPU Debug;Release;GPU + 11.0 From a4625830c418bb06a753c796e5c0f363bd1dbffa Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Thu, 24 Nov 2022 18:31:49 -0600 Subject: [PATCH 18/39] Fix NeuralNetXor example. --- src/SharpCV.Examples/SharpCV.Examples.csproj | 2 +- .../TensorFlowNET.Examples.FSharp.fsproj | 2 +- .../NeuralNetworks/NeuralNetXor.cs | 111 +++++------------ .../NeuralNetworks/NeuralNetXorEager.cs | 117 ++++++++++++++++++ src/TensorFlowNET.Examples/Program.cs | 4 +- 5 files changed, 151 insertions(+), 85 deletions(-) create mode 100644 src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorEager.cs diff --git a/src/SharpCV.Examples/SharpCV.Examples.csproj b/src/SharpCV.Examples/SharpCV.Examples.csproj index 70befc0..d88e9a3 100644 --- a/src/SharpCV.Examples/SharpCV.Examples.csproj +++ b/src/SharpCV.Examples/SharpCV.Examples.csproj @@ -9,7 +9,7 @@ - + diff --git a/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj b/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj index a06ae27..3a4b9aa 100644 --- a/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj +++ b/src/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj @@ -39,7 +39,7 @@ - + diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs index 997582f..fd8b877 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs @@ -37,39 +37,11 @@ public class NeuralNetXor : SciSharpExample, IExample public ExampleConfig InitConfig() => Config = new ExampleConfig { - Name = "NN XOR", + Name = "NN XOR in Graph Mode", Enabled = true, IsImportingGraph = false }; - private (Operation, Tensor, Tensor) make_graph(Tensor features, Tensor labels, int num_hidden = 8) - { - var stddev = 1 / Math.Sqrt(2); - var hidden_weights = tf.Variable(tf.truncated_normal(new int[] { 2, num_hidden }, seed: 1, stddev: (float)stddev)); - - // Shape [4, num_hidden] - var hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights)); - - var output_weights = tf.Variable(tf.truncated_normal( - new[] { num_hidden, 1 }, - seed: 17, - stddev: (float)(1 / Math.Sqrt(num_hidden)) - )); - - // Shape [4, 1] - var logits = tf.matmul(hidden_activations, output_weights); - - // Shape [4] - var predictions = tf.tanh(tf.squeeze(logits)); - var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name: "loss"); - - var gs = tf.Variable(0, trainable: false, name: "global_step"); - var optimizer = tf.train.GradientDescentOptimizer(0.2f); - var train_op = optimizer.minimize(loss, global_step: gs); - - return (train_op, loss, gs); - } - public bool Run() { tf.compat.v1.disable_eager_execution(); @@ -77,9 +49,7 @@ public bool Run() PrepareData(); float loss_value = 0; - if (tf.Context.executing_eagerly()) - loss_value = RunEagerMode(); - else if (Config.IsImportingGraph) + if (Config.IsImportingGraph) loss_value = RunWithImportedGraph(); else loss_value = RunWithBuiltGraph(); @@ -87,55 +57,6 @@ public bool Run() return loss_value < 0.0628; } - private float RunEagerMode() - { - var learning_rate = 0.01f; - var num_hidden = 8; - var display_step = 1000; - var stddev = 1 / Math.Sqrt(2); - var features = tf.constant(data); - var labels = tf.constant(label); - - var hidden_weights = tf.Variable(tf.random.truncated_normal((2, num_hidden), seed: 1, stddev: (float)stddev)); - - var optimizer = keras.optimizers.SGD(learning_rate); - - // Run training for the given number of steps. - foreach (var step in range(1, num_steps + 1)) - { - using var g = tf.GradientTape(); - - // Shape [4, num_hidden] - var hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights)); - - var output_weights = tf.Variable(tf.truncated_normal( - (num_hidden, 1), - seed: 17, - stddev: (float)(1 / Math.Sqrt(num_hidden)) - )); - - // Shape [4, 1] - var logits = tf.matmul(hidden_activations, output_weights); - - // Shape [4] - var predictions = tf.tanh(tf.squeeze(logits)); - var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name: "loss"); - - // should stop recording - // Compute gradients. - var gradients = g.gradient(loss, output_weights); - - // Update W and b following gradients. - optimizer.apply_gradients((gradients, output_weights)); - - if (step % display_step == 0) - { - print($"step: {step}, loss: {loss.numpy()}"); - } - } - return 0; - } - private float RunWithImportedGraph() { var graph = tf.Graph().as_default(); @@ -205,6 +126,34 @@ private float RunWithBuiltGraph() return loss_value; } + private (Operation, Tensor, Tensor) make_graph(Tensor features, Tensor labels, int num_hidden = 8) + { + var stddev = 1 / Math.Sqrt(2); + var hidden_weights = tf.Variable(tf.truncated_normal(new int[] { 2, num_hidden }, seed: 1, stddev: (float)stddev)); + + // Shape [4, num_hidden] + var hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights)); + + var output_weights = tf.Variable(tf.truncated_normal( + new[] { num_hidden, 1 }, + seed: 17, + stddev: (float)(1 / Math.Sqrt(num_hidden)) + )); + + // Shape [4, 1] + var logits = tf.matmul(hidden_activations, output_weights); + + // Shape [4] + var predictions = tf.tanh(tf.squeeze(logits)); + var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name: "loss"); + + var gs = tf.Variable(0, trainable: false, name: "global_step"); + var optimizer = tf.train.GradientDescentOptimizer(0.2f); + var train_op = optimizer.minimize(loss, global_step: gs); + + return (train_op, loss, gs); + } + public override void PrepareData() { data = new float[,] diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorEager.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorEager.cs new file mode 100644 index 0000000..8ccc6ed --- /dev/null +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorEager.cs @@ -0,0 +1,117 @@ +/***************************************************************************** + Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +******************************************************************************/ + +using System; +using Tensorflow.NumPy; +using static Tensorflow.Binding; +using static Tensorflow.KerasApi; + +namespace TensorFlowNET.Examples +{ + /// + /// Simple vanilla neural net solving the famous XOR problem + /// https://github.com/amygdala/tensorflow-workshop/blob/master/workshop_sections/getting_started/xor/README.md + /// + public class NeuralNetXorEager : SciSharpExample, IExample + { + public int num_steps = 10000; + + private NDArray data; + private NDArray label; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "NN XOR in Eager Mode", + Enabled = true + }; + + public bool Run() + { + PrepareData(); + var loss_value = RunEagerMode(); + return loss_value < 0.0628; + } + + private float RunEagerMode() + { + var learning_rate = 0.01f; + var num_hidden = 8; + var display_step = 1000; + var stddev = 1 / Math.Sqrt(2); + var features = tf.constant(data); + var labels = tf.constant(label); + + var hidden_weights = tf.Variable(tf.random.truncated_normal((2, num_hidden), seed: 1, stddev: (float)stddev)); + + // Shape [4, num_hidden] + var hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights)); + + var output_weights = tf.Variable(tf.truncated_normal( + (num_hidden, 1), + seed: 17, + stddev: (float)(1 / Math.Sqrt(num_hidden)) + )); + + var optimizer = keras.optimizers.SGD(learning_rate); + + // Run training for the given number of steps. + foreach (var step in range(1, num_steps + 1)) + { + using var g = tf.GradientTape(); + + // Shape [4, 1] + var logits = tf.matmul(hidden_activations, output_weights); + + // Shape [4] + var predictions = tf.tanh(tf.squeeze(logits)); + var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name: "loss"); + + // should stop recording + // Compute gradients. + var gradients = g.gradient(loss, output_weights); + + // Update W and b following gradients. + optimizer.apply_gradients((gradients, output_weights)); + + if (step % display_step == 0) + { + print($"step: {step}, loss: {loss.numpy()}"); + } + } + return 0; + } + + public override void PrepareData() + { + data = new float[,] + { + {1, 0 }, + {1, 1 }, + {0, 0 }, + {0, 1 } + }; + + label = new float[,] + { + {1 }, + {0 }, + {0 }, + {1 } + }; + } + } +} diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index 8e0d8ef..b563525 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -36,8 +36,8 @@ static void Main(string[] args) var examples = Assembly.GetEntryAssembly().GetTypes() .Where(x => x.GetInterfaces().Contains(typeof(IExample))) //.Where(x => x.Name == nameof(WeatherPrediction)) - //.Where(x => x.Name == nameof(NeuralNetXorKeras)) - .Where(x => x.Name == nameof(DigitRecognitionCnnKeras)) + //.Where(x => x.Name == nameof(NeuralNetXorEager)) + //.Where(x => x.Name == nameof(DigitRecognitionCnnKeras)) .ToArray(); Console.WriteLine(Environment.OSVersion, Color.Yellow); From 351920790538819e9422d484a964a13fb75d62e2 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sat, 26 Nov 2022 16:01:14 -0600 Subject: [PATCH 19/39] Fix batch_size for Keras Input. --- SciSharp STACK Examples.sln | 68 +++++++++++++++++-- src/SharpCV.Examples/SharpCV.Examples.csproj | 7 +- src/TensorFlowNET.Examples/Program.cs | 2 +- .../TensorFlowNET.Examples.csproj | 10 +-- 4 files changed, 75 insertions(+), 12 deletions(-) diff --git a/SciSharp STACK Examples.sln b/SciSharp STACK Examples.sln index 5f28d5e..041feac 100644 --- a/SciSharp STACK Examples.sln +++ b/SciSharp STACK Examples.sln @@ -17,6 +17,12 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.TimeSeries" EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.Core", "..\SciSharp.Models\SciSharp.Models.Core\SciSharp.Models.Core.csproj", "{71245256-9FE4-4D17-AE33-43945FEF6910}" EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.ObjectDetection", "..\SciSharp.Models\SciSharp.Models.ObjectDetection\SciSharp.Models.ObjectDetection.csproj", "{927655AB-B23E-4E45-85E7-16804B05D1A8}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SharpCV", "..\SharpCV\src\SharpCV\SharpCV.csproj", "{50934DBB-6C5F-4E61-87AB-0C1D748AF420}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.ImageClassification", "..\SciSharp.Models\SciSharp.Models.ImageClassification\SciSharp.Models.ImageClassification.csproj", "{FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}" +EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Any CPU = Debug|Any CPU @@ -126,8 +132,8 @@ Global {C9B305D6-43C9-4BC7-820D-5907041BB97F}.Debug|x64.Build.0 = Debug|Any CPU {C9B305D6-43C9-4BC7-820D-5907041BB97F}.Debug|x86.ActiveCfg = Debug|Any CPU {C9B305D6-43C9-4BC7-820D-5907041BB97F}.Debug|x86.Build.0 = Debug|Any CPU - {C9B305D6-43C9-4BC7-820D-5907041BB97F}.GPU|Any CPU.ActiveCfg = Release|Any CPU - {C9B305D6-43C9-4BC7-820D-5907041BB97F}.GPU|Any CPU.Build.0 = Release|Any CPU + {C9B305D6-43C9-4BC7-820D-5907041BB97F}.GPU|Any CPU.ActiveCfg = Debug|Any CPU + {C9B305D6-43C9-4BC7-820D-5907041BB97F}.GPU|Any CPU.Build.0 = Debug|Any CPU {C9B305D6-43C9-4BC7-820D-5907041BB97F}.GPU|x64.ActiveCfg = Release|Any CPU {C9B305D6-43C9-4BC7-820D-5907041BB97F}.GPU|x64.Build.0 = Release|Any CPU {C9B305D6-43C9-4BC7-820D-5907041BB97F}.GPU|x86.ActiveCfg = Release|Any CPU @@ -144,8 +150,8 @@ Global {71245256-9FE4-4D17-AE33-43945FEF6910}.Debug|x64.Build.0 = Debug|Any CPU {71245256-9FE4-4D17-AE33-43945FEF6910}.Debug|x86.ActiveCfg = Debug|Any CPU {71245256-9FE4-4D17-AE33-43945FEF6910}.Debug|x86.Build.0 = Debug|Any CPU - {71245256-9FE4-4D17-AE33-43945FEF6910}.GPU|Any CPU.ActiveCfg = Release|Any CPU - {71245256-9FE4-4D17-AE33-43945FEF6910}.GPU|Any CPU.Build.0 = Release|Any CPU + {71245256-9FE4-4D17-AE33-43945FEF6910}.GPU|Any CPU.ActiveCfg = Debug|Any CPU + {71245256-9FE4-4D17-AE33-43945FEF6910}.GPU|Any CPU.Build.0 = Debug|Any CPU {71245256-9FE4-4D17-AE33-43945FEF6910}.GPU|x64.ActiveCfg = Release|Any CPU {71245256-9FE4-4D17-AE33-43945FEF6910}.GPU|x64.Build.0 = Release|Any CPU {71245256-9FE4-4D17-AE33-43945FEF6910}.GPU|x86.ActiveCfg = Release|Any CPU @@ -156,6 +162,60 @@ Global {71245256-9FE4-4D17-AE33-43945FEF6910}.Release|x64.Build.0 = Release|Any CPU {71245256-9FE4-4D17-AE33-43945FEF6910}.Release|x86.ActiveCfg = Release|Any CPU {71245256-9FE4-4D17-AE33-43945FEF6910}.Release|x86.Build.0 = Release|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Debug|Any CPU.Build.0 = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Debug|x64.ActiveCfg = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Debug|x64.Build.0 = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Debug|x86.ActiveCfg = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Debug|x86.Build.0 = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.GPU|Any CPU.ActiveCfg = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.GPU|Any CPU.Build.0 = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.GPU|x64.ActiveCfg = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.GPU|x64.Build.0 = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.GPU|x86.ActiveCfg = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.GPU|x86.Build.0 = Debug|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Release|Any CPU.ActiveCfg = Release|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Release|Any CPU.Build.0 = Release|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Release|x64.ActiveCfg = Release|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Release|x64.Build.0 = Release|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Release|x86.ActiveCfg = Release|Any CPU + {927655AB-B23E-4E45-85E7-16804B05D1A8}.Release|x86.Build.0 = Release|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Debug|Any CPU.Build.0 = Debug|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Debug|x64.ActiveCfg = Debug|x64 + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Debug|x64.Build.0 = Debug|x64 + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Debug|x86.ActiveCfg = Debug|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Debug|x86.Build.0 = Debug|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.GPU|Any CPU.ActiveCfg = Debug|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.GPU|Any CPU.Build.0 = Debug|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.GPU|x64.ActiveCfg = Debug|x64 + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.GPU|x64.Build.0 = Debug|x64 + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.GPU|x86.ActiveCfg = Debug|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.GPU|x86.Build.0 = Debug|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Release|Any CPU.ActiveCfg = Release|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Release|Any CPU.Build.0 = Release|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Release|x64.ActiveCfg = Release|x64 + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Release|x64.Build.0 = Release|x64 + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Release|x86.ActiveCfg = Release|Any CPU + {50934DBB-6C5F-4E61-87AB-0C1D748AF420}.Release|x86.Build.0 = Release|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Debug|Any CPU.Build.0 = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Debug|x64.ActiveCfg = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Debug|x64.Build.0 = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Debug|x86.ActiveCfg = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Debug|x86.Build.0 = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.GPU|Any CPU.ActiveCfg = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.GPU|Any CPU.Build.0 = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.GPU|x64.ActiveCfg = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.GPU|x64.Build.0 = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.GPU|x86.ActiveCfg = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.GPU|x86.Build.0 = Debug|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Release|Any CPU.ActiveCfg = Release|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Release|Any CPU.Build.0 = Release|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Release|x64.ActiveCfg = Release|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Release|x64.Build.0 = Release|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Release|x86.ActiveCfg = Release|Any CPU + {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Release|x86.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE diff --git a/src/SharpCV.Examples/SharpCV.Examples.csproj b/src/SharpCV.Examples/SharpCV.Examples.csproj index d88e9a3..e60e865 100644 --- a/src/SharpCV.Examples/SharpCV.Examples.csproj +++ b/src/SharpCV.Examples/SharpCV.Examples.csproj @@ -10,8 +10,11 @@ - - + + + + + diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index b563525..f3ed018 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -37,7 +37,7 @@ static void Main(string[] args) .Where(x => x.GetInterfaces().Contains(typeof(IExample))) //.Where(x => x.Name == nameof(WeatherPrediction)) //.Where(x => x.Name == nameof(NeuralNetXorEager)) - //.Where(x => x.Name == nameof(DigitRecognitionCnnKeras)) + .Where(x => x.Name == nameof(MnistInYOLOv3)) .ToArray(); Console.WriteLine(Environment.OSVersion, Color.Yellow); diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 02cc029..0a47ceb 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -49,11 +49,8 @@ - - - - - + + @@ -62,7 +59,10 @@ + + + From ea6826a7cb5b7f0af6944baf1a526392842f5708 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sat, 3 Dec 2022 12:44:00 -0600 Subject: [PATCH 20/39] Fix Sequential model.summary missing layers. #960 --- SciSharp STACK Examples.sln | 20 ++++ src/TensorFlowNET.Examples/GAN/MnistGAN.cs | 9 +- .../SentimentClassification.cs | 103 ++++++++++++++++++ .../ObjectDetection/MnistInYOLOv3.cs | 4 +- src/TensorFlowNET.Examples/Program.cs | 4 +- .../TensorFlowNET.Examples.csproj | 1 + 6 files changed, 136 insertions(+), 5 deletions(-) create mode 100644 src/TensorFlowNET.Examples/NaturalLanguageProcessing/SentimentClassification.cs diff --git a/SciSharp STACK Examples.sln b/SciSharp STACK Examples.sln index 041feac..a25a422 100644 --- a/SciSharp STACK Examples.sln +++ b/SciSharp STACK Examples.sln @@ -23,6 +23,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SharpCV", "..\SharpCV\src\S EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.ImageClassification", "..\SciSharp.Models\SciSharp.Models.ImageClassification\SciSharp.Models.ImageClassification.csproj", "{FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}" EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.TextClassification", "..\SciSharp.Models\SciSharp.Models.TextClassification\SciSharp.Models.TextClassification.csproj", "{D5243F2C-4EAE-45AD-849C-A7FD79B599C5}" +EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Any CPU = Debug|Any CPU @@ -216,6 +218,24 @@ Global {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Release|x64.Build.0 = Release|Any CPU {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Release|x86.ActiveCfg = Release|Any CPU {FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}.Release|x86.Build.0 = Release|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Debug|Any CPU.Build.0 = Debug|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Debug|x64.ActiveCfg = Debug|x64 + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Debug|x64.Build.0 = Debug|x64 + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Debug|x86.ActiveCfg = Debug|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Debug|x86.Build.0 = Debug|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.GPU|Any CPU.ActiveCfg = Debug|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.GPU|Any CPU.Build.0 = Debug|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.GPU|x64.ActiveCfg = Debug|x64 + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.GPU|x64.Build.0 = Debug|x64 + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.GPU|x86.ActiveCfg = Debug|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.GPU|x86.Build.0 = Debug|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Release|Any CPU.ActiveCfg = Release|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Release|Any CPU.Build.0 = Release|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Release|x64.ActiveCfg = Release|x64 + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Release|x64.Build.0 = Release|x64 + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Release|x86.ActiveCfg = Release|Any CPU + {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Release|x86.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE diff --git a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs index 2069452..d90fa7a 100644 --- a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs +++ b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs @@ -16,8 +16,12 @@ namespace TensorFlowNET.Examples.GAN public class MnistGAN : SciSharpExample, IExample { float LeakyReLU_alpha = 0.2f; + +#if GPU + int epochs = 2000; // Better effect, but longer time +#else int epochs = 20; - //int epochs = 2000; // Better effect, but longer time +#endif int batch_size = 64; string imgpath = "dcgan\\imgs"; @@ -65,7 +69,8 @@ private Model Make_Generator_model() Tensorflow.Keras.Activation activation = null; var model = keras.Sequential(); - model.add(keras.layers.Dense(img_rows / 4 * img_cols / 4 * 256, activation: activation, input_shape: 100)); + model.add(keras.layers.Input(shape: 100)); + model.add(keras.layers.Dense(img_rows / 4 * img_cols / 4 * 256, activation: activation)); model.add(keras.layers.BatchNormalization(momentum: 0.8f)); model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); model.add(keras.layers.Reshape((7, 7, 256))); diff --git a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/SentimentClassification.cs b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/SentimentClassification.cs new file mode 100644 index 0000000..08760ee --- /dev/null +++ b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/SentimentClassification.cs @@ -0,0 +1,103 @@ +using SciSharp.Models.TimeSeries; +using SciSharp.Models; +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using System.IO; +using static Tensorflow.Binding; +using static Tensorflow.KerasApi; + +namespace TensorFlowNET.Examples +{ + public class SentimentClassification : SciSharpExample, IExample + { + ITimeSeriesTask task; + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Text Sentiment Classification", + Enabled = true + }; + + public bool Run() + { + var wizard = new ModelWizard(); + task = wizard.AddTimeSeriesTask(new TaskOptions + { + WeightsPath = @"timeseries_linear_v1\saved_weights.h5" + }); + task.SetModelArgs(new TimeSeriesModelArgs + { + }); + + return true; + } + + public override void PrepareData() + { + // tf.debugging.set_log_device_placement(true); + string url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"; + var dataset = keras.utils.get_file("aclImdb_v1.tar.gz", url, + untar: true, + cache_dir: Path.GetTempPath(), + cache_subdir: "aclImdb_v1"); + var data_dir = Path.Combine(dataset, "aclImdb"); + var train_dir = Path.Combine(data_dir, "train"); + + int batch_size = 32; + int seed = 42; + var raw_train_ds = keras.preprocessing.text_dataset_from_directory( + train_dir, + batch_size: batch_size, + validation_split: 0.2f, + subset: "training", + seed: seed); + + /*foreach (var (text_batch, label_batch) in raw_train_ds.take(1)) + { + foreach (var i in range(3)) + { + print("Review", text_batch.StringData()[i]); + print("Label", label_batch.numpy()[i]); + } + } + + print("Label 0 corresponds to", raw_train_ds.class_names[0]); + print("Label 1 corresponds to", raw_train_ds.class_names[1]); + + var raw_val_ds = keras.preprocessing.text_dataset_from_directory( + train_dir, + batch_size: batch_size, + validation_split: 0.2f, + subset: "validation", + seed: seed); + + var test_dir = Path.Combine(data_dir, "test"); + var raw_test_ds = keras.preprocessing.text_dataset_from_directory( + test_dir, + batch_size: batch_size); + + var max_features = 10000; + var sequence_length = 250; + + Func custom_standardization = input_data => + { + var lowercase = tf.strings.lower(input_data); + var stripped_html = tf.strings.regex_replace(lowercase, "
", " "); + return tf.strings.regex_replace(stripped_html, + "'[!\"\\#\\$%\\&\'\\(\\)\\*\\+,\\-\\./:;<=>\\?@\\[\\\\\\]\\^_`\\{\\|\\}\\~]'", + ""); + }; + + var vectorize_layer = keras.layers.preprocessing.TextVectorization(standardize: custom_standardization, + max_tokens: max_features, + output_mode: "int", + output_sequence_length: sequence_length); + + var train_text = raw_train_ds.map(inputs => inputs[0]); + //vectorize_layer.adapt(train_text);*/ + } + } +} diff --git a/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs b/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs index 3cee6a6..0d6ecaa 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs @@ -31,7 +31,7 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "MNIST in YOLOv3", - Enabled = true + Enabled = false }; public bool Run() @@ -39,6 +39,7 @@ public bool Run() cfg = new YoloConfig("YOLOv3"); (trainingData, testingData) = PrepareData(); Train(); + Test(); return true; } @@ -67,6 +68,7 @@ public override void Test() { ModelPath = @"./YOLOv3/yolov3.h5" }); + task.SetModelArgs(cfg); var result = task.Test(new TestingOptions { diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index f3ed018..463b9e5 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -36,8 +36,8 @@ static void Main(string[] args) var examples = Assembly.GetEntryAssembly().GetTypes() .Where(x => x.GetInterfaces().Contains(typeof(IExample))) //.Where(x => x.Name == nameof(WeatherPrediction)) - //.Where(x => x.Name == nameof(NeuralNetXorEager)) - .Where(x => x.Name == nameof(MnistInYOLOv3)) + //.Where(x => x.Name == nameof(SentimentClassification)) + //.Where(x => x.Name == nameof(MnistInYOLOv3)) .ToArray(); Console.WriteLine(Environment.OSVersion, Color.Yellow); diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 0a47ceb..afca37b 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -61,6 +61,7 @@ + From 044b7cf638d907452c844fbef0497b7cd69a9e11 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sat, 3 Dec 2022 15:42:59 -0600 Subject: [PATCH 21/39] Add WebAPI example. #958 --- SciSharp STACK Examples.sln | 20 +++ .../ImageClassificationController.cs | 53 ++++++++ src/SciSharp.WebApi/Program.cs | 27 ++++ src/SciSharp.WebApi/SciSharp.WebApi.csproj | 18 +++ .../Services/ImageClassificationService.cs | 121 ++++++++++++++++++ .../appsettings.Development.json | 8 ++ src/SciSharp.WebApi/appsettings.json | 9 ++ 7 files changed, 256 insertions(+) create mode 100644 src/SciSharp.WebApi/Controllers/ImageClassificationController.cs create mode 100644 src/SciSharp.WebApi/Program.cs create mode 100644 src/SciSharp.WebApi/SciSharp.WebApi.csproj create mode 100644 src/SciSharp.WebApi/Services/ImageClassificationService.cs create mode 100644 src/SciSharp.WebApi/appsettings.Development.json create mode 100644 src/SciSharp.WebApi/appsettings.json diff --git a/SciSharp STACK Examples.sln b/SciSharp STACK Examples.sln index a25a422..4c1da97 100644 --- a/SciSharp STACK Examples.sln +++ b/SciSharp STACK Examples.sln @@ -25,6 +25,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.ImageClassi EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.TextClassification", "..\SciSharp.Models\SciSharp.Models.TextClassification\SciSharp.Models.TextClassification.csproj", "{D5243F2C-4EAE-45AD-849C-A7FD79B599C5}" EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.WebApi", "src\SciSharp.WebApi\SciSharp.WebApi.csproj", "{E1D6E827-3FC6-476A-8875-1FF8C1D398CE}" +EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Any CPU = Debug|Any CPU @@ -236,6 +238,24 @@ Global {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Release|x64.Build.0 = Release|x64 {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Release|x86.ActiveCfg = Release|Any CPU {D5243F2C-4EAE-45AD-849C-A7FD79B599C5}.Release|x86.Build.0 = Release|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Debug|Any CPU.Build.0 = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Debug|x64.ActiveCfg = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Debug|x64.Build.0 = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Debug|x86.ActiveCfg = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Debug|x86.Build.0 = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.GPU|Any CPU.ActiveCfg = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.GPU|Any CPU.Build.0 = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.GPU|x64.ActiveCfg = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.GPU|x64.Build.0 = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.GPU|x86.ActiveCfg = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.GPU|x86.Build.0 = Debug|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Release|Any CPU.ActiveCfg = Release|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Release|Any CPU.Build.0 = Release|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Release|x64.ActiveCfg = Release|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Release|x64.Build.0 = Release|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Release|x86.ActiveCfg = Release|Any CPU + {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Release|x86.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE diff --git a/src/SciSharp.WebApi/Controllers/ImageClassificationController.cs b/src/SciSharp.WebApi/Controllers/ImageClassificationController.cs new file mode 100644 index 0000000..3f308e9 --- /dev/null +++ b/src/SciSharp.WebApi/Controllers/ImageClassificationController.cs @@ -0,0 +1,53 @@ +using Microsoft.AspNetCore.Mvc; +using Tensorflow.NumPy; +using WebApi.Services; + +namespace WebApi.Controllers +{ + [ApiController] + [Route("[controller]")] + public class ImageClassificationController : ControllerBase + { + private readonly ILogger _logger; + private readonly ImageClassificationService _imageClassifier; + + + public ImageClassificationController(ILogger logger, ImageClassificationService imageClassifier) + { + _logger = logger; + _imageClassifier = imageClassifier; + } + + [HttpGet("train")] + public IActionResult Train() + { + _imageClassifier.Train(); + return Ok(); + } + + [HttpGet("predict")] + public IActionResult Predict() + { + // Validate incoming bytes + /*var length = request.ContentLength; + if (length is null) + { + return BadRequest("Missing Content-Length header."); + } + else if (length != 28 * 28) + { + return BadRequest("Content-Length should be exactly 28x28 bytes."); + } + + // Read HTTP request body into buffer + var buffer = new byte[(int)length]; + await request.Body.ReadAsync(buffer, 0, (int)length);*/ + + // Give their bytes to the neural network as input + var prediction = _imageClassifier.Predict(new byte[28 * 28]); + + // Send back what we think it is, as an array of 10 doubles + return Ok(prediction); + } + } +} \ No newline at end of file diff --git a/src/SciSharp.WebApi/Program.cs b/src/SciSharp.WebApi/Program.cs new file mode 100644 index 0000000..ad9cd6b --- /dev/null +++ b/src/SciSharp.WebApi/Program.cs @@ -0,0 +1,27 @@ +using WebApi.Services; + +var builder = WebApplication.CreateBuilder(args); + +// Add services to the container. +// Build a new neural network on startup, including training +builder.Services.AddSingleton(); + +builder.Services.AddControllers(); +// Learn more about configuring Swagger/OpenAPI at https://aka.ms/aspnetcore/swashbuckle +builder.Services.AddEndpointsApiExplorer(); +builder.Services.AddSwaggerGen(); + +var app = builder.Build(); + +// Configure the HTTP request pipeline. +if (app.Environment.IsDevelopment()) +{ + app.UseSwagger(); + app.UseSwaggerUI(); +} + +app.UseAuthorization(); + +app.MapControllers(); + +app.Run(); diff --git a/src/SciSharp.WebApi/SciSharp.WebApi.csproj b/src/SciSharp.WebApi/SciSharp.WebApi.csproj new file mode 100644 index 0000000..f55cf01 --- /dev/null +++ b/src/SciSharp.WebApi/SciSharp.WebApi.csproj @@ -0,0 +1,18 @@ + + + + net6.0 + enable + enable + + + + + + + + + + + + diff --git a/src/SciSharp.WebApi/Services/ImageClassificationService.cs b/src/SciSharp.WebApi/Services/ImageClassificationService.cs new file mode 100644 index 0000000..fe81202 --- /dev/null +++ b/src/SciSharp.WebApi/Services/ImageClassificationService.cs @@ -0,0 +1,121 @@ +using System.Diagnostics; +using Tensorflow; +using Tensorflow.Keras.Engine; +using Tensorflow.NumPy; +using static Tensorflow.KerasApi; + +namespace WebApi.Services +{ + public class ImageClassificationService + { + const float learningRate = 0.001f; + const int batchSize = 100; + const int epochs = 10; + + private Sequential baseModel; + private Sequential predictionModel; + + private NDArray trainImages; + private NDArray trainLabels; + + private NDArray testImages; + private NDArray testLabels; + + public ImageClassificationService() + { + + } + + public void Train() + { + // !! + // Here to silence CS8618 warning + trainImages = default!; + trainLabels = default!; + testImages = default!; + testLabels = default!; + baseModel = default!; + // !! + + // Prepare datasets + PrepareData(); + + // Prepare the model + PrepareModel(); + + // Train the model + { + var stopwatch = new Stopwatch(); + Console.WriteLine("Starting training..."); + stopwatch.Start(); + + baseModel.fit(trainImages, trainLabels, batchSize, epochs); + + stopwatch.Stop(); + Console.WriteLine($"Took {stopwatch.ElapsedMilliseconds / 1000} seconds"); + } + + baseModel.evaluate(testImages, testLabels); + } + + + private void PrepareData() + { + // Load the MNIST dataset + var data = keras.datasets.mnist.load_data(); + (trainImages, trainLabels) = data.Train; + (testImages, testLabels) = data.Test; + + // Pre-process it, turning them all into doubles between 0-1 instead of bytes between 0-255. We do this even + // though we receive data from the user as raw bytes (0-255), neural networks work better with doubles than + // integers + trainImages /= 255.0f; + testImages /= 255.0f; + } + + + private void PrepareModel() + { + baseModel = keras.Sequential(); + baseModel.add(keras.layers.Input((28, 28))); + baseModel.add(keras.layers.Flatten()); + baseModel.add(keras.layers.Dense(256, activation: "relu")); + baseModel.add(keras.layers.Dense(128, activation: "relu")); + baseModel.add(keras.layers.Dense(10)); + + baseModel.compile( + loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true), + optimizer: keras.optimizers.Adam(learningRate), + metrics: new string[] { "accuracy" } + ); + } + + public float[] Predict(byte[] data) + { + if (data.Length != 28 * 28) + { + Console.Error.WriteLine("Data must be exactly 28x28 (784) bytes. Truncating or padding to match."); + Array.Resize(ref data, 28 * 28); + } + + // Reshape our input data into 1x (28x28) arrays (AKA one 1x28x28 tensor, AKA a 3D vector) + var array = np.array(data); + array = array.reshape((1, 28, 28)); + array = array / 255.0f; + + // Create a copy of the same model, but with a Softmax layer at the end to get outputs. + if(predictionModel == null) + { + predictionModel = keras.Sequential(); + predictionModel.add(baseModel); + predictionModel.add(keras.layers.Softmax(-1)); + predictionModel.compile(); + } + + Tensors predictions = predictionModel.predict(array); + + var prediction = predictions[0].numpy().reshape(-1); + return prediction.ToArray(); + } + } +} diff --git a/src/SciSharp.WebApi/appsettings.Development.json b/src/SciSharp.WebApi/appsettings.Development.json new file mode 100644 index 0000000..0c208ae --- /dev/null +++ b/src/SciSharp.WebApi/appsettings.Development.json @@ -0,0 +1,8 @@ +{ + "Logging": { + "LogLevel": { + "Default": "Information", + "Microsoft.AspNetCore": "Warning" + } + } +} diff --git a/src/SciSharp.WebApi/appsettings.json b/src/SciSharp.WebApi/appsettings.json new file mode 100644 index 0000000..10f68b8 --- /dev/null +++ b/src/SciSharp.WebApi/appsettings.json @@ -0,0 +1,9 @@ +{ + "Logging": { + "LogLevel": { + "Default": "Information", + "Microsoft.AspNetCore": "Warning" + } + }, + "AllowedHosts": "*" +} From f084e6f4e1da3672f9fdb3cb7cbf523862a1f65d Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sun, 4 Dec 2022 23:04:57 -0600 Subject: [PATCH 22/39] Change model.build parameter to input_shape. --- .../ImageProcessing/MnistCnnKerasSubclass.cs | 51 +++++++++++-------- 1 file changed, 30 insertions(+), 21 deletions(-) diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index 7bf59c1..3bcf825 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -31,7 +31,7 @@ namespace TensorFlowNET.Examples /// This example is using a low-level approach to better understand all mechanics behind building convolutional neural networks and the training process. /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb /// - public class DigitRecognitionCnnKeras : SciSharpExample, IExample + public class MnistCnnKerasSubclass : SciSharpExample, IExample { // MNIST dataset parameters. int num_classes = 10; @@ -39,7 +39,7 @@ public class DigitRecognitionCnnKeras : SciSharpExample, IExample // Training parameters. float learning_rate = 0.001f; int training_steps = 100; - int batch_size = 32; + int batch_size = 128; int display_step = 10; float accuracy_test = 0.0f; @@ -61,6 +61,14 @@ public bool Run() PrepareData(); + Train(); + Test(); + + return accuracy_test > 0.85; + } + + public override void Train() + { // Build neural network model. var conv_net = new ConvNet(new ConvNetArgs { @@ -94,7 +102,26 @@ public bool Run() print($"Test Accuracy: {accuracy_test}"); } - return accuracy_test > 0.85; + conv_net.save_weights("model.weights"); + } + + public override void Test() + { + var conv_net = new ConvNet(new ConvNetArgs + { + NumClasses = num_classes + }); + + conv_net.load_weights("model.weights"); + + // Test model on validation set. + { + x_test = x_test["::100"]; + y_test = y_test["::100"]; + var pred = conv_net.Apply(x_test); + accuracy_test = (float)accuracy(pred, y_test); + print($"Test Accuracy: {accuracy_test}"); + } } void run_optimization(ConvNet conv_net, OptimizerV2 optimizer, Tensor x, Tensor y) @@ -110,24 +137,6 @@ void run_optimization(ConvNet conv_net, OptimizerV2 optimizer, Tensor x, Tensor optimizer.apply_gradients(zip(gradients, conv_net.trainable_variables.Select(x => x as ResourceVariable))); } - Tensor conv2d(Tensor x, IVariableV1 W, IVariableV1 b, int strides = 1) - { - x = tf.nn.conv2d(x, W.AsTensor(), new int[] { 1, strides, strides, 1 }, padding: "SAME"); - x = tf.nn.bias_add(x, b); - return tf.nn.relu(x); - } - - /// - /// MaxPool2D wrapper. - /// - /// - /// - /// - Tensor maxpool2d(Tensor x, int k = 2) - { - return tf.nn.max_pool(x, ksize: new[] { 1, k, k, 1 }, strides: new[] { 1, k, k, 1 }, padding: "SAME"); - } - Tensor cross_entropy_loss(Tensor x, Tensor y) { // Convert labels to int 64 for tf cross-entropy function. From 27a287135054e5f648c4bc57857687dccb59f9b2 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Fri, 30 Dec 2022 09:18:55 -0600 Subject: [PATCH 23/39] Fix conv_net.load_weights #956 --- src/TensorFlowNET.Examples/GAN/MnistGAN.cs | 5 +++-- .../ImageProcessing/ImageClassificationKeras.cs | 2 +- .../ImageProcessing/MnistCnnKerasSubclass.cs | 13 ++++++++----- src/TensorFlowNET.Examples/Program.cs | 2 +- .../TensorFlowNET.Examples.csproj | 1 - 5 files changed, 13 insertions(+), 10 deletions(-) diff --git a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs index d90fa7a..33a6b5e 100644 --- a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs +++ b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs @@ -18,11 +18,12 @@ public class MnistGAN : SciSharpExample, IExample float LeakyReLU_alpha = 0.2f; #if GPU - int epochs = 2000; // Better effect, but longer time + int epochs = 1000; // Better effect, but longer time + int batch_size = 16; #else int epochs = 20; -#endif int batch_size = 64; +#endif string imgpath = "dcgan\\imgs"; string modelpath = "dcgan\\models"; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs index 6e4a227..e111bc5 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs @@ -17,7 +17,7 @@ public class ImageClassificationKeras : SciSharpExample, IExample { int batch_size = 32; int epochs = 3; - Shape img_dim = (180, 180); + Shape img_dim = (64, 64); IDatasetV2 train_ds, val_ds; Model model; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index 3bcf825..2c891b1 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -62,7 +62,8 @@ public bool Run() PrepareData(); Train(); - Test(); + + // Test(); return accuracy_test > 0.85; } @@ -102,7 +103,7 @@ public override void Train() print($"Test Accuracy: {accuracy_test}"); } - conv_net.save_weights("model.weights"); + conv_net.save_weights("weights.h5"); } public override void Test() @@ -112,12 +113,14 @@ public override void Test() NumClasses = num_classes }); - conv_net.load_weights("model.weights"); - - // Test model on validation set. + // Test model on testing set. { x_test = x_test["::100"]; y_test = y_test["::100"]; + + conv_net.build(x_test.shape); + conv_net.load_weights("weights.h5"); + var pred = conv_net.Apply(x_test); accuracy_test = (float)accuracy(pred, y_test); print($"Test Accuracy: {accuracy_test}"); diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index 463b9e5..6a63fa6 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -37,7 +37,7 @@ static void Main(string[] args) .Where(x => x.GetInterfaces().Contains(typeof(IExample))) //.Where(x => x.Name == nameof(WeatherPrediction)) //.Where(x => x.Name == nameof(SentimentClassification)) - //.Where(x => x.Name == nameof(MnistInYOLOv3)) + .Where(x => x.Name == nameof(MnistCnnKerasSubclass)) .ToArray(); Console.WriteLine(Environment.OSVersion, Color.Yellow); diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index afca37b..d4f063c 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -47,7 +47,6 @@ - From 45477595509d7b2e7417aac9858e032143392994 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Fri, 30 Dec 2022 12:43:36 -0600 Subject: [PATCH 24/39] Remove _layers in Layer. --- .../BasicModels/LinearRegressionKeras.cs | 2 +- src/TensorFlowNET.Examples/GAN/MnistGAN.cs | 8 ++++---- .../ImageProcessing/DigitRecognitionRnnKeras.cs | 4 ++-- .../ImageProcessing/MnistCnnKerasSubclass.cs | 4 ++-- .../NeuralNetworks/FullyConnectedKeras.cs | 4 ++-- src/TensorFlowNET.Examples/Program.cs | 2 +- 6 files changed, 12 insertions(+), 12 deletions(-) diff --git a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs index e2c2a37..7b5f9f7 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs @@ -55,7 +55,7 @@ public override void BuildModel() metrics: new[] { "acc" }); model.fit(train_X, train_Y, epochs: 100); - var weights = model.trainable_variables; + var weights = model.TrainableVariables; print($"weight: {weights[0].numpy()}, bias: {weights[1].numpy()}"); } diff --git a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs index 33a6b5e..923eb93 100644 --- a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs +++ b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs @@ -160,11 +160,11 @@ public override void Train() g_loss = BinaryCrossentropy(d_logits, tf.ones_like(d_logits)); d_loss = d_loss_real + d_loss_fake; - var grad = tape.gradient(d_loss, D.trainable_variables); - d_optimizer.apply_gradients(zip(grad, D.trainable_variables.Select(x => x as ResourceVariable))); + var grad = tape.gradient(d_loss, D.TrainableVariables); + d_optimizer.apply_gradients(zip(grad, D.TrainableVariables.Select(x => x as ResourceVariable))); - grad = tape.gradient(g_loss, G.trainable_variables); - g_optimizer.apply_gradients(zip(grad, G.trainable_variables.Select(x => x as ResourceVariable))); + grad = tape.gradient(g_loss, G.TrainableVariables); + g_optimizer.apply_gradients(zip(grad, G.TrainableVariables.Select(x => x as ResourceVariable))); } if (i % 5 == 0 && i != 0) { diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs index 8d4a429..ba7ced6 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs @@ -154,10 +154,10 @@ public void Optimize(Tensor x, Tensor y) var loss = CrossEntropyLoss(pred, y); // Compute gradients. - var gradients = g.gradient(loss, trainable_variables); + var gradients = g.gradient(loss, TrainableVariables); // Update W and b following gradients. - optimizer.apply_gradients(zip(gradients, trainable_variables.Select(x => x as ResourceVariable))); + optimizer.apply_gradients(zip(gradients, TrainableVariables.Select(x => x as ResourceVariable))); } /// diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index 2c891b1..8cba525 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -134,10 +134,10 @@ void run_optimization(ConvNet conv_net, OptimizerV2 optimizer, Tensor x, Tensor var loss = cross_entropy_loss(pred, y); // Compute gradients. - var gradients = g.gradient(loss, conv_net.trainable_variables); + var gradients = g.gradient(loss, conv_net.TrainableVariables); // Update W and b following gradients. - optimizer.apply_gradients(zip(gradients, conv_net.trainable_variables.Select(x => x as ResourceVariable))); + optimizer.apply_gradients(zip(gradients, conv_net.TrainableVariables.Select(x => x as ResourceVariable))); } Tensor cross_entropy_loss(Tensor x, Tensor y) diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs index a9248a0..74ed8a0 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs @@ -121,10 +121,10 @@ public bool Run() var loss = cross_entropy_loss(pred, y); // Compute gradients. - var gradients = g.gradient(loss, neural_net.trainable_variables); + var gradients = g.gradient(loss, neural_net.TrainableVariables); // Update W and b following gradients. - optimizer.apply_gradients(zip(gradients, neural_net.trainable_variables.Select(x => x as ResourceVariable))); + optimizer.apply_gradients(zip(gradients, neural_net.TrainableVariables.Select(x => x as ResourceVariable))); }; diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index 6a63fa6..8b6c790 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -37,7 +37,7 @@ static void Main(string[] args) .Where(x => x.GetInterfaces().Contains(typeof(IExample))) //.Where(x => x.Name == nameof(WeatherPrediction)) //.Where(x => x.Name == nameof(SentimentClassification)) - .Where(x => x.Name == nameof(MnistCnnKerasSubclass)) + //.Where(x => x.Name == nameof(MnistCnnKerasSubclass)) .ToArray(); Console.WriteLine(Environment.OSVersion, Color.Yellow); From ad48b9066ec877ac05cbaba6e943dc36578151c9 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sun, 1 Jan 2023 17:06:07 -0600 Subject: [PATCH 25/39] Fix TransferLearning.Test null bug. --- .../ImageProcessing/MnistCnnKerasSubclass.cs | 4 ++-- .../ImageProcessing/TransferLearningWithInceptionV3.cs | 6 +++++- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index 8cba525..eaa691d 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -62,7 +62,7 @@ public bool Run() PrepareData(); Train(); - + // Test(); return accuracy_test > 0.85; @@ -230,7 +230,7 @@ protected override Tensors Call(Tensors inputs, Tensor state = null, bool? train inputs = maxpool2.Apply(inputs); inputs = flatten.Apply(inputs); inputs = fc1.Apply(inputs); - inputs = dropout.Apply(inputs, is_training: training.Value); + inputs = dropout.Apply(inputs); inputs = output.Apply(inputs); if (!training.Value) diff --git a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs index ae862d7..a03bbc4 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs @@ -43,6 +43,7 @@ public ExampleConfig InitConfig() public bool Run() { + PrepareData(); Train(); Test(); Predict(); @@ -50,7 +51,7 @@ public bool Run() return accuracy > 0.75f; } - public override void Train() + public override void PrepareData() { // get a set of images to teach the network about the new classes string fileName = "flower_photos.tgz"; @@ -58,7 +59,10 @@ public override void Train() string url = $"http://download.tensorflow.org/example_images/{fileName}"; Web.Download(url, dataDir, fileName); Compress.ExtractTGZ(Path.Join(dataDir, fileName), dataDir); + } + public override void Train() + { // using wizard to train model var wizard = new ModelWizard(); var task = wizard.AddImageClassificationTask(new TaskOptions From 276022840385f1e6c67d1b4fff712c72ddfb7442 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sun, 15 Jan 2023 21:34:36 -0600 Subject: [PATCH 26/39] Upgrade to tensorflow keras 0.10.0 --- SciSharp STACK Examples.sln | 160 ------------------ src/SciSharp.WebApi/SciSharp.WebApi.csproj | 7 +- src/SharpCV.Examples/SharpCV.Examples.csproj | 6 +- .../ImageProcessing/MnistCnnKerasSubclass.cs | 4 +- .../TextGeneration.cs | 48 ++++++ src/TensorFlowNET.Examples/Program.cs | 4 +- .../TensorFlowNET.Examples.csproj | 16 +- .../TimeSeries/WeatherPrediction.cs | 4 +- 8 files changed, 63 insertions(+), 186 deletions(-) create mode 100644 src/TensorFlowNET.Examples/NaturalLanguageProcessing/TextGeneration.cs diff --git a/SciSharp STACK Examples.sln b/SciSharp STACK Examples.sln index 4c1da97..1aa6e77 100644 --- a/SciSharp STACK Examples.sln +++ b/SciSharp STACK Examples.sln @@ -9,22 +9,6 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SharpCV.Examples", "src\Sha EndProject Project("{6EC3EE1D-3C4E-46DD-8F32-0CC8E7565705}") = "TensorFlowNET.Examples.FSharp", "src\TensorFlowNET.Examples.FSharp\TensorFlowNET.Examples.FSharp.fsproj", "{2B536506-0C0D-42EA-9C10-8BBF27332602}" EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Binding", "..\TensorFlow.NET\src\TensorFlowNET.Core\Tensorflow.Binding.csproj", "{07E6F1E1-8BB1-4CEE-8246-E33F7DC9709E}" -EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Keras", "..\TensorFlow.NET\src\TensorFlowNET.Keras\Tensorflow.Keras.csproj", "{A43790DD-05AD-410F-99DC-BFEFE2CDE5EA}" -EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.TimeSeries", "..\SciSharp.Models\SciSharp.Models.TimeSeries\SciSharp.Models.TimeSeries.csproj", "{C9B305D6-43C9-4BC7-820D-5907041BB97F}" -EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.Core", "..\SciSharp.Models\SciSharp.Models.Core\SciSharp.Models.Core.csproj", "{71245256-9FE4-4D17-AE33-43945FEF6910}" -EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.ObjectDetection", "..\SciSharp.Models\SciSharp.Models.ObjectDetection\SciSharp.Models.ObjectDetection.csproj", "{927655AB-B23E-4E45-85E7-16804B05D1A8}" -EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SharpCV", "..\SharpCV\src\SharpCV\SharpCV.csproj", "{50934DBB-6C5F-4E61-87AB-0C1D748AF420}" -EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.ImageClassification", "..\SciSharp.Models\SciSharp.Models.ImageClassification\SciSharp.Models.ImageClassification.csproj", "{FA0B3C1B-9364-40D9-A7E8-54A6045F7B67}" -EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.TextClassification", "..\SciSharp.Models\SciSharp.Models.TextClassification\SciSharp.Models.TextClassification.csproj", "{D5243F2C-4EAE-45AD-849C-A7FD79B599C5}" -EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.WebApi", "src\SciSharp.WebApi\SciSharp.WebApi.csproj", "{E1D6E827-3FC6-476A-8875-1FF8C1D398CE}" EndProject Global @@ -94,150 +78,6 @@ Global {2B536506-0C0D-42EA-9C10-8BBF27332602}.Release|x64.Build.0 = Release|Any CPU {2B536506-0C0D-42EA-9C10-8BBF27332602}.Release|x86.ActiveCfg = Release|Any CPU {2B536506-0C0D-42EA-9C10-8BBF27332602}.Release|x86.Build.0 = Release|Any CPU - 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- - - - + + diff --git a/src/SharpCV.Examples/SharpCV.Examples.csproj b/src/SharpCV.Examples/SharpCV.Examples.csproj index e60e865..1ed56d1 100644 --- a/src/SharpCV.Examples/SharpCV.Examples.csproj +++ b/src/SharpCV.Examples/SharpCV.Examples.csproj @@ -10,11 +10,7 @@ - - - - - + diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index eaa691d..7bedbf9 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -118,12 +118,12 @@ public override void Test() x_test = x_test["::100"]; y_test = y_test["::100"]; - conv_net.build(x_test.shape); + /*conv_net.build(x_test.shape); conv_net.load_weights("weights.h5"); var pred = conv_net.Apply(x_test); accuracy_test = (float)accuracy(pred, y_test); - print($"Test Accuracy: {accuracy_test}"); + print($"Test Accuracy: {accuracy_test}");*/ } } diff --git a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/TextGeneration.cs b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/TextGeneration.cs new file mode 100644 index 0000000..77bf828 --- /dev/null +++ b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/TextGeneration.cs @@ -0,0 +1,48 @@ +using SciSharp.Models.ImageClassification; +using SciSharp.Models; +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using TensorFlowNET.Examples; +using SciSharp.Models.TextClassification; + +namespace TensorFlowNET.NaturalLanguageProcessing +{ + public class TextGeneration : SciSharpExample, IExample + { + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Text Generation", + Enabled = true + }; + + public bool Run() + { + PrepareData(); + Train(); + return true; + } + + public override void PrepareData() + { + + } + + public override void Train() + { + // using wizard to train model + var wizard = new ModelWizard(); + var task = wizard.AddTextGenerationTask(new TaskOptions + { + DataDir = @"", + }); + task.Train(new TrainingOptions + { + TrainingSteps = 100 + }); + } + } +} diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index 8b6c790..3dab9e9 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -35,9 +35,9 @@ static void Main(string[] args) var examples = Assembly.GetEntryAssembly().GetTypes() .Where(x => x.GetInterfaces().Contains(typeof(IExample))) - //.Where(x => x.Name == nameof(WeatherPrediction)) + //.Where(x => x.Name == nameof(NeuralNetXorKeras)) //.Where(x => x.Name == nameof(SentimentClassification)) - //.Where(x => x.Name == nameof(MnistCnnKerasSubclass)) + //.Where(x => x.Name == nameof(TextGeneration)) .ToArray(); Console.WriteLine(Environment.OSVersion, Color.Yellow); diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index d4f063c..583aa8a 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -42,28 +42,24 @@ + + + + + + - - - - - - - - - - diff --git a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs index 9a9fe39..aedf816 100644 --- a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs +++ b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs @@ -37,8 +37,8 @@ public bool Run() (training_ds, val_ds, test_ds) = task.GenerateDataset(PrepareData); Train(); - Test(); - Predict(); + //Test(); + //Predict(); return true; } From ba3d3f108592a06705b323553416880bdc5a0756 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sat, 28 Jan 2023 20:07:57 -0600 Subject: [PATCH 27/39] Fixed MnistGAN example #72. --- src/TensorFlowNET.Examples/BasicEagerApi.cs | 71 +- src/TensorFlowNET.Examples/ExampleConfig.cs | 31 +- src/TensorFlowNET.Examples/GAN/MnistGAN.cs | 381 +++--- src/TensorFlowNET.Examples/HelloWorld.cs | 70 +- .../ImageProcessing/CnnInYourOwnData.cs | 1025 ++++++++--------- .../ImageProcessing/DigitRecognitionCNN.cs | 223 ++-- .../DigitRecognitionCnnEager.cs | 323 +++--- .../ImageProcessing/DigitRecognitionLSTM.cs | 235 ++-- .../ImageProcessing/DigitRecognitionNN.cs | 263 +++-- .../ImageProcessing/DigitRecognitionRNN.cs | 241 ++-- .../DigitRecognitionRnnKeras.cs | 297 +++-- .../ImageProcessing/ImageBackgroundRemoval.cs | 91 +- .../ImageClassificationKeras.cs | 145 ++- .../ImageRecognitionInception.cs | 169 ++- .../ImageProcessing/InceptionArchGoogLeNet.cs | 171 ++- .../ImageProcessing/MnistCnnKerasSubclass.cs | 353 +++--- .../MnistFnnKerasFunctional.cs | 119 +- .../ImageProcessing/ToyResNet.cs | 129 +-- .../TransferLearningWithInceptionV3.cs | 156 ++- .../SentimentClassification.cs | 147 ++- .../NeuralNetworks/FullyConnected.cs | 205 ++-- .../NeuralNetworks/FullyConnectedEager.cs | 269 +++-- .../NeuralNetworks/FullyConnectedInQueue.cs | 153 ++- .../NeuralNetworks/FullyConnectedKeras.cs | 309 +++-- .../NeuralNetworks/NeuralNetXor.cs | 247 ++-- .../NeuralNetworks/NeuralNetXorEager.cs | 147 ++- .../NeuralNetworks/NeuralNetXorKeras.cs | 63 +- .../ObjectDetection/DetectInMobilenet.cs | 219 ++-- .../ObjectDetection/MnistInYOLOv3.cs | 111 +- .../ObjectDetection/YoloCoco.cs | 459 ++++---- src/TensorFlowNET.Examples/Program.cs | 6 +- src/TensorFlowNET.Examples/SciSharpExample.cs | 65 +- .../TensorFlowNET.Examples.csproj | 4 +- .../BinaryTextClassification.cs | 183 ++- .../TextProcessing/CnnTextClassification.cs | 467 ++++---- .../CnnTextClassificationKeras.cs | 49 +- .../TextProcessing/NamedEntityRecognition.cs | 33 +- .../TextProcessing/Word2Vec.cs | 361 +++--- .../TextProcessing/cnn_models/VdCnn.cs | 2 +- .../TimeSeries/WeatherPrediction.cs | 215 ++-- src/python/.vscode/launch.json | 16 + .../.vscode/launch.json | 2 +- .../image_classification.py | 10 +- 43 files changed, 4134 insertions(+), 4101 deletions(-) create mode 100644 src/python/.vscode/launch.json diff --git a/src/TensorFlowNET.Examples/BasicEagerApi.cs b/src/TensorFlowNET.Examples/BasicEagerApi.cs index c6afd3b..bab925b 100644 --- a/src/TensorFlowNET.Examples/BasicEagerApi.cs +++ b/src/TensorFlowNET.Examples/BasicEagerApi.cs @@ -2,45 +2,44 @@ using Tensorflow; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Basic introduction to TensorFlow's Eager API. +/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_eager_api.py +/// +public class BasicEagerApi : SciSharpExample, IExample { - /// - /// Basic introduction to TensorFlow's Eager API. - /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_eager_api.py - /// - public class BasicEagerApi : SciSharpExample, IExample + private Tensor a, b, c, d; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Basic Eager" + }; + + public bool Run() { - private Tensor a, b, c, d; + // Set Eager API + Console.WriteLine("Setting Eager mode..."); + tf.enable_eager_execution(); - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Basic Eager" - }; + // Define constant tensors + Console.WriteLine("Define constant tensors"); + a = tf.constant(2); + Console.WriteLine($"a = {a}"); + b = tf.constant(3); + Console.WriteLine($"b = {b}"); - public bool Run() - { - // Set Eager API - Console.WriteLine("Setting Eager mode..."); - tf.enable_eager_execution(); - - // Define constant tensors - Console.WriteLine("Define constant tensors"); - a = tf.constant(2); - Console.WriteLine($"a = {a}"); - b = tf.constant(3); - Console.WriteLine($"b = {b}"); - - // Run the operation without the need for tf.Session - Console.WriteLine("Running operations, without tf.Session"); - c = a + b; - Console.WriteLine($"a + b = {c}"); - d = a * b; - Console.WriteLine($"a * b = {d}"); - - // Full compatibility with Numpy - - return true; - } + // Run the operation without the need for tf.Session + Console.WriteLine("Running operations, without tf.Session"); + c = a + b; + Console.WriteLine($"a + b = {c}"); + d = a * b; + Console.WriteLine($"a * b = {d}"); + + // Full compatibility with Numpy + + return true; } } diff --git a/src/TensorFlowNET.Examples/ExampleConfig.cs b/src/TensorFlowNET.Examples/ExampleConfig.cs index 73ef663..e31111c 100644 --- a/src/TensorFlowNET.Examples/ExampleConfig.cs +++ b/src/TensorFlowNET.Examples/ExampleConfig.cs @@ -1,20 +1,19 @@ -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +public class ExampleConfig { - public class ExampleConfig - { - /// - /// Example name - /// - public string Name { get; set; } + /// + /// Example name + /// + public string Name { get; set; } - /// - /// True to run example - /// - public bool Enabled { get; set; } = true; + /// + /// True to run example + /// + public bool Enabled { get; set; } = true; - /// - /// Set true to import the computation graph instead of building it. - /// - public bool IsImportingGraph { get; set; } = false; - } + /// + /// Set true to import the computation graph instead of building it. + /// + public bool IsImportingGraph { get; set; } = false; } diff --git a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs index 923eb93..11f0399 100644 --- a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs +++ b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs @@ -6,142 +6,170 @@ using static Tensorflow.Binding; using static Tensorflow.KerasApi; using Tensorflow.NumPy; +using System.Drawing; +using System.IO; -namespace TensorFlowNET.Examples.GAN +namespace TensorFlowNET.Examples; + +/// +/// https://www.tensorflow.org/tutorials/generative/dcgan +/// AtCode:JG5FLDRWHY9FEZ9S V559.83530 Provided by big crabs +/// +public class MnistGAN : SciSharpExample, IExample { - /// - /// https://www.tensorflow.org/tutorials/generative/dcgan - /// AtCode:JG5FLDRWHY9FEZ9S V559.83530 Provided by big crabs - /// - public class MnistGAN : SciSharpExample, IExample - { - float LeakyReLU_alpha = 0.2f; + float LeakyReLU_alpha = 0.2f; #if GPU - int epochs = 1000; // Better effect, but longer time - int batch_size = 16; + int epochs = 5000; //1000; // Better effect, but longer time + int batch_size = 256; //16; #else - int epochs = 20; - int batch_size = 64; + int epochs = 1000; //20; + int batch_size = 64; #endif - string imgpath = "dcgan\\imgs"; - string modelpath = "dcgan\\models"; - Shape img_shape; - int latent_dim = 100; - int img_rows = 28; - int img_cols = 28; - int channels = 1; + string imgpath = "dcgan\\imgs"; + string modelpath = "dcgan\\models"; + Shape img_shape; + int latent_dim = 100; + int img_rows = 28; + int img_cols = 28; + int channels = 1; - DatasetPass data; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "GAN MNIST", - Enabled = true - }; + DatasetPass data; - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.enable_eager_execution(); + Name = "GAN MNIST", + Enabled = true + }; - PrepareData(); - Train(); - //Test(); + public bool Run() + { + tf.enable_eager_execution(); - return true; - } + PrepareData(); + Train(); + //Test(); - public override void PrepareData() - { - data = keras.datasets.mnist.load_data(); + return true; + } - img_shape = (img_rows, img_cols, channels); - if (img_cols % 4 != 0 || img_rows % 4 != 0) - { - throw new Exception("The width and height of the image must be a multiple of 4"); - } - System.IO.Directory.CreateDirectory(imgpath); - System.IO.Directory.CreateDirectory(modelpath); - } - private Model Make_Generator_model() - { - Tensorflow.Keras.Activation activation = null; - - var model = keras.Sequential(); - model.add(keras.layers.Input(shape: 100)); - model.add(keras.layers.Dense(img_rows / 4 * img_cols / 4 * 256, activation: activation)); - model.add(keras.layers.BatchNormalization(momentum: 0.8f)); - model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); - model.add(keras.layers.Reshape((7, 7, 256))); - - model.add(keras.layers.UpSampling2D()); - model.add(keras.layers.Conv2D(128, 3, 1, padding: "same", activation: activation)); - model.add(keras.layers.BatchNormalization(momentum: 0.8f)); - model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); - - model.add(keras.layers.UpSampling2D()); - model.add(keras.layers.Conv2D(64, 3, 1, padding: "same", activation: activation)); - model.add(keras.layers.BatchNormalization(momentum: 0.8f)); - model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); - - model.add(keras.layers.Conv2D(32, 3, 1, padding: "same", activation: activation)); - model.add(keras.layers.BatchNormalization(momentum: 0.8f)); - model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); - - model.add(keras.layers.Conv2D(1, 3, 1, padding: "same", activation: "tanh")); - model.summary(); - return model; - } + public override void PrepareData() + { + data = keras.datasets.mnist.load_data(); - private Model Make_Discriminator_model() + img_shape = (img_rows, img_cols, channels); + if (img_cols % 4 != 0 || img_rows % 4 != 0) { - Tensorflow.Keras.Activation activation = null; - var image = keras.Input(img_shape); - - var x = keras.layers.Conv2D(128, kernel_size: 3, strides: (2, 2), padding: "same", activation: activation).Apply(image); - x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); - x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); - - x = keras.layers.Conv2D(256, 3, (2, 2), "same", activation: activation).Apply(x); - x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); - x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); - - x = keras.layers.Conv2D(512, 3, (2, 2), "same", activation: activation).Apply(x); - x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); - x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + throw new Exception("The width and height of the image must be a multiple of 4"); + } + Directory.CreateDirectory(imgpath); + Directory.CreateDirectory(modelpath); + } + private Model Make_Generator_model() + { + Tensorflow.Keras.Activation activation = null; + + var model = keras.Sequential(); + model.add(keras.layers.Input(shape: 100)); + model.add(keras.layers.Dense(784, activation: activation)); + model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); + model.add(keras.layers.Dense(784, activation: activation)); + model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); + model.add(keras.layers.Reshape((28, 28, 1))); + model.add(keras.layers.Tanh()); + model.summary(); + return model; + + + //Tensorflow.Keras.Activation activation = null; + //var model = keras.Sequential(); + //model.add(keras.layers.Input(shape: 100)); + //model.add(keras.layers.Dense(img_rows / 4 * img_cols / 4 * 256, activation: activation)); + //model.add(keras.layers.BatchNormalization(momentum: 0.8f)); + //model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); + //model.add(keras.layers.Reshape((7, 7, 256))); + //model.add(keras.layers.UpSampling2D()); + //model.add(keras.layers.Conv2D(128, 3, 1, padding: "same", activation: activation)); + //model.add(keras.layers.BatchNormalization(momentum: 0.8f)); + //model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); + //model.add(keras.layers.UpSampling2D()); + //model.add(keras.layers.Conv2D(64, 3, 1, padding: "same", activation: activation)); + //model.add(keras.layers.BatchNormalization(momentum: 0.8f)); + //model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); + //model.add(keras.layers.Conv2D(32, 3, 1, padding: "same", activation: activation)); + //model.add(keras.layers.BatchNormalization(momentum: 0.8f)); + //model.add(keras.layers.LeakyReLU(LeakyReLU_alpha)); + //model.add(keras.layers.Conv2D(1, 3, 1, padding: "same", activation: "tanh")); + //model.summary(); + //return model; + } - x = keras.layers.Conv2D(1024, 3, (2, 2), "same", activation: activation).Apply(x); - x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); - x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + private Model Make_Discriminator_model() + { + Tensorflow.Keras.Activation activation = null; + var image = keras.Input(img_shape); + var x = keras.layers.Reshape(784).Apply(image); + x = keras.layers.Dense(784, activation: activation).Apply(x); + x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + x = keras.layers.Dense(784, activation: activation).Apply(x); + x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + x = keras.layers.Dense(1, activation: "sigmoid").Apply(x); + var model = keras.Model(image, x); + model.summary(); + return model; + + + //Tensorflow.Keras.Activation activation = null; + //var image = keras.Input(img_shape); + //var x = keras.layers.Conv2D(128, kernel_size: 3, strides: (2, 2), padding: "same", activation: activation).Apply(image); + //x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + //x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); + //x = keras.layers.Conv2D(256, 3, (2, 2), "same", activation: activation).Apply(x); + //x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); + //x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + //x = keras.layers.Conv2D(512, 3, (2, 2), "same", activation: activation).Apply(x); + //x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); + //x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + //x = keras.layers.Conv2D(1024, 3, (2, 2), "same", activation: activation).Apply(x); + //x = keras.layers.BatchNormalization(momentum: 0.8f).Apply(x); + //x = keras.layers.LeakyReLU(LeakyReLU_alpha).Apply(x); + //x = keras.layers.Flatten().Apply(x); + //x = keras.layers.Dense(1, activation: "sigmoid").Apply(x); + //var model = keras.Model(image, x); + //model.summary(); + //return model; + } - x = keras.layers.Flatten().Apply(x); - x = keras.layers.Dense(1, activation: "sigmoid").Apply(x); + public override void Train() + { + NDArray X_train = data.Train.Item1; // [60000,28,28] + X_train = X_train / 127.5 - 1; + X_train = np.expand_dims(X_train, 3); + X_train = X_train.astype(np.float32); - var model = keras.Model(image, x); - model.summary(); + var G = Make_Generator_model(); + var D = Make_Discriminator_model(); - return model; - } + float d_lr = 2e-4f; + float g_lr = 2e-4f; + var d_optimizer = keras.optimizers.Adam(d_lr, 0.5f); + var g_optimizer = keras.optimizers.Adam(g_lr, 0.5f); + int showstep = 10; - public override void Train() + for (var i = 0; i <= epochs; i++) { - NDArray X_train = data.Train.Item1; - X_train = X_train / 127.5 - 1; - X_train = np.expand_dims(X_train, 3); - X_train = X_train.astype(np.float32); - - var G = Make_Generator_model(); - var D = Make_Discriminator_model(); - - float d_lr = 2e-4f; - float g_lr = 2e-4f; - var d_optimizer = keras.optimizers.Adam(d_lr, 0.5f); - var g_optimizer = keras.optimizers.Adam(g_lr, 0.5f); - int showstep = 10; - - for (var i = 0; i <= epochs; i++) + while (File.Exists("dcgan\\models\\Model_" + (i + 100) + "_g.weights") && File.Exists("dcgan\\models\\Model_" + (i + 100) + "_d.weights")) + i += 100; + if (File.Exists("dcgan\\models\\Model_" + i + "_g.weights") && File.Exists("dcgan\\models\\Model_" + i + "_d.weights")) + { + Console.WriteLine("Loading weights for epoch " + i); + G.load_weights("dcgan\\models\\Model_" + i + "_g.weights"); + D.save_weights("dcgan\\models\\Model_" + i + "_d.weights"); + PredictImage(G, i); + } + else { var idx = np.random.randint(0, (int)X_train.shape[0], size: batch_size); var imgs = X_train[idx]; @@ -183,64 +211,89 @@ public override void Train() } } } + } - private Tensor BinaryCrossentropy(Tensor x, Tensor y) - { - var shape = tf.reduce_prod(tf.shape(x)); - var count = tf.cast(shape, TF_DataType.TF_FLOAT); - x = tf.clip_by_value(x, 1e-6f, 1.0f - 1e-6f); - var z = y * tf.log(x) + (1 - y) * tf.log(1 - x); - var result = -1.0f / count * tf.reduce_sum(z); - return result; - } + private Tensor BinaryCrossentropy(Tensor x, Tensor y) + { + var shape = tf.reduce_prod(tf.shape(x)); + var count = tf.cast(shape, TF_DataType.TF_FLOAT); + x = tf.clip_by_value(x, 1e-6f, 1.0f - 1e-6f); + var z = y * tf.log(x) + (1 - y) * tf.log(1 - x); + var result = -1.0f / count * tf.reduce_sum(z); + return result; + } - private void PredictImage(Model g, int step) - { - var r = 5; - var c = 5; - - var noise = np.random.normal(0, 1, new int[] { r * c, latent_dim }); - noise = noise.astype(np.float32); - Tensor tensor_result = g.predict(noise); - var gen_imgs = tensor_result.numpy(); - SaveImage(gen_imgs, step); - } + private void PredictImage(Model g, int step) + { + var r = 5; + var c = 5; + + var noise = np.random.normal(0, 1, new int[] { r * c, latent_dim }); + noise = noise.astype(np.float32); + Tensor tensor_result = g.predict(noise); + var gen_imgs = tensor_result.numpy(); + SaveImage(gen_imgs, step); + } - private void SaveImage(NDArray gen_imgs, int step) + private void SaveImage(NDArray gen_imgs, int step) + { + int size = 4; + gen_imgs = gen_imgs * 0.5 + 0.5; // 25x28x28x1 [0.0-1.0] + //var c = 5; + //var r = gen_imgs.shape[0] / c; + //var nDArray = np.zeros((img_rows * r, img_cols * c), dtype: np.float32); + //for (var i = 0; i < r; i++) + //{ + // for (var j = 0; j < c; j++) + // { + // var x = new Slice(i * img_rows, (i + 1) * img_cols); + // var y = new Slice(j * img_rows, (j + 1) * img_cols); + // var v = gen_imgs[i * r + j].reshape((img_rows, img_cols)); + // nDArray[x, y] = v; + // } + //} + + //var t = nDArray.reshape((img_rows * r, img_cols * c)) * 255; + //GrayToRGB(t.astype(np.uint8)).ToBitmap().Save(imgpath + "/image" + step + ".jpg"); // .ToBitmap() missing + + var generatedImages = gen_imgs.ToArray(); // Tensor.Numpy[25]... + + Image image = new Bitmap(28 * 5 * size, 28 * 5 * size); + for (int i = 0; i < generatedImages.Length; i++) { - gen_imgs = gen_imgs * 0.5 + 0.5; - var c = 5; - var r = gen_imgs.shape[0] / c; - var nDArray = np.zeros((img_rows * r, img_cols * c), dtype: np.float32); - for (var i = 0; i < r; i++) - { - for (var j = 0; j < c; j++) + var values = generatedImages[i].reshape(784).ToArray(); + float min = values.Min(); + float max = values.Max(); + float slope = 0.0f; + if (max > min) slope = 255.0f / (max - min); + Graphics g = Graphics.FromImage(image); + for (int y = 0; y < 28; y++) + for (int x = 0; x < 28; x++) { - var x = new Slice(i * img_rows, (i + 1) * img_cols); - var y = new Slice(j * img_rows, (j + 1) * img_cols); - var v = gen_imgs[i * r + j].reshape((img_rows, img_cols)); - nDArray[x, y] = v; + var value = (int)((values[y * 28 + x] - min) * slope); + Brush thisBrush = new SolidBrush(Color.FromArgb(255, value, value, value)); + g.FillRectangle(thisBrush, x * size + ((i % 5) * 28 * size), y * size + ((i / 5) * 28 * size), size, size); } - } - - var t = nDArray.reshape((img_rows * r, img_cols * c)) * 255; - // GrayToRGB(t.astype(np.uint8)).ToBitmap().Save(imgpath + "/image" + step + ".jpg"); } + image.Save(imgpath + "/image" + (step / 10).ToString("D8") + ".jpg"); - private NDArray GrayToRGB(NDArray img2D) - { - var img4A = np.full_like(img2D, 255); - var img3D = np.expand_dims(img2D, 2); - var r = np.dstack(img3D, img3D, img3D, img4A); - var img4 = np.expand_dims(r, 0); - return img4; - } - public override void Test() - { - var G = Make_Generator_model(); - G.load_weights(modelpath + "\\Model_100_g.weights"); - PredictImage(G, 1); - } + + } + + private NDArray GrayToRGB(NDArray img2D) + { + var img4A = np.full_like(img2D, 255); + var img3D = np.expand_dims(img2D, 2); + var r = np.dstack(img3D, img3D, img3D, img4A); + var img4 = np.expand_dims(r, 0); + return img4; + } + + public override void Test() + { + var G = Make_Generator_model(); + G.load_weights(modelpath + "\\Model_100_g.weights"); + PredictImage(G, 1); } } diff --git a/src/TensorFlowNET.Examples/HelloWorld.cs b/src/TensorFlowNET.Examples/HelloWorld.cs index 9095e23..44931cb 100644 --- a/src/TensorFlowNET.Examples/HelloWorld.cs +++ b/src/TensorFlowNET.Examples/HelloWorld.cs @@ -1,47 +1,45 @@ using System; using System.Linq; -using Tensorflow.NumPy; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples -{ - /// - /// A very simple "hello world" using TensorFlow v2 tensors. - /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb - /// - public class HelloWorld : SciSharpExample, IExample - { - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Hello World" - }; +namespace TensorFlowNET.Examples; - public HelloWorld() - { - str = string.Join("", Enumerable.Range(0, 1024 * 1024 * 20).Select(x => "X")); - } - public static string str; - public bool Run() +/// +/// A very simple "hello world" using TensorFlow v2 tensors. +/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb +/// +public class HelloWorld : SciSharpExample, IExample +{ + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - // Eager model is enabled by default. - tf.enable_eager_execution(); + Name = "Hello World" + }; + + public HelloWorld() + { + str = string.Join("", Enumerable.Range(0, 1024 * 1024 * 20).Select(x => "X")); + } + public static string str; + public bool Run() + { + // Eager model is enabled by default. + tf.enable_eager_execution(); - /* Create a Constant op - The op is added as a node to the default graph. - - The value returned by the constructor represents the output - of the Constant op. */ - // var str = string.Join("", Enumerable.Range(0, 1024 * 1024 * 20).Select(x => "X")); - using var hello = tf.constant(str); - - // tf.Tensor: shape=(), dtype=string, numpy=b'Hello, TensorFlow.NET!' - // print(hello); + /* Create a Constant op + The op is added as a node to the default graph. + + The value returned by the constructor represents the output + of the Constant op. */ + // var str = string.Join("", Enumerable.Range(0, 1024 * 1024 * 20).Select(x => "X")); + using var hello = tf.constant(str); + + // tf.Tensor: shape=(), dtype=string, numpy=b'Hello, TensorFlow.NET!' + // print(hello); - var tensor = hello.StringData(); + var tensor = hello.StringData(); - //return tensor.ToString() == $"'{str}'"; - return true; - } + //return tensor.ToString() == $"'{str}'"; + return true; } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs index 039dc1e..acd78ec 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs @@ -30,610 +30,609 @@ limitations under the License. using static SharpCV.Binding; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Convolutional Neural Network classifier for Local Classify Images. +/// CNN architecture with two convolutional layers, followed by two fully-connected layers at the end. +/// Use Stochastic Gradient Descent (SGD) optimizer. +/// Learning Rate reduces per 10 epochs. +/// TODO: Thread Queue will be added to improve images loading efficiency later. +/// https://www.easy-tensorflow.com/tf-tutorials/convolutional-neural-nets-cnns/cnn1 +/// +class CnnInYourOwnData : SciSharpExample, IExample { - /// - /// Convolutional Neural Network classifier for Local Classify Images. - /// CNN architecture with two convolutional layers, followed by two fully-connected layers at the end. - /// Use Stochastic Gradient Descent (SGD) optimizer. - /// Learning Rate reduces per 10 epochs. - /// TODO: Thread Queue will be added to improve images loading efficiency later. - /// https://www.easy-tensorflow.com/tf-tutorials/convolutional-neural-nets-cnns/cnn1 - /// - class CnnInYourOwnData : SciSharpExample, IExample - { - string[] ArrayFileName_Train, ArrayFileName_Validation, ArrayFileName_Test; - long[] ArrayLabel_Train, ArrayLabel_Validation, ArrayLabel_Test; - Dictionary Dict_Label; - NDArray y_train; - NDArray x_valid, y_valid; - NDArray x_test, y_test; - int img_h = 64;// MNIST images are 64x64 - int img_w = 64;// MNIST images are 64x64 - int img_mean = 0; - int img_std = 255; - int n_channels = 1;//Gray Image ,one channel - int n_classes;// Number of classes - - Tensor x, y; // Placeholders for inputs (x) and outputs(y) - Tensor loss, accuracy, cls_prediction, prob; - Tensor optimizer; - Tensor normalized; - Tensor decodeJpeg; - - int display_freq = 2; - float accuracy_test = 0f; - float loss_test = 1f; - - // Network configuration - // 1st Convolutional Layer - int filter_size1 = 5; // Convolution filters are 5 x 5 pixels. - int num_filters1 = 16; // There are 16 of these filters. - int stride1 = 1; // The stride of the sliding window - - // 2nd Convolutional Layer - int filter_size2 = 5; // Convolution filters are 5 x 5 pixels. - int num_filters2 = 32;// There are 32 of these filters. - int stride2 = 1; // The stride of the sliding window - - // Fully-connected layer. - int h1 = 128; // Number of neurons in fully-connected layer. - - // Hyper-parameters - int epochs = 5; // accuracy > 98% - int batch_size = 100; - float learning_rate_base = 0.001f; - float learning_rate_decay = 0.1f; - uint learning_rate_step = 2; - float learning_rate_min = 0.000001f; - - NDArray Test_Cls, Test_Data; - - IVariableV1 global_steps; - IVariableV1 learning_rate; - - bool SaverBest = true; - double max_accuracy = 0; - - string path_model; - int TrainQueueCapa = 3; - Session sess; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "CNN in Your Own Data (Graph)", - Enabled = true, - IsImportingGraph = false - }; - - public bool Run() + string[] ArrayFileName_Train, ArrayFileName_Validation, ArrayFileName_Test; + long[] ArrayLabel_Train, ArrayLabel_Validation, ArrayLabel_Test; + Dictionary Dict_Label; + NDArray y_train; + NDArray x_valid, y_valid; + NDArray x_test, y_test; + int img_h = 64;// MNIST images are 64x64 + int img_w = 64;// MNIST images are 64x64 + int img_mean = 0; + int img_std = 255; + int n_channels = 1;//Gray Image ,one channel + int n_classes;// Number of classes + + Tensor x, y; // Placeholders for inputs (x) and outputs(y) + Tensor loss, accuracy, cls_prediction, prob; + Tensor optimizer; + Tensor normalized; + Tensor decodeJpeg; + + int display_freq = 2; + float accuracy_test = 0f; + float loss_test = 1f; + + // Network configuration + // 1st Convolutional Layer + int filter_size1 = 5; // Convolution filters are 5 x 5 pixels. + int num_filters1 = 16; // There are 16 of these filters. + int stride1 = 1; // The stride of the sliding window + + // 2nd Convolutional Layer + int filter_size2 = 5; // Convolution filters are 5 x 5 pixels. + int num_filters2 = 32;// There are 32 of these filters. + int stride2 = 1; // The stride of the sliding window + + // Fully-connected layer. + int h1 = 128; // Number of neurons in fully-connected layer. + + // Hyper-parameters + int epochs = 5; // accuracy > 98% + int batch_size = 100; + float learning_rate_base = 0.001f; + float learning_rate_decay = 0.1f; + uint learning_rate_step = 2; + float learning_rate_min = 0.000001f; + + NDArray Test_Cls, Test_Data; + + IVariableV1 global_steps; + IVariableV1 learning_rate; + + bool SaverBest = true; + double max_accuracy = 0; + + string path_model; + int TrainQueueCapa = 3; + Session sess; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.compat.v1.disable_eager_execution(); - - PrepareData(); - BuildGraph(); + Name = "CNN in Your Own Data (Graph)", + Enabled = true, + IsImportingGraph = false + }; - sess = tf.Session(); - Train(); - Test(); + public bool Run() + { + tf.compat.v1.disable_eager_execution(); + + PrepareData(); + BuildGraph(); - TestDataOutput(); + sess = tf.Session(); + Train(); + Test(); - FreezeModel(); - LoadAndPredict(); + TestDataOutput(); - return accuracy_test > 0.98; - } + FreezeModel(); + LoadAndPredict(); - public void LoadAndPredict() - { - using var graph = tf.Graph().as_default(); - graph.Import(Path.Combine(Config.Name, "model.pb")); - Tensor x = graph.OperationByName("Input/X"); - Tensor prediction = graph.OperationByName("Train/Prediction/predictions"); - Tensor probility = graph.OperationByName("Train/Prediction/prob"); - - using var sess = tf.Session(graph); - var (prediction_result, probility_result) = sess.run((prediction, probility), (x, x_test)); - print($"Prediction result: {prediction_result}"); - } + return accuracy_test > 0.98; + } - public override string FreezeModel() - { - return tf.train.freeze_graph(Config.Name + "\\MODEL", "model", new[] - { - "Train/Prediction/predictions", - "Train/Prediction/prob" - }); - } + public void LoadAndPredict() + { + using var graph = tf.Graph().as_default(); + graph.Import(Path.Combine(Config.Name, "model.pb")); + Tensor x = graph.OperationByName("Input/X"); + Tensor prediction = graph.OperationByName("Train/Prediction/predictions"); + Tensor probility = graph.OperationByName("Train/Prediction/prob"); + + using var sess = tf.Session(graph); + var (prediction_result, probility_result) = sess.run((prediction, probility), (x, x_test)); + print($"Prediction result: {prediction_result}"); + } - #region PrepareData - public override void PrepareData() + public override string FreezeModel() + { + return tf.train.freeze_graph(Config.Name + "\\MODEL", "model", new[] { - string url = "https://github.com/SciSharp/SciSharp-Stack-Examples/raw/master/data/data_CnnInYourOwnData.zip"; - Directory.CreateDirectory(Config.Name); - Web.Download(url, Config.Name, "data_CnnInYourOwnData.zip"); - Compress.UnZip(Config.Name + "\\data_CnnInYourOwnData.zip", Config.Name); + "Train/Prediction/predictions", + "Train/Prediction/prob" + }); + } - FillDictionaryLabel(Config.Name + "\\train"); + #region PrepareData + public override void PrepareData() + { + string url = "https://github.com/SciSharp/SciSharp-Stack-Examples/raw/master/data/data_CnnInYourOwnData.zip"; + Directory.CreateDirectory(Config.Name); + Web.Download(url, Config.Name, "data_CnnInYourOwnData.zip"); + Compress.UnZip(Config.Name + "\\data_CnnInYourOwnData.zip", Config.Name); - ArrayFileName_Train = Directory.GetFiles(Config.Name + "\\train", "*.*", SearchOption.AllDirectories); - ArrayLabel_Train = GetLabelArray(ArrayFileName_Train); + FillDictionaryLabel(Config.Name + "\\train"); - ArrayFileName_Validation = Directory.GetFiles(Config.Name + "\\validation", "*.*", SearchOption.AllDirectories); - ArrayLabel_Validation = GetLabelArray(ArrayFileName_Validation); + ArrayFileName_Train = Directory.GetFiles(Config.Name + "\\train", "*.*", SearchOption.AllDirectories); + ArrayLabel_Train = GetLabelArray(ArrayFileName_Train); - ArrayFileName_Test = Directory.GetFiles(Config.Name + "\\test", "*.*", SearchOption.AllDirectories); - ArrayLabel_Test = GetLabelArray(ArrayFileName_Test); + ArrayFileName_Validation = Directory.GetFiles(Config.Name + "\\validation", "*.*", SearchOption.AllDirectories); + ArrayLabel_Validation = GetLabelArray(ArrayFileName_Validation); - //shuffle array - (ArrayFileName_Train, ArrayLabel_Train) = ShuffleArray(ArrayLabel_Train.Length, ArrayFileName_Train, ArrayLabel_Train); - (ArrayFileName_Validation, ArrayLabel_Validation) = ShuffleArray(ArrayLabel_Validation.Length, ArrayFileName_Validation, ArrayLabel_Validation); - (ArrayFileName_Test, ArrayLabel_Test) = ShuffleArray(ArrayLabel_Test.Length, ArrayFileName_Test, ArrayLabel_Test); + ArrayFileName_Test = Directory.GetFiles(Config.Name + "\\test", "*.*", SearchOption.AllDirectories); + ArrayLabel_Test = GetLabelArray(ArrayFileName_Test); - LoadImagesToNDArray(); - } - /// - /// Load Validation and Test data to NDarray, Train data is too large ,we load by training process - /// - private void LoadImagesToNDArray() + //shuffle array + (ArrayFileName_Train, ArrayLabel_Train) = ShuffleArray(ArrayLabel_Train.Length, ArrayFileName_Train, ArrayLabel_Train); + (ArrayFileName_Validation, ArrayLabel_Validation) = ShuffleArray(ArrayLabel_Validation.Length, ArrayFileName_Validation, ArrayLabel_Validation); + (ArrayFileName_Test, ArrayLabel_Test) = ShuffleArray(ArrayLabel_Test.Length, ArrayFileName_Test, ArrayLabel_Test); + + LoadImagesToNDArray(); + } + /// + /// Load Validation and Test data to NDarray, Train data is too large ,we load by training process + /// + private void LoadImagesToNDArray() + { + //Load labels + y_valid = np.eye(Dict_Label.Count, dtype: tf.float32)[np.array(ArrayLabel_Validation)]; + y_test = np.eye(Dict_Label.Count, dtype: tf.float32)[np.array(ArrayLabel_Test)]; + print("Load Labels To NDArray : OK!"); + + //Load Images + x_valid = np.zeros((ArrayFileName_Validation.Length, img_h, img_w, n_channels), dtype: tf.float32); + x_test = np.zeros((ArrayFileName_Test.Length, img_h, img_w, n_channels), dtype: tf.float32); + LoadImage(ArrayFileName_Validation, x_valid, "validation"); + LoadImage(ArrayFileName_Test, x_test, "test"); + print("Loading images finished!"); + } + private void LoadImage(string[] a, NDArray b, string c) + { + using var graph = tf.Graph().as_default(); + + for (int i = 0; i < a.Length; i++) { - //Load labels - y_valid = np.eye(Dict_Label.Count, dtype: tf.float32)[np.array(ArrayLabel_Validation)]; - y_test = np.eye(Dict_Label.Count, dtype: tf.float32)[np.array(ArrayLabel_Test)]; - print("Load Labels To NDArray : OK!"); - - //Load Images - x_valid = np.zeros((ArrayFileName_Validation.Length, img_h, img_w, n_channels), dtype: tf.float32); - x_test = np.zeros((ArrayFileName_Test.Length, img_h, img_w, n_channels), dtype: tf.float32); - LoadImage(ArrayFileName_Validation, x_valid, "validation"); - LoadImage(ArrayFileName_Test, x_test, "test"); - print("Loading images finished!"); + b[i] = ReadTensorFromImageFile(a[i], graph); + Console.Write($"Loading image: {a[i]}..."); + Console.CursorLeft = 0; } - private void LoadImage(string[] a, NDArray b, string c) - { - using var graph = tf.Graph().as_default(); - - for (int i = 0; i < a.Length; i++) - { - b[i] = ReadTensorFromImageFile(a[i], graph); - Console.Write($"Loading image: {a[i]}..."); - Console.CursorLeft = 0; - } - Console.WriteLine(); - Console.WriteLine($"Loaded {a.Length} images for " + c); - } + Console.WriteLine(); + Console.WriteLine($"Loaded {a.Length} images for " + c); + } - private NDArray ReadTensorFromImageFile(string file_name, Graph graph) + private NDArray ReadTensorFromImageFile(string file_name, Graph graph) + { + var file_reader = tf.io.read_file(file_name, "file_reader"); + var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: n_channels, name: "DecodeJpeg"); + var cast = tf.cast(decodeJpeg, tf.float32); + var dims_expander = tf.expand_dims(cast, 0); + var resize = tf.constant(new int[] { img_h, img_w }); + var bilinear = tf.image.resize_bilinear(dims_expander, resize); + var sub = tf.subtract(bilinear, new float[] { img_mean }); + var normalized = tf.divide(sub, new float[] { img_std }); + + using (var sess = tf.Session(graph)) { - var file_reader = tf.io.read_file(file_name, "file_reader"); - var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: n_channels, name: "DecodeJpeg"); - var cast = tf.cast(decodeJpeg, tf.float32); - var dims_expander = tf.expand_dims(cast, 0); - var resize = tf.constant(new int[] { img_h, img_w }); - var bilinear = tf.image.resize_bilinear(dims_expander, resize); - var sub = tf.subtract(bilinear, new float[] { img_mean }); - var normalized = tf.divide(sub, new float[] { img_std }); - - using (var sess = tf.Session(graph)) - { - return sess.run(normalized); - } + return sess.run(normalized); } + } - /// - /// Shuffle Images and Labels Array - /// - /// - /// - /// - /// - public (string[], long[]) ShuffleArray(int count, string[] images, long[] labels) + /// + /// Shuffle Images and Labels Array + /// + /// + /// + /// + /// + public (string[], long[]) ShuffleArray(int count, string[] images, long[] labels) + { + ArrayList mylist = new ArrayList(); + string[] new_images = new string[count]; + long[] new_labels = new long[count]; + Random r = new Random(); + for (int i = 0; i < count; i++) { - ArrayList mylist = new ArrayList(); - string[] new_images = new string[count]; - long[] new_labels = new long[count]; - Random r = new Random(); - for (int i = 0; i < count; i++) - { - mylist.Add(i); - } + mylist.Add(i); + } - for (int i = 0; i < count; i++) - { - int rand = r.Next(mylist.Count); - new_images[i] = images[(int)(mylist[rand])]; - new_labels[i] = labels[(int)(mylist[rand])]; - mylist.RemoveAt(rand); - } - print("shuffle array list： " + count.ToString()); - return (new_images, new_labels); + for (int i = 0; i < count; i++) + { + int rand = r.Next(mylist.Count); + new_images[i] = images[(int)(mylist[rand])]; + new_labels[i] = labels[(int)(mylist[rand])]; + mylist.RemoveAt(rand); } + print("shuffle array list： " + count.ToString()); + return (new_images, new_labels); + } - /// - /// Get Label Array with Dictionary - /// - /// - /// - private long[] GetLabelArray(string[] FilesArray) + /// + /// Get Label Array with Dictionary + /// + /// + /// + private long[] GetLabelArray(string[] FilesArray) + { + var ArrayLabel = new long[FilesArray.Length]; + for (int i = 0; i < ArrayLabel.Length; i++) { - var ArrayLabel = new long[FilesArray.Length]; - for (int i = 0; i < ArrayLabel.Length; i++) - { - string[] labels = FilesArray[i].Split('\\'); - string label = labels[labels.Length - 2]; - ArrayLabel[i] = Dict_Label.Single(k => k.Value == label).Key; - } - return ArrayLabel; + string[] labels = FilesArray[i].Split('\\'); + string label = labels[labels.Length - 2]; + ArrayLabel[i] = Dict_Label.Single(k => k.Value == label).Key; } + return ArrayLabel; + } - /// - /// Get Dictionary , Key is Order Number , Value is Label - /// - /// - private void FillDictionaryLabel(string DirPath) + /// + /// Get Dictionary , Key is Order Number , Value is Label + /// + /// + private void FillDictionaryLabel(string DirPath) + { + string[] str_dir = Directory.GetDirectories(DirPath, "*", SearchOption.TopDirectoryOnly); + int str_dir_num = str_dir.Length; + if (str_dir_num > 0) { - string[] str_dir = Directory.GetDirectories(DirPath, "*", SearchOption.TopDirectoryOnly); - int str_dir_num = str_dir.Length; - if (str_dir_num > 0) + Dict_Label = new Dictionary(); + for (int i = 0; i < str_dir_num; i++) { - Dict_Label = new Dictionary(); - for (int i = 0; i < str_dir_num; i++) - { - string label = (str_dir[i].Replace(DirPath + "\\", "")).Split('\\').First(); - Dict_Label.Add(i, label); - print(i.ToString() + " : " + label); - } - n_classes = Dict_Label.Count; + string label = (str_dir[i].Replace(DirPath + "\\", "")).Split('\\').First(); + Dict_Label.Add(i, label); + print(i.ToString() + " : " + label); } + n_classes = Dict_Label.Count; } - #endregion + } + #endregion - #region BuildGraph - public override Graph BuildGraph() + #region BuildGraph + public override Graph BuildGraph() + { + var graph = new Graph().as_default(); + + tf_with(tf.name_scope("Input"), delegate + { + x = tf.placeholder(tf.float32, shape: (-1, img_h, img_w, n_channels), name: "X"); + y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y"); + }); + + var conv1 = conv_layer(x, filter_size1, num_filters1, stride1, name: "conv1"); + var pool1 = max_pool(conv1, ksize: 2, stride: 2, name: "pool1"); + var conv2 = conv_layer(pool1, filter_size2, num_filters2, stride2, name: "conv2"); + var pool2 = max_pool(conv2, ksize: 2, stride: 2, name: "pool2"); + var layer_flat = flatten_layer(pool2); + var fc1 = fc_layer(layer_flat, h1, "FC1", use_relu: true); + var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false); + + //Some important parameter saved with graph , easy to load later + //var img_h_t = tf.constant(img_h, name: "img_h"); + //var img_w_t = tf.constant(img_w, name: "img_w"); + //var img_mean_t = tf.constant(img_mean, name: "img_mean"); + //var img_std_t = tf.constant(img_std, name: "img_std"); + //var channels_t = tf.constant(n_channels, name: "img_channels"); + + //learning rate decay + global_steps = tf.Variable(0, trainable: false); + learning_rate = tf.Variable(learning_rate_base); + + //create train images graph + tf_with(tf.variable_scope("LoadImage"), delegate { - var graph = new Graph().as_default(); + decodeJpeg = tf.placeholder(tf.byte8, name: "DecodeJpeg"); + var cast = tf.cast(decodeJpeg, tf.float32); + var dims_expander = tf.expand_dims(cast, 0); + var resize = tf.constant(new int[] { img_h, img_w }); + var bilinear = tf.image.resize_bilinear(dims_expander, resize); + var sub = tf.subtract(bilinear, new float[] { img_mean }); + normalized = tf.divide(sub, new float[] { img_std }, name: "normalized"); + }); - tf_with(tf.name_scope("Input"), delegate + tf_with(tf.variable_scope("Train"), delegate + { + tf_with(tf.variable_scope("Loss"), delegate { - x = tf.placeholder(tf.float32, shape: (-1, img_h, img_w, n_channels), name: "X"); - y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y"); + loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits), name: "loss"); }); - var conv1 = conv_layer(x, filter_size1, num_filters1, stride1, name: "conv1"); - var pool1 = max_pool(conv1, ksize: 2, stride: 2, name: "pool1"); - var conv2 = conv_layer(pool1, filter_size2, num_filters2, stride2, name: "conv2"); - var pool2 = max_pool(conv2, ksize: 2, stride: 2, name: "pool2"); - var layer_flat = flatten_layer(pool2); - var fc1 = fc_layer(layer_flat, h1, "FC1", use_relu: true); - var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false); - - //Some important parameter saved with graph , easy to load later - //var img_h_t = tf.constant(img_h, name: "img_h"); - //var img_w_t = tf.constant(img_w, name: "img_w"); - //var img_mean_t = tf.constant(img_mean, name: "img_mean"); - //var img_std_t = tf.constant(img_std, name: "img_std"); - //var channels_t = tf.constant(n_channels, name: "img_channels"); - - //learning rate decay - global_steps = tf.Variable(0, trainable: false); - learning_rate = tf.Variable(learning_rate_base); - - //create train images graph - tf_with(tf.variable_scope("LoadImage"), delegate + tf_with(tf.variable_scope("Optimizer"), delegate { - decodeJpeg = tf.placeholder(tf.byte8, name: "DecodeJpeg"); - var cast = tf.cast(decodeJpeg, tf.float32); - var dims_expander = tf.expand_dims(cast, 0); - var resize = tf.constant(new int[] { img_h, img_w }); - var bilinear = tf.image.resize_bilinear(dims_expander, resize); - var sub = tf.subtract(bilinear, new float[] { img_mean }); - normalized = tf.divide(sub, new float[] { img_std }, name: "normalized"); + optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss, global_step: global_steps); }); - tf_with(tf.variable_scope("Train"), delegate + tf_with(tf.variable_scope("Accuracy"), delegate { - tf_with(tf.variable_scope("Loss"), delegate - { - loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits), name: "loss"); - }); - - tf_with(tf.variable_scope("Optimizer"), delegate - { - optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss, global_step: global_steps); - }); - - tf_with(tf.variable_scope("Accuracy"), delegate - { - var correct_prediction = tf.equal(tf.math.argmax(output_logits, 1), tf.math.argmax(y, 1), name: "correct_pred"); - accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy"); - }); - - tf_with(tf.variable_scope("Prediction"), delegate - { - cls_prediction = tf.math.argmax(output_logits, axis: 1, name: "predictions"); - prob = tf.nn.softmax(output_logits, axis: 1, name: "prob"); - }); + var correct_prediction = tf.equal(tf.math.argmax(output_logits, 1), tf.math.argmax(y, 1), name: "correct_pred"); + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy"); }); - return graph; - } - /// - /// Create a 2D convolution layer - /// - /// input from previous layer - /// size of each filter - /// number of filters(or output feature maps) - /// filter stride - /// layer name - /// The output array - private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride, string name) - { - return tf_with(tf.variable_scope(name), delegate + tf_with(tf.variable_scope("Prediction"), delegate { - var num_in_channel = x.shape[x.ndim - 1]; - var shape = new int[] { filter_size, filter_size, (int)num_in_channel, num_filters }; - var W = weight_variable("W", shape); - // var tf.summary.histogram("weight", W); - var b = bias_variable("b", new[] { num_filters }); - // tf.summary.histogram("bias", b); - var layer = tf.nn.conv2d(x, W.AsTensor(), - strides: new int[] { 1, stride, stride, 1 }, - padding: "SAME"); - layer += b.AsTensor(); - return tf.nn.relu(layer); + cls_prediction = tf.math.argmax(output_logits, axis: 1, name: "predictions"); + prob = tf.nn.softmax(output_logits, axis: 1, name: "prob"); }); - } + }); + return graph; + } - /// - /// Create a max pooling layer - /// - /// input to max-pooling layer - /// size of the max-pooling filter - /// stride of the max-pooling filter - /// layer name - /// The output array - private Tensor max_pool(Tensor x, int ksize, int stride, string name) + /// + /// Create a 2D convolution layer + /// + /// input from previous layer + /// size of each filter + /// number of filters(or output feature maps) + /// filter stride + /// layer name + /// The output array + private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride, string name) + { + return tf_with(tf.variable_scope(name), delegate { - return tf.nn.max_pool(x, - ksize: new[] { 1, ksize, ksize, 1 }, - strides: new[] { 1, stride, stride, 1 }, - padding: "SAME", - name: name); - } + var num_in_channel = x.shape[x.ndim - 1]; + var shape = new int[] { filter_size, filter_size, (int)num_in_channel, num_filters }; + var W = weight_variable("W", shape); + // var tf.summary.histogram("weight", W); + var b = bias_variable("b", new[] { num_filters }); + // tf.summary.histogram("bias", b); + var layer = tf.nn.conv2d(x, W.AsTensor(), + strides: new int[] { 1, stride, stride, 1 }, + padding: "SAME"); + layer += b.AsTensor(); + return tf.nn.relu(layer); + }); + } - /// - /// Flattens the output of the convolutional layer to be fed into fully-connected layer - /// - /// input array - /// flattened array - private Tensor flatten_layer(Tensor layer) + /// + /// Create a max pooling layer + /// + /// input to max-pooling layer + /// size of the max-pooling filter + /// stride of the max-pooling filter + /// layer name + /// The output array + private Tensor max_pool(Tensor x, int ksize, int stride, string name) + { + return tf.nn.max_pool(x, + ksize: new[] { 1, ksize, ksize, 1 }, + strides: new[] { 1, stride, stride, 1 }, + padding: "SAME", + name: name); + } + + /// + /// Flattens the output of the convolutional layer to be fed into fully-connected layer + /// + /// input array + /// flattened array + private Tensor flatten_layer(Tensor layer) + { + return tf_with(tf.variable_scope("Flatten_layer"), delegate { - return tf_with(tf.variable_scope("Flatten_layer"), delegate - { - var layer_shape = layer.shape; - var num_features = layer_shape[new Slice(1, 4)].size; - var layer_flat = tf.reshape(layer, new[] { -1, num_features }); + var layer_shape = layer.shape; + var num_features = layer_shape[new Slice(1, 4)].size; + var layer_flat = tf.reshape(layer, new[] { -1, num_features }); - return layer_flat; - }); - } + return layer_flat; + }); + } - /// - /// Create a weight variable with appropriate initialization - /// - /// - /// - /// - private IVariableV1 weight_variable(string name, int[] shape) - { - var initer = tf.truncated_normal_initializer(stddev: 0.01f); - return tf.compat.v1.get_variable(name, - dtype: tf.float32, - shape: shape, - initializer: initer); - } + /// + /// Create a weight variable with appropriate initialization + /// + /// + /// + /// + private IVariableV1 weight_variable(string name, int[] shape) + { + var initer = tf.truncated_normal_initializer(stddev: 0.01f); + return tf.compat.v1.get_variable(name, + dtype: tf.float32, + shape: shape, + initializer: initer); + } - /// - /// Create a bias variable with appropriate initialization - /// - /// - /// - /// - private IVariableV1 bias_variable(string name, int[] shape) - { - var initial = tf.constant(0f, shape: shape, dtype: tf.float32); - return tf.compat.v1.get_variable(name, - dtype: tf.float32, - initializer: initial); - } + /// + /// Create a bias variable with appropriate initialization + /// + /// + /// + /// + private IVariableV1 bias_variable(string name, int[] shape) + { + var initial = tf.constant(0f, shape: shape, dtype: tf.float32); + return tf.compat.v1.get_variable(name, + dtype: tf.float32, + initializer: initial); + } - /// - /// Create a fully-connected layer - /// - /// input from previous layer - /// number of hidden units in the fully-connected layer - /// layer name - /// boolean to add ReLU non-linearity (or not) - /// The output array - private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true) + /// + /// Create a fully-connected layer + /// + /// input from previous layer + /// number of hidden units in the fully-connected layer + /// layer name + /// boolean to add ReLU non-linearity (or not) + /// The output array + private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true) + { + return tf_with(tf.variable_scope(name), delegate { - return tf_with(tf.variable_scope(name), delegate - { - var in_dim = x.shape[1]; + var in_dim = x.shape[1]; - var W = weight_variable("W_" + name, shape: new[] { (int)in_dim, num_units }); - var b = bias_variable("b_" + name, new[] { num_units }); + var W = weight_variable("W_" + name, shape: new[] { (int)in_dim, num_units }); + var b = bias_variable("b_" + name, new[] { num_units }); - var layer = tf.matmul(x, W.AsTensor()) + b.AsTensor(); - if (use_relu) - layer = tf.nn.relu(layer); + var layer = tf.matmul(x, W.AsTensor()) + b.AsTensor(); + if (use_relu) + layer = tf.nn.relu(layer); - return layer; - }); - } - #endregion + return layer; + }); + } + #endregion - #region Train - public override void Train() - { - // Number of training iterations in each epoch - var num_tr_iter = (ArrayLabel_Train.Length) / batch_size; + #region Train + public override void Train() + { + // Number of training iterations in each epoch + var num_tr_iter = (ArrayLabel_Train.Length) / batch_size; - var init = tf.global_variables_initializer(); - sess.run(init); + var init = tf.global_variables_initializer(); + sess.run(init); - var saver = tf.train.Saver(tf.global_variables(), max_to_keep: 10); + var saver = tf.train.Saver(tf.global_variables(), max_to_keep: 10); - path_model = Config.Name + "\\MODEL"; - Directory.CreateDirectory(path_model); + path_model = Config.Name + "\\MODEL"; + Directory.CreateDirectory(path_model); - float loss_val = 100.0f; - float accuracy_val = 0f; + float loss_val = 100.0f; + float accuracy_val = 0f; - var sw = new Stopwatch(); - sw.Start(); - foreach (var epoch in range(epochs)) - { - print($"Training epoch: {epoch + 1}"); - // Randomly shuffle the training data at the beginning of each epoch - (ArrayFileName_Train, ArrayLabel_Train) = ShuffleArray(ArrayLabel_Train.Length, ArrayFileName_Train, ArrayLabel_Train); - y_train = np.eye(Dict_Label.Count, dtype: tf.float32)[new NDArray(ArrayLabel_Train)]; + var sw = new Stopwatch(); + sw.Start(); + foreach (var epoch in range(epochs)) + { + print($"Training epoch: {epoch + 1}"); + // Randomly shuffle the training data at the beginning of each epoch + (ArrayFileName_Train, ArrayLabel_Train) = ShuffleArray(ArrayLabel_Train.Length, ArrayFileName_Train, ArrayLabel_Train); + y_train = np.eye(Dict_Label.Count, dtype: tf.float32)[new NDArray(ArrayLabel_Train)]; - //decay learning rate - if (learning_rate_step != 0) + //decay learning rate + if (learning_rate_step != 0) + { + if ((epoch != 0) && (epoch % learning_rate_step == 0)) { - if ((epoch != 0) && (epoch % learning_rate_step == 0)) - { - learning_rate_base = learning_rate_base * learning_rate_decay; - if (learning_rate_base <= learning_rate_min) { learning_rate_base = learning_rate_min; } - sess.run(tf.assign(learning_rate, learning_rate_base)); - } + learning_rate_base = learning_rate_base * learning_rate_decay; + if (learning_rate_base <= learning_rate_min) { learning_rate_base = learning_rate_min; } + sess.run(tf.assign(learning_rate, learning_rate_base)); } + } - //Load local images asynchronously,use queue,improve train efficiency - BlockingCollection<(NDArray c_x, NDArray c_y, int iter)> BlockC = new BlockingCollection<(NDArray C1, NDArray C2, int iter)>(TrainQueueCapa); - Task.Run(() => - { - foreach (var iteration in range(num_tr_iter)) - { - var start = iteration * batch_size; - var end = (iteration + 1) * batch_size; - (NDArray x_batch, NDArray y_batch) = GetNextBatch(sess, ArrayFileName_Train, y_train, start, end); - BlockC.Add((x_batch, y_batch, iteration)); - } - BlockC.CompleteAdding(); - }); - - foreach (var item in BlockC.GetConsumingEnumerable()) + //Load local images asynchronously,use queue,improve train efficiency + BlockingCollection<(NDArray c_x, NDArray c_y, int iter)> BlockC = new BlockingCollection<(NDArray C1, NDArray C2, int iter)>(TrainQueueCapa); + Task.Run(() => + { + foreach (var iteration in range(num_tr_iter)) { - sess.run(optimizer, (x, item.c_x), (y, item.c_y)); - - if (item.iter % display_freq == 0) - { - // Calculate and display the batch loss and accuracy - var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, item.c_x), new FeedItem(y, item.c_y)); - loss_val = result[0]; - accuracy_val = result[1]; - print("CNN：" + ($"iter {item.iter.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")} {sw.ElapsedMilliseconds}ms")); - sw.Restart(); - } + var start = iteration * batch_size; + var end = (iteration + 1) * batch_size; + (NDArray x_batch, NDArray y_batch) = GetNextBatch(sess, ArrayFileName_Train, y_train, start, end); + BlockC.Add((x_batch, y_batch, iteration)); } + BlockC.CompleteAdding(); + }); - // Run validation after every epoch - (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, x_valid), (y, y_valid)); - print("CNN：" + "---------------------------------------------------------"); - print("CNN：" + $"global steps: {sess.run(global_steps)[0]}, learning rate: {sess.run(learning_rate)[0]}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}"); - print("CNN：" + "---------------------------------------------------------"); + foreach (var item in BlockC.GetConsumingEnumerable()) + { + sess.run(optimizer, (x, item.c_x), (y, item.c_y)); - if (SaverBest) + if (item.iter % display_freq == 0) { - if (accuracy_val > max_accuracy) - { - max_accuracy = accuracy_val; - saver.save(sess, path_model + "\\CNN_Best"); - print("CKPT Model is saved."); - } + // Calculate and display the batch loss and accuracy + var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, item.c_x), new FeedItem(y, item.c_y)); + loss_val = result[0]; + accuracy_val = result[1]; + print("CNN：" + ($"iter {item.iter.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")} {sw.ElapsedMilliseconds}ms")); + sw.Restart(); } - else + } + + // Run validation after every epoch + (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, x_valid), (y, y_valid)); + print("CNN：" + "---------------------------------------------------------"); + print("CNN：" + $"global steps: {sess.run(global_steps)[0]}, learning rate: {sess.run(learning_rate)[0]}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}"); + print("CNN：" + "---------------------------------------------------------"); + + if (SaverBest) + { + if (accuracy_val > max_accuracy) { - saver.save(sess, path_model + string.Format("\\CNN_Epoch_{0}_Loss_{1}_Acc_{2}", epoch, loss_val, accuracy_val)); + max_accuracy = accuracy_val; + saver.save(sess, path_model + "\\CNN_Best"); print("CKPT Model is saved."); } } - Write_Dictionary(path_model + "\\dic.txt", Dict_Label); + else + { + saver.save(sess, path_model + string.Format("\\CNN_Epoch_{0}_Loss_{1}_Acc_{2}", epoch, loss_val, accuracy_val)); + print("CKPT Model is saved."); + } } + Write_Dictionary(path_model + "\\dic.txt", Dict_Label); + } - void Write_Dictionary(string path, Dictionary mydic) - { - FileStream fs = new FileStream(path, FileMode.Create); - StreamWriter sw = new StreamWriter(fs); - foreach (var d in mydic) { sw.Write(d.Key + "," + d.Value + "\r\n"); } - sw.Flush(); - sw.Close(); - fs.Close(); - print("Write_Dictionary"); - } + void Write_Dictionary(string path, Dictionary mydic) + { + FileStream fs = new FileStream(path, FileMode.Create); + StreamWriter sw = new StreamWriter(fs); + foreach (var d in mydic) { sw.Write(d.Key + "," + d.Value + "\r\n"); } + sw.Flush(); + sw.Close(); + fs.Close(); + print("Write_Dictionary"); + } - (NDArray, NDArray) Randomize(NDArray x, NDArray y) - { - var perm = np.random.permutation((int)y.shape[0]); - np.random.shuffle(perm); - return (x[perm], y[perm]); - } + (NDArray, NDArray) Randomize(NDArray x, NDArray y) + { + var perm = np.random.permutation((int)y.shape[0]); + np.random.shuffle(perm); + return (x[perm], y[perm]); + } - (NDArray, NDArray) GetNextBatch(NDArray x, NDArray y, int start, int end) - { - var slice = new Slice(start, end); - var x_batch = x[slice]; - var y_batch = y[slice]; - return (x_batch, y_batch); - } + (NDArray, NDArray) GetNextBatch(NDArray x, NDArray y, int start, int end) + { + var slice = new Slice(start, end); + var x_batch = x[slice]; + var y_batch = y[slice]; + return (x_batch, y_batch); + } - (NDArray, NDArray) GetNextBatch(Session sess, string[] x, NDArray y, int start, int end) + (NDArray, NDArray) GetNextBatch(Session sess, string[] x, NDArray y, int start, int end) + { + NDArray x_batch = np.zeros((end - start, img_h, img_w, n_channels), dtype: tf.float32); + int n = 0; + for (int i = start; i < end; i++) { - NDArray x_batch = np.zeros((end - start, img_h, img_w, n_channels), dtype: tf.float32); - int n = 0; - for (int i = start; i < end; i++) - { - NDArray img4 = cv2.imread(x[i], IMREAD_COLOR.IMREAD_GRAYSCALE); - img4 = img4.reshape((img4.shape[0], img4.shape[1], 1)); - x_batch[n] = sess.run(normalized, (decodeJpeg, img4)); - n++; - } - var slice = new Slice(start, end); - var y_batch = y[slice]; - return (x_batch, y_batch); + NDArray img4 = cv2.imread(x[i], IMREAD_COLOR.IMREAD_GRAYSCALE); + img4 = img4.reshape((img4.shape[0], img4.shape[1], 1)); + x_batch[n] = sess.run(normalized, (decodeJpeg, img4)); + n++; } - #endregion + var slice = new Slice(start, end); + var y_batch = y[slice]; + return (x_batch, y_batch); + } + #endregion - public override void Test() - { - (loss_test, accuracy_test) = sess.run((loss, accuracy), (x, x_test), (y, y_test)); - print("CNN：" + "---------------------------------------------------------"); - print("CNN：" + $"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}"); - print("CNN：" + "---------------------------------------------------------"); + public override void Test() + { + (loss_test, accuracy_test) = sess.run((loss, accuracy), (x, x_test), (y, y_test)); + print("CNN：" + "---------------------------------------------------------"); + print("CNN：" + $"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}"); + print("CNN：" + "---------------------------------------------------------"); - (Test_Cls, Test_Data) = sess.run((cls_prediction, prob), (x, x_test)); - } + (Test_Cls, Test_Data) = sess.run((cls_prediction, prob), (x, x_test)); + } - void TestDataOutput() + void TestDataOutput() + { + for (int i = 0; i < ArrayLabel_Test.Length; i++) { - for (int i = 0; i < ArrayLabel_Test.Length; i++) - { - long real = ArrayLabel_Test[i]; - int predict = Test_Cls[i]; - var probability = Test_Data[i, predict]; - string result = (real == predict) ? "OK" : "NG"; - string fileName = ArrayFileName_Test[i]; - string real_str = Dict_Label[real]; - string predict_str = Dict_Label[predict]; - print((i + 1).ToString() + "|" + "result:" + result + "|" + "real_str:" + real_str + "|" - + "predict_str:" + predict_str + "|" + "probability:" + (float)probability + "|" - + "fileName:" + fileName); - } + long real = ArrayLabel_Test[i]; + int predict = Test_Cls[i]; + var probability = Test_Data[i, predict]; + string result = (real == predict) ? "OK" : "NG"; + string fileName = ArrayFileName_Test[i]; + string real_str = Dict_Label[real]; + string predict_str = Dict_Label[predict]; + print((i + 1).ToString() + "|" + "result:" + result + "|" + "real_str:" + real_str + "|" + + "predict_str:" + predict_str + "|" + "probability:" + (float)probability + "|" + + "fileName:" + fileName); } } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs index 44d5ba9..c28ce3e 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs @@ -23,129 +23,128 @@ limitations under the License. using Tensorflow.NumPy; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Convolutional Neural Network classifier for Hand Written Digits +/// CNN architecture with two convolutional layers, followed by two fully-connected layers at the end. +/// Use Stochastic Gradient Descent (SGD) optimizer. +/// https://www.easy-tensorflow.com/tf-tutorials/convolutional-neural-nets-cnns/cnn1 +/// +public class DigitRecognitionCNN : SciSharpExample, IExample { - /// - /// Convolutional Neural Network classifier for Hand Written Digits - /// CNN architecture with two convolutional layers, followed by two fully-connected layers at the end. - /// Use Stochastic Gradient Descent (SGD) optimizer. - /// https://www.easy-tensorflow.com/tf-tutorials/convolutional-neural-nets-cnns/cnn1 - /// - public class DigitRecognitionCNN : SciSharpExample, IExample - { - Datasets mnist; + Datasets mnist; - float accuracy_test = 0f; + float accuracy_test = 0f; - NDArray x_train, y_train; - NDArray x_valid, y_valid; - NDArray x_test, y_test; + NDArray x_train, y_train; + NDArray x_valid, y_valid; + NDArray x_test, y_test; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "MNIST CNN (Graph)", - Enabled = true - }; - - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - PrepareData(); - Train(); - Test(); - Predict(); + Name = "MNIST CNN (Graph)", + Enabled = true + }; + + public bool Run() + { + PrepareData(); + Train(); + Test(); + Predict(); - return accuracy_test > 0.95; - } + return accuracy_test > 0.95; + } - public override void Train() + public override void Train() + { + // using wizard to train model + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions { - // using wizard to train model - var wizard = new ModelWizard(); - var task = wizard.AddImageClassificationTask(new TaskOptions - { - InputShape = (28, 28, 1), - NumberOfClass = 10, - }); - task.SetModelArgs(new ConvArgs - { - NumberOfNeurons = 128 - }); - task.Train(new TrainingOptions - { - Epochs = 5, - TrainingData = new FeatureAndLabel(x_train, y_train), - ValidationData = new FeatureAndLabel(x_valid, y_valid) - }); - } - - public override void Test() + InputShape = (28, 28, 1), + NumberOfClass = 10, + }); + task.SetModelArgs(new ConvArgs { - var wizard = new ModelWizard(); - var task = wizard.AddImageClassificationTask(new TaskOptions - { - ModelPath = @"image_classification_cnn_v1\saved_model.pb" - }); - var result = task.Test(new TestingOptions - { - TestingData = new FeatureAndLabel(x_test, y_test) - }); - accuracy_test = result.Accuracy; - } - - public override void Predict() + NumberOfNeurons = 128 + }); + task.Train(new TrainingOptions { - // predict image - var wizard = new ModelWizard(); - var task = wizard.AddImageClassificationTask(new TaskOptions - { - LabelPath = @"image_classification_cnn_v1\labels.txt", - ModelPath = @"image_classification_cnn_v1\saved_model.pb" - }); - - var input = x_test["0:1"]; - var result = task.Predict(input); - long output = np.argmax(y_test[0]); - Debug.Assert(result.Label == output.ToString()); - - input = x_test["1:2"]; - result = task.Predict(input); - output = np.argmax(y_test[1]); - Debug.Assert(result.Label == output.ToString()); - } - - public override void PrepareData() + Epochs = 5, + TrainingData = new FeatureAndLabel(x_train, y_train), + ValidationData = new FeatureAndLabel(x_valid, y_valid) + }); + } + + public override void Test() + { + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions { - Directory.CreateDirectory("image_classification_cnn_v1"); - var loader = new MnistModelLoader(); - mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; - (x_train, y_train) = Reformat(mnist.Train.Data, mnist.Train.Labels); - (x_valid, y_valid) = Reformat(mnist.Validation.Data, mnist.Validation.Labels); - (x_test, y_test) = Reformat(mnist.Test.Data, mnist.Test.Labels); - - print("Size of:"); - print($"- Training-set:\t\t{len(mnist.Train.Data)}"); - print($"- Validation-set:\t{len(mnist.Validation.Data)}"); - - // generate labels - var labels = range(0, 9).Select(x => x.ToString()); - File.WriteAllLines(@"image_classification_cnn_v1\labels.txt", labels); - } - - /// - /// Reformats the data to the format acceptable for convolutional layers - /// - /// - /// - /// - private (NDArray, NDArray) Reformat(NDArray x, NDArray y) + ModelPath = @"image_classification_cnn_v1\saved_model.pb" + }); + var result = task.Test(new TestingOptions { - var (unique_y, _) = np.unique(np.argmax(y, 1)); - var (img_size, num_ch, num_class) = ((int)np.sqrt(x.shape[1]).astype(np.int32), 1, len(unique_y)); - var dataset = x.reshape((x.shape[0], img_size, img_size, num_ch)).astype(np.float32); - //y[0] = np.arange(num_class) == y[0]; - //var labels = (np.arange(num_class) == y.reshape(y.shape[0], 1, y.shape[1])).astype(np.float32); - return (dataset, y); - } + TestingData = new FeatureAndLabel(x_test, y_test) + }); + accuracy_test = result.Accuracy; + } + + public override void Predict() + { + // predict image + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions + { + LabelPath = @"image_classification_cnn_v1\labels.txt", + ModelPath = @"image_classification_cnn_v1\saved_model.pb" + }); + + var input = x_test["0:1"]; + var result = task.Predict(input); + long output = np.argmax(y_test[0]); + Debug.Assert(result.Label == output.ToString()); + + input = x_test["1:2"]; + result = task.Predict(input); + output = np.argmax(y_test[1]); + Debug.Assert(result.Label == output.ToString()); + } + + public override void PrepareData() + { + Directory.CreateDirectory("image_classification_cnn_v1"); + var loader = new MnistModelLoader(); + mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; + (x_train, y_train) = Reformat(mnist.Train.Data, mnist.Train.Labels); + (x_valid, y_valid) = Reformat(mnist.Validation.Data, mnist.Validation.Labels); + (x_test, y_test) = Reformat(mnist.Test.Data, mnist.Test.Labels); + + print("Size of:"); + print($"- Training-set:\t\t{len(mnist.Train.Data)}"); + print($"- Validation-set:\t{len(mnist.Validation.Data)}"); + + // generate labels + var labels = range(0, 10).Select(x => x.ToString()); + File.WriteAllLines(@"image_classification_cnn_v1\labels.txt", labels); + } + + /// + /// Reformats the data to the format acceptable for convolutional layers + /// + /// + /// + /// + private (NDArray, NDArray) Reformat(NDArray x, NDArray y) + { + var (unique_y, _) = np.unique(np.argmax(y, 1)); + var (img_size, num_ch, num_class) = ((int)np.sqrt(x.shape[1]).astype(np.int32), 1, len(unique_y)); + var dataset = x.reshape((x.shape[0], img_size, img_size, num_ch)).astype(np.float32); + //y[0] = np.arange(num_class) == y[0]; + //var labels = (np.arange(num_class) == y.reshape(y.shape[0], 1, y.shape[1])).astype(np.float32); + return (dataset, y); } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs index c0888b2..2ad4599 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs @@ -21,197 +21,196 @@ limitations under the License. using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Build a convolutional neural network with TensorFlow v2. +/// This example is using a low-level approach to better understand all mechanics behind building convolutional neural networks and the training process. +/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb +/// +public class DigitRecognitionCnnEager : SciSharpExample, IExample { - /// - /// Build a convolutional neural network with TensorFlow v2. - /// This example is using a low-level approach to better understand all mechanics behind building convolutional neural networks and the training process. - /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb - /// - public class DigitRecognitionCnnEager : SciSharpExample, IExample - { - // MNIST dataset parameters. - int num_classes = 10; // total classes (0-9 digits). + // MNIST dataset parameters. + int num_classes = 10; // total classes (0-9 digits). - // Training parameters. - float learning_rate = 0.001f; - int training_steps = 100; - int batch_size = 32; - int display_step = 10; + // Training parameters. + float learning_rate = 0.001f; + int training_steps = 100; + int batch_size = 32; + int display_step = 10; - // Network parameters. - int conv1_filters = 32; // number of filters for 1st conv layer. - int conv2_filters = 64; // number of filters for 2nd conv layer. - int fc1_units = 1024; // number of neurons for 1st fully-connected layer. + // Network parameters. + int conv1_filters = 32; // number of filters for 1st conv layer. + int conv2_filters = 64; // number of filters for 2nd conv layer. + int fc1_units = 1024; // number of neurons for 1st fully-connected layer. - float accuracy_test = 0.0f; + float accuracy_test = 0.0f; - IDatasetV2 train_data; - NDArray x_test, y_test, x_train, y_train; - IVariableV1 wc1, wc2, wd1, wout; - IVariableV1 bc1, bc2, bd1, bout; + IDatasetV2 train_data; + NDArray x_test, y_test, x_train, y_train; + IVariableV1 wc1, wc2, wd1, wout; + IVariableV1 bc1, bc2, bd1, bout; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "MNIST CNN (Eager)", - Enabled = true, - IsImportingGraph = false - }; - - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.enable_eager_execution(); + Name = "MNIST CNN (Eager)", + Enabled = true, + IsImportingGraph = false + }; - PrepareData(); - Train(); - Test(); - - return accuracy_test >= 0.80; - } + public bool Run() + { + tf.enable_eager_execution(); + + PrepareData(); + Train(); + Test(); + + return accuracy_test >= 0.80; + } - public override void Train() + public override void Train() + { + // A random value generator to initialize weights. + var random_normal = tf.initializers.random_normal_initializer(); + + // Conv Layer 1: 5x5 conv, 1 input, 32 filters (MNIST has 1 color channel only). + wc1 = tf.Variable(random_normal.Apply(new InitializerArgs((5, 5, 1, conv1_filters)))); + // Conv Layer 2: 5x5 conv, 32 inputs, 64 filters. + wc2 = tf.Variable(random_normal.Apply(new InitializerArgs((5, 5, conv1_filters, conv2_filters)))); + // FC Layer 1: 7*7*64 inputs, 1024 units. + wd1 = tf.Variable(random_normal.Apply(new InitializerArgs((7 * 7 * 64, fc1_units)))); + // FC Out Layer: 1024 inputs, 10 units (total number of classes) + wout = tf.Variable(random_normal.Apply(new InitializerArgs((fc1_units, num_classes)))); + + bc1 = tf.Variable(tf.zeros(conv1_filters)); + bc2 = tf.Variable(tf.zeros(conv2_filters)); + bd1 = tf.Variable(tf.zeros(fc1_units)); + bout = tf.Variable(tf.zeros(num_classes)); + + // ADAM optimizer. + var optimizer = keras.optimizers.Adam(learning_rate); + + // Run training for the given number of steps. + foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1)) { - // A random value generator to initialize weights. - var random_normal = tf.initializers.random_normal_initializer(); - - // Conv Layer 1: 5x5 conv, 1 input, 32 filters (MNIST has 1 color channel only). - wc1 = tf.Variable(random_normal.Apply(new InitializerArgs((5, 5, 1, conv1_filters)))); - // Conv Layer 2: 5x5 conv, 32 inputs, 64 filters. - wc2 = tf.Variable(random_normal.Apply(new InitializerArgs((5, 5, conv1_filters, conv2_filters)))); - // FC Layer 1: 7*7*64 inputs, 1024 units. - wd1 = tf.Variable(random_normal.Apply(new InitializerArgs((7 * 7 * 64, fc1_units)))); - // FC Out Layer: 1024 inputs, 10 units (total number of classes) - wout = tf.Variable(random_normal.Apply(new InitializerArgs((fc1_units, num_classes)))); - - bc1 = tf.Variable(tf.zeros(conv1_filters)); - bc2 = tf.Variable(tf.zeros(conv2_filters)); - bd1 = tf.Variable(tf.zeros(fc1_units)); - bout = tf.Variable(tf.zeros(num_classes)); - - // ADAM optimizer. - var optimizer = keras.optimizers.Adam(learning_rate); - - // Run training for the given number of steps. - foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1)) + // Run the optimization to update W and b values. + run_optimization(optimizer, batch_x, batch_y); + + if (step % display_step == 0) { - // Run the optimization to update W and b values. - run_optimization(optimizer, batch_x, batch_y); - - if (step % display_step == 0) - { - var pred = conv_net(batch_x); - var loss = cross_entropy(pred, batch_y); - var acc = accuracy(pred, batch_y); - print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}"); - } + var pred = conv_net(batch_x); + var loss = cross_entropy(pred, batch_y); + var acc = accuracy(pred, batch_y); + print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}"); } } + } - public override void Test() - { - // Test model on validation set. - x_test = x_test["::100"]; - y_test = y_test["::100"]; - var pred = conv_net(x_test); - accuracy_test = (float)accuracy(pred, y_test); - print($"Test Accuracy: {accuracy_test}"); - } + public override void Test() + { + // Test model on validation set. + x_test = x_test["::100"]; + y_test = y_test["::100"]; + var pred = conv_net(x_test); + accuracy_test = (float)accuracy(pred, y_test); + print($"Test Accuracy: {accuracy_test}"); + } - void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y) - { - using var g = tf.GradientTape(); - var pred = conv_net(x); - var loss = cross_entropy(pred, y); + void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y) + { + using var g = tf.GradientTape(); + var pred = conv_net(x); + var loss = cross_entropy(pred, y); - // Compute gradients. - var trainable_variables = new IVariableV1[] { wc1, wc2, wd1, wout, bc1, bc2, bd1, bout }; - var gradients = g.gradient(loss, trainable_variables); + // Compute gradients. + var trainable_variables = new IVariableV1[] { wc1, wc2, wd1, wout, bc1, bc2, bd1, bout }; + var gradients = g.gradient(loss, trainable_variables); - // Update W and b following gradients. - optimizer.apply_gradients(zip(gradients, trainable_variables.Select(x => x as ResourceVariable))); - } + // Update W and b following gradients. + optimizer.apply_gradients(zip(gradients, trainable_variables.Select(x => x as ResourceVariable))); + } - Tensor conv2d(Tensor x, IVariableV1 W, IVariableV1 b, int strides = 1) - { - x = tf.nn.conv2d(x, W.AsTensor(), new int[] { 1, strides, strides, 1 }, padding: "SAME"); - x = tf.nn.bias_add(x, b); - return tf.nn.relu(x); - } + Tensor conv2d(Tensor x, IVariableV1 W, IVariableV1 b, int strides = 1) + { + x = tf.nn.conv2d(x, W.AsTensor(), new int[] { 1, strides, strides, 1 }, padding: "SAME"); + x = tf.nn.bias_add(x, b); + return tf.nn.relu(x); + } - /// - /// MaxPool2D wrapper. - /// - /// - /// - /// - Tensor maxpool2d(Tensor x, int k = 2) - { - return tf.nn.max_pool(x, ksize: new[] { 1, k, k, 1 }, strides: new[] { 1, k, k, 1 }, padding: "SAME"); - } + /// + /// MaxPool2D wrapper. + /// + /// + /// + /// + Tensor maxpool2d(Tensor x, int k = 2) + { + return tf.nn.max_pool(x, ksize: new[] { 1, k, k, 1 }, strides: new[] { 1, k, k, 1 }, padding: "SAME"); + } - Tensor conv_net(Tensor x) - { - // Input shape: [-1, 28, 28, 1]. A batch of 28x28x1 (grayscale) images. - x = tf.reshape(x, (-1, 28, 28, 1)); + Tensor conv_net(Tensor x) + { + // Input shape: [-1, 28, 28, 1]. A batch of 28x28x1 (grayscale) images. + x = tf.reshape(x, (-1, 28, 28, 1)); - // Convolution Layer. Output shape: [-1, 28, 28, 32]. - var conv1 = conv2d(x, wc1, bc1); + // Convolution Layer. Output shape: [-1, 28, 28, 32]. + var conv1 = conv2d(x, wc1, bc1); - // Max Pooling (down-sampling). Output shape: [-1, 14, 14, 32]. - conv1 = maxpool2d(conv1, k: 2); + // Max Pooling (down-sampling). Output shape: [-1, 14, 14, 32]. + conv1 = maxpool2d(conv1, k: 2); - // Convolution Layer. Output shape: [-1, 14, 14, 64]. - var conv2 = conv2d(conv1, wc2, bc2); + // Convolution Layer. Output shape: [-1, 14, 14, 64]. + var conv2 = conv2d(conv1, wc2, bc2); - // Max Pooling (down-sampling). Output shape: [-1, 7, 7, 64]. - conv2 = maxpool2d(conv2, k: 2); + // Max Pooling (down-sampling). Output shape: [-1, 7, 7, 64]. + conv2 = maxpool2d(conv2, k: 2); - // Reshape conv2 output to fit fully connected layer input, Output shape: [-1, 7*7*64]. - var fc1 = tf.reshape(conv2, (-1, wd1.shape.dims[0])); + // Reshape conv2 output to fit fully connected layer input, Output shape: [-1, 7*7*64]. + var fc1 = tf.reshape(conv2, (-1, wd1.shape.dims[0])); - // Fully connected layer, Output shape: [-1, 1024]. - fc1 = tf.add(tf.matmul(fc1, wd1.AsTensor()), bd1.AsTensor()); - // Apply ReLU to fc1 output for non-linearity. - fc1 = tf.nn.relu(fc1); + // Fully connected layer, Output shape: [-1, 1024]. + fc1 = tf.add(tf.matmul(fc1, wd1.AsTensor()), bd1.AsTensor()); + // Apply ReLU to fc1 output for non-linearity. + fc1 = tf.nn.relu(fc1); - // Fully connected layer, Output shape: [-1, 10]. - var output = tf.add(tf.matmul(fc1, wout.AsTensor()), bout.AsTensor()); - // Apply softmax to normalize the logits to a probability distribution. - return tf.nn.softmax(output); - } + // Fully connected layer, Output shape: [-1, 10]. + var output = tf.add(tf.matmul(fc1, wout.AsTensor()), bout.AsTensor()); + // Apply softmax to normalize the logits to a probability distribution. + return tf.nn.softmax(output); + } - Tensor cross_entropy(Tensor y_pred, Tensor y_true) - { - // Encode label to a one hot vector. - y_true = tf.one_hot(y_true, depth: num_classes); - // Clip prediction values to avoid log(0) error. - y_pred = tf.clip_by_value(y_pred, 1e-9f, 1.0f); - // Compute cross-entropy. - return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred))); - } + Tensor cross_entropy(Tensor y_pred, Tensor y_true) + { + // Encode label to a one hot vector. + y_true = tf.one_hot(y_true, depth: num_classes); + // Clip prediction values to avoid log(0) error. + y_pred = tf.clip_by_value(y_pred, 1e-9f, 1.0f); + // Compute cross-entropy. + return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred))); + } - Tensor accuracy(Tensor y_pred, Tensor y_true) - { - // Predicted class is the index of highest score in prediction vector (i.e. argmax). - var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); - return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); - } + Tensor accuracy(Tensor y_pred, Tensor y_true) + { + // Predicted class is the index of highest score in prediction vector (i.e. argmax). + var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); + return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); + } - public override void PrepareData() - { - ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data(); - // Convert to float32. - // (x_train, x_test) = (np.array(x_train, np.float32), np.array(x_test, np.float32)); - // Normalize images value from [0, 255] to [0, 1]. - (x_train, x_test) = (x_train / 255.0f, x_test / 255.0f); - - train_data = tf.data.Dataset.from_tensor_slices(x_train, y_train); - train_data = train_data.repeat() - .shuffle(5000) - .batch(batch_size) - .prefetch(1) - .take(training_steps); - } + public override void PrepareData() + { + ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data(); + // Convert to float32. + // (x_train, x_test) = (np.array(x_train, np.float32), np.array(x_test, np.float32)); + // Normalize images value from [0, 255] to [0, 1]. + (x_train, x_test) = (x_train / 255.0f, x_test / 255.0f); + + train_data = tf.data.Dataset.from_tensor_slices(x_train, y_train); + train_data = train_data.repeat() + .shuffle(5000) + .batch(batch_size) + .prefetch(1) + .take(training_steps); } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs index afb0bce..794d1c4 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionLSTM.cs @@ -20,155 +20,154 @@ limitations under the License. using Tensorflow.Operations; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Bi-directional Recurrent Neural Network. +/// +/// To classify images using a bidirectional recurrent neural network, we consider +/// every image row as a sequence of pixels.Because MNIST image shape is 28*28px, +/// we will then handle 28 sequences of 28 steps for every sample. +/// +/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py +/// +public class DigitRecognitionLSTM : SciSharpExample, IExample { - /// - /// Bi-directional Recurrent Neural Network. - /// - /// To classify images using a bidirectional recurrent neural network, we consider - /// every image row as a sequence of pixels.Because MNIST image shape is 28*28px, - /// we will then handle 28 sequences of 28 steps for every sample. - /// - /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py - /// - public class DigitRecognitionLSTM : SciSharpExample, IExample - { - // Training Parameters - float learning_rate = 0.001f; - int training_steps = 1000; - int batch_size = 128; - int display_step = 100; - - // Network Parameters - int num_input = 28; - int timesteps = 28; - int num_hidden = 128; // hidden layer num of features - int num_classes = 10; // MNIST total classes (0-9 digits) + // Training Parameters + float learning_rate = 0.001f; + int training_steps = 1000; + int batch_size = 128; + int display_step = 100; - Datasets mnist; + // Network Parameters + int num_input = 28; + int timesteps = 28; + int num_hidden = 128; // hidden layer num of features + int num_classes = 10; // MNIST total classes (0-9 digits) - Tensor X, Y; - Tensor loss_op, accuracy, prediction; - Operation train_op; + Datasets mnist; - float accuracy_test = 0f; - Session sess; + Tensor X, Y; + Tensor loss_op, accuracy, prediction; + Operation train_op; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "MNIST LSTM (Graph)", - Enabled = false, - IsImportingGraph = false - }; + float accuracy_test = 0f; + Session sess; - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.compat.v1.disable_eager_execution(); + Name = "MNIST LSTM (Graph)", + Enabled = false, + IsImportingGraph = false + }; - PrepareData(); - BuildGraph(); + public bool Run() + { + tf.compat.v1.disable_eager_execution(); - sess = tf.Session(); + PrepareData(); + BuildGraph(); - Train(); - Test(); + sess = tf.Session(); - return accuracy_test > 0.40; - } + Train(); + Test(); - public override Graph BuildGraph() - { - var graph = new Graph().as_default(); + return accuracy_test > 0.40; + } - X = tf.placeholder(tf.float32, (-1, timesteps, num_input)); - Y = tf.placeholder(tf.float32, (-1, num_classes)); + public override Graph BuildGraph() + { + var graph = new Graph().as_default(); - // Hidden layer weights => 2*n_hidden because of forward + backward cells - var weights = tf.Variable(tf.random.normal((2 * num_hidden, num_classes))); - var biases = tf.Variable(tf.random.normal(num_classes)); + X = tf.placeholder(tf.float32, (-1, timesteps, num_input)); + Y = tf.placeholder(tf.float32, (-1, num_classes)); - // Unstack to get a list of 'timesteps' tensors of shape (batch_size, num_input) - var x = tf.unstack(X, timesteps, 1); + // Hidden layer weights => 2*n_hidden because of forward + backward cells + var weights = tf.Variable(tf.random.normal((2 * num_hidden, num_classes))); + var biases = tf.Variable(tf.random.normal(num_classes)); - // Define lstm cells with tensorflow - // Forward direction cell - var lstm_fw_cell = new BasicLstmCell(num_hidden, forget_bias: 1.0f); - // Backward direction cell - var lstm_bw_cell = new BasicLstmCell(num_hidden, forget_bias: 1.0f); + // Unstack to get a list of 'timesteps' tensors of shape (batch_size, num_input) + var x = tf.unstack(X, timesteps, 1); - // Get lstm cell output - var (outputs, _, _) = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype: tf.float32); + // Define lstm cells with tensorflow + // Forward direction cell + var lstm_fw_cell = new BasicLstmCell(num_hidden, forget_bias: 1.0f); + // Backward direction cell + var lstm_bw_cell = new BasicLstmCell(num_hidden, forget_bias: 1.0f); - // Linear activation, using rnn inner loop last output - var logits = tf.matmul(outputs.Last(), weights) + biases; - prediction = tf.nn.softmax(logits); + // Get lstm cell output + var (outputs, _, _) = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype: tf.float32); - // Define loss and optimizer - loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( - logits: logits, labels: Y)); - var optimizer = tf.train.GradientDescentOptimizer(learning_rate: learning_rate); - train_op = optimizer.minimize(loss_op); + // Linear activation, using rnn inner loop last output + var logits = tf.matmul(outputs.Last(), weights) + biases; + prediction = tf.nn.softmax(logits); - // Evaluate model (with test logits, for dropout to be disabled) - var correct_pred = tf.equal(tf.math.argmax(prediction, 1), tf.math.argmax(Y, 1)); - accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)); + // Define loss and optimizer + loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits: logits, labels: Y)); + var optimizer = tf.train.GradientDescentOptimizer(learning_rate: learning_rate); + train_op = optimizer.minimize(loss_op); - return graph; - } + // Evaluate model (with test logits, for dropout to be disabled) + var correct_pred = tf.equal(tf.math.argmax(prediction, 1), tf.math.argmax(Y, 1)); + accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)); - public override void Train() - { - float loss_val = 100.0f; - float accuracy_val = 0f; + return graph; + } - var init = tf.global_variables_initializer(); - sess.run(init); + public override void Train() + { + float loss_val = 100.0f; + float accuracy_val = 0f; - var sw = new Stopwatch(); - sw.Start(); - foreach (var step in range(1, training_steps + 1)) - { - var (batch_x, batch_y) = mnist.Train.GetNextBatch(batch_size); + var init = tf.global_variables_initializer(); + sess.run(init); - // Reshape data to get 28 seq of 28 elements - batch_x = batch_x.reshape((batch_size, timesteps, num_input)); + var sw = new Stopwatch(); + sw.Start(); + foreach (var step in range(1, training_steps + 1)) + { + var (batch_x, batch_y) = mnist.Train.GetNextBatch(batch_size); - // Run optimization op (backprop) - sess.run(train_op, (X, batch_x), (Y, batch_y)); + // Reshape data to get 28 seq of 28 elements + batch_x = batch_x.reshape((batch_size, timesteps, num_input)); - if (step % display_step == 0 || step == 1) - { - // Calculate batch loss and accuracy - (loss_val, accuracy_val) = sess.run((loss_op, accuracy), (X, batch_x), (Y, batch_y)); - print($"Step {step}: Minibatch Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("0.000")} {sw.ElapsedMilliseconds}ms"); - sw.Restart(); - } - } + // Run optimization op (backprop) + sess.run(train_op, (X, batch_x), (Y, batch_y)); - print("Optimization Finished!"); + if (step % display_step == 0 || step == 1) + { + // Calculate batch loss and accuracy + (loss_val, accuracy_val) = sess.run((loss_op, accuracy), (X, batch_x), (Y, batch_y)); + print($"Step {step}: Minibatch Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("0.000")} {sw.ElapsedMilliseconds}ms"); + sw.Restart(); + } } - public override void Test() - { - // Calculate accuracy for 128 mnist test images - var (x_test, y_test) = (mnist.Test.Data[":128"], mnist.Test.Labels[":128"]); - x_test = x_test.reshape((-1, timesteps, num_input)); - accuracy_test = sess.run(accuracy, new FeedItem(X, x_test), new FeedItem(Y, y_test)); - print("---------------------------------------------------------"); - print($"Testing Accuracy: {accuracy_test.ToString("P")}"); - print("---------------------------------------------------------"); - } + print("Optimization Finished!"); + } - public override void PrepareData() - { - var loader = new MnistModelLoader(); - mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; + public override void Test() + { + // Calculate accuracy for 128 mnist test images + var (x_test, y_test) = (mnist.Test.Data[":128"], mnist.Test.Labels[":128"]); + x_test = x_test.reshape((-1, timesteps, num_input)); + accuracy_test = sess.run(accuracy, new FeedItem(X, x_test), new FeedItem(Y, y_test)); + print("---------------------------------------------------------"); + print($"Testing Accuracy: {accuracy_test.ToString("P")}"); + print("---------------------------------------------------------"); + } - print("Size of:"); - print($"- Training-set:\t\t{len(mnist.Train.Data)}"); - print($"- Validation-set:\t{len(mnist.Validation.Data)}"); - print($"- Test-set:\t\t{len(mnist.Test.Data)}"); - } + public override void PrepareData() + { + var loader = new MnistModelLoader(); + mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; + + print("Size of:"); + print($"- Training-set:\t\t{len(mnist.Train.Data)}"); + print($"- Validation-set:\t{len(mnist.Validation.Data)}"); + print($"- Test-set:\t\t{len(mnist.Test.Data)}"); } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs index a696e89..4a948b1 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs @@ -18,165 +18,164 @@ limitations under the License. using Tensorflow; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Neural Network classifier for Hand Written Digits +/// Sample Neural Network architecture with two layers implemented for classifying MNIST digits. +/// Use Stochastic Gradient Descent (SGD) optimizer. +/// http://www.easy-tf.com/tf-tutorials/neural-networks +/// +public class DigitRecognitionNN : SciSharpExample, IExample { - /// - /// Neural Network classifier for Hand Written Digits - /// Sample Neural Network architecture with two layers implemented for classifying MNIST digits. - /// Use Stochastic Gradient Descent (SGD) optimizer. - /// http://www.easy-tf.com/tf-tutorials/neural-networks - /// - public class DigitRecognitionNN : SciSharpExample, IExample + const int img_h = 28; + const int img_w = 28; + int img_size_flat = img_h * img_w; // 784, the total number of pixels + int n_classes = 10; // Number of classes, one class per digit + // Hyper-parameters + int epochs = 10; + int batch_size = 100; + float learning_rate = 0.001f; + int h1 = 200; // number of nodes in the 1st hidden layer + Datasets mnist; + + Tensor x, y; + Tensor loss, accuracy; + Operation optimizer; + + int display_freq = 100; + float accuracy_test = 0f; + float loss_test = 1f; + Session sess; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Digits Recognition Neural Network", + Enabled = false, + IsImportingGraph = false + }; + + public bool Run() { - const int img_h = 28; - const int img_w = 28; - int img_size_flat = img_h * img_w; // 784, the total number of pixels - int n_classes = 10; // Number of classes, one class per digit - // Hyper-parameters - int epochs = 10; - int batch_size = 100; - float learning_rate = 0.001f; - int h1 = 200; // number of nodes in the 1st hidden layer - Datasets mnist; - - Tensor x, y; - Tensor loss, accuracy; - Operation optimizer; - - int display_freq = 100; - float accuracy_test = 0f; - float loss_test = 1f; - Session sess; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Digits Recognition Neural Network", - Enabled = false, - IsImportingGraph = false - }; + tf.compat.v1.disable_eager_execution(); - public bool Run() - { - tf.compat.v1.disable_eager_execution(); + PrepareData(); + BuildGraph(); - PrepareData(); - BuildGraph(); + sess = tf.Session(); - sess = tf.Session(); + Train(); + Test(); - Train(); - Test(); + return loss_test < 0.09 && accuracy_test > 0.95; + } - return loss_test < 0.09 && accuracy_test > 0.95; - } + public override Graph BuildGraph() + { + var graph = new Graph().as_default(); + + // Placeholders for inputs (x) and outputs(y) + x = tf.placeholder(tf.float32, shape: (-1, img_size_flat), name: "X"); + y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y"); + + // Create a fully-connected layer with h1 nodes as hidden layer + var fc1 = fc_layer(x, h1, "FC1", use_relu: true); + // Create a fully-connected layer with n_classes nodes as output layer + var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false); + // Define the loss function, optimizer, and accuracy + var logits = tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits); + loss = tf.reduce_mean(logits, name: "loss"); + optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss); + var correct_prediction = tf.equal(tf.math.argmax(output_logits, 1), tf.math.argmax(y, 1), name: "correct_pred"); + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy"); + + // Network predictions + var cls_prediction = tf.math.argmax(output_logits, axis: 1, name: "predictions"); + + return graph; + } - public override Graph BuildGraph() - { - var graph = new Graph().as_default(); - - // Placeholders for inputs (x) and outputs(y) - x = tf.placeholder(tf.float32, shape: (-1, img_size_flat), name: "X"); - y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y"); - - // Create a fully-connected layer with h1 nodes as hidden layer - var fc1 = fc_layer(x, h1, "FC1", use_relu: true); - // Create a fully-connected layer with n_classes nodes as output layer - var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false); - // Define the loss function, optimizer, and accuracy - var logits = tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits); - loss = tf.reduce_mean(logits, name: "loss"); - optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss); - var correct_prediction = tf.equal(tf.math.argmax(output_logits, 1), tf.math.argmax(y, 1), name: "correct_pred"); - accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy"); - - // Network predictions - var cls_prediction = tf.math.argmax(output_logits, axis: 1, name: "predictions"); - - return graph; - } + private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true) + { + var in_dim = x.shape[1]; - private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true) - { - var in_dim = x.shape[1]; + var initer = tf.truncated_normal_initializer(stddev: 0.01f); + var W = tf.compat.v1.get_variable("W_" + name, + dtype: tf.float32, + shape: (in_dim, num_units), + initializer: initer); - var initer = tf.truncated_normal_initializer(stddev: 0.01f); - var W = tf.compat.v1.get_variable("W_" + name, - dtype: tf.float32, - shape: (in_dim, num_units), - initializer: initer); + var initial = tf.constant(0f, shape: num_units); + var b = tf.compat.v1.get_variable("b_" + name, + dtype: tf.float32, + initializer: initial); - var initial = tf.constant(0f, shape: num_units); - var b = tf.compat.v1.get_variable("b_" + name, - dtype: tf.float32, - initializer: initial); + var layer = tf.matmul(x, W.AsTensor()) + b.AsTensor(); + if (use_relu) + layer = tf.nn.relu(layer); - var layer = tf.matmul(x, W.AsTensor()) + b.AsTensor(); - if (use_relu) - layer = tf.nn.relu(layer); + return layer; + } - return layer; - } + public override void PrepareData() + { + var loader = new MnistModelLoader(); + mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; + } - public override void PrepareData() - { - var loader = new MnistModelLoader(); - mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; - } + public override void Train() + { + // Number of training iterations in each epoch + var num_tr_iter = (int)mnist.Train.Labels.shape[0] / batch_size; - public override void Train() - { - // Number of training iterations in each epoch - var num_tr_iter = (int)mnist.Train.Labels.shape[0] / batch_size; + var init = tf.global_variables_initializer(); + sess.run(init); - var init = tf.global_variables_initializer(); - sess.run(init); + float loss_val = 100.0f; + float accuracy_val = 0f; - float loss_val = 100.0f; - float accuracy_val = 0f; + var sw = new Stopwatch(); + sw.Start(); - var sw = new Stopwatch(); - sw.Start(); + foreach (var epoch in range(epochs)) + { + print($"Training epoch: {epoch + 1}"); + // Randomly shuffle the training data at the beginning of each epoch + var (x_train, y_train) = mnist.Randomize(mnist.Train.Data, mnist.Train.Labels); - foreach (var epoch in range(epochs)) + foreach (var iteration in range(num_tr_iter)) { - print($"Training epoch: {epoch + 1}"); - // Randomly shuffle the training data at the beginning of each epoch - var (x_train, y_train) = mnist.Randomize(mnist.Train.Data, mnist.Train.Labels); + var start = iteration * batch_size; + var end = (iteration + 1) * batch_size; + var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end); - foreach (var iteration in range(num_tr_iter)) + // Run optimization op (backprop) + sess.run(optimizer, (x, x_batch), (y, y_batch)); + + if (iteration % display_freq == 0) { - var start = iteration * batch_size; - var end = (iteration + 1) * batch_size; - var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end); - - // Run optimization op (backprop) - sess.run(optimizer, (x, x_batch), (y, y_batch)); - - if (iteration % display_freq == 0) - { - // Calculate and display the batch loss and accuracy - (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, x_batch), (y, y_batch)); - print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")} {sw.ElapsedMilliseconds}ms"); - sw.Restart(); - } + // Calculate and display the batch loss and accuracy + (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, x_batch), (y, y_batch)); + print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")} {sw.ElapsedMilliseconds}ms"); + sw.Restart(); } - - // Run validation after every epoch - (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, mnist.Validation.Data), (y, mnist.Validation.Labels)); - print("---------------------------------------------------------"); - print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}"); - print("---------------------------------------------------------"); } - } - - public override void Test() - { - (loss_test, accuracy_test) = sess.run((loss, accuracy), (x, mnist.Test.Data), (y, mnist.Test.Labels)); + // Run validation after every epoch + (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, mnist.Validation.Data), (y, mnist.Validation.Labels)); print("---------------------------------------------------------"); - print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}"); + print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}"); print("---------------------------------------------------------"); } + + } + + public override void Test() + { + (loss_test, accuracy_test) = sess.run((loss, accuracy), (x, mnist.Test.Data), (y, mnist.Test.Labels)); + print("---------------------------------------------------------"); + print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}"); + print("---------------------------------------------------------"); } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs index 56501ef..35e9314 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs @@ -19,153 +19,152 @@ limitations under the License. using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples -{ - /// - /// Recurrent Neural Network for handwritten digits MNIST. - /// https://medium.com/machine-learning-algorithms/mnist-using-recurrent-neural-network-2d070a5915a2 - /// - public class DigitRecognitionRNN : SciSharpExample, IExample - { - // Hyper-parameters - int n_neurons = 128; - float learning_rate = 0.001f; - int batch_size = 128; - int n_epochs = 5; +namespace TensorFlowNET.Examples; - int n_steps = 28; - int n_inputs = 28; - int n_outputs = 10; +/// +/// Recurrent Neural Network for handwritten digits MNIST. +/// https://medium.com/machine-learning-algorithms/mnist-using-recurrent-neural-network-2d070a5915a2 +/// +public class DigitRecognitionRNN : SciSharpExample, IExample +{ + // Hyper-parameters + int n_neurons = 128; + float learning_rate = 0.001f; + int batch_size = 128; + int n_epochs = 5; - Datasets mnist; + int n_steps = 28; + int n_inputs = 28; + int n_outputs = 10; - Tensor X, y; - Tensor loss, accuracy, prediction; - Operation optimizer; + Datasets mnist; - int display_freq = 100; - float accuracy_test = 0f; - float loss_test = 1f; + Tensor X, y; + Tensor loss, accuracy, prediction; + Operation optimizer; - NDArray x_train, y_train; - NDArray x_valid, y_valid; - NDArray x_test, y_test; + int display_freq = 100; + float accuracy_test = 0f; + float loss_test = 1f; - Session sess; + NDArray x_train, y_train; + NDArray x_valid, y_valid; + NDArray x_test, y_test; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "MNIST RNN (Graph)", - Enabled = false, - IsImportingGraph = false - }; + Session sess; - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.compat.v1.disable_eager_execution(); + Name = "MNIST RNN (Graph)", + Enabled = false, + IsImportingGraph = false + }; - PrepareData(); - BuildGraph(); + public bool Run() + { + tf.compat.v1.disable_eager_execution(); - sess = tf.Session(); + PrepareData(); + BuildGraph(); - Train(); - Test(); + sess = tf.Session(); - return accuracy_test > 0.95; - } + Train(); + Test(); - public override Graph BuildGraph() - { - var graph = new Graph().as_default(); - - X = tf.placeholder(tf.float32, new[] { -1, n_steps, n_inputs }); - y = tf.placeholder(tf.int32, new[] { -1 }); - var cell = tf.nn.rnn_cell.BasicRNNCell(num_units: n_neurons); - var (output, state) = tf.nn.dynamic_rnn(cell, X, dtype: tf.float32); - var logits = tf.keras.layers.Dense(n_outputs).Apply(state); - var cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: y, logits: logits); - loss = tf.reduce_mean(cross_entropy); - var adam = tf.train.AdamOptimizer(learning_rate: learning_rate); - optimizer = adam.minimize(loss); - prediction = tf.nn.in_top_k(logits, y, 1); - accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32)); - - return graph; - } + return accuracy_test > 0.95; + } - public override void Train() - { - float loss_val = 100.0f; - float accuracy_val = 0f; + public override Graph BuildGraph() + { + var graph = new Graph().as_default(); + + X = tf.placeholder(tf.float32, new[] { -1, n_steps, n_inputs }); + y = tf.placeholder(tf.int32, new[] { -1 }); + var cell = tf.nn.rnn_cell.BasicRNNCell(num_units: n_neurons); + var (output, state) = tf.nn.dynamic_rnn(cell, X, dtype: tf.float32); + var logits = tf.keras.layers.Dense(n_outputs).Apply(state); + var cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: y, logits: logits); + loss = tf.reduce_mean(cross_entropy); + var adam = tf.train.AdamOptimizer(learning_rate: learning_rate); + optimizer = adam.minimize(loss); + prediction = tf.nn.in_top_k(logits, y, 1); + accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32)); + + return graph; + } - // Number of training iterations in each epoch - var n_batches = (int)y_train.shape[0] / batch_size; + public override void Train() + { + float loss_val = 100.0f; + float accuracy_val = 0f; - var init = tf.global_variables_initializer(); - sess.run(init); + // Number of training iterations in each epoch + var n_batches = (int)y_train.shape[0] / batch_size; - foreach (var epoch in range(n_epochs)) - { - print($"Training epoch: {epoch + 1}"); - // Randomly shuffle the training data at the beginning of each epoch - (x_train, y_train) = mnist.Randomize(x_train, y_train); + var init = tf.global_variables_initializer(); + sess.run(init); - foreach (var iteration in range(n_batches)) + foreach (var epoch in range(n_epochs)) + { + print($"Training epoch: {epoch + 1}"); + // Randomly shuffle the training data at the beginning of each epoch + (x_train, y_train) = mnist.Randomize(x_train, y_train); + + foreach (var iteration in range(n_batches)) + { + var start = iteration * batch_size; + var end = (iteration + 1) * batch_size; + var (X_train, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end); + X_train = X_train.reshape((-1, n_steps, n_inputs)); + y_batch = np.argmax(y_batch, axis: 1); + // Run optimization op (backprop) + sess.run(optimizer, new FeedItem(X, X_train), new FeedItem(y, y_batch)); + + if (iteration % display_freq == 0) { - var start = iteration * batch_size; - var end = (iteration + 1) * batch_size; - var (X_train, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end); - X_train = X_train.reshape((-1, n_steps, n_inputs)); - y_batch = np.argmax(y_batch, axis: 1); - // Run optimization op (backprop) - sess.run(optimizer, new FeedItem(X, X_train), new FeedItem(y, y_batch)); - - if (iteration % display_freq == 0) - { - // Calculate and display the batch loss and accuracy - (loss_val, accuracy_val) = sess.run((loss, accuracy), (X, X_train), (y, y_batch)); - print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}"); - } + // Calculate and display the batch loss and accuracy + (loss_val, accuracy_val) = sess.run((loss, accuracy), (X, X_train), (y, y_batch)); + print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}"); } - - // Run validation after every epoch - (loss_val, accuracy_val) = sess.run((loss, accuracy), (X, x_valid), (y, y_valid)); - - print("---------------------------------------------------------"); - print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}"); - print("---------------------------------------------------------"); } - } - public override void Test() - { - var result = sess.run(new[] { loss, accuracy }, new FeedItem(X, x_test), new FeedItem(y, y_test)); - loss_test = result[0]; - accuracy_test = result[1]; + // Run validation after every epoch + (loss_val, accuracy_val) = sess.run((loss, accuracy), (X, x_valid), (y, y_valid)); + print("---------------------------------------------------------"); - print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}"); + print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}"); print("---------------------------------------------------------"); } + } - public override void PrepareData() - { - var loader = new MnistModelLoader(); - mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; - (x_train, y_train) = (mnist.Train.Data, mnist.Train.Labels); - (x_valid, y_valid) = (mnist.Validation.Data, mnist.Validation.Labels); - (x_test, y_test) = (mnist.Test.Data, mnist.Test.Labels); - - y_valid = np.argmax(y_valid, axis: 1); - x_valid = np.reshape(x_valid, (- 1, n_steps, n_inputs)); - y_test = np.argmax(y_test, axis: 1); - x_test = np.reshape(x_test, (-1, n_steps, n_inputs)); - - print("Size of:"); - print($"- Training-set:\t\t{len(mnist.Train.Data)}"); - print($"- Validation-set:\t{len(mnist.Validation.Data)}"); - print($"- Test-set:\t\t{len(mnist.Test.Data)}"); - } + public override void Test() + { + var result = sess.run(new[] { loss, accuracy }, new FeedItem(X, x_test), new FeedItem(y, y_test)); + loss_test = result[0]; + accuracy_test = result[1]; + print("---------------------------------------------------------"); + print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}"); + print("---------------------------------------------------------"); + } + + public override void PrepareData() + { + var loader = new MnistModelLoader(); + mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result; + (x_train, y_train) = (mnist.Train.Data, mnist.Train.Labels); + (x_valid, y_valid) = (mnist.Validation.Data, mnist.Validation.Labels); + (x_test, y_test) = (mnist.Test.Data, mnist.Test.Labels); + + y_valid = np.argmax(y_valid, axis: 1); + x_valid = np.reshape(x_valid, (- 1, n_steps, n_inputs)); + y_test = np.argmax(y_test, axis: 1); + x_test = np.reshape(x_test, (-1, n_steps, n_inputs)); + + print("Size of:"); + print($"- Training-set:\t\t{len(mnist.Train.Data)}"); + print($"- Validation-set:\t{len(mnist.Validation.Data)}"); + print($"- Test-set:\t\t{len(mnist.Test.Data)}"); } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs index ba7ced6..1a9e3f8 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs @@ -24,182 +24,181 @@ limitations under the License. using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples -{ - /// - /// Build a recurrent neural network (LSTM) with TensorFlow 2.0. - /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb - /// - public class DigitRecognitionRnnKeras : SciSharpExample, IExample - { - float accuracy = 0f; - int display_step = 100; - int batch_size = 32; - int training_steps = 1000; - LSTMModel lstm_net; - IDatasetV2 train_data; - NDArray x_test, y_test, x_train, y_train; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "MNIST RNN (Keras)", - Enabled = false, - IsImportingGraph = false - }; +namespace TensorFlowNET.Examples; - public bool Run() +/// +/// Build a recurrent neural network (LSTM) with TensorFlow 2.0. +/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb +/// +public class DigitRecognitionRnnKeras : SciSharpExample, IExample +{ + float accuracy = 0f; + int display_step = 100; + int batch_size = 32; + int training_steps = 1000; + LSTMModel lstm_net; + IDatasetV2 train_data; + NDArray x_test, y_test, x_train, y_train; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.enable_eager_execution(); + Name = "MNIST RNN (Keras)", + Enabled = false, + IsImportingGraph = false + }; - PrepareData(); - BuildModel(); - Train(); - Test(); + public bool Run() + { + tf.enable_eager_execution(); - return accuracy > 0.95; - } + PrepareData(); + BuildModel(); + Train(); + Test(); - /// - /// Build LSTM model. - /// - public override void BuildModel() + return accuracy > 0.95; + } + + /// + /// Build LSTM model. + /// + public override void BuildModel() + { + lstm_net = new LSTMModel(new LSTMModelArgs { - lstm_net = new LSTMModel(new LSTMModelArgs - { - NumUnits = 32, - NumClasses = 10, - LearningRate = 0.001f - }); - } + NumUnits = 32, + NumClasses = 10, + LearningRate = 0.001f + }); + } - public override void Train() + public override void Train() + { + // Run training for the given number of steps. + foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1)) { - // Run training for the given number of steps. - foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1)) + // Run the optimization to update W and b values. + lstm_net.Optimize(batch_x, batch_y); + + if (step % display_step == 0) { - // Run the optimization to update W and b values. - lstm_net.Optimize(batch_x, batch_y); - - if (step % display_step == 0) - { - var pred = lstm_net.Predict(batch_x); - var loss = lstm_net.CrossEntropyLoss(pred, batch_y); - var acc = lstm_net.Accuracy(pred, batch_y); - print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}"); - } + var pred = lstm_net.Predict(batch_x); + var loss = lstm_net.CrossEntropyLoss(pred, batch_y); + var acc = lstm_net.Accuracy(pred, batch_y); + print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}"); } } + } - public override void Test() - { - - } + public override void Test() + { - public override void PrepareData() - { - ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data(); - // Convert to float32. - // (x_train, x_test) = (np.array(x_train, np.float32), np.array(x_test, np.float32)); - // Normalize images value from [0, 255] to [0, 1]. - (x_train, x_test) = (x_train / 255.0f, x_test / 255.0f); - - train_data = tf.data.Dataset.from_tensor_slices(x_train, y_train); - train_data = train_data.repeat() - .shuffle(5000) - .batch(batch_size) - .prefetch(1) - .take(training_steps); - } } - internal class LSTMModel : Model + public override void PrepareData() { - OptimizerV2 optimizer; - ILayer lstm; - ILayer output; + ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data(); + // Convert to float32. + // (x_train, x_test) = (np.array(x_train, np.float32), np.array(x_test, np.float32)); + // Normalize images value from [0, 255] to [0, 1]. + (x_train, x_test) = (x_train / 255.0f, x_test / 255.0f); + + train_data = tf.data.Dataset.from_tensor_slices(x_train, y_train); + train_data = train_data.repeat() + .shuffle(5000) + .batch(batch_size) + .prefetch(1) + .take(training_steps); + } +} - public LSTMModel(LSTMModelArgs args) - : base(args) - { - optimizer = keras.optimizers.Adam(args.LearningRate); +internal class LSTMModel : Model +{ + OptimizerV2 optimizer; + ILayer lstm; + ILayer output; - var layers = keras.layers; - lstm = layers.LSTM(args.NumUnits); - output = layers.Dense(args.NumClasses); - } + public LSTMModel(LSTMModelArgs args) + : base(args) + { + optimizer = keras.optimizers.Adam(args.LearningRate); - protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null) - { - // LSTM layer. - inputs = lstm.Apply(inputs); - // Output layer (num_classes). - inputs = output.Apply(inputs); - if (!training.Value) - inputs = tf.nn.softmax(inputs); - return inputs; - } + var layers = keras.layers; + lstm = layers.LSTM(args.NumUnits); + output = layers.Dense(args.NumClasses); + } - /// - /// Optimization process. - /// - /// - /// - public void Optimize(Tensor x, Tensor y) - { - using var g = tf.GradientTape(); - // Forward pass. - var pred = Apply(x, training: true); - // Compute loss. - var loss = CrossEntropyLoss(pred, y); + protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null) + { + // LSTM layer. + inputs = lstm.Apply(inputs); + // Output layer (num_classes). + inputs = output.Apply(inputs); + if (!training.Value) + inputs = tf.nn.softmax(inputs); + return inputs; + } - // Compute gradients. - var gradients = g.gradient(loss, TrainableVariables); + /// + /// Optimization process. + /// + /// + /// + public void Optimize(Tensor x, Tensor y) + { + using var g = tf.GradientTape(); + // Forward pass. + var pred = Apply(x, training: true); + // Compute loss. + var loss = CrossEntropyLoss(pred, y); - // Update W and b following gradients. - optimizer.apply_gradients(zip(gradients, TrainableVariables.Select(x => x as ResourceVariable))); - } + // Compute gradients. + var gradients = g.gradient(loss, TrainableVariables); - /// - /// Cross-Entropy Loss. - /// Note that this will apply 'softmax' to the logits. - /// - /// - /// - /// - public Tensor CrossEntropyLoss(Tensor x, Tensor y) - { - // Convert labels to int 64 for tf cross-entropy function. - y = tf.cast(y, tf.int64); - // Apply softmax to logits and compute cross-entropy. - var loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: y, logits: x); - // Average loss across the batch. - return tf.reduce_mean(loss); - } + // Update W and b following gradients. + optimizer.apply_gradients(zip(gradients, TrainableVariables.Select(x => x as ResourceVariable))); + } - /// - /// Accuracy metric. - /// - /// - /// - /// - public Tensor Accuracy(Tensor yPred, Tensor yTrue) - { - // Predicted class is the index of highest score in prediction vector (i.e. argmax). - var correct_prediction = tf.equal(tf.math.argmax(yPred, 1), tf.cast(yTrue, tf.int64)); - return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); - } + /// + /// Cross-Entropy Loss. + /// Note that this will apply 'softmax' to the logits. + /// + /// + /// + /// + public Tensor CrossEntropyLoss(Tensor x, Tensor y) + { + // Convert labels to int 64 for tf cross-entropy function. + y = tf.cast(y, tf.int64); + // Apply softmax to logits and compute cross-entropy. + var loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: y, logits: x); + // Average loss across the batch. + return tf.reduce_mean(loss); + } - public Tensor Predict(Tensor x) - { - return Apply(x, training: true); - } + /// + /// Accuracy metric. + /// + /// + /// + /// + public Tensor Accuracy(Tensor yPred, Tensor yTrue) + { + // Predicted class is the index of highest score in prediction vector (i.e. argmax). + var correct_prediction = tf.equal(tf.math.argmax(yPred, 1), tf.cast(yTrue, tf.int64)); + return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); } - internal class LSTMModelArgs : ModelArgs + public Tensor Predict(Tensor x) { - public int NumUnits { get; set; } - public int NumClasses { get; set; } - public float LearningRate { get; set; } + return Apply(x, training: true); } } + +internal class LSTMModelArgs : ModelArgs +{ + public int NumUnits { get; set; } + public int NumClasses { get; set; } + public float LearningRate { get; set; } +} diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs index 11ebf77..40b04df 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs @@ -3,59 +3,58 @@ using Tensorflow.Keras.Utils; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// This example removes the background from an input image. +/// +/// https://github.com/susheelsk/image-background-removal +/// +public class ImageBackgroundRemoval : SciSharpExample, IExample { - /// - /// This example removes the background from an input image. - /// - /// https://github.com/susheelsk/image-background-removal - /// - public class ImageBackgroundRemoval : SciSharpExample, IExample - { - string dataDir = "deeplabv3"; - string modelDir = "deeplabv3_mnv2_pascal_train_aug"; - string modelName = "frozen_inference_graph.pb"; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Image Background Removal", - Enabled = false, - IsImportingGraph = true - }; - - public bool Run() - { - PrepareData(); + string dataDir = "deeplabv3"; + string modelDir = "deeplabv3_mnv2_pascal_train_aug"; + string modelName = "frozen_inference_graph.pb"; - // import GraphDef from pb file - var graph = new Graph().as_default(); - graph.Import(Path.Join(dataDir, modelDir, modelName)); + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Image Background Removal", + Enabled = false, + IsImportingGraph = true + }; - Tensor output = graph.OperationByName("SemanticPredictions"); + public bool Run() + { + PrepareData(); - using (var sess = tf.Session(graph)) - { - // Runs inference on a single image. - sess.run(output, new FeedItem(output, "[np.asarray(resized_image)]")); - } + // import GraphDef from pb file + var graph = new Graph().as_default(); + graph.Import(Path.Join(dataDir, modelDir, modelName)); - return false; - } + Tensor output = graph.OperationByName("SemanticPredictions"); - public override void PrepareData() + using (var sess = tf.Session(graph)) { - // get mobile_net_model file - string fileName = "deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz"; - string url = $"http://download.tensorflow.org/models/{fileName}"; - Web.Download(url, dataDir, fileName); - Compress.ExtractTGZ(Path.Join(dataDir, fileName), dataDir); - - // xception_model, better accuracy - /*fileName = "deeplabv3_pascal_train_aug_2018_01_04.tar.gz"; - url = $"http://download.tensorflow.org/models/{fileName}"; - Web.Download(url, modelDir, fileName); - Compress.ExtractTGZ(Path.Join(modelDir, fileName), modelDir);*/ + // Runs inference on a single image. + sess.run(output, new FeedItem(output, "[np.asarray(resized_image)]")); } + + return false; + } + + public override void PrepareData() + { + // get mobile_net_model file + string fileName = "deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz"; + string url = $"http://download.tensorflow.org/models/{fileName}"; + Web.Download(url, dataDir, fileName); + Compress.ExtractTGZ(Path.Join(dataDir, fileName), dataDir); + + // xception_model, better accuracy + /*fileName = "deeplabv3_pascal_train_aug_2018_01_04.tar.gz"; + url = $"http://download.tensorflow.org/models/{fileName}"; + Web.Download(url, modelDir, fileName); + Compress.ExtractTGZ(Path.Join(modelDir, fileName), modelDir);*/ } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs index e111bc5..c73d160 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs @@ -7,95 +7,94 @@ using System.IO; using Tensorflow.Keras.Engine; -namespace TensorFlowNET.Examples -{ - /// - /// This tutorial shows how to classify images of flowers. - /// https://www.tensorflow.org/tutorials/images/classification - /// - public class ImageClassificationKeras : SciSharpExample, IExample - { - int batch_size = 32; - int epochs = 3; - Shape img_dim = (64, 64); - IDatasetV2 train_ds, val_ds; - Model model; +namespace TensorFlowNET.Examples; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Image Classification (Keras)", - Enabled = true - }; +/// +/// This tutorial shows how to classify images of flowers. +/// https://www.tensorflow.org/tutorials/images/classification +/// +public class ImageClassificationKeras : SciSharpExample, IExample +{ + int batch_size = 32; + int epochs = 3; + Shape img_dim = (64, 64); + IDatasetV2 train_ds, val_ds; + Model model; - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.enable_eager_execution(); + Name = "Image Classification (Keras)", + Enabled = true + }; + + public bool Run() + { + tf.enable_eager_execution(); - PrepareData(); - BuildModel(); - Train(); + PrepareData(); + BuildModel(); + Train(); - return true; - } + return true; + } - public override void BuildModel() + public override void BuildModel() + { + int num_classes = 5; + // var normalization_layer = tf.keras.layers.Rescaling(1.0f / 255); + var layers = keras.layers; + model = keras.Sequential(new List { - int num_classes = 5; - // var normalization_layer = tf.keras.layers.Rescaling(1.0f / 255); - var layers = keras.layers; - model = keras.Sequential(new List - { - layers.Rescaling(1.0f / 255, input_shape: (img_dim.dims[0], img_dim.dims[1], 3)), - layers.Conv2D(16, 3, padding: "same", activation: keras.activations.Relu), - layers.MaxPooling2D(), - /*layers.Conv2D(32, 3, padding: "same", activation: "relu"), - layers.MaxPooling2D(), - layers.Conv2D(64, 3, padding: "same", activation: "relu"), - layers.MaxPooling2D(),*/ - layers.Flatten(), - layers.Dense(128, activation: keras.activations.Relu), - layers.Dense(num_classes) - }); - - model.compile(optimizer: keras.optimizers.Adam(), - loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true), - metrics: new[] { "accuracy" }); + layers.Rescaling(1.0f / 255, input_shape: (img_dim.dims[0], img_dim.dims[1], 3)), + layers.Conv2D(16, 3, padding: "same", activation: keras.activations.Relu), + layers.MaxPooling2D(), + /*layers.Conv2D(32, 3, padding: "same", activation: "relu"), + layers.MaxPooling2D(), + layers.Conv2D(64, 3, padding: "same", activation: "relu"), + layers.MaxPooling2D(),*/ + layers.Flatten(), + layers.Dense(128, activation: keras.activations.Relu), + layers.Dense(num_classes) + }); - model.summary(); - } + model.compile(optimizer: keras.optimizers.Adam(), + loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true), + metrics: new[] { "accuracy" }); - public override void Train() - { - model.fit(train_ds, validation_data: val_ds, epochs: epochs); - } + model.summary(); + } - public override void PrepareData() - { - string fileName = "flower_photos.tgz"; - string url = $"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"; - string data_dir = Path.Combine(Path.GetTempPath(), "flower_photos"); - Web.Download(url, data_dir, fileName); - Compress.ExtractTGZ(Path.Join(data_dir, fileName), data_dir); - data_dir = Path.Combine(data_dir, "flower_photos"); + public override void Train() + { + model.fit(train_ds, validation_data: val_ds, epochs: epochs); + } - // convert to tensor - train_ds = keras.preprocessing.image_dataset_from_directory(data_dir, - validation_split: 0.2f, - subset: "training", - seed: 123, - image_size: img_dim, - batch_size: batch_size); + public override void PrepareData() + { + string fileName = "flower_photos.tgz"; + string url = $"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"; + string data_dir = Path.Combine(Path.GetTempPath(), "flower_photos"); + Web.Download(url, data_dir, fileName); + Compress.ExtractTGZ(Path.Join(data_dir, fileName), data_dir); + data_dir = Path.Combine(data_dir, "flower_photos"); - val_ds = keras.preprocessing.image_dataset_from_directory(data_dir, + // convert to tensor + train_ds = keras.preprocessing.image_dataset_from_directory(data_dir, validation_split: 0.2f, - subset: "validation", + subset: "training", seed: 123, image_size: img_dim, batch_size: batch_size); - train_ds = train_ds.shuffle(1000).prefetch(buffer_size: -1); - val_ds = val_ds.prefetch(buffer_size: -1); - } + val_ds = keras.preprocessing.image_dataset_from_directory(data_dir, + validation_split: 0.2f, + subset: "validation", + seed: 123, + image_size: img_dim, + batch_size: batch_size); + + train_ds = train_ds.shuffle(1000).prefetch(buffer_size: -1); + val_ds = val_ds.prefetch(buffer_size: -1); } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs index 221ddd6..c0da6a1 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs @@ -8,113 +8,112 @@ using static Tensorflow.Binding; using Console = Colorful.Console; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Inception v3 is a widely-used image recognition model +/// that has been shown to attain greater than 78.1% accuracy on the ImageNet dataset. +/// The model is the culmination of many ideas developed by multiple researchers over the years. +/// +public class ImageRecognitionInception : SciSharpExample, IExample { - /// - /// Inception v3 is a widely-used image recognition model - /// that has been shown to attain greater than 78.1% accuracy on the ImageNet dataset. - /// The model is the culmination of many ideas developed by multiple researchers over the years. - /// - public class ImageRecognitionInception : SciSharpExample, IExample - { - string dir = "ImageRecognitionInception"; - string pbFile = "tensorflow_inception_graph.pb"; - string labelFile = "imagenet_comp_graph_label_strings.txt"; - List file_ndarrays = new List(); - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Image Recognition Inception", - Enabled = true, - IsImportingGraph = false - }; + string dir = "ImageRecognitionInception"; + string pbFile = "tensorflow_inception_graph.pb"; + string labelFile = "imagenet_comp_graph_label_strings.txt"; + List file_ndarrays = new List(); - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.compat.v1.disable_eager_execution(); + Name = "Image Recognition Inception", + Enabled = true, + IsImportingGraph = false + }; + + public bool Run() + { + tf.compat.v1.disable_eager_execution(); - PrepareData(); + PrepareData(); - var graph = tf.Graph().as_default(); - //import GraphDef from pb file - graph.Import(Path.Join(dir, pbFile)); + var graph = tf.Graph().as_default(); + //import GraphDef from pb file + graph.Import(Path.Join(dir, pbFile)); - var input_name = "input"; - var output_name = "output"; + var input_name = "input"; + var output_name = "output"; - var input_operation = graph.OperationByName(input_name); - var output_operation = graph.OperationByName(output_name); + var input_operation = graph.OperationByName(input_name); + var output_operation = graph.OperationByName(output_name); - var labels = File.ReadAllLines(Path.Join(dir, labelFile)); - var result_labels = new List(); - var sw = new Stopwatch(); + var labels = File.ReadAllLines(Path.Join(dir, labelFile)); + var result_labels = new List(); + var sw = new Stopwatch(); - using (var sess = tf.Session(graph)) + using (var sess = tf.Session(graph)) + { + foreach (var nd in file_ndarrays) { - foreach (var nd in file_ndarrays) - { - sw.Restart(); + sw.Restart(); - var results = sess.run(output_operation.outputs[0], (input_operation.outputs[0], nd)); - results = np.squeeze(results); - int idx = np.argmax(results); + var results = sess.run(output_operation.outputs[0], (input_operation.outputs[0], nd)); + results = np.squeeze(results); + int idx = np.argmax(results); - Console.WriteLine($"{labels[idx]} {results[idx]} in {sw.ElapsedMilliseconds}ms", Color.Tan); - result_labels.Add(labels[idx]); - } + Console.WriteLine($"{labels[idx]} {results[idx]} in {sw.ElapsedMilliseconds}ms", Color.Tan); + result_labels.Add(labels[idx]); } - - return result_labels.Contains("military uniform"); } - private NDArray ReadTensorFromImageFile(string file_name, - int input_height = 224, - int input_width = 224, - int input_mean = 117, - int input_std = 1) - { - var graph = tf.Graph().as_default(); - - var file_reader = tf.io.read_file(file_name, "file_reader"); - var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg"); - var cast = tf.cast(decodeJpeg, tf.float32); - var dims_expander = tf.expand_dims(cast, 0); - var resize = tf.constant(new int[] { input_height, input_width }); - var bilinear = tf.image.resize_bilinear(dims_expander, resize); - var sub = tf.subtract(bilinear, new float[] { input_mean }); - var normalized = tf.divide(sub, new float[] { input_std }); - - using (var sess = tf.Session(graph)) - return sess.run(normalized); - } + return result_labels.Contains("military uniform"); + } - public override void PrepareData() - { - Directory.CreateDirectory(dir); + private NDArray ReadTensorFromImageFile(string file_name, + int input_height = 224, + int input_width = 224, + int input_mean = 117, + int input_std = 1) + { + var graph = tf.Graph().as_default(); + + var file_reader = tf.io.read_file(file_name, "file_reader"); + var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg"); + var cast = tf.cast(decodeJpeg, tf.float32); + var dims_expander = tf.expand_dims(cast, 0); + var resize = tf.constant(new int[] { input_height, input_width }); + var bilinear = tf.image.resize_bilinear(dims_expander, resize); + var sub = tf.subtract(bilinear, new float[] { input_mean }); + var normalized = tf.divide(sub, new float[] { input_std }); + + using (var sess = tf.Session(graph)) + return sess.run(normalized); + } - // get model file - string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip"; + public override void PrepareData() + { + Directory.CreateDirectory(dir); - Web.Download(url, dir, "inception5h.zip"); + // get model file + string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip"; - Compress.UnZip(Path.Join(dir, "inception5h.zip"), dir); + Web.Download(url, dir, "inception5h.zip"); - // download sample picture - Directory.CreateDirectory(Path.Join(dir, "img")); - url = $"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/grace_hopper.jpg"; - Web.Download(url, Path.Join(dir, "img"), "grace_hopper.jpg"); + Compress.UnZip(Path.Join(dir, "inception5h.zip"), dir); - url = $"https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/shasta-daisy.jpg"; - Web.Download(url, Path.Join(dir, "img"), "shasta-daisy.jpg"); + // download sample picture + Directory.CreateDirectory(Path.Join(dir, "img")); + url = $"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/grace_hopper.jpg"; + Web.Download(url, Path.Join(dir, "img"), "grace_hopper.jpg"); - // load image file - var files = Directory.GetFiles(Path.Join(dir, "img")); - for (int i = 0; i < files.Length; i++) - { - var nd = ReadTensorFromImageFile(files[i]); - file_ndarrays.Add(nd); - } + url = $"https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/shasta-daisy.jpg"; + Web.Download(url, Path.Join(dir, "img"), "shasta-daisy.jpg"); + + // load image file + var files = Directory.GetFiles(Path.Join(dir, "img")); + for (int i = 0; i < files.Length; i++) + { + var nd = ReadTensorFromImageFile(files[i]); + file_ndarrays.Add(nd); } } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs b/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs index 7eef4e7..3fea155 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs @@ -6,107 +6,106 @@ using Tensorflow.NumPy; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Inception Architecture for Computer Vision +/// Port from tensorflow\examples\label_image\label_image.py +/// +public class InceptionArchGoogLeNet : SciSharpExample, IExample { - /// - /// Inception Architecture for Computer Vision - /// Port from tensorflow\examples\label_image\label_image.py - /// - public class InceptionArchGoogLeNet : SciSharpExample, IExample - { - string dir = "label_image_data"; - string pbFile = "inception_v3_2016_08_28_frozen.pb"; - string labelFile = "imagenet_slim_labels.txt"; - string picFile = "grace_hopper.jpg"; - int input_height = 299; - int input_width = 299; - int input_mean = 0; - int input_std = 255; - string input_name = "import/input"; - string output_name = "import/InceptionV3/Predictions/Reshape_1"; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Inception Arch GoogLeNet", - Enabled = false, - IsImportingGraph = false - }; - - public bool Run() + string dir = "label_image_data"; + string pbFile = "inception_v3_2016_08_28_frozen.pb"; + string labelFile = "imagenet_slim_labels.txt"; + string picFile = "grace_hopper.jpg"; + int input_height = 299; + int input_width = 299; + int input_mean = 0; + int input_std = 255; + string input_name = "import/input"; + string output_name = "import/InceptionV3/Predictions/Reshape_1"; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - PrepareData(); + Name = "Inception Arch GoogLeNet", + Enabled = false, + IsImportingGraph = false + }; - var labels = File.ReadAllLines(Path.Join(dir, labelFile)); + public bool Run() + { + PrepareData(); - var nd = ReadTensorFromImageFile(Path.Join(dir, picFile), - input_height: input_height, - input_width: input_width, - input_mean: input_mean, - input_std: input_std); + var labels = File.ReadAllLines(Path.Join(dir, labelFile)); - var graph = new Graph(); - graph.Import(Path.Join(dir, pbFile)); - var input_operation = graph.get_operation_by_name(input_name); - var output_operation = graph.get_operation_by_name(output_name); + var nd = ReadTensorFromImageFile(Path.Join(dir, picFile), + input_height: input_height, + input_width: input_width, + input_mean: input_mean, + input_std: input_std); - NDArray results; - using (var sess = tf.Session(graph)) - { - results = sess.run(output_operation.outputs[0], - new FeedItem(input_operation.outputs[0], nd)); - } + var graph = new Graph(); + graph.Import(Path.Join(dir, pbFile)); + var input_operation = graph.get_operation_by_name(input_name); + var output_operation = graph.get_operation_by_name(output_name); - results = np.squeeze(results); + NDArray results; + using (var sess = tf.Session(graph)) + { + results = sess.run(output_operation.outputs[0], + new FeedItem(input_operation.outputs[0], nd)); + } - var argsort = np.argsort(results); - var top_k = argsort.ToArray() - .Skip((int)results.size - 5) - .Reverse() - .ToArray(); + results = np.squeeze(results); - foreach (float idx in top_k) - Console.WriteLine($"{picFile}: {idx} {labels[(int)idx]}, {results[(int)idx]}"); + var argsort = np.argsort(results); + var top_k = argsort.ToArray() + .Skip((int)results.size - 5) + .Reverse() + .ToArray(); - return true; - } + foreach (float idx in top_k) + Console.WriteLine($"{picFile}: {idx} {labels[(int)idx]}, {results[(int)idx]}"); - private NDArray ReadTensorFromImageFile(string file_name, - int input_height = 299, - int input_width = 299, - int input_mean = 0, - int input_std = 255) - { - var graph = tf.Graph().as_default(); - - var file_reader = tf.io.read_file(file_name, "file_reader"); - var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader"); - var caster = tf.cast(image_reader, tf.float32); - var dims_expander = tf.expand_dims(caster, 0); - var resize = tf.constant(new int[] { input_height, input_width }); - var bilinear = tf.image.resize_bilinear(dims_expander, resize); - var sub = tf.subtract(bilinear, new float[] { input_mean }); - var normalized = tf.divide(sub, new float[] { input_std }); - - using (var sess = tf.Session(graph)) - return sess.run(normalized); - } + return true; + } - public override void PrepareData() - { - Directory.CreateDirectory(dir); + private NDArray ReadTensorFromImageFile(string file_name, + int input_height = 299, + int input_width = 299, + int input_mean = 0, + int input_std = 255) + { + var graph = tf.Graph().as_default(); + + var file_reader = tf.io.read_file(file_name, "file_reader"); + var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader"); + var caster = tf.cast(image_reader, tf.float32); + var dims_expander = tf.expand_dims(caster, 0); + var resize = tf.constant(new int[] { input_height, input_width }); + var bilinear = tf.image.resize_bilinear(dims_expander, resize); + var sub = tf.subtract(bilinear, new float[] { input_mean }); + var normalized = tf.divide(sub, new float[] { input_std }); + + using (var sess = tf.Session(graph)) + return sess.run(normalized); + } - // get model file - string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz"; + public override void PrepareData() + { + Directory.CreateDirectory(dir); - Web.Download(url, dir, $"{pbFile}.tar.gz"); + // get model file + string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz"; - Compress.ExtractTGZ(Path.Join(dir, $"{pbFile}.tar.gz"), dir); + Web.Download(url, dir, $"{pbFile}.tar.gz"); - // download sample picture - string pic = "grace_hopper.jpg"; - url = $"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/{pic}"; - Web.Download(url, dir, pic); - } + Compress.ExtractTGZ(Path.Join(dir, $"{pbFile}.tar.gz"), dir); + + // download sample picture + string pic = "grace_hopper.jpg"; + url = $"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/{pic}"; + Web.Download(url, dir, pic); } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index 7bedbf9..45885ed 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -24,225 +24,224 @@ limitations under the License. using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Build a convolutional neural network with TensorFlow v2. +/// This example is using a low-level approach to better understand all mechanics behind building convolutional neural networks and the training process. +/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb +/// +public class MnistCnnKerasSubclass : SciSharpExample, IExample { - /// - /// Build a convolutional neural network with TensorFlow v2. - /// This example is using a low-level approach to better understand all mechanics behind building convolutional neural networks and the training process. - /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb - /// - public class MnistCnnKerasSubclass : SciSharpExample, IExample - { - // MNIST dataset parameters. - int num_classes = 10; - - // Training parameters. - float learning_rate = 0.001f; - int training_steps = 100; - int batch_size = 128; - int display_step = 10; + // MNIST dataset parameters. + int num_classes = 10; - float accuracy_test = 0.0f; + // Training parameters. + float learning_rate = 0.001f; + int training_steps = 100; + int batch_size = 128; + int display_step = 10; - IDatasetV2 train_data; - NDArray x_test, y_test, x_train, y_train; + float accuracy_test = 0.0f; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "MNIST CNN (Keras Subclass)", - Enabled = true, - IsImportingGraph = false - }; + IDatasetV2 train_data; + NDArray x_test, y_test, x_train, y_train; - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.enable_eager_execution(); + Name = "MNIST CNN (Keras Subclass)", + Enabled = true, + IsImportingGraph = false + }; - PrepareData(); + public bool Run() + { + tf.enable_eager_execution(); - Train(); - - // Test(); + PrepareData(); - return accuracy_test > 0.85; - } + Train(); + + // Test(); + + return accuracy_test > 0.85; + } - public override void Train() + public override void Train() + { + // Build neural network model. + var conv_net = new ConvNet(new ConvNetArgs { - // Build neural network model. - var conv_net = new ConvNet(new ConvNetArgs - { - NumClasses = num_classes - }); + NumClasses = num_classes + }); - // ADAM optimizer. - var optimizer = keras.optimizers.Adam(learning_rate); + // ADAM optimizer. + var optimizer = keras.optimizers.Adam(learning_rate); - // Run training for the given number of steps. - foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1)) - { - // Run the optimization to update W and b values. - run_optimization(conv_net, optimizer, batch_x, batch_y); - - if (step % display_step == 0) - { - var pred = conv_net.Apply(batch_x); - var loss = cross_entropy_loss(pred, batch_y); - var acc = accuracy(pred, batch_y); - print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}"); - } - } + // Run training for the given number of steps. + foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1)) + { + // Run the optimization to update W and b values. + run_optimization(conv_net, optimizer, batch_x, batch_y); - // Test model on validation set. + if (step % display_step == 0) { - x_test = x_test["::100"]; - y_test = y_test["::100"]; - var pred = conv_net.Apply(x_test); - accuracy_test = (float)accuracy(pred, y_test); - print($"Test Accuracy: {accuracy_test}"); + var pred = conv_net.Apply(batch_x); + var loss = cross_entropy_loss(pred, batch_y); + var acc = accuracy(pred, batch_y); + print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}"); } + } - conv_net.save_weights("weights.h5"); + // Test model on validation set. + { + x_test = x_test["::100"]; + y_test = y_test["::100"]; + var pred = conv_net.Apply(x_test); + accuracy_test = (float)accuracy(pred, y_test); + print($"Test Accuracy: {accuracy_test}"); } - public override void Test() + conv_net.save_weights("weights.h5"); + } + + public override void Test() + { + var conv_net = new ConvNet(new ConvNetArgs { - var conv_net = new ConvNet(new ConvNetArgs - { - NumClasses = num_classes - }); + NumClasses = num_classes + }); - // Test model on testing set. - { - x_test = x_test["::100"]; - y_test = y_test["::100"]; + // Test model on testing set. + { + x_test = x_test["::100"]; + y_test = y_test["::100"]; - /*conv_net.build(x_test.shape); - conv_net.load_weights("weights.h5"); + /*conv_net.build(x_test.shape); + conv_net.load_weights("weights.h5"); - var pred = conv_net.Apply(x_test); - accuracy_test = (float)accuracy(pred, y_test); - print($"Test Accuracy: {accuracy_test}");*/ - } + var pred = conv_net.Apply(x_test); + accuracy_test = (float)accuracy(pred, y_test); + print($"Test Accuracy: {accuracy_test}");*/ } + } - void run_optimization(ConvNet conv_net, OptimizerV2 optimizer, Tensor x, Tensor y) - { - using var g = tf.GradientTape(); - var pred = conv_net.Apply(x, training: true); - var loss = cross_entropy_loss(pred, y); + void run_optimization(ConvNet conv_net, OptimizerV2 optimizer, Tensor x, Tensor y) + { + using var g = tf.GradientTape(); + var pred = conv_net.Apply(x, training: true); + var loss = cross_entropy_loss(pred, y); - // Compute gradients. - var gradients = g.gradient(loss, conv_net.TrainableVariables); + // Compute gradients. + var gradients = g.gradient(loss, conv_net.TrainableVariables); - // Update W and b following gradients. - optimizer.apply_gradients(zip(gradients, conv_net.TrainableVariables.Select(x => x as ResourceVariable))); - } + // Update W and b following gradients. + optimizer.apply_gradients(zip(gradients, conv_net.TrainableVariables.Select(x => x as ResourceVariable))); + } - Tensor cross_entropy_loss(Tensor x, Tensor y) - { - // Convert labels to int 64 for tf cross-entropy function. - y = tf.cast(y, tf.int64); - // Apply softmax to logits and compute cross-entropy. - var loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: y, logits: x); - // Average loss across the batch. - return tf.reduce_mean(loss); - } + Tensor cross_entropy_loss(Tensor x, Tensor y) + { + // Convert labels to int 64 for tf cross-entropy function. + y = tf.cast(y, tf.int64); + // Apply softmax to logits and compute cross-entropy. + var loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: y, logits: x); + // Average loss across the batch. + return tf.reduce_mean(loss); + } - Tensor accuracy(Tensor y_pred, Tensor y_true) - { - // # Predicted class is the index of highest score in prediction vector (i.e. argmax). - var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); - return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); - } + Tensor accuracy(Tensor y_pred, Tensor y_true) + { + // # Predicted class is the index of highest score in prediction vector (i.e. argmax). + var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); + return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); + } - public override void PrepareData() - { - ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data(); - // Convert to float32. - // (x_train, x_test) = (np.array(x_train, np.float32), np.array(x_test, np.float32)); - // Normalize images value from [0, 255] to [0, 1]. - (x_train, x_test) = (x_train / 255.0f, x_test / 255.0f); - - train_data = tf.data.Dataset.from_tensor_slices(x_train, y_train); - train_data = train_data.repeat() - .shuffle(5000) - .batch(batch_size) - .prefetch(1) - .take(training_steps); - } + public override void PrepareData() + { + ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data(); + // Convert to float32. + // (x_train, x_test) = (np.array(x_train, np.float32), np.array(x_test, np.float32)); + // Normalize images value from [0, 255] to [0, 1]. + (x_train, x_test) = (x_train / 255.0f, x_test / 255.0f); + + train_data = tf.data.Dataset.from_tensor_slices(x_train, y_train); + train_data = train_data.repeat() + .shuffle(5000) + .batch(batch_size) + .prefetch(1) + .take(training_steps); + } - public class ConvNet : Model + public class ConvNet : Model + { + ILayer conv1; + ILayer maxpool1; + ILayer conv2; + ILayer maxpool2; + ILayer flatten; + ILayer fc1; + ILayer dropout; + ILayer output; + + public ConvNet(ConvNetArgs args) + : base(args) { - ILayer conv1; - ILayer maxpool1; - ILayer conv2; - ILayer maxpool2; - ILayer flatten; - ILayer fc1; - ILayer dropout; - ILayer output; - - public ConvNet(ConvNetArgs args) - : base(args) - { - var layers = keras.layers; - - // Convolution Layer with 32 filters and a kernel size of 5. - conv1 = layers.Conv2D(32, kernel_size: 5, activation: keras.activations.Relu); + var layers = keras.layers; - // Max Pooling (down-sampling) with kernel size of 2 and strides of 2. - maxpool1 = layers.MaxPooling2D(2, strides: 2); + // Convolution Layer with 32 filters and a kernel size of 5. + conv1 = layers.Conv2D(32, kernel_size: 5, activation: keras.activations.Relu); - // Convolution Layer with 64 filters and a kernel size of 3. - conv2 = layers.Conv2D(64, kernel_size: 3, activation: keras.activations.Relu); - // Max Pooling (down-sampling) with kernel size of 2 and strides of 2. - maxpool2 = layers.MaxPooling2D(2, strides: 2); + // Max Pooling (down-sampling) with kernel size of 2 and strides of 2. + maxpool1 = layers.MaxPooling2D(2, strides: 2); - // Flatten the data to a 1-D vector for the fully connected layer. - flatten = layers.Flatten(); + // Convolution Layer with 64 filters and a kernel size of 3. + conv2 = layers.Conv2D(64, kernel_size: 3, activation: keras.activations.Relu); + // Max Pooling (down-sampling) with kernel size of 2 and strides of 2. + maxpool2 = layers.MaxPooling2D(2, strides: 2); - // Fully connected layer. - fc1 = layers.Dense(1024); - // Apply Dropout (if is_training is False, dropout is not applied). - dropout = layers.Dropout(rate: 0.5f); + // Flatten the data to a 1-D vector for the fully connected layer. + flatten = layers.Flatten(); - // Output layer, class prediction. - output = layers.Dense(args.NumClasses); + // Fully connected layer. + fc1 = layers.Dense(1024); + // Apply Dropout (if is_training is False, dropout is not applied). + dropout = layers.Dropout(rate: 0.5f); - StackLayers(conv1, maxpool1, conv2, maxpool2, flatten, fc1, dropout, output); - } + // Output layer, class prediction. + output = layers.Dense(args.NumClasses); - /// - /// Set forward pass. - /// - /// - /// - /// - /// - protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null) - { - inputs = tf.reshape(inputs, (-1, 28, 28, 1)); - inputs = conv1.Apply(inputs); - inputs = maxpool1.Apply(inputs); - inputs = conv2.Apply(inputs); - inputs = maxpool2.Apply(inputs); - inputs = flatten.Apply(inputs); - inputs = fc1.Apply(inputs); - inputs = dropout.Apply(inputs); - inputs = output.Apply(inputs); - - if (!training.Value) - inputs = tf.nn.softmax(inputs); - - return inputs; - } + StackLayers(conv1, maxpool1, conv2, maxpool2, flatten, fc1, dropout, output); } - public class ConvNetArgs : ModelArgs + /// + /// Set forward pass. + /// + /// + /// + /// + /// + protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null) { - public int NumClasses { get; set; } + inputs = tf.reshape(inputs, (-1, 28, 28, 1)); + inputs = conv1.Apply(inputs); + inputs = maxpool1.Apply(inputs); + inputs = conv2.Apply(inputs); + inputs = maxpool2.Apply(inputs); + inputs = flatten.Apply(inputs); + inputs = fc1.Apply(inputs); + inputs = dropout.Apply(inputs); + inputs = output.Apply(inputs); + + if (!training.Value) + inputs = tf.nn.softmax(inputs); + + return inputs; } } + + public class ConvNetArgs : ModelArgs + { + public int NumClasses { get; set; } + } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs index caf0bbc..4e64f73 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs @@ -19,81 +19,80 @@ limitations under the License. using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// The Keras functional API is a way to create models that are more flexible than the tf.keras.Sequential API. +/// The functional API can handle models with non-linear topology, shared layers, and even multiple inputs or outputs. +/// https://keras.io/guides/functional_api/ +/// +public class MnistFnnKerasFunctional : SciSharpExample, IExample { - /// - /// The Keras functional API is a way to create models that are more flexible than the tf.keras.Sequential API. - /// The functional API can handle models with non-linear topology, shared layers, and even multiple inputs or outputs. - /// https://keras.io/guides/functional_api/ - /// - public class MnistFnnKerasFunctional : SciSharpExample, IExample - { - Model model; - NDArray x_train, y_train, x_test, y_test; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "MNIST FNN (Keras Functional)", - Enabled = true - }; + Model model; + NDArray x_train, y_train, x_test, y_test; - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.enable_eager_execution(); + Name = "MNIST FNN (Keras Functional)", + Enabled = true + }; - PrepareData(); - BuildModel(); - Train(); + public bool Run() + { + tf.enable_eager_execution(); - return true; - } + PrepareData(); + BuildModel(); + Train(); - public override void PrepareData() - { - (x_train, y_train, x_test, y_test) = keras.datasets.mnist.load_data(); - x_train = x_train.reshape((60000, 784)) / 255f; - x_test = x_test.reshape((10000, 784)) / 255f; - } + return true; + } - public override void BuildModel() - { - // input layer - var inputs = keras.Input(shape: 784); + public override void PrepareData() + { + (x_train, y_train, x_test, y_test) = keras.datasets.mnist.load_data(); + x_train = x_train.reshape((60000, 784)) / 255f; + x_test = x_test.reshape((10000, 784)) / 255f; + } - // 1st dense layer - var outputs = layers.Dense(64, activation: keras.activations.Relu).Apply(inputs); + public override void BuildModel() + { + // input layer + var inputs = keras.Input(shape: 784); - // 2nd dense layer - outputs = layers.Dense(64, activation: keras.activations.Relu).Apply(outputs); + // 1st dense layer + var outputs = layers.Dense(64, activation: keras.activations.Relu).Apply(inputs); - // output layer - outputs = layers.Dense(10).Apply(outputs); + // 2nd dense layer + outputs = layers.Dense(64, activation: keras.activations.Relu).Apply(outputs); - // build keras model - model = keras.Model(inputs, outputs, name: "mnist_model"); - // show model summary - model.summary(); + // output layer + outputs = layers.Dense(10).Apply(outputs); - // compile keras model into tensorflow's static graph - model.compile(loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true), - optimizer: keras.optimizers.RMSprop(), - metrics: new[] { "accuracy" }); - } + // build keras model + model = keras.Model(inputs, outputs, name: "mnist_model"); + // show model summary + model.summary(); - public override void Train() - { - // train model by feeding data and labels. - model.fit(x_train, y_train, batch_size: 64, epochs: 2, validation_split: 0.2f); + // compile keras model into tensorflow's static graph + model.compile(loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true), + optimizer: keras.optimizers.RMSprop(), + metrics: new[] { "accuracy" }); + } + + public override void Train() + { + // train model by feeding data and labels. + model.fit(x_train, y_train, batch_size: 64, epochs: 2, validation_split: 0.2f); - // evluate the model - model.evaluate(x_test, y_test, verbose: 2); + // evluate the model + model.evaluate(x_test, y_test, verbose: 2); - // save and serialize model - model.save("mnist_model"); + // save and serialize model + model.save("mnist_model"); - // recreate the exact same model purely from the file: - // model = keras.models.load_model("path_to_my_model"); - } + // recreate the exact same model purely from the file: + // model = keras.models.load_model("path_to_my_model"); } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs b/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs index c6ab454..94a7b46 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs @@ -21,84 +21,83 @@ limitations under the License. using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// A toy ResNet model +/// https://keras.io/guides/functional_api/ +/// +public class ToyResNet : SciSharpExample, IExample { - /// - /// A toy ResNet model - /// https://keras.io/guides/functional_api/ - /// - public class ToyResNet : SciSharpExample, IExample - { - Model model; - NDArray x_train, y_train; - NDArray x_test, y_test; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Toy ResNet", - Enabled = true - }; - - public bool Run() + Model model; + NDArray x_train, y_train; + NDArray x_test, y_test; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.enable_eager_execution(); - - BuildModel(); - PrepareData(); - Train(); + Name = "Toy ResNet", + Enabled = true + }; - return true; - } + public bool Run() + { + tf.enable_eager_execution(); + + BuildModel(); + PrepareData(); + Train(); - public override void BuildModel() - { - var inputs = keras.Input(shape: (32, 32, 3), name: "img"); - var x = layers.Conv2D(32, 3, activation: "relu").Apply(inputs); - x = layers.Conv2D(64, 3, activation: "relu").Apply(x); + return true; + } + + public override void BuildModel() + { + var inputs = keras.Input(shape: (32, 32, 3), name: "img"); + var x = layers.Conv2D(32, 3, activation: "relu").Apply(inputs); + x = layers.Conv2D(64, 3, activation: "relu").Apply(x); - x = layers.BatchNormalization().Apply(x); - var block_1_output = layers.MaxPooling2D(3).Apply(x); + x = layers.BatchNormalization().Apply(x); + var block_1_output = layers.MaxPooling2D(3).Apply(x); - x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_1_output); - x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x); - var block_2_output = layers.Add().Apply(new Tensors(x, block_1_output)); + x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_1_output); + x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x); + var block_2_output = layers.Add().Apply(new Tensors(x, block_1_output)); - x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_2_output); - x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x); - var block_3_output = layers.Add().Apply(new Tensors(x, block_2_output)); + x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_2_output); + x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x); + var block_3_output = layers.Add().Apply(new Tensors(x, block_2_output)); - x = layers.Conv2D(64, 3, activation: "relu").Apply(block_3_output); - x = layers.GlobalAveragePooling2D().Apply(x); - x = layers.Dense(256, activation: "relu").Apply(x); - x = layers.Dropout(0.5f).Apply(x); - var outputs = layers.Dense(10).Apply(x); + x = layers.Conv2D(64, 3, activation: "relu").Apply(block_3_output); + x = layers.GlobalAveragePooling2D().Apply(x); + x = layers.Dense(256, activation: "relu").Apply(x); + x = layers.Dropout(0.5f).Apply(x); + var outputs = layers.Dense(10).Apply(x); - model = keras.Model(inputs, outputs, name: "toy_resnet"); - model.summary(); + model = keras.Model(inputs, outputs, name: "toy_resnet"); + model.summary(); - model.compile(optimizer: keras.optimizers.RMSprop(1e-3f), - loss: keras.losses.CategoricalCrossentropy(from_logits: true), - metrics: new[] { "acc" }); - } + model.compile(optimizer: keras.optimizers.RMSprop(1e-3f), + loss: keras.losses.CategoricalCrossentropy(from_logits: true), + metrics: new[] { "acc" }); + } - public override void PrepareData() - { - ((x_train, y_train), (x_test, y_test)) = keras.datasets.cifar10.load_data(); + public override void PrepareData() + { + ((x_train, y_train), (x_test, y_test)) = keras.datasets.cifar10.load_data(); - x_train = x_train / 255.0f; - x_test = x_test / 255.0f; + x_train = x_train / 255.0f; + x_test = x_test / 255.0f; - y_train = np_utils.to_categorical(y_train, 10); - y_test = np_utils.to_categorical(y_test, 10); - } + y_train = np_utils.to_categorical(y_train, 10); + y_test = np_utils.to_categorical(y_test, 10); + } - public override void Train() - { - model.fit(x_train[new Slice(0, 2000)], y_train[new Slice(0, 2000)], - batch_size: 64, - epochs: 3, - validation_split: 0.2f); - } + public override void Train() + { + model.fit(x_train[new Slice(0, 2000)], y_train[new Slice(0, 2000)], + batch_size: 64, + epochs: 3, + validation_split: 0.2f); } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs index a03bbc4..b2fb2a1 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/TransferLearningWithInceptionV3.cs @@ -18,98 +18,96 @@ limitations under the License. using SciSharp.Models.ImageClassification; using System; using System.Collections.Generic; -using System.Diagnostics; using System.IO; using Tensorflow.Keras.Utils; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// In this tutorial, we will reuse the feature extraction capabilities from powerful image classifiers trained on ImageNet +/// and simply train a new classification layer on top. Transfer learning is a technique that shortcuts much of this +/// by taking a piece of a model that has already been trained on a related task and reusing it in a new model. +/// +/// https://www.tensorflow.org/hub/tutorials/image_retraining +/// +public class TransferLearningWithInceptionV3 : SciSharpExample, IExample { - /// - /// In this tutorial, we will reuse the feature extraction capabilities from powerful image classifiers trained on ImageNet - /// and simply train a new classification layer on top. Transfer learning is a technique that shortcuts much of this - /// by taking a piece of a model that has already been trained on a related task and reusing it in a new model. - /// - /// https://www.tensorflow.org/hub/tutorials/image_retraining - /// - public class TransferLearningWithInceptionV3 : SciSharpExample, IExample + float accuracy; + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Transfer Learning With InceptionV3 (Graph)", + Enabled = true + }; + + public bool Run() { - float accuracy; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Transfer Learning With InceptionV3 (Graph)", - Enabled = true - }; + PrepareData(); + Train(); + Test(); + Predict(); + + return accuracy > 0.75f; + } - public bool Run() - { - PrepareData(); - Train(); - Test(); - Predict(); - - return accuracy > 0.75f; - } + public override void PrepareData() + { + // get a set of images to teach the network about the new classes + string fileName = "flower_photos.tgz"; + string dataDir = "image_classification_v1"; + string url = $"http://download.tensorflow.org/example_images/{fileName}"; + Web.Download(url, dataDir, fileName); + Compress.ExtractTGZ(Path.Join(dataDir, fileName), dataDir); + } - public override void PrepareData() + public override void Train() + { + // using wizard to train model + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions { - // get a set of images to teach the network about the new classes - string fileName = "flower_photos.tgz"; - string dataDir = "image_classification_v1"; - string url = $"http://download.tensorflow.org/example_images/{fileName}"; - Web.Download(url, dataDir, fileName); - Compress.ExtractTGZ(Path.Join(dataDir, fileName), dataDir); - } - - public override void Train() + DataDir = @"image_classification_v1\flower_photos", + }); + task.Train(new TrainingOptions { - // using wizard to train model - var wizard = new ModelWizard(); - var task = wizard.AddImageClassificationTask(new TaskOptions - { - DataDir = @"image_classification_v1\flower_photos", - }); - task.Train(new TrainingOptions - { - TrainingSteps = 100 - }); - } + TrainingSteps = 100 + }); + } - /// - /// Prediction - /// labels mapping, it's from output_lables.txt - /// 0 - daisy - /// 1 - dandelion - /// 2 - roses - /// 3 - sunflowers - /// 4 - tulips - /// - public override void Predict() + /// + /// Prediction + /// labels mapping, it's from output_lables.txt + /// 0 - daisy + /// 1 - dandelion + /// 2 - roses + /// 3 - sunflowers + /// 4 - tulips + /// + public override void Predict() + { + // predict image + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions { - // predict image - var wizard = new ModelWizard(); - var task = wizard.AddImageClassificationTask(new TaskOptions - { - ModelPath = @"image_classification_v1\saved_model.pb" - }); + ModelPath = @"image_classification_v1\saved_model.pb" + }); - var imgPath = Path.Join("image_classification_v1", "flower_photos", "daisy", "5547758_eea9edfd54_n.jpg"); - var input = ImageUtil.ReadImageFromFile(imgPath); - var result = task.Predict(input); - } + var imgPath = Path.Join("image_classification_v1", "flower_photos", "daisy", "5547758_eea9edfd54_n.jpg"); + var input = ImageUtil.ReadImageFromFile(imgPath); + var result = task.Predict(input); + } - public override void Test() + public override void Test() + { + var wizard = new ModelWizard(); + var task = wizard.AddImageClassificationTask(new TaskOptions + { + DataDir = @"image_classification_v1\flower_photos", + ModelPath = @"image_classification_v1\saved_model.pb" + }); + var result = task.Test(new TestingOptions { - var wizard = new ModelWizard(); - var task = wizard.AddImageClassificationTask(new TaskOptions - { - DataDir = @"image_classification_v1\flower_photos", - ModelPath = @"image_classification_v1\saved_model.pb" - }); - var result = task.Test(new TestingOptions - { - }); - accuracy = result.Accuracy; - } + }); + accuracy = result.Accuracy; } } diff --git a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/SentimentClassification.cs b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/SentimentClassification.cs index 08760ee..72cb334 100644 --- a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/SentimentClassification.cs +++ b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/SentimentClassification.cs @@ -9,95 +9,94 @@ using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +public class SentimentClassification : SciSharpExample, IExample { - public class SentimentClassification : SciSharpExample, IExample - { - ITimeSeriesTask task; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Text Sentiment Classification", - Enabled = true - }; + ITimeSeriesTask task; + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Text Sentiment Classification", + Enabled = true + }; - public bool Run() + public bool Run() + { + var wizard = new ModelWizard(); + task = wizard.AddTimeSeriesTask(new TaskOptions { - var wizard = new ModelWizard(); - task = wizard.AddTimeSeriesTask(new TaskOptions - { - WeightsPath = @"timeseries_linear_v1\saved_weights.h5" - }); - task.SetModelArgs(new TimeSeriesModelArgs - { - }); + WeightsPath = @"timeseries_linear_v1\saved_weights.h5" + }); + task.SetModelArgs(new TimeSeriesModelArgs + { + }); - return true; - } + return true; + } - public override void PrepareData() - { - // tf.debugging.set_log_device_placement(true); - string url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"; - var dataset = keras.utils.get_file("aclImdb_v1.tar.gz", url, - untar: true, - cache_dir: Path.GetTempPath(), - cache_subdir: "aclImdb_v1"); - var data_dir = Path.Combine(dataset, "aclImdb"); - var train_dir = Path.Combine(data_dir, "train"); + public override void PrepareData() + { + // tf.debugging.set_log_device_placement(true); + string url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"; + var dataset = keras.utils.get_file("aclImdb_v1.tar.gz", url, + untar: true, + cache_dir: Path.GetTempPath(), + cache_subdir: "aclImdb_v1"); + var data_dir = Path.Combine(dataset, "aclImdb"); + var train_dir = Path.Combine(data_dir, "train"); - int batch_size = 32; - int seed = 42; - var raw_train_ds = keras.preprocessing.text_dataset_from_directory( - train_dir, - batch_size: batch_size, - validation_split: 0.2f, - subset: "training", - seed: seed); + int batch_size = 32; + int seed = 42; + var raw_train_ds = keras.preprocessing.text_dataset_from_directory( + train_dir, + batch_size: batch_size, + validation_split: 0.2f, + subset: "training", + seed: seed); - /*foreach (var (text_batch, label_batch) in raw_train_ds.take(1)) + /*foreach (var (text_batch, label_batch) in raw_train_ds.take(1)) + { + foreach (var i in range(3)) { - foreach (var i in range(3)) - { - print("Review", text_batch.StringData()[i]); - print("Label", label_batch.numpy()[i]); - } + print("Review", text_batch.StringData()[i]); + print("Label", label_batch.numpy()[i]); } + } - print("Label 0 corresponds to", raw_train_ds.class_names[0]); - print("Label 1 corresponds to", raw_train_ds.class_names[1]); + print("Label 0 corresponds to", raw_train_ds.class_names[0]); + print("Label 1 corresponds to", raw_train_ds.class_names[1]); - var raw_val_ds = keras.preprocessing.text_dataset_from_directory( - train_dir, - batch_size: batch_size, - validation_split: 0.2f, - subset: "validation", - seed: seed); + var raw_val_ds = keras.preprocessing.text_dataset_from_directory( + train_dir, + batch_size: batch_size, + validation_split: 0.2f, + subset: "validation", + seed: seed); - var test_dir = Path.Combine(data_dir, "test"); - var raw_test_ds = keras.preprocessing.text_dataset_from_directory( - test_dir, - batch_size: batch_size); + var test_dir = Path.Combine(data_dir, "test"); + var raw_test_ds = keras.preprocessing.text_dataset_from_directory( + test_dir, + batch_size: batch_size); - var max_features = 10000; - var sequence_length = 250; + var max_features = 10000; + var sequence_length = 250; - Func custom_standardization = input_data => - { - var lowercase = tf.strings.lower(input_data); - var stripped_html = tf.strings.regex_replace(lowercase, "
", " "); - return tf.strings.regex_replace(stripped_html, - "'[!\"\\#\\$%\\&\'\\(\\)\\*\\+,\\-\\./:;<=>\\?@\\[\\\\\\]\\^_`\\{\\|\\}\\~]'", - ""); - }; + Func custom_standardization = input_data => + { + var lowercase = tf.strings.lower(input_data); + var stripped_html = tf.strings.regex_replace(lowercase, "
", " "); + return tf.strings.regex_replace(stripped_html, + "'[!\"\\#\\$%\\&\'\\(\\)\\*\\+,\\-\\./:;<=>\\?@\\[\\\\\\]\\^_`\\{\\|\\}\\~]'", + ""); + }; - var vectorize_layer = keras.layers.preprocessing.TextVectorization(standardize: custom_standardization, - max_tokens: max_features, - output_mode: "int", - output_sequence_length: sequence_length); + var vectorize_layer = keras.layers.preprocessing.TextVectorization(standardize: custom_standardization, + max_tokens: max_features, + output_mode: "int", + output_sequence_length: sequence_length); - var train_text = raw_train_ds.map(inputs => inputs[0]); - //vectorize_layer.adapt(train_text);*/ - } + var train_text = raw_train_ds.map(inputs => inputs[0]); + //vectorize_layer.adapt(train_text);*/ } } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs index 1f10231..b8bdabf 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs @@ -18,133 +18,132 @@ limitations under the License. using Tensorflow; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// How to optimise your input pipeline with queues and multi-threading +/// https://blog.metaflow.fr/tensorflow-how-to-optimise-your-input-pipeline-with-queues-and-multi-threading-e7c3874157e0 +/// +public class FullyConnected : SciSharpExample, IExample { - /// - /// How to optimise your input pipeline with queues and multi-threading - /// https://blog.metaflow.fr/tensorflow-how-to-optimise-your-input-pipeline-with-queues-and-multi-threading-e7c3874157e0 - /// - public class FullyConnected : SciSharpExample, IExample + Tensor input = null; + Tensor x_inputs_data = null; + Tensor y_inputs_data = null; + Tensor accuracy = null; + Tensor y_true = null; + Tensor loss_op = null; + Operation train_op = null; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Fully Connected Neural Network (Graph)", + Enabled = false, + IsImportingGraph = false + }; + + public override Graph BuildGraph() { - Tensor input = null; - Tensor x_inputs_data = null; - Tensor y_inputs_data = null; - Tensor accuracy = null; - Tensor y_true = null; - Tensor loss_op = null; - Operation train_op = null; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Fully Connected Neural Network (Graph)", - Enabled = false, - IsImportingGraph = false - }; + var g = tf.get_default_graph(); - public override Graph BuildGraph() + Tensor z = null; + + tf_with(tf.variable_scope("placeholder"), delegate { - var g = tf.get_default_graph(); + input = tf.placeholder(tf.float32, shape: (-1, 1024)); + y_true = tf.placeholder(tf.int32, shape: (-1, 1)); + }); - Tensor z = null; + tf_with(tf.variable_scope("FullyConnected"), delegate + { + var w = tf.compat.v1.get_variable("w", shape: (1024, 1024), initializer: tf.random_normal_initializer(stddev: 0.1f)); + var b = tf.compat.v1.get_variable("b", shape: 1024, initializer: tf.constant_initializer(0.1)); + z = tf.matmul(input, w.AsTensor()) + b.AsTensor(); + var y = tf.nn.relu(z); - tf_with(tf.variable_scope("placeholder"), delegate - { - input = tf.placeholder(tf.float32, shape: (-1, 1024)); - y_true = tf.placeholder(tf.int32, shape: (-1, 1)); - }); + var w2 = tf.compat.v1.get_variable("w2", shape: (1024, 1), initializer: tf.random_normal_initializer(stddev: 0.1f)); + var b2 = tf.compat.v1.get_variable("b2", shape: 1, initializer: tf.constant_initializer(0.1)); + z = tf.matmul(y, w2.AsTensor()) + b2.AsTensor(); + }); - tf_with(tf.variable_scope("FullyConnected"), delegate - { - var w = tf.compat.v1.get_variable("w", shape: (1024, 1024), initializer: tf.random_normal_initializer(stddev: 0.1f)); - var b = tf.compat.v1.get_variable("b", shape: 1024, initializer: tf.constant_initializer(0.1)); - z = tf.matmul(input, w.AsTensor()) + b.AsTensor(); - var y = tf.nn.relu(z); + tf_with(tf.variable_scope("Loss"), delegate + { + var losses = tf.nn.sigmoid_cross_entropy_with_logits(tf.cast(y_true, tf.float32), z); + loss_op = tf.reduce_mean(losses); + }); - var w2 = tf.compat.v1.get_variable("w2", shape: (1024, 1), initializer: tf.random_normal_initializer(stddev: 0.1f)); - var b2 = tf.compat.v1.get_variable("b2", shape: 1, initializer: tf.constant_initializer(0.1)); - z = tf.matmul(y, w2.AsTensor()) + b2.AsTensor(); - }); + tf_with(tf.variable_scope("Accuracy"), delegate + { + var y_pred = tf.cast(z > 0, tf.int32); + accuracy = tf.reduce_mean(tf.cast(tf.equal(y_pred, y_true), tf.float32)); + // accuracy = tf.Print(accuracy, data =[accuracy], message = "accuracy:") + }); - tf_with(tf.variable_scope("Loss"), delegate - { - var losses = tf.nn.sigmoid_cross_entropy_with_logits(tf.cast(y_true, tf.float32), z); - loss_op = tf.reduce_mean(losses); - }); + // We add the training operation, ... + var adam = tf.train.AdamOptimizer(0.01f); + train_op = adam.minimize(loss_op, name: "train_op"); - tf_with(tf.variable_scope("Accuracy"), delegate - { - var y_pred = tf.cast(z > 0, tf.int32); - accuracy = tf.reduce_mean(tf.cast(tf.equal(y_pred, y_true), tf.float32)); - // accuracy = tf.Print(accuracy, data =[accuracy], message = "accuracy:") - }); + return g; + } - // We add the training operation, ... - var adam = tf.train.AdamOptimizer(0.01f); - train_op = adam.minimize(loss_op, name: "train_op"); + public override void PrepareData() + { + // batches of 128 samples, each containing 1024 data points + x_inputs_data = tf.random.normal(new[] { 128, 1024 }, mean: 0, stddev: 1); + // We will try to predict this law: + // predict 1 if the sum of the elements is positive and 0 otherwise + y_inputs_data = tf.cast(tf.reduce_sum(x_inputs_data, axis: 1, keepdims: true) > 0, tf.int32); + } - return g; - } + public bool Run() + { + tf.compat.v1.disable_eager_execution(); - public override void PrepareData() - { - // batches of 128 samples, each containing 1024 data points - x_inputs_data = tf.random.normal(new[] { 128, 1024 }, mean: 0, stddev: 1); - // We will try to predict this law: - // predict 1 if the sum of the elements is positive and 0 otherwise - y_inputs_data = tf.cast(tf.reduce_sum(x_inputs_data, axis: 1, keepdims: true) > 0, tf.int32); - } + PrepareData(); + BuildGraph(); + Train(); - public bool Run() - { - tf.compat.v1.disable_eager_execution(); + return true; + } - PrepareData(); - BuildGraph(); - Train(); + public override void Train() + { + var sw = new Stopwatch(); + sw.Start(); - return true; - } + var config = new ConfigProto + { + IntraOpParallelismThreads = 1, + InterOpParallelismThreads = 1, + LogDevicePlacement = true + }; - public override void Train() + using (var sess = tf.Session(config)) { - var sw = new Stopwatch(); - sw.Start(); + // init variables + sess.run(tf.global_variables_initializer()); - var config = new ConfigProto - { - IntraOpParallelismThreads = 1, - InterOpParallelismThreads = 1, - LogDevicePlacement = true - }; + // check the accuracy before training + var (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); + sess.run(accuracy, (input, x_input), (y_true, y_input)); - using (var sess = tf.Session(config)) + // training + foreach (var i in range(5000)) { - // init variables - sess.run(tf.global_variables_initializer()); - - // check the accuracy before training - var (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - sess.run(accuracy, (input, x_input), (y_true, y_input)); - - // training - foreach (var i in range(5000)) - { - // by sampling some input data (fetching) - (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - var (_, loss) = sess.run((train_op, loss_op), (input, x_input), (y_true, y_input)); - - // We regularly check the loss - if (i % 500 == 0) - print($"iter:{i} - loss:{loss}"); - } - - // Finally, we check our final accuracy + // by sampling some input data (fetching) (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - sess.run(accuracy, (input, x_input), (y_true, y_input)); + var (_, loss) = sess.run((train_op, loss_op), (input, x_input), (y_true, y_input)); + + // We regularly check the loss + if (i % 500 == 0) + print($"iter:{i} - loss:{loss}"); } - print($"Time taken: {sw.Elapsed.TotalSeconds}s"); + // Finally, we check our final accuracy + (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); + sess.run(accuracy, (input, x_input), (y_true, y_input)); } + + print($"Time taken: {sw.Elapsed.TotalSeconds}s"); } } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs index d86b196..16c4536 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs @@ -21,155 +21,154 @@ limitations under the License. using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow v2. +/// This example is using a low-level approach to better understand all mechanics behind building neural networks and the training process. +/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb +/// +public class FullyConnectedEager : SciSharpExample, IExample { - /// - /// Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow v2. - /// This example is using a low-level approach to better understand all mechanics behind building neural networks and the training process. - /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb - /// - public class FullyConnectedEager : SciSharpExample, IExample - { - int num_classes = 10; // total classes (0-9 digits). - int num_features = 784; // data features (img shape: 28*28). - - // Training parameters. - float learning_rate = 0.001f; - int training_steps = 1000; - int batch_size = 256; - int display_step = 100; - - // Network parameters. - int n_hidden_1 = 128; // 1st layer number of neurons. - int n_hidden_2 = 256; // 2nd layer number of neurons. - - IDatasetV2 train_data; - NDArray x_test, y_test, x_train, y_train; - IVariableV1 h1, h2, wout, b1, b2, bout; - float accuracy_test = 0f; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Fully Connected Neural Network (Eager)", - Enabled = true, - IsImportingGraph = false - }; + int num_classes = 10; // total classes (0-9 digits). + int num_features = 784; // data features (img shape: 28*28). + + // Training parameters. + float learning_rate = 0.001f; + int training_steps = 1000; + int batch_size = 256; + int display_step = 100; + + // Network parameters. + int n_hidden_1 = 128; // 1st layer number of neurons. + int n_hidden_2 = 256; // 2nd layer number of neurons. + + IDatasetV2 train_data; + NDArray x_test, y_test, x_train, y_train; + IVariableV1 h1, h2, wout, b1, b2, bout; + float accuracy_test = 0f; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Fully Connected Neural Network (Eager)", + Enabled = true, + IsImportingGraph = false + }; - public bool Run() + public bool Run() + { + tf.enable_eager_execution(); + + PrepareData(); + + // Store layers weight & bias + // A random value generator to initialize weights. + var random_normal = tf.initializers.random_normal_initializer(); + h1 = tf.Variable(random_normal.Apply(new InitializerArgs((num_features, n_hidden_1)))); + h2 = tf.Variable(random_normal.Apply(new InitializerArgs((n_hidden_1, n_hidden_2)))); + wout = tf.Variable(random_normal.Apply(new InitializerArgs((n_hidden_2, num_classes)))); + b1 = tf.Variable(tf.zeros(n_hidden_1)); + b2 = tf.Variable(tf.zeros(n_hidden_2)); + bout = tf.Variable(tf.zeros(num_classes)); + var trainable_variables = new IVariableV1[] { h1, h2, wout, b1, b2, bout }; + + // Stochastic gradient descent optimizer. + var optimizer = keras.optimizers.SGD(learning_rate); + + // Run training for the given number of steps. + foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1)) { - tf.enable_eager_execution(); - - PrepareData(); - - // Store layers weight & bias - // A random value generator to initialize weights. - var random_normal = tf.initializers.random_normal_initializer(); - h1 = tf.Variable(random_normal.Apply(new InitializerArgs((num_features, n_hidden_1)))); - h2 = tf.Variable(random_normal.Apply(new InitializerArgs((n_hidden_1, n_hidden_2)))); - wout = tf.Variable(random_normal.Apply(new InitializerArgs((n_hidden_2, num_classes)))); - b1 = tf.Variable(tf.zeros(n_hidden_1)); - b2 = tf.Variable(tf.zeros(n_hidden_2)); - bout = tf.Variable(tf.zeros(num_classes)); - var trainable_variables = new IVariableV1[] { h1, h2, wout, b1, b2, bout }; - - // Stochastic gradient descent optimizer. - var optimizer = keras.optimizers.SGD(learning_rate); - - // Run training for the given number of steps. - foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1)) - { - // Run the optimization to update W and b values. - run_optimization(optimizer, batch_x, batch_y, trainable_variables); - - if (step % display_step == 0) - { - var pred = neural_net(batch_x); - var loss = cross_entropy(pred, batch_y); - var acc = accuracy(pred, batch_y); - print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}"); - } - } + // Run the optimization to update W and b values. + run_optimization(optimizer, batch_x, batch_y, trainable_variables); - // Test model on validation set. + if (step % display_step == 0) { - var pred = neural_net(x_test); - accuracy_test = (float)accuracy(pred, y_test); - print($"Test Accuracy: {accuracy_test}"); + var pred = neural_net(batch_x); + var loss = cross_entropy(pred, batch_y); + var acc = accuracy(pred, batch_y); + print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}"); } - - return accuracy_test > 0.85; } - void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y, IVariableV1[] trainable_variables) + // Test model on validation set. { - using var g = tf.GradientTape(); - var pred = neural_net(x); - var loss = cross_entropy(pred, y); + var pred = neural_net(x_test); + accuracy_test = (float)accuracy(pred, y_test); + print($"Test Accuracy: {accuracy_test}"); + } - // Compute gradients. - var gradients = g.gradient(loss, trainable_variables); + return accuracy_test > 0.85; + } - // Update W and b following gradients. - optimizer.apply_gradients(zip(gradients, trainable_variables.Select(x => x as ResourceVariable))); - } + void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y, IVariableV1[] trainable_variables) + { + using var g = tf.GradientTape(); + var pred = neural_net(x); + var loss = cross_entropy(pred, y); - Tensor cross_entropy(Tensor y_pred, Tensor y_true) - { - // Encode label to a one hot vector. - y_true = tf.one_hot(y_true, depth: num_classes); - // Clip prediction values to avoid log(0) error. - y_pred = tf.clip_by_value(y_pred, 1e-9f, 1.0f); - // Compute cross-entropy. - return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred))); - } + // Compute gradients. + var gradients = g.gradient(loss, trainable_variables); - Tensor accuracy(Tensor y_pred, Tensor y_true) - { - // Predicted class is the index of highest score in prediction vector (i.e. argmax). - var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); - return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); - } + // Update W and b following gradients. + optimizer.apply_gradients(zip(gradients, trainable_variables.Select(x => x as ResourceVariable))); + } - /// - /// Create model - /// - /// - /// - Tensor neural_net(Tensor x) - { - // Hidden fully connected layer with 128 neurons. - var layer_1 = tf.add(tf.matmul(x, h1.AsTensor()), b1.AsTensor()); - // Apply sigmoid to layer_1 output for non-linearity. - layer_1 = tf.nn.sigmoid(layer_1); - - // Hidden fully connected layer with 256 neurons. - var layer_2 = tf.add(tf.matmul(layer_1, h2.AsTensor()), b2.AsTensor()); - // Apply sigmoid to layer_2 output for non-linearity. - layer_2 = tf.nn.sigmoid(layer_2); - - // Output fully connected layer with a neuron for each class. - var out_layer = tf.matmul(layer_2, wout.AsTensor()) + bout.AsTensor(); - // Apply softmax to normalize the logits to a probability distribution. - return tf.nn.softmax(out_layer); - } + Tensor cross_entropy(Tensor y_pred, Tensor y_true) + { + // Encode label to a one hot vector. + y_true = tf.one_hot(y_true, depth: num_classes); + // Clip prediction values to avoid log(0) error. + y_pred = tf.clip_by_value(y_pred, 1e-9f, 1.0f); + // Compute cross-entropy. + return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred))); + } - public override void PrepareData() - { - // Prepare MNIST data. - ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data(); - // Flatten images to 1-D vector of 784 features (28*28). - (x_train, x_test) = (x_train.reshape((-1, num_features)), x_test.reshape((-1, num_features))); - // Normalize images value from [0, 255] to [0, 1]. - (x_train, x_test) = (x_train / 255f, x_test / 255f); - - // Use tf.data API to shuffle and batch data. - train_data = tf.data.Dataset.from_tensor_slices(x_train, y_train); - train_data = train_data.repeat() - .shuffle(5000) - .batch(batch_size) - .prefetch(1) - .take(training_steps); - } + Tensor accuracy(Tensor y_pred, Tensor y_true) + { + // Predicted class is the index of highest score in prediction vector (i.e. argmax). + var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); + return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); + } + + /// + /// Create model + /// + /// + /// + Tensor neural_net(Tensor x) + { + // Hidden fully connected layer with 128 neurons. + var layer_1 = tf.add(tf.matmul(x, h1.AsTensor()), b1.AsTensor()); + // Apply sigmoid to layer_1 output for non-linearity. + layer_1 = tf.nn.sigmoid(layer_1); + + // Hidden fully connected layer with 256 neurons. + var layer_2 = tf.add(tf.matmul(layer_1, h2.AsTensor()), b2.AsTensor()); + // Apply sigmoid to layer_2 output for non-linearity. + layer_2 = tf.nn.sigmoid(layer_2); + + // Output fully connected layer with a neuron for each class. + var out_layer = tf.matmul(layer_2, wout.AsTensor()) + bout.AsTensor(); + // Apply softmax to normalize the logits to a probability distribution. + return tf.nn.softmax(out_layer); + } + + public override void PrepareData() + { + // Prepare MNIST data. + ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data(); + // Flatten images to 1-D vector of 784 features (28*28). + (x_train, x_test) = (x_train.reshape((-1, num_features)), x_test.reshape((-1, num_features))); + // Normalize images value from [0, 255] to [0, 1]. + (x_train, x_test) = (x_train / 255f, x_test / 255f); + + // Use tf.data API to shuffle and batch data. + train_data = tf.data.Dataset.from_tensor_slices(x_train, y_train); + train_data = train_data.repeat() + .shuffle(5000) + .batch(batch_size) + .prefetch(1) + .take(training_steps); } } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedInQueue.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedInQueue.cs index 21a1b14..cc3aeaf 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedInQueue.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedInQueue.cs @@ -18,97 +18,96 @@ limitations under the License. using Tensorflow; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// How to optimise your input pipeline with queues and multi-threading +/// https://blog.metaflow.fr/tensorflow-how-to-optimise-your-input-pipeline-with-queues-and-multi-threading-e7c3874157e0 +/// +public class FullyConnectedInQueue : SciSharpExample, IExample { - /// - /// How to optimise your input pipeline with queues and multi-threading - /// https://blog.metaflow.fr/tensorflow-how-to-optimise-your-input-pipeline-with-queues-and-multi-threading-e7c3874157e0 - /// - public class FullyConnectedInQueue : SciSharpExample, IExample + Tensor input = null; + Tensor x_inputs_data = null; + Tensor y_inputs_data = null; + Tensor accuracy = null; + Tensor y_true = null; + Tensor loss_op = null; + Operation train_op = null; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Fully Connected Neural Network In Queue", + Enabled = false, + IsImportingGraph = false + }; + + public override Graph BuildGraph() { - Tensor input = null; - Tensor x_inputs_data = null; - Tensor y_inputs_data = null; - Tensor accuracy = null; - Tensor y_true = null; - Tensor loss_op = null; - Operation train_op = null; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Fully Connected Neural Network In Queue", - Enabled = false, - IsImportingGraph = false - }; + var g = tf.get_default_graph(); - public override Graph BuildGraph() + // We build our small model: a basic two layers neural net with ReLU + tf_with(tf.variable_scope("queue"), delegate { - var g = tf.get_default_graph(); + // enqueue 5 batches + var q = tf.FIFOQueue(capacity: 5, dtype: tf.float32); + // We use the "enqueue" operation so 1 element of the queue is the full batch + var enqueue_op = q.enqueue(x_inputs_data); + // var numberOfThreads = 1; + // var qr = tf.train.QueueRunner(q, [enqueue_op] * numberOfThreads); + }); + + return g; + } - // We build our small model: a basic two layers neural net with ReLU - tf_with(tf.variable_scope("queue"), delegate - { - // enqueue 5 batches - var q = tf.FIFOQueue(capacity: 5, dtype: tf.float32); - // We use the "enqueue" operation so 1 element of the queue is the full batch - var enqueue_op = q.enqueue(x_inputs_data); - // var numberOfThreads = 1; - // var qr = tf.train.QueueRunner(q, [enqueue_op] * numberOfThreads); - }); - - return g; - } + public override void PrepareData() + { + // batches of 128 samples, each containing 1024 data points + x_inputs_data = tf.random.normal(new[] { 128, 1024 }, mean: 0, stddev: 1); + // We will try to predict this law: + // predict 1 if the sum of the elements is positive and 0 otherwise + y_inputs_data = tf.cast(tf.reduce_sum(x_inputs_data, axis: 1, keepdims: true) > 0, tf.int32); + } - public override void PrepareData() - { - // batches of 128 samples, each containing 1024 data points - x_inputs_data = tf.random.normal(new[] { 128, 1024 }, mean: 0, stddev: 1); - // We will try to predict this law: - // predict 1 if the sum of the elements is positive and 0 otherwise - y_inputs_data = tf.cast(tf.reduce_sum(x_inputs_data, axis: 1, keepdims: true) > 0, tf.int32); - } + public bool Run() + { + PrepareData(); + BuildGraph(); + Train(); + return true; + } - public bool Run() - { - PrepareData(); - BuildGraph(); - Train(); - return true; - } + public override void Train() + { + var sw = new Stopwatch(); + sw.Start(); - public override void Train() + using (var sess = tf.Session()) { - var sw = new Stopwatch(); - sw.Start(); + // init variables + sess.run(tf.global_variables_initializer()); + + // check the accuracy before training + var (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); + sess.run(accuracy, (input, x_input), (y_true, y_input)); - using (var sess = tf.Session()) + // training + foreach (var i in range(5000)) { - // init variables - sess.run(tf.global_variables_initializer()); - - // check the accuracy before training - var (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - sess.run(accuracy, (input, x_input), (y_true, y_input)); - - // training - foreach (var i in range(5000)) - { - // by sampling some input data (fetching) - (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - var (_, loss) = sess.run((train_op, loss_op), (input, x_input), (y_true, y_input)); - - // We regularly check the loss - if (i % 500 == 0) - print($"iter:{i} - loss:{loss}"); - } - - // Finally, we check our final accuracy + // by sampling some input data (fetching) (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - sess.run(accuracy, (input, x_input), (y_true, y_input)); + var (_, loss) = sess.run((train_op, loss_op), (input, x_input), (y_true, y_input)); + + // We regularly check the loss + if (i % 500 == 0) + print($"iter:{i} - loss:{loss}"); } - print($"Time taken: {sw.Elapsed.TotalSeconds}s"); + // Finally, we check our final accuracy + (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); + sess.run(accuracy, (input, x_input), (y_true, y_input)); } + + print($"Time taken: {sw.Elapsed.TotalSeconds}s"); } } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs index 74ed8a0..644169c 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs @@ -24,187 +24,186 @@ limitations under the License. using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples -{ - /// - /// Build a convolutional neural network with TensorFlow v2. - /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb - /// - public class FullyConnectedKeras : SciSharpExample, IExample - { - // MNIST dataset parameters. - int num_classes = 10; // 0 to 9 digits - int num_features = 784; // 28*28 - - // Training parameters. - float learning_rate = 0.1f; - int display_step = 100; - int batch_size = 256; - int training_steps = 1000; - - float accuracy; - IDatasetV2 train_data; - NDArray x_test, y_test, x_train, y_train; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Fully Connected Neural Network (Keras)", - Enabled = true, - IsImportingGraph = false - }; +namespace TensorFlowNET.Examples; - public override void PrepareData() +/// +/// Build a convolutional neural network with TensorFlow v2. +/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb +/// +public class FullyConnectedKeras : SciSharpExample, IExample +{ + // MNIST dataset parameters. + int num_classes = 10; // 0 to 9 digits + int num_features = 784; // 28*28 + + // Training parameters. + float learning_rate = 0.1f; + int display_step = 100; + int batch_size = 256; + int training_steps = 1000; + + float accuracy; + IDatasetV2 train_data; + NDArray x_test, y_test, x_train, y_train; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - // Prepare MNIST data. - ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data(); - // Flatten images to 1-D vector of 784 features (28*28). - (x_train, x_test) = (x_train.reshape((-1, num_features)), x_test.reshape((-1, num_features))); - // Normalize images value from [0, 255] to [0, 1]. - (x_train, x_test) = (x_train / 255f, x_test / 255f); - - // Use tf.data API to shuffle and batch data. - train_data = tf.data.Dataset.from_tensor_slices(x_train, y_train); - train_data = train_data.repeat() - .shuffle(5000) - .batch(batch_size) - .prefetch(1) - .take(training_steps); - } + Name = "Fully Connected Neural Network (Keras)", + Enabled = true, + IsImportingGraph = false + }; - public bool Run() - { - tf.enable_eager_execution(); + public override void PrepareData() + { + // Prepare MNIST data. + ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data(); + // Flatten images to 1-D vector of 784 features (28*28). + (x_train, x_test) = (x_train.reshape((-1, num_features)), x_test.reshape((-1, num_features))); + // Normalize images value from [0, 255] to [0, 1]. + (x_train, x_test) = (x_train / 255f, x_test / 255f); + + // Use tf.data API to shuffle and batch data. + train_data = tf.data.Dataset.from_tensor_slices(x_train, y_train); + train_data = train_data.repeat() + .shuffle(5000) + .batch(batch_size) + .prefetch(1) + .take(training_steps); + } - PrepareData(); + public bool Run() + { + tf.enable_eager_execution(); - // Build neural network model. - var neural_net = new NeuralNet(new NeuralNetArgs - { - NumClasses = num_classes, - NeuronOfHidden1 = 128, - Activation1 = keras.activations.Relu, - NeuronOfHidden2 = 256, - Activation2 = keras.activations.Relu - }); - - // Cross-Entropy Loss. - // Note that this will apply 'softmax' to the logits. - Func cross_entropy_loss = (x, y) => - { - // Convert labels to int 64 for tf cross-entropy function. - y = tf.cast(y, tf.int64); - // Apply softmax to logits and compute cross-entropy. - var loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: y, logits: x); - // Average loss across the batch. - return tf.reduce_mean(loss); - }; - - // Accuracy metric. - Func accuracy = (y_pred, y_true) => - { - // Predicted class is the index of highest score in prediction vector (i.e. argmax). - var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); - return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); - }; + PrepareData(); - // Stochastic gradient descent optimizer. - var optimizer = keras.optimizers.SGD(learning_rate); + // Build neural network model. + var neural_net = new NeuralNet(new NeuralNetArgs + { + NumClasses = num_classes, + NeuronOfHidden1 = 128, + Activation1 = keras.activations.Relu, + NeuronOfHidden2 = 256, + Activation2 = keras.activations.Relu + }); + + // Cross-Entropy Loss. + // Note that this will apply 'softmax' to the logits. + Func cross_entropy_loss = (x, y) => + { + // Convert labels to int 64 for tf cross-entropy function. + y = tf.cast(y, tf.int64); + // Apply softmax to logits and compute cross-entropy. + var loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: y, logits: x); + // Average loss across the batch. + return tf.reduce_mean(loss); + }; + + // Accuracy metric. + Func accuracy = (y_pred, y_true) => + { + // Predicted class is the index of highest score in prediction vector (i.e. argmax). + var correct_prediction = tf.equal(tf.math.argmax(y_pred, 1), tf.cast(y_true, tf.int64)); + return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis: -1); + }; - // Optimization process. - Action run_optimization = (x, y) => - { - // Wrap computation inside a GradientTape for automatic differentiation. - using var g = tf.GradientTape(); - // Forward pass. - var pred = neural_net.Apply(x, training: true); - var loss = cross_entropy_loss(pred, y); + // Stochastic gradient descent optimizer. + var optimizer = keras.optimizers.SGD(learning_rate); - // Compute gradients. - var gradients = g.gradient(loss, neural_net.TrainableVariables); + // Optimization process. + Action run_optimization = (x, y) => + { + // Wrap computation inside a GradientTape for automatic differentiation. + using var g = tf.GradientTape(); + // Forward pass. + var pred = neural_net.Apply(x, training: true); + var loss = cross_entropy_loss(pred, y); - // Update W and b following gradients. - optimizer.apply_gradients(zip(gradients, neural_net.TrainableVariables.Select(x => x as ResourceVariable))); - }; + // Compute gradients. + var gradients = g.gradient(loss, neural_net.TrainableVariables); + // Update W and b following gradients. + optimizer.apply_gradients(zip(gradients, neural_net.TrainableVariables.Select(x => x as ResourceVariable))); + }; - // Run training for the given number of steps. - foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1)) - { - // Run the optimization to update W and b values. - run_optimization(batch_x, batch_y); - - if (step % display_step == 0) - { - var pred = neural_net.Apply(batch_x, training: true); - var loss = cross_entropy_loss(pred, batch_y); - var acc = accuracy(pred, batch_y); - print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}"); - } - } - // Test model on validation set. + // Run training for the given number of steps. + foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1)) + { + // Run the optimization to update W and b values. + run_optimization(batch_x, batch_y); + + if (step % display_step == 0) { - var pred = neural_net.Apply(x_test, training: false); - this.accuracy = (float)accuracy(pred, y_test); - print($"Test Accuracy: {this.accuracy}"); + var pred = neural_net.Apply(batch_x, training: true); + var loss = cross_entropy_loss(pred, batch_y); + var acc = accuracy(pred, batch_y); + print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}"); } - - return this.accuracy > 0.92f; } - public class NeuralNet : Model + // Test model on validation set. { - ILayer fc1; - ILayer fc2; - ILayer output; + var pred = neural_net.Apply(x_test, training: false); + this.accuracy = (float)accuracy(pred, y_test); + print($"Test Accuracy: {this.accuracy}"); + } - public NeuralNet(NeuralNetArgs args) : - base(args) - { - var layers = keras.layers; + return this.accuracy > 0.92f; + } + + public class NeuralNet : Model + { + ILayer fc1; + ILayer fc2; + ILayer output; - // First fully-connected hidden layer. - fc1 = layers.Dense(args.NeuronOfHidden1, activation: args.Activation1); + public NeuralNet(NeuralNetArgs args) : + base(args) + { + var layers = keras.layers; - // Second fully-connected hidden layer. - fc2 = layers.Dense(args.NeuronOfHidden2, activation: args.Activation2); + // First fully-connected hidden layer. + fc1 = layers.Dense(args.NeuronOfHidden1, activation: args.Activation1); - output = layers.Dense(args.NumClasses); + // Second fully-connected hidden layer. + fc2 = layers.Dense(args.NeuronOfHidden2, activation: args.Activation2); - StackLayers(fc1, fc2, output); - } + output = layers.Dense(args.NumClasses); - // Set forward pass. - protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null) - { - inputs = fc1.Apply(inputs); - inputs = fc2.Apply(inputs); - inputs = output.Apply(inputs); - if (!training.Value) - inputs = tf.nn.softmax(inputs); - return inputs; - } + StackLayers(fc1, fc2, output); } - /// - /// Network parameters. - /// - public class NeuralNetArgs : ModelArgs + // Set forward pass. + protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null) { - /// - /// 1st layer number of neurons. - /// - public int NeuronOfHidden1 { get; set; } - public Activation Activation1 { get; set; } - - /// - /// 2nd layer number of neurons. - /// - public int NeuronOfHidden2 { get; set; } - public Activation Activation2 { get; set; } - - public int NumClasses { get; set; } + inputs = fc1.Apply(inputs); + inputs = fc2.Apply(inputs); + inputs = output.Apply(inputs); + if (!training.Value) + inputs = tf.nn.softmax(inputs); + return inputs; } } + + /// + /// Network parameters. + /// + public class NeuralNetArgs : ModelArgs + { + /// + /// 1st layer number of neurons. + /// + public int NeuronOfHidden1 { get; set; } + public Activation Activation1 { get; set; } + + /// + /// 2nd layer number of neurons. + /// + public int NeuronOfHidden2 { get; set; } + public Activation Activation2 { get; set; } + + public int NumClasses { get; set; } + } } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs index fd8b877..1c4e4a7 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs @@ -21,163 +21,162 @@ limitations under the License. using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Simple vanilla neural net solving the famous XOR problem +/// https://github.com/amygdala/tensorflow-workshop/blob/master/workshop_sections/getting_started/xor/README.md +/// +public class NeuralNetXor : SciSharpExample, IExample { - /// - /// Simple vanilla neural net solving the famous XOR problem - /// https://github.com/amygdala/tensorflow-workshop/blob/master/workshop_sections/getting_started/xor/README.md - /// - public class NeuralNetXor : SciSharpExample, IExample - { - public int num_steps = 10000; + public int num_steps = 10000; - private NDArray data; - private NDArray label; + private NDArray data; + private NDArray label; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "NN XOR in Graph Mode", - Enabled = true, - IsImportingGraph = false - }; - - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.compat.v1.disable_eager_execution(); + Name = "NN XOR in Graph Mode", + Enabled = true, + IsImportingGraph = false + }; - PrepareData(); - float loss_value = 0; + public bool Run() + { + tf.compat.v1.disable_eager_execution(); - if (Config.IsImportingGraph) - loss_value = RunWithImportedGraph(); - else - loss_value = RunWithBuiltGraph(); + PrepareData(); + float loss_value = 0; - return loss_value < 0.0628; - } + if (Config.IsImportingGraph) + loss_value = RunWithImportedGraph(); + else + loss_value = RunWithBuiltGraph(); - private float RunWithImportedGraph() - { - var graph = tf.Graph().as_default(); + return loss_value < 0.0628; + } - tf.train.import_meta_graph("graph/xor.meta"); + private float RunWithImportedGraph() + { + var graph = tf.Graph().as_default(); - Tensor features = graph.get_operation_by_name("Placeholder"); - Tensor labels = graph.get_operation_by_name("Placeholder_1"); - Tensor loss = graph.get_operation_by_name("loss"); - Tensor train_op = graph.get_operation_by_name("train_op"); - Tensor global_step = graph.get_operation_by_name("global_step"); + tf.train.import_meta_graph("graph/xor.meta"); - var init = tf.global_variables_initializer(); - float loss_value = 0; - // Start tf session - using (var sess = tf.Session(graph)) + Tensor features = graph.get_operation_by_name("Placeholder"); + Tensor labels = graph.get_operation_by_name("Placeholder_1"); + Tensor loss = graph.get_operation_by_name("loss"); + Tensor train_op = graph.get_operation_by_name("train_op"); + Tensor global_step = graph.get_operation_by_name("global_step"); + + var init = tf.global_variables_initializer(); + float loss_value = 0; + // Start tf session + using (var sess = tf.Session(graph)) + { + sess.run(init); + var step = 0; + + var y_ = np.array(new int[] { 1, 0, 0, 1 }); + while (step < num_steps) { - sess.run(init); - var step = 0; - - var y_ = np.array(new int[] { 1, 0, 0, 1 }); - while (step < num_steps) - { - // original python: - //_, step, loss_value = sess.run( - // [train_op, gs, loss], - // feed_dict={features: xy, labels: y_} - // ) - (_, step, loss_value) = sess.run((train_op, global_step, loss), (features, data), (labels, y_)); - if (step == 1 || step % 1000 == 0) - Console.WriteLine($"Step {step} loss: {loss_value}"); - } - Console.WriteLine($"Final loss: {loss_value}"); + // original python: + //_, step, loss_value = sess.run( + // [train_op, gs, loss], + // feed_dict={features: xy, labels: y_} + // ) + (_, step, loss_value) = sess.run((train_op, global_step, loss), (features, data), (labels, y_)); + if (step == 1 || step % 1000 == 0) + Console.WriteLine($"Step {step} loss: {loss_value}"); } - - return loss_value; + Console.WriteLine($"Final loss: {loss_value}"); } - private float RunWithBuiltGraph() - { - var graph = tf.Graph().as_default(); + return loss_value; + } + + private float RunWithBuiltGraph() + { + var graph = tf.Graph().as_default(); - var features = tf.placeholder(tf.float32, (4, 2)); - var labels = tf.placeholder(tf.int32, 4); + var features = tf.placeholder(tf.float32, (4, 2)); + var labels = tf.placeholder(tf.int32, 4); - var (train_op, loss, gs) = make_graph(features, labels); + var (train_op, loss, gs) = make_graph(features, labels); - var init = tf.global_variables_initializer(); + var init = tf.global_variables_initializer(); - float loss_value = 0; - // Start tf session - using (var sess = tf.Session(graph)) + float loss_value = 0; + // Start tf session + using (var sess = tf.Session(graph)) + { + sess.run(init); + var step = 0; + + var y_ = np.array(new int[] { 1, 0, 0, 1 }); + while (step < num_steps) { - sess.run(init); - var step = 0; - - var y_ = np.array(new int[] { 1, 0, 0, 1 }); - while (step < num_steps) - { - (_, step, loss_value) = sess.run((train_op, gs, loss), (features, data), (labels, y_)); - if (step == 1 || step % 1000 == 0) - Console.WriteLine($"Step {step} loss: {loss_value}"); - } - Console.WriteLine($"Final loss: {loss_value}"); + (_, step, loss_value) = sess.run((train_op, gs, loss), (features, data), (labels, y_)); + if (step == 1 || step % 1000 == 0) + Console.WriteLine($"Step {step} loss: {loss_value}"); } - - return loss_value; + Console.WriteLine($"Final loss: {loss_value}"); } - private (Operation, Tensor, Tensor) make_graph(Tensor features, Tensor labels, int num_hidden = 8) - { - var stddev = 1 / Math.Sqrt(2); - var hidden_weights = tf.Variable(tf.truncated_normal(new int[] { 2, num_hidden }, seed: 1, stddev: (float)stddev)); + return loss_value; + } + + private (Operation, Tensor, Tensor) make_graph(Tensor features, Tensor labels, int num_hidden = 8) + { + var stddev = 1 / Math.Sqrt(2); + var hidden_weights = tf.Variable(tf.truncated_normal(new int[] { 2, num_hidden }, seed: 1, stddev: (float)stddev)); - // Shape [4, num_hidden] - var hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights)); + // Shape [4, num_hidden] + var hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights)); - var output_weights = tf.Variable(tf.truncated_normal( - new[] { num_hidden, 1 }, - seed: 17, - stddev: (float)(1 / Math.Sqrt(num_hidden)) - )); + var output_weights = tf.Variable(tf.truncated_normal( + new[] { num_hidden, 1 }, + seed: 17, + stddev: (float)(1 / Math.Sqrt(num_hidden)) + )); - // Shape [4, 1] - var logits = tf.matmul(hidden_activations, output_weights); + // Shape [4, 1] + var logits = tf.matmul(hidden_activations, output_weights); - // Shape [4] - var predictions = tf.tanh(tf.squeeze(logits)); - var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name: "loss"); + // Shape [4] + var predictions = tf.tanh(tf.squeeze(logits)); + var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name: "loss"); - var gs = tf.Variable(0, trainable: false, name: "global_step"); - var optimizer = tf.train.GradientDescentOptimizer(0.2f); - var train_op = optimizer.minimize(loss, global_step: gs); + var gs = tf.Variable(0, trainable: false, name: "global_step"); + var optimizer = tf.train.GradientDescentOptimizer(0.2f); + var train_op = optimizer.minimize(loss, global_step: gs); - return (train_op, loss, gs); - } + return (train_op, loss, gs); + } - public override void PrepareData() + public override void PrepareData() + { + data = new float[,] { - data = new float[,] - { - {1, 0 }, - {1, 1 }, - {0, 0 }, - {0, 1 } - }; + {1, 0 }, + {1, 1 }, + {0, 0 }, + {0, 1 } + }; - label = new float[,] - { - {1 }, - {0 }, - {0 }, - {1 } - }; + label = new float[,] + { + {1 }, + {0 }, + {0 }, + {1 } + }; - if (Config.IsImportingGraph) - { - // download graph meta data - string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/xor.meta"; - Web.Download(url, "graph", "xor.meta"); - } + if (Config.IsImportingGraph) + { + // download graph meta data + string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/xor.meta"; + Web.Download(url, "graph", "xor.meta"); } } } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorEager.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorEager.cs index 8ccc6ed..b37350a 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorEager.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorEager.cs @@ -19,99 +19,98 @@ limitations under the License. using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Simple vanilla neural net solving the famous XOR problem +/// https://github.com/amygdala/tensorflow-workshop/blob/master/workshop_sections/getting_started/xor/README.md +/// +public class NeuralNetXorEager : SciSharpExample, IExample { - /// - /// Simple vanilla neural net solving the famous XOR problem - /// https://github.com/amygdala/tensorflow-workshop/blob/master/workshop_sections/getting_started/xor/README.md - /// - public class NeuralNetXorEager : SciSharpExample, IExample - { - public int num_steps = 10000; + public int num_steps = 10000; - private NDArray data; - private NDArray label; + private NDArray data; + private NDArray label; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "NN XOR in Eager Mode", - Enabled = true - }; - - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - PrepareData(); - var loss_value = RunEagerMode(); - return loss_value < 0.0628; - } + Name = "NN XOR in Eager Mode", + Enabled = true + }; - private float RunEagerMode() - { - var learning_rate = 0.01f; - var num_hidden = 8; - var display_step = 1000; - var stddev = 1 / Math.Sqrt(2); - var features = tf.constant(data); - var labels = tf.constant(label); + public bool Run() + { + PrepareData(); + var loss_value = RunEagerMode(); + return loss_value < 0.0628; + } - var hidden_weights = tf.Variable(tf.random.truncated_normal((2, num_hidden), seed: 1, stddev: (float)stddev)); + private float RunEagerMode() + { + var learning_rate = 0.01f; + var num_hidden = 8; + var display_step = 1000; + var stddev = 1 / Math.Sqrt(2); + var features = tf.constant(data); + var labels = tf.constant(label); - // Shape [4, num_hidden] - var hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights)); + var hidden_weights = tf.Variable(tf.random.truncated_normal((2, num_hidden), seed: 1, stddev: (float)stddev)); - var output_weights = tf.Variable(tf.truncated_normal( - (num_hidden, 1), - seed: 17, - stddev: (float)(1 / Math.Sqrt(num_hidden)) - )); + // Shape [4, num_hidden] + var hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights)); - var optimizer = keras.optimizers.SGD(learning_rate); + var output_weights = tf.Variable(tf.truncated_normal( + (num_hidden, 1), + seed: 17, + stddev: (float)(1 / Math.Sqrt(num_hidden)) + )); - // Run training for the given number of steps. - foreach (var step in range(1, num_steps + 1)) - { - using var g = tf.GradientTape(); + var optimizer = keras.optimizers.SGD(learning_rate); - // Shape [4, 1] - var logits = tf.matmul(hidden_activations, output_weights); + // Run training for the given number of steps. + foreach (var step in range(1, num_steps + 1)) + { + using var g = tf.GradientTape(); + + // Shape [4, 1] + var logits = tf.matmul(hidden_activations, output_weights); - // Shape [4] - var predictions = tf.tanh(tf.squeeze(logits)); - var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name: "loss"); + // Shape [4] + var predictions = tf.tanh(tf.squeeze(logits)); + var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name: "loss"); - // should stop recording - // Compute gradients. - var gradients = g.gradient(loss, output_weights); + // should stop recording + // Compute gradients. + var gradients = g.gradient(loss, output_weights); - // Update W and b following gradients. - optimizer.apply_gradients((gradients, output_weights)); + // Update W and b following gradients. + optimizer.apply_gradients((gradients, output_weights)); - if (step % display_step == 0) - { - print($"step: {step}, loss: {loss.numpy()}"); - } + if (step % display_step == 0) + { + print($"step: {step}, loss: {loss.numpy()}"); } - return 0; } + return 0; + } - public override void PrepareData() + public override void PrepareData() + { + data = new float[,] { - data = new float[,] - { - {1, 0 }, - {1, 1 }, - {0, 0 }, - {0, 1 } - }; + {1, 0 }, + {1, 1 }, + {0, 0 }, + {0, 1 } + }; - label = new float[,] - { - {1 }, - {0 }, - {0 }, - {1 } - }; - } + label = new float[,] + { + {1 }, + {0 }, + {0 }, + {1 } + }; } } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs index e41b124..70ce195 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs @@ -21,39 +21,38 @@ limitations under the License. using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Simple vanilla neural net solving the famous XOR problem +/// https://github.com/amygdala/tensorflow-workshop/blob/master/workshop_sections/getting_started/xor/README.md +/// +public class NeuralNetXorKeras : SciSharpExample, IExample { - /// - /// Simple vanilla neural net solving the famous XOR problem - /// https://github.com/amygdala/tensorflow-workshop/blob/master/workshop_sections/getting_started/xor/README.md - /// - public class NeuralNetXorKeras : SciSharpExample, IExample - { - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "NN XOR in Keras", - Enabled = true - }; - - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.enable_eager_execution(); - - var x = np.array(new float[,] { { 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 } }); - var y = np.array(new float[,] { { 0 }, { 1 }, { 1 }, { 0 } }); - - var model = keras.Sequential(); - model.add(keras.Input(2)); - model.add(keras.layers.Dense(32, keras.activations.Relu)); - model.add(keras.layers.Dense(1, keras.activations.Sigmoid)); - model.compile(optimizer: keras.optimizers.Adam(), - loss: keras.losses.MeanSquaredError(), - new[] { "accuracy" }); - model.fit(x, y, 1, 100); - model.evaluate(x, y); - Tensor result = model.predict(x, 4); - return result.ToArray() is [< 0.5f, > 0.5f, > 0.5f, < 0.5f]; - } + Name = "NN XOR in Keras", + Enabled = true + }; + + public bool Run() + { + tf.enable_eager_execution(); + + var x = np.array(new float[,] { { 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 } }); + var y = np.array(new float[,] { { 0 }, { 1 }, { 1 }, { 0 } }); + + var model = keras.Sequential(); + model.add(keras.Input(2)); + model.add(keras.layers.Dense(32, keras.activations.Relu)); + model.add(keras.layers.Dense(1, keras.activations.Sigmoid)); + model.compile(optimizer: keras.optimizers.Adam(), + loss: keras.losses.MeanSquaredError(), + new[] { "accuracy" }); + model.fit(x, y, 1, 100); + model.evaluate(x, y); + Tensor result = model.predict(x, 4); + return result.ToArray() is [< 0.5f, > 0.5f, > 0.5f, < 0.5f]; } } diff --git a/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs b/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs index 17de909..1baa205 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs @@ -25,149 +25,148 @@ limitations under the License. using TensorFlowNET.Examples.Utility; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +public class DetectInMobilenet : SciSharpExample, IExample { - public class DetectInMobilenet : SciSharpExample, IExample - { - public float MIN_SCORE = 0.5f; + public float MIN_SCORE = 0.5f; - string modelDir = "ssd_mobilenet_v1_coco_2018_01_28"; - string imageDir = "images"; - string pbFile = "frozen_inference_graph.pb"; + string modelDir = "ssd_mobilenet_v1_coco_2018_01_28"; + string imageDir = "images"; + string pbFile = "frozen_inference_graph.pb"; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Object Detection in MobileNet (Graph)", - Enabled = true, - IsImportingGraph = true - }; - - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.compat.v1.disable_eager_execution(); + Name = "Object Detection in MobileNet (Graph)", + Enabled = true, + IsImportingGraph = true + }; - PrepareData(); + public bool Run() + { + tf.compat.v1.disable_eager_execution(); - Predict(); + PrepareData(); - return true; - } + Predict(); - public override Graph ImportGraph() - { - var graph = new Graph().as_default(); - graph.Import(Path.Join(modelDir, pbFile)); + return true; + } - return graph; - } + public override Graph ImportGraph() + { + var graph = new Graph().as_default(); + graph.Import(Path.Join(modelDir, pbFile)); - public override void Predict() - { - // read in the input image - var imgArr = ReadTensorFromImageFile(Path.Join(imageDir, "input.jpg")); + return graph; + } - var graph = Config.IsImportingGraph ? ImportGraph() : BuildGraph(); + public override void Predict() + { + // read in the input image + var imgArr = ReadTensorFromImageFile(Path.Join(imageDir, "input.jpg")); - using (var sess = tf.Session(graph)) - { - Tensor tensorNum = graph.OperationByName("num_detections"); - Tensor tensorBoxes = graph.OperationByName("detection_boxes"); - Tensor tensorScores = graph.OperationByName("detection_scores"); - Tensor tensorClasses = graph.OperationByName("detection_classes"); - Tensor imgTensor = graph.OperationByName("image_tensor"); - Tensor[] outTensorArr = new Tensor[] { tensorNum, tensorBoxes, tensorScores, tensorClasses }; + var graph = Config.IsImportingGraph ? ImportGraph() : BuildGraph(); + + using (var sess = tf.Session(graph)) + { + Tensor tensorNum = graph.OperationByName("num_detections"); + Tensor tensorBoxes = graph.OperationByName("detection_boxes"); + Tensor tensorScores = graph.OperationByName("detection_scores"); + Tensor tensorClasses = graph.OperationByName("detection_classes"); + Tensor imgTensor = graph.OperationByName("image_tensor"); + Tensor[] outTensorArr = new Tensor[] { tensorNum, tensorBoxes, tensorScores, tensorClasses }; - var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr)); + var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr)); - buildOutputImage(results); - } + buildOutputImage(results); } + } - public override void PrepareData() - { - // get model file - string url = "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz"; - Web.Download(url, modelDir, "ssd_mobilenet_v1_coco.tar.gz"); + public override void PrepareData() + { + // get model file + string url = "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz"; + Web.Download(url, modelDir, "ssd_mobilenet_v1_coco.tar.gz"); - Compress.ExtractTGZ(Path.Join(modelDir, "ssd_mobilenet_v1_coco.tar.gz"), "./"); + Compress.ExtractTGZ(Path.Join(modelDir, "ssd_mobilenet_v1_coco.tar.gz"), "./"); - // download sample picture - url = $"https://github.com/tensorflow/models/raw/master/research/object_detection/test_images/image2.jpg"; - Web.Download(url, imageDir, "input.jpg"); + // download sample picture + url = $"https://github.com/tensorflow/models/raw/master/research/object_detection/test_images/image2.jpg"; + Web.Download(url, imageDir, "input.jpg"); - // download the pbtxt file - url = $"https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt"; - Web.Download(url, modelDir, "mscoco_label_map.pbtxt"); - } + // download the pbtxt file + url = $"https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt"; + Web.Download(url, modelDir, "mscoco_label_map.pbtxt"); + } - private NDArray ReadTensorFromImageFile(string file_name) - { - var graph = tf.Graph().as_default(); + private NDArray ReadTensorFromImageFile(string file_name) + { + var graph = tf.Graph().as_default(); - var file_reader = tf.io.read_file(file_name, "file_reader"); - var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg"); - var casted = tf.cast(decodeJpeg, TF_DataType.TF_UINT8); - var dims_expander = tf.expand_dims(casted, 0); + var file_reader = tf.io.read_file(file_name, "file_reader"); + var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg"); + var casted = tf.cast(decodeJpeg, TF_DataType.TF_UINT8); + var dims_expander = tf.expand_dims(casted, 0); - using (var sess = tf.Session(graph)) - return sess.run(dims_expander); - } + using (var sess = tf.Session(graph)) + return sess.run(dims_expander); + } - private void buildOutputImage(NDArray[] resultArr) - { - // get pbtxt items - PbtxtItems pbTxtItems = PbtxtParser.ParsePbtxtFile(Path.Join(modelDir, "mscoco_label_map.pbtxt")); + private void buildOutputImage(NDArray[] resultArr) + { + // get pbtxt items + PbtxtItems pbTxtItems = PbtxtParser.ParsePbtxtFile(Path.Join(modelDir, "mscoco_label_map.pbtxt")); - // get bitmap - Bitmap bitmap = new Bitmap(Path.Join(imageDir, "input.jpg")); + // get bitmap + Bitmap bitmap = new Bitmap(Path.Join(imageDir, "input.jpg")); - var scores = resultArr[2].ToArray(); - var boxes = resultArr[1].ToArray(); - var id = np.squeeze(resultArr[3]).ToArray(); - for (int i = 0; i < scores.Length; i++) + var scores = resultArr[2].ToArray(); + var boxes = resultArr[1].ToArray(); + var id = np.squeeze(resultArr[3]).ToArray(); + for (int i = 0; i < scores.Length; i++) + { + float score = scores[i]; + if (score > MIN_SCORE) { - float score = scores[i]; - if (score > MIN_SCORE) + float top = boxes[i * 4] * bitmap.Height; + float left = boxes[i * 4 + 1] * bitmap.Width; + float bottom = boxes[i * 4 + 2] * bitmap.Height; + float right = boxes[i * 4 + 3] * bitmap.Width; + + Rectangle rect = new Rectangle() { - float top = boxes[i * 4] * bitmap.Height; - float left = boxes[i * 4 + 1] * bitmap.Width; - float bottom = boxes[i * 4 + 2] * bitmap.Height; - float right = boxes[i * 4 + 3] * bitmap.Width; - - Rectangle rect = new Rectangle() - { - X = (int)left, - Y = (int)top, - Width = (int)(right - left), - Height = (int)(bottom - top) - }; - - string name = pbTxtItems.items.Where(w => w.id == id[i]).Select(s => s.display_name).FirstOrDefault(); - - drawObjectOnBitmap(bitmap, rect, score, name); - } - } + X = (int)left, + Y = (int)top, + Width = (int)(right - left), + Height = (int)(bottom - top) + }; + + string name = pbTxtItems.items.Where(w => w.id == id[i]).Select(s => s.display_name).FirstOrDefault(); - string path = Path.Join(imageDir, "output.jpg"); - bitmap.Save(path); - Console.WriteLine($"Processed image is saved as {path}"); + drawObjectOnBitmap(bitmap, rect, score, name); + } } - private void drawObjectOnBitmap(Bitmap bmp, Rectangle rect, float score, string name) + string path = Path.Join(imageDir, "output.jpg"); + bitmap.Save(path); + Console.WriteLine($"Processed image is saved as {path}"); + } + + private void drawObjectOnBitmap(Bitmap bmp, Rectangle rect, float score, string name) + { + using (Graphics graphic = Graphics.FromImage(bmp)) { - using (Graphics graphic = Graphics.FromImage(bmp)) - { - graphic.SmoothingMode = SmoothingMode.AntiAlias; + graphic.SmoothingMode = SmoothingMode.AntiAlias; - using (Pen pen = new Pen(Color.Red, 2)) - { - graphic.DrawRectangle(pen, rect); + using (Pen pen = new Pen(Color.Red, 2)) + { + graphic.DrawRectangle(pen, rect); - Point p = new Point(rect.Right + 5, rect.Top + 5); - string text = string.Format("{0}:{1}%", name, (int)(score * 100)); - graphic.DrawString(text, new Font("Verdana", 8), Brushes.Red, p); - } + Point p = new Point(rect.Right + 5, rect.Top + 5); + string text = string.Format("{0}:{1}%", name, (int)(score * 100)); + graphic.DrawString(text, new Font("Verdana", 8), Brushes.Red, p); } } } diff --git a/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs b/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs index 0d6ecaa..c556cd5 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs @@ -19,71 +19,70 @@ limitations under the License. using System; using System.IO; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +public class MnistInYOLOv3 : SciSharpExample, IExample { - public class MnistInYOLOv3 : SciSharpExample, IExample + YoloConfig cfg; + float accuracy_test = 0f; + YoloDataset trainingData, testingData; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "MNIST in YOLOv3", + Enabled = false + }; + + public bool Run() { - YoloConfig cfg; - float accuracy_test = 0f; - YoloDataset trainingData, testingData; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "MNIST in YOLOv3", - Enabled = false - }; + cfg = new YoloConfig("YOLOv3"); + (trainingData, testingData) = PrepareData(); + Train(); + Test(); + return true; + } - public bool Run() + public override void Train() + { + // using wizard to train model + var wizard = new ModelWizard(); + var task = wizard.AddObjectDetectionTask(new TaskOptions { - cfg = new YoloConfig("YOLOv3"); - (trainingData, testingData) = PrepareData(); - Train(); - Test(); - return true; - } + InputShape = (28, 28, 1), + NumberOfClass = 10, + }); + task.SetModelArgs(cfg); - public override void Train() + task.Train(new YoloTrainingOptions { - // using wizard to train model - var wizard = new ModelWizard(); - var task = wizard.AddObjectDetectionTask(new TaskOptions - { - InputShape = (28, 28, 1), - NumberOfClass = 10, - }); - task.SetModelArgs(cfg); - - task.Train(new YoloTrainingOptions - { - TrainingData = trainingData, - TestingData = testingData - }); - } + TrainingData = trainingData, + TestingData = testingData + }); + } - public override void Test() + public override void Test() + { + var wizard = new ModelWizard(); + var task = wizard.AddObjectDetectionTask(new TaskOptions { - var wizard = new ModelWizard(); - var task = wizard.AddObjectDetectionTask(new TaskOptions - { - ModelPath = @"./YOLOv3/yolov3.h5" - }); - task.SetModelArgs(cfg); - var result = task.Test(new TestingOptions - { - - }); - accuracy_test = result.Accuracy; - } - - public (YoloDataset, YoloDataset) PrepareData() + ModelPath = @"./YOLOv3/yolov3.h5" + }); + task.SetModelArgs(cfg); + var result = task.Test(new TestingOptions { - string dataDir = Path.Combine("YOLOv3", "data"); - Directory.CreateDirectory(dataDir); + + }); + accuracy_test = result.Accuracy; + } + + public (YoloDataset, YoloDataset) PrepareData() + { + string dataDir = Path.Combine("YOLOv3", "data"); + Directory.CreateDirectory(dataDir); - var trainset = new YoloDataset("train", cfg); - var testset = new YoloDataset("test", cfg); - return (trainset, testset); - } + var trainset = new YoloDataset("train", cfg); + var testset = new YoloDataset("test", cfg); + return (trainset, testset); } } diff --git a/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs b/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs index d6e82bf..c52642b 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs @@ -25,80 +25,115 @@ limitations under the License. using static SharpCV.Binding; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// https://github.com/shimat/opencvsharp/wiki/Capturing-Video +/// +public class YoloCoco : SciSharpExample, IExample { - /// - /// https://github.com/shimat/opencvsharp/wiki/Capturing-Video - /// - public class YoloCoco : SciSharpExample, IExample + int input_size = 416; + int num_classes = 1; + + string[] return_elements = new[] { - int input_size = 416; - int num_classes = 1; + "input/input_data:0", + "pred_sbbox/concat_2:0", + "pred_mbbox/concat_2:0", + "pred_lbbox/concat_2:0" + }; + + Tensor[] return_tensors; - string[] return_elements = new[] + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - "input/input_data:0", - "pred_sbbox/concat_2:0", - "pred_mbbox/concat_2:0", - "pred_lbbox/concat_2:0" + Name = "YoloCoco", + Enabled = false, + IsImportingGraph = true }; - Tensor[] return_tensors; + public bool Run() + { + PrepareData(); + Predict(); + return true; + } - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "YoloCoco", - Enabled = false, - IsImportingGraph = true - }; + public override void PrepareData() + { + // download video + string url = "https://raw.githubusercontent.com/YunYang1994/tensorflow-yolov3/master/docs/images/road.mp4"; + Web.Download(url, Config.Name, "road.mp4"); + } - public bool Run() - { - PrepareData(); - Predict(); - return true; - } + public override Graph ImportGraph() + { + var graph = tf.Graph().as_default(); - public override void PrepareData() - { - // download video - string url = "https://raw.githubusercontent.com/YunYang1994/tensorflow-yolov3/master/docs/images/road.mp4"; - Web.Download(url, Config.Name, "road.mp4"); - } + var bytes = File.ReadAllBytes(Path.Combine(Config.Name, "yolov3_coco.pb")); + var graphDef = GraphDef.Parser.ParseFrom(bytes); + return_tensors = tf.import_graph_def(graphDef, return_elements: return_elements) + .Select(x => x as Tensor) + .ToArray(); - public override Graph ImportGraph() + return graph; + } + + public override void Predict() + { + PredictFromImage(); + // PredictFromVideo(); + } + + public void PredictFromImage() + { + var graph = ImportGraph(); + using (var sess = new Session(graph)) { - var graph = tf.Graph().as_default(); + var original_image = cv2.imread(@"D:\SciSharp\SciSharp-Stack-Examples\data\images\cat_face.jpg"); + original_image = cv2.cvtColor(original_image, ColorConversionCodes.COLOR_BGR2RGB); + var original_image_size = ((int)original_image.shape[0], (int)original_image.shape[1]); + var image_data = image_preporcess(original_image, (input_size, input_size)); + image_data = image_data[np.newaxis, Slice.Ellipsis]; - var bytes = File.ReadAllBytes(Path.Combine(Config.Name, "yolov3_coco.pb")); - var graphDef = GraphDef.Parser.ParseFrom(bytes); - return_tensors = tf.import_graph_def(graphDef, return_elements: return_elements) - .Select(x => x as Tensor) - .ToArray(); + var (pred_sbbox, pred_mbbox, pred_lbbox) = sess.run((return_tensors[1], return_tensors[2], return_tensors[3]), + (return_tensors[0], image_data)); - return graph; + var pred_bbox = np.concatenate(new[] + { + np.reshape(pred_sbbox, (-1, 5 + num_classes)), + np.reshape(pred_mbbox, (-1, 5 + num_classes)), + np.reshape(pred_lbbox, (-1, 5 + num_classes)) + }, axis: 0); + + var bboxes = postprocess_boxes(pred_bbox, original_image_size, input_size, 0.3f); + var bboxess = nms(bboxes, 0.75f, method: "nms"); + var image = draw_bbox(original_image, bboxess); + cv2.imshow("Detected Objects in TensorFlow.NET", image); + cv2.waitKey(); } + } - public override void Predict() + public void PredictFromVideo() + { + var graph = ImportGraph(); + using (var sess = new Session(graph)) { - PredictFromImage(); - // PredictFromVideo(); - } + // Opens MP4 file (ffmpeg is probably needed) + var vid = cv2.VideoCapture(Path.Combine(Config.Name, "road.mp4")); - public void PredictFromImage() - { - var graph = ImportGraph(); - using (var sess = new Session(graph)) + int sleepTime = (int)Math.Round(1000 / 24.0); + + var (loaded, frame) = vid.read(); + while (loaded) { - var original_image = cv2.imread(@"D:\SciSharp\SciSharp-Stack-Examples\data\images\cat_face.jpg"); - original_image = cv2.cvtColor(original_image, ColorConversionCodes.COLOR_BGR2RGB); - var original_image_size = ((int)original_image.shape[0], (int)original_image.shape[1]); - var image_data = image_preporcess(original_image, (input_size, input_size)); + var frame_size = ((int)frame.shape[0], (int)frame.shape[1]); + var image_data = image_preporcess(frame, (input_size, input_size)); image_data = image_data[np.newaxis, Slice.Ellipsis]; var (pred_sbbox, pred_mbbox, pred_lbbox) = sess.run((return_tensors[1], return_tensors[2], return_tensors[3]), - (return_tensors[0], image_data)); + (return_tensors[0], image_data)); var pred_bbox = np.concatenate(new[] { @@ -107,212 +142,176 @@ public void PredictFromImage() np.reshape(pred_lbbox, (-1, 5 + num_classes)) }, axis: 0); - var bboxes = postprocess_boxes(pred_bbox, original_image_size, input_size, 0.3f); - var bboxess = nms(bboxes, 0.75f, method: "nms"); - var image = draw_bbox(original_image, bboxess); - cv2.imshow("Detected Objects in TensorFlow.NET", image); - cv2.waitKey(); - } - } + var bboxes = postprocess_boxes(pred_bbox, frame_size, input_size, 0.3f); + var bboxess = nms(bboxes, 0.45f, method: "nms"); + var image = draw_bbox(frame, bboxess); - public void PredictFromVideo() - { - var graph = ImportGraph(); - using (var sess = new Session(graph)) - { - // Opens MP4 file (ffmpeg is probably needed) - var vid = cv2.VideoCapture(Path.Combine(Config.Name, "road.mp4")); + cv2.imshow("objects", image); + cv2.waitKey(sleepTime); - int sleepTime = (int)Math.Round(1000 / 24.0); + (loaded, frame) = vid.read(); + } + } + } - var (loaded, frame) = vid.read(); - while (loaded) - { - var frame_size = ((int)frame.shape[0], (int)frame.shape[1]); - var image_data = image_preporcess(frame, (input_size, input_size)); - image_data = image_data[np.newaxis, Slice.Ellipsis]; + private NDArray image_preporcess(Mat image, (int, int) target_size) + { + image = cv2.cvtColor(image, ColorConversionCodes.COLOR_BGR2RGB); + var (ih, iw) = target_size; + var (h, w) = (image.shape[0] + 0.0f, image.shape[1] + 0.0f); + var scale = min(iw / w, ih / h); + var (nw, nh) = ((int)Math.Round(scale * w), (int)Math.Round(scale * h)); + var image_resized = cv2.resize(image, (nw, nh)); + var image_padded = np.full((ih, iw, 3), fill_value: 128.0f); + var (dw, dh) = ((iw - nw) / 2, (ih - nh) / 2); + image_padded[new Slice(dh, nh + dh), new Slice(dw, nw + dw), Slice.All] = image_resized; + image_padded = image_padded / 255; + + return image_padded; + } - var (pred_sbbox, pred_mbbox, pred_lbbox) = sess.run((return_tensors[1], return_tensors[2], return_tensors[3]), - (return_tensors[0], image_data)); + private NDArray postprocess_boxes(NDArray pred_bbox, (int, int) org_img_shape, float input_size, float score_threshold) + { + var valid_scale = new[] { 0, np.inf }; - var pred_bbox = np.concatenate(new[] - { - np.reshape(pred_sbbox, (-1, 5 + num_classes)), - np.reshape(pred_mbbox, (-1, 5 + num_classes)), - np.reshape(pred_lbbox, (-1, 5 + num_classes)) - }, axis: 0); + var pred_xywh = pred_bbox[Slice.All, new Slice(0, 4)]; + var pred_conf = pred_bbox[Slice.All, 4]; + var pred_prob = pred_bbox[Slice.All, new Slice(5)]; - var bboxes = postprocess_boxes(pred_bbox, frame_size, input_size, 0.3f); - var bboxess = nms(bboxes, 0.45f, method: "nms"); - var image = draw_bbox(frame, bboxess); + // (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax) + var pred_coor = np.concatenate(new[] + { + pred_xywh[Slice.All, new Slice(stop: 2)] - pred_xywh[Slice.All, new Slice(2)] * 0.5f, + pred_xywh[Slice.All, new Slice(stop: 2)] + pred_xywh[Slice.All, new Slice(2)] * 0.5f + }, axis: -1); - cv2.imshow("objects", image); - cv2.waitKey(sleepTime); + // (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org) + var (org_h, org_w) = org_img_shape; + var resize_ratio = min(input_size / org_w, input_size / org_h); + var dw = (input_size - resize_ratio * org_w) / 2; + var dh = (input_size - resize_ratio * org_h) / 2; - (loaded, frame) = vid.read(); - } - } - } + pred_coor[Slice.All, new Slice(0, step: 2)] = 1.0 * (pred_coor[Slice.All, new Slice(0, step: 2)] - dw) / resize_ratio; + pred_coor[Slice.All, new Slice(1, step: 2)] = 1.0 * (pred_coor[Slice.All, new Slice(1, step: 2)] - dh) / resize_ratio; - private NDArray image_preporcess(Mat image, (int, int) target_size) + // (3) clip some boxes those are out of range + pred_coor = np.concatenate(new[] { - image = cv2.cvtColor(image, ColorConversionCodes.COLOR_BGR2RGB); - var (ih, iw) = target_size; - var (h, w) = (image.shape[0] + 0.0f, image.shape[1] + 0.0f); - var scale = min(iw / w, ih / h); - var (nw, nh) = ((int)Math.Round(scale * w), (int)Math.Round(scale * h)); - var image_resized = cv2.resize(image, (nw, nh)); - var image_padded = np.full((ih, iw, 3), fill_value: 128.0f); - var (dw, dh) = ((iw - nw) / 2, (ih - nh) / 2); - image_padded[new Slice(dh, nh + dh), new Slice(dw, nw + dw), Slice.All] = image_resized; - image_padded = image_padded / 255; - - return image_padded; - } + np.maximum(pred_coor[Slice.All, new Slice(stop: 2)], np.array(new[] { 0, 0 })), + np.minimum(pred_coor[Slice.All, new Slice(2)], np.array(new[] { org_w - 1, org_h - 1 })) + }, axis: -1); + + var invalid_mask = np.logical_or(pred_coor[Slice.All, 0] > pred_coor[Slice.All, 2], pred_coor[Slice.All, 1] > pred_coor[Slice.All, 3]); + pred_coor[invalid_mask] = 0; + + // (4) discard some invalid boxes + var coor_diff = pred_coor[Slice.All, new Slice(2, 4)] - pred_coor[Slice.All, new Slice(0, 2)]; + var bboxes_scale = np.sqrt(np.prod(coor_diff, axis: -1)); + var scale_mask = np.logical_and(bboxes_scale > 0d, bboxes_scale < double.MaxValue); + + // (5) discard some boxes with low scores + NDArray coors; + var classes = np.argmax(pred_prob, axis: -1); + var scores = pred_conf * pred_prob[np.arange(len(pred_coor)), classes]; + var score_mask = scores > score_threshold; + var mask = np.logical_and(scale_mask, score_mask); + (coors, scores, classes) = (pred_coor[mask], scores[mask], classes[mask]); + + return np.concatenate(new[] { coors, scores[Slice.All, np.newaxis], classes[Slice.All, np.newaxis] }, axis: -1); + } - private NDArray postprocess_boxes(NDArray pred_bbox, (int, int) org_img_shape, float input_size, float score_threshold) + private NDArray[] nms(NDArray bboxes, float iou_threshold, float sigma = 0.3f, string method = "nms") + { + var classes_in_img = bboxes[Slice.All, 5].ToArray().Distinct().ToArray(); + var best_bboxes = new List(); + foreach (var cls in classes_in_img) { - var valid_scale = new[] { 0, np.inf }; - - var pred_xywh = pred_bbox[Slice.All, new Slice(0, 4)]; - var pred_conf = pred_bbox[Slice.All, 4]; - var pred_prob = pred_bbox[Slice.All, new Slice(5)]; - - // (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax) - var pred_coor = np.concatenate(new[] + var cls_mask = bboxes[Slice.All, 5] == cls; + var cls_bboxes = bboxes[cls_mask]; + while (len(cls_bboxes) > 0) { - pred_xywh[Slice.All, new Slice(stop: 2)] - pred_xywh[Slice.All, new Slice(2)] * 0.5f, - pred_xywh[Slice.All, new Slice(stop: 2)] + pred_xywh[Slice.All, new Slice(2)] * 0.5f - }, axis: -1); + var max_ind = np.argmax(cls_bboxes[Slice.All, 4]); + var best_bbox = cls_bboxes[max_ind]; + best_bboxes.append(best_bbox); + cls_bboxes = np.concatenate(new[] { cls_bboxes[new Slice(stop: max_ind)], cls_bboxes[new Slice(max_ind + 1)] }); + NDArray iou = bboxes_iou(best_bbox[np.newaxis, new Slice(stop: 4)], cls_bboxes[Slice.All, new Slice(stop: 4)]); - // (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org) - var (org_h, org_w) = org_img_shape; - var resize_ratio = min(input_size / org_w, input_size / org_h); - var dw = (input_size - resize_ratio * org_w) / 2; - var dh = (input_size - resize_ratio * org_h) / 2; + if (len(iou) == 0) + continue; - pred_coor[Slice.All, new Slice(0, step: 2)] = 1.0 * (pred_coor[Slice.All, new Slice(0, step: 2)] - dw) / resize_ratio; - pred_coor[Slice.All, new Slice(1, step: 2)] = 1.0 * (pred_coor[Slice.All, new Slice(1, step: 2)] - dh) / resize_ratio; + var weight = np.ones(new Shape(len(iou)), dtype: np.float32); - // (3) clip some boxes those are out of range - pred_coor = np.concatenate(new[] - { - np.maximum(pred_coor[Slice.All, new Slice(stop: 2)], np.array(new[] { 0, 0 })), - np.minimum(pred_coor[Slice.All, new Slice(2)], np.array(new[] { org_w - 1, org_h - 1 })) - }, axis: -1); - - var invalid_mask = np.logical_or(pred_coor[Slice.All, 0] > pred_coor[Slice.All, 2], pred_coor[Slice.All, 1] > pred_coor[Slice.All, 3]); - pred_coor[invalid_mask] = 0; - - // (4) discard some invalid boxes - var coor_diff = pred_coor[Slice.All, new Slice(2, 4)] - pred_coor[Slice.All, new Slice(0, 2)]; - var bboxes_scale = np.sqrt(np.prod(coor_diff, axis: -1)); - var scale_mask = np.logical_and(bboxes_scale > 0d, bboxes_scale < double.MaxValue); - - // (5) discard some boxes with low scores - NDArray coors; - var classes = np.argmax(pred_prob, axis: -1); - var scores = pred_conf * pred_prob[np.arange(len(pred_coor)), classes]; - var score_mask = scores > score_threshold; - var mask = np.logical_and(scale_mask, score_mask); - (coors, scores, classes) = (pred_coor[mask], scores[mask], classes[mask]); - - return np.concatenate(new[] { coors, scores[Slice.All, np.newaxis], classes[Slice.All, np.newaxis] }, axis: -1); - } + if (method == "nms") + { + var iou_mask = iou > iou_threshold; + if (iou_mask.ndim == 0) + iou_mask = iou_mask.reshape(1); + if (iou_mask.size > 0) + weight[iou_mask] = 0.0f; + } + else if (method == "soft-nms") + { + weight = np.exp(-(1.0 * np.sqrt(iou) / sigma)); + } - private NDArray[] nms(NDArray bboxes, float iou_threshold, float sigma = 0.3f, string method = "nms") - { - var classes_in_img = bboxes[Slice.All, 5].ToArray().Distinct().ToArray(); - var best_bboxes = new List(); - foreach (var cls in classes_in_img) - { - var cls_mask = bboxes[Slice.All, 5] == cls; - var cls_bboxes = bboxes[cls_mask]; - while (len(cls_bboxes) > 0) + //if(len(cls_bboxes) > 0) { - var max_ind = np.argmax(cls_bboxes[Slice.All, 4]); - var best_bbox = cls_bboxes[max_ind]; - best_bboxes.append(best_bbox); - cls_bboxes = np.concatenate(new[] { cls_bboxes[new Slice(stop: max_ind)], cls_bboxes[new Slice(max_ind + 1)] }); - NDArray iou = bboxes_iou(best_bbox[np.newaxis, new Slice(stop: 4)], cls_bboxes[Slice.All, new Slice(stop: 4)]); - - if (len(iou) == 0) - continue; - - var weight = np.ones(new Shape(len(iou)), dtype: np.float32); - - if (method == "nms") - { - var iou_mask = iou > iou_threshold; - if (iou_mask.ndim == 0) - iou_mask = iou_mask.reshape(1); - if (iou_mask.size > 0) - weight[iou_mask] = 0.0f; - } - else if (method == "soft-nms") - { - weight = np.exp(-(1.0 * np.sqrt(iou) / sigma)); - } - - //if(len(cls_bboxes) > 0) - { - cls_bboxes[Slice.All, 4] = cls_bboxes[Slice.All, 4] * weight; - var score_mask = cls_bboxes[Slice.All, 4] > 0f; - cls_bboxes = cls_bboxes[score_mask]; - } + cls_bboxes[Slice.All, 4] = cls_bboxes[Slice.All, 4] * weight; + var score_mask = cls_bboxes[Slice.All, 4] > 0f; + cls_bboxes = cls_bboxes[score_mask]; } } - - return best_bboxes.ToArray(); } - private NDArray bboxes_iou(NDArray boxes1, NDArray boxes2) - { - if (boxes2.size == 0) - return boxes2; + return best_bboxes.ToArray(); + } - var boxes1_area = (boxes1[Slice.Ellipsis, 2] - boxes1[Slice.Ellipsis, 0]) * (boxes1[Slice.Ellipsis, 3] - boxes1[Slice.Ellipsis, 1]); - var boxes2_area = (boxes2[Slice.Ellipsis, 2] - boxes2[Slice.Ellipsis, 0]) * (boxes2[Slice.Ellipsis, 3] - boxes2[Slice.Ellipsis, 1]); + private NDArray bboxes_iou(NDArray boxes1, NDArray boxes2) + { + if (boxes2.size == 0) + return boxes2; - var left_up = np.maximum(boxes1[Slice.Ellipsis, new Slice(stop: 2)], boxes2[Slice.Ellipsis, new Slice(stop: 2)]); - var right_down = np.minimum(boxes1[Slice.Ellipsis, new Slice(2)], boxes2[Slice.Ellipsis, new Slice(2)]); + var boxes1_area = (boxes1[Slice.Ellipsis, 2] - boxes1[Slice.Ellipsis, 0]) * (boxes1[Slice.Ellipsis, 3] - boxes1[Slice.Ellipsis, 1]); + var boxes2_area = (boxes2[Slice.Ellipsis, 2] - boxes2[Slice.Ellipsis, 0]) * (boxes2[Slice.Ellipsis, 3] - boxes2[Slice.Ellipsis, 1]); - var inter_section = np.maximum(right_down - left_up, 0.0); - var inter_area = inter_section[Slice.Ellipsis, 0] * inter_section[Slice.Ellipsis, 1]; - var union_area = boxes1_area + boxes2_area - inter_area; - var ious = np.maximum(1.0 * inter_area / union_area, np.array(1.1920929e-7)); + var left_up = np.maximum(boxes1[Slice.Ellipsis, new Slice(stop: 2)], boxes2[Slice.Ellipsis, new Slice(stop: 2)]); + var right_down = np.minimum(boxes1[Slice.Ellipsis, new Slice(2)], boxes2[Slice.Ellipsis, new Slice(2)]); - return ious; - } + var inter_section = np.maximum(right_down - left_up, 0.0); + var inter_area = inter_section[Slice.Ellipsis, 0] * inter_section[Slice.Ellipsis, 1]; + var union_area = boxes1_area + boxes2_area - inter_area; + var ious = np.maximum(1.0 * inter_area / union_area, np.array(1.1920929e-7)); - private Mat draw_bbox(Mat image, NDArray[] bboxes) - { - // var rnd = new Random(); - var classes = File.ReadAllLines(@"D:\SciSharp\SciSharp-Stack-Examples\data\classes\coco.names"); - var num_classes = len(classes); - var (image_h, image_w) = (image.shape[0], image.shape[1]); - // var hsv_tuples = range(num_classes).Select(x => (rnd.Next(255), rnd.Next(255), rnd.Next(255))).ToArray(); + return ious; + } - foreach (var (i, bbox) in enumerate(bboxes)) - { - var coor = bbox[new Slice(stop: 4)].astype(np.int32); - var fontScale = 0.5; - float score = bbox[4]; - var class_ind = (float)bbox[5]; - var bbox_color = (0, 0, 250);// hsv_tuples[rnd.Next(num_classes)]; - var bbox_thick = (int)(0.6 * (image_h + image_w) / 600); - cv2.rectangle(image, (coor[0], coor[1]), (coor[2], coor[3]), bbox_color, bbox_thick); - - // show label; - var bbox_mess = $"{classes[(int)class_ind]}: {score.ToString("P")}"; - var t_size = cv2.getTextSize(bbox_mess, HersheyFonts.HERSHEY_SIMPLEX, fontScale, thickness: bbox_thick / 2); - /*cv2.rectangle(image, (coor[0], coor[1]), (coor[0] + t_size.Width, coor[1] - t_size.Height - 3), bbox_color, -1); - cv2.putText(image, bbox_mess, (coor[0], coor[1] - 2), HersheyFonts.HERSHEY_SIMPLEX, - fontScale, (0, 0, 0), bbox_thick / 2, lineType: LineTypes.LINE_AA);*/ - throw new NotImplementedException(""); - } + private Mat draw_bbox(Mat image, NDArray[] bboxes) + { + // var rnd = new Random(); + var classes = File.ReadAllLines(@"D:\SciSharp\SciSharp-Stack-Examples\data\classes\coco.names"); + var num_classes = len(classes); + var (image_h, image_w) = (image.shape[0], image.shape[1]); + // var hsv_tuples = range(num_classes).Select(x => (rnd.Next(255), rnd.Next(255), rnd.Next(255))).ToArray(); - return image; + foreach (var (i, bbox) in enumerate(bboxes)) + { + var coor = bbox[new Slice(stop: 4)].astype(np.int32); + var fontScale = 0.5; + float score = bbox[4]; + var class_ind = (float)bbox[5]; + var bbox_color = (0, 0, 250);// hsv_tuples[rnd.Next(num_classes)]; + var bbox_thick = (int)(0.6 * (image_h + image_w) / 600); + cv2.rectangle(image, (coor[0], coor[1]), (coor[2], coor[3]), bbox_color, bbox_thick); + + // show label; + var bbox_mess = $"{classes[(int)class_ind]}: {score.ToString("P")}"; + var t_size = cv2.getTextSize(bbox_mess, HersheyFonts.HERSHEY_SIMPLEX, fontScale, thickness: bbox_thick / 2); + /*cv2.rectangle(image, (coor[0], coor[1]), (coor[0] + t_size.Width, coor[1] - t_size.Height - 3), bbox_color, -1); + cv2.putText(image, bbox_mess, (coor[0], coor[1] - 2), HersheyFonts.HERSHEY_SIMPLEX, + fontScale, (0, 0, 0), bbox_thick / 2, lineType: LineTypes.LINE_AA);*/ + throw new NotImplementedException(""); } + + return image; } } diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index 3dab9e9..c9892d6 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -35,9 +35,9 @@ static void Main(string[] args) var examples = Assembly.GetEntryAssembly().GetTypes() .Where(x => x.GetInterfaces().Contains(typeof(IExample))) - //.Where(x => x.Name == nameof(NeuralNetXorKeras)) - //.Where(x => x.Name == nameof(SentimentClassification)) - //.Where(x => x.Name == nameof(TextGeneration)) + //.Where(x => x.Name == nameof(MnistGAN)) + //.Where(x => x.Name == nameof(ImageClassificationKeras)) + //.Where(x => x.Name == nameof(WeatherPrediction)) .ToArray(); Console.WriteLine(Environment.OSVersion, Color.Yellow); diff --git a/src/TensorFlowNET.Examples/SciSharpExample.cs b/src/TensorFlowNET.Examples/SciSharpExample.cs index a331322..2de8a6c 100644 --- a/src/TensorFlowNET.Examples/SciSharpExample.cs +++ b/src/TensorFlowNET.Examples/SciSharpExample.cs @@ -2,50 +2,49 @@ using Tensorflow; using Tensorflow.Keras.Layers; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +public class SciSharpExample { - public class SciSharpExample - { - public ExampleConfig Config { get; set; } - protected LayersApi layers = new LayersApi(); + public ExampleConfig Config { get; set; } + protected LayersApi layers = new LayersApi(); - public virtual void BuildModel() - { + public virtual void BuildModel() + { - } + } - public virtual Graph BuildGraph() - { - throw new NotImplementedException(); - } + public virtual Graph BuildGraph() + { + throw new NotImplementedException(); + } - public virtual Graph ImportGraph() - { - throw new NotImplementedException(); - } + public virtual Graph ImportGraph() + { + throw new NotImplementedException(); + } - public virtual void PrepareData() - { - throw new NotImplementedException(); - } + public virtual void PrepareData() + { + throw new NotImplementedException(); + } - public virtual void Train() - { + public virtual void Train() + { - } + } - public virtual void Test() - { + public virtual void Test() + { - } + } - public virtual void Predict() - { - } + public virtual void Predict() + { + } - public virtual string FreezeModel() - { - throw new NotImplementedException(); - } + public virtual string FreezeModel() + { + throw new NotImplementedException(); } } diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 583aa8a..4569d48 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -51,12 +51,12 @@ - + - + diff --git a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs index 1abaece..c096fcb 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs @@ -7,115 +7,114 @@ using static Tensorflow.Binding; using static Tensorflow.KerasApi; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// This tutorial demonstrates text classification starting from plain text files stored on disk. +/// You'll train a binary classifier to perform sentiment analysis on an IMDB dataset. +/// At the end of the notebook, there is an exercise for you to try, in which you'll train a +/// multiclass classifier to predict the tag for a programming question on Stack Overflow. +/// https://www.tensorflow.org/tutorials/keras/text_classification +/// +public class BinaryTextClassification : SciSharpExample, IExample { - /// - /// This tutorial demonstrates text classification starting from plain text files stored on disk. - /// You'll train a binary classifier to perform sentiment analysis on an IMDB dataset. - /// At the end of the notebook, there is an exercise for you to try, in which you'll train a - /// multiclass classifier to predict the tag for a programming question on Stack Overflow. - /// https://www.tensorflow.org/tutorials/keras/text_classification - /// - public class BinaryTextClassification : SciSharpExample, IExample - { - NDArray train_data, train_labels, test_data, test_labels; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Binary Text Classification", - Enabled = false - }; + NDArray train_data, train_labels, test_data, test_labels; - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - PrepareData(); + Name = "Binary Text Classification", + Enabled = false + }; - // Console.WriteLine($"Training entries: {train_data.shape[0]}, labels: {train_labels.shape[0]}"); + public bool Run() + { + PrepareData(); - // A dictionary mapping words to an integer index - /*train_data = keras.preprocessing.sequence.pad_sequences(train_data, - value: word_index[""], - padding: "post", - maxlen: 256); + // Console.WriteLine($"Training entries: {train_data.shape[0]}, labels: {train_labels.shape[0]}"); - test_data = keras.preprocessing.sequence.pad_sequences(test_data, - value: word_index[""], - padding: "post", - maxlen: 256);*/ + // A dictionary mapping words to an integer index + /*train_data = keras.preprocessing.sequence.pad_sequences(train_data, + value: word_index[""], + padding: "post", + maxlen: 256); - // input shape is the vocabulary count used for the movie reviews (10,000 words) + test_data = keras.preprocessing.sequence.pad_sequences(test_data, + value: word_index[""], + padding: "post", + maxlen: 256);*/ - var model = keras.Sequential(); - //var layer = tf.keras.layers.Embedding(vocab_size, 16); - //model.add(layer); + // input shape is the vocabulary count used for the movie reviews (10,000 words) - return false; - } + var model = keras.Sequential(); + //var layer = tf.keras.layers.Embedding(vocab_size, 16); + //model.add(layer); + + return false; + } - public override void PrepareData() + public override void PrepareData() + { + // tf.debugging.set_log_device_placement(true); + string url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"; + var dataset = keras.utils.get_file("aclImdb_v1.tar.gz", url, + untar: true, + cache_dir: Path.GetTempPath(), + cache_subdir: "aclImdb_v1"); + var data_dir = Path.Combine(dataset, "aclImdb"); + var train_dir = Path.Combine(data_dir, "train"); + + int batch_size = 32; + int seed = 42; + var raw_train_ds = keras.preprocessing.text_dataset_from_directory( + train_dir, + batch_size: batch_size, + validation_split: 0.2f, + subset: "training", + seed: seed); + + foreach (var (text_batch, label_batch) in raw_train_ds.take(1)) { - // tf.debugging.set_log_device_placement(true); - string url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"; - var dataset = keras.utils.get_file("aclImdb_v1.tar.gz", url, - untar: true, - cache_dir: Path.GetTempPath(), - cache_subdir: "aclImdb_v1"); - var data_dir = Path.Combine(dataset, "aclImdb"); - var train_dir = Path.Combine(data_dir, "train"); - - int batch_size = 32; - int seed = 42; - var raw_train_ds = keras.preprocessing.text_dataset_from_directory( - train_dir, - batch_size: batch_size, - validation_split: 0.2f, - subset: "training", - seed: seed); - - foreach (var (text_batch, label_batch) in raw_train_ds.take(1)) + foreach (var i in range(3)) { - foreach (var i in range(3)) - { - print("Review", text_batch.StringData()[i]); - print("Label", label_batch.numpy()[i]); - } + print("Review", text_batch.StringData()[i]); + print("Label", label_batch.numpy()[i]); } + } - print("Label 0 corresponds to", raw_train_ds.class_names[0]); - print("Label 1 corresponds to", raw_train_ds.class_names[1]); + print("Label 0 corresponds to", raw_train_ds.class_names[0]); + print("Label 1 corresponds to", raw_train_ds.class_names[1]); - var raw_val_ds = keras.preprocessing.text_dataset_from_directory( - train_dir, - batch_size: batch_size, - validation_split: 0.2f, - subset: "validation", - seed: seed); + var raw_val_ds = keras.preprocessing.text_dataset_from_directory( + train_dir, + batch_size: batch_size, + validation_split: 0.2f, + subset: "validation", + seed: seed); - var test_dir = Path.Combine(data_dir, "test"); - var raw_test_ds = keras.preprocessing.text_dataset_from_directory( - test_dir, - batch_size: batch_size); + var test_dir = Path.Combine(data_dir, "test"); + var raw_test_ds = keras.preprocessing.text_dataset_from_directory( + test_dir, + batch_size: batch_size); - var max_features = 10000; - var sequence_length = 250; + var max_features = 10000; + var sequence_length = 250; - Func custom_standardization = input_data => - { - var lowercase = tf.strings.lower(input_data); - var stripped_html = tf.strings.regex_replace(lowercase, "
", " "); - return tf.strings.regex_replace(stripped_html, - "'[!\"\\#\\$%\\&\'\\(\\)\\*\\+,\\-\\./:;<=>\\?@\\[\\\\\\]\\^_`\\{\\|\\}\\~]'", - ""); - }; - - var vectorize_layer = keras.layers.preprocessing.TextVectorization(standardize: custom_standardization, - max_tokens: max_features, - output_mode: "int", - output_sequence_length: sequence_length); - - var train_text = raw_train_ds.map(inputs => inputs[0]); - //vectorize_layer.adapt(train_text); - } + Func custom_standardization = input_data => + { + var lowercase = tf.strings.lower(input_data); + var stripped_html = tf.strings.regex_replace(lowercase, "
", " "); + return tf.strings.regex_replace(stripped_html, + "'[!\"\\#\\$%\\&\'\\(\\)\\*\\+,\\-\\./:;<=>\\?@\\[\\\\\\]\\^_`\\{\\|\\}\\~]'", + ""); + }; + + var vectorize_layer = keras.layers.preprocessing.TextVectorization(standardize: custom_standardization, + max_tokens: max_features, + output_mode: "int", + output_sequence_length: sequence_length); + + var train_text = raw_train_ds.map(inputs => inputs[0]); + //vectorize_layer.adapt(train_text); } } diff --git a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs index 05d74b7..cde3191 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs @@ -26,300 +26,299 @@ limitations under the License. using TensorFlowNET.Examples.Text; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// https://github.com/dongjun-Lee/text-classification-models-tf +/// +public class CnnTextClassification : SciSharpExample, IExample { - /// - /// https://github.com/dongjun-Lee/text-classification-models-tf - /// - public class CnnTextClassification : SciSharpExample, IExample - { - public int? DataLimit = null; + public int? DataLimit = null; - const string dataDir = "cnn_text"; + const string dataDir = "cnn_text"; - string TRAIN_PATH = $"{dataDir}/dbpedia_csv/train.csv"; + string TRAIN_PATH = $"{dataDir}/dbpedia_csv/train.csv"; - int NUM_CLASS = 14; - int BATCH_SIZE = 64; - int NUM_EPOCHS = 10; - int WORD_MAX_LEN = 100; - int CHAR_MAX_LEN = 1014; + int NUM_CLASS = 14; + int BATCH_SIZE = 64; + int NUM_EPOCHS = 10; + int WORD_MAX_LEN = 100; + int CHAR_MAX_LEN = 1014; - float loss_value = 0; - double max_accuracy = 0; + float loss_value = 0; + double max_accuracy = 0; - int alphabet_size = -1; - int vocabulary_size = -1; - NDArray train_x, test_x, train_y, test_y; + int alphabet_size = -1; + int vocabulary_size = -1; + NDArray train_x, test_x, train_y, test_y; - ITextModel textModel; - public string ModelName = "word_cnn"; // word_cnn | char_cnn | vd_cnn | word_rnn | att_rnn | rcnn + ITextModel textModel; + public string ModelName = "word_cnn"; // word_cnn | char_cnn | vd_cnn | word_rnn | att_rnn | rcnn - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "CNN Text Classification (Graph)", - Enabled = true, - IsImportingGraph = true - }; - - public bool Run() + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - tf.compat.v1.disable_eager_execution(); + Name = "CNN Text Classification (Graph)", + Enabled = true, + IsImportingGraph = true + }; - PrepareData(); - Predict(); - Test(); - Train(); - FreezeModel(); + public bool Run() + { + tf.compat.v1.disable_eager_execution(); - return max_accuracy > 0.9; - } + PrepareData(); + Predict(); + Test(); + Train(); + FreezeModel(); - // TODO: this originally is an SKLearn utility function. it randomizes train and test which we don't do here - private (NDArray, NDArray, NDArray, NDArray) train_test_split(NDArray x, NDArray y, float test_size = 0.3f) - { - Console.WriteLine("Splitting in Training and Testing data..."); - var len = x.shape[0]; - //int classes = y.Data().Distinct().Count(); - //int samples = len / classes; - int train_size = (int)Math.Round(len * (1 - test_size)); - train_x = x[new Slice(stop: train_size), new Slice()]; - test_x = x[new Slice(start: train_size), new Slice()]; - train_y = y[new Slice(stop: train_size)]; - test_y = y[new Slice(start: train_size)]; - Console.WriteLine("\tDONE"); - - return (train_x, test_x, train_y, test_y); - } + return max_accuracy > 0.9; + } - private void FillWithShuffledLabels(int[][] x, int[] y, int[][] shuffled_x, int[] shuffled_y, Random random, Dictionary> labels) + // TODO: this originally is an SKLearn utility function. it randomizes train and test which we don't do here + private (NDArray, NDArray, NDArray, NDArray) train_test_split(NDArray x, NDArray y, float test_size = 0.3f) + { + Console.WriteLine("Splitting in Training and Testing data..."); + var len = x.shape[0]; + //int classes = y.Data().Distinct().Count(); + //int samples = len / classes; + int train_size = (int)Math.Round(len * (1 - test_size)); + train_x = x[new Slice(stop: train_size), new Slice()]; + test_x = x[new Slice(start: train_size), new Slice()]; + train_y = y[new Slice(stop: train_size)]; + test_y = y[new Slice(start: train_size)]; + Console.WriteLine("\tDONE"); + + return (train_x, test_x, train_y, test_y); + } + + private void FillWithShuffledLabels(int[][] x, int[] y, int[][] shuffled_x, int[] shuffled_y, Random random, Dictionary> labels) + { + int i = 0; + var label_keys = labels.Keys.ToArray(); + while (i < shuffled_x.Length) { - int i = 0; - var label_keys = labels.Keys.ToArray(); - while (i < shuffled_x.Length) + var key = label_keys[random.Next(label_keys.Length)]; + var set = labels[key]; + var index = set.First(); + if (set.Count == 0) { - var key = label_keys[random.Next(label_keys.Length)]; - var set = labels[key]; - var index = set.First(); - if (set.Count == 0) - { - labels.Remove(key); // remove the set as it is empty - label_keys = labels.Keys.ToArray(); - } - shuffled_x[i] = x[index]; - shuffled_y[i] = y[index]; - i++; + labels.Remove(key); // remove the set as it is empty + label_keys = labels.Keys.ToArray(); } + shuffled_x[i] = x[index]; + shuffled_y[i] = y[index]; + i++; } + } - private IEnumerable<(NDArray, NDArray, int)> batch_iter(NDArray inputs, NDArray outputs, int batch_size, int num_epochs) + private IEnumerable<(NDArray, NDArray, int)> batch_iter(NDArray inputs, NDArray outputs, int batch_size, int num_epochs) + { + var num_batches_per_epoch = (len(inputs) - 1) / batch_size + 1; + var total_batches = num_batches_per_epoch * num_epochs; + foreach (var epoch in range(num_epochs)) { - var num_batches_per_epoch = (len(inputs) - 1) / batch_size + 1; - var total_batches = num_batches_per_epoch * num_epochs; - foreach (var epoch in range(num_epochs)) + foreach (var batch_num in range(num_batches_per_epoch)) { - foreach (var batch_num in range(num_batches_per_epoch)) - { - var start_index = batch_num * batch_size; - var end_index = Math.Min((batch_num + 1) * batch_size, len(inputs)); - if (end_index <= start_index) - break; - yield return (inputs[new Slice(start_index, end_index)], outputs[new Slice(start_index, end_index)], total_batches); - } + var start_index = batch_num * batch_size; + var end_index = Math.Min((batch_num + 1) * batch_size, len(inputs)); + if (end_index <= start_index) + break; + yield return (inputs[new Slice(start_index, end_index)], outputs[new Slice(start_index, end_index)], total_batches); } } + } - public override void PrepareData() - { - // full dataset https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz - var url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/dbpedia_subset.zip"; - Web.Download(url, dataDir, "dbpedia_subset.zip"); - Compress.UnZip(Path.Combine(dataDir, "dbpedia_subset.zip"), Path.Combine(dataDir, "dbpedia_csv")); + public override void PrepareData() + { + // full dataset https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz + var url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/dbpedia_subset.zip"; + Web.Download(url, dataDir, "dbpedia_subset.zip"); + Compress.UnZip(Path.Combine(dataDir, "dbpedia_subset.zip"), Path.Combine(dataDir, "dbpedia_csv")); - Console.WriteLine("Building dataset..."); - int[,] x; - int[] y; + Console.WriteLine("Building dataset..."); + int[,] x; + int[] y; - if (ModelName == "char_cnn") - { - (x, y, alphabet_size) = DataHelpers.build_char_dataset(TRAIN_PATH, "char_cnn", CHAR_MAX_LEN); - } - else - { - var word_dict = DataHelpers.build_word_dict(TRAIN_PATH); - vocabulary_size = len(word_dict); - (x, y) = DataHelpers.build_word_dataset(TRAIN_PATH, word_dict, WORD_MAX_LEN); - } + if (ModelName == "char_cnn") + { + (x, y, alphabet_size) = DataHelpers.build_char_dataset(TRAIN_PATH, "char_cnn", CHAR_MAX_LEN); + } + else + { + var word_dict = DataHelpers.build_word_dict(TRAIN_PATH); + vocabulary_size = len(word_dict); + (x, y) = DataHelpers.build_word_dataset(TRAIN_PATH, word_dict, WORD_MAX_LEN); + } - Console.WriteLine("\tDONE "); + Console.WriteLine("\tDONE "); - (train_x, test_x, train_y, test_y) = train_test_split(x, y, test_size: 0.15f); - Console.WriteLine("Training set size: " + train_x.shape[0]); - Console.WriteLine("Test set size: " + test_x.shape[0]); - } + (train_x, test_x, train_y, test_y) = train_test_split(x, y, test_size: 0.15f); + Console.WriteLine("Training set size: " + train_x.shape[0]); + Console.WriteLine("Test set size: " + test_x.shape[0]); + } - public override Graph ImportGraph() + public override Graph ImportGraph() + { + var graph = tf.Graph().as_default(); + + // download graph meta data + var meta_file = "word_cnn.meta"; + var meta_path = Path.Combine("graph", meta_file); + if (File.GetLastWriteTime(meta_path) < new DateTime(2019, 05, 11)) { - var graph = tf.Graph().as_default(); + // delete old cached file which contains errors + Console.WriteLine("Discarding cached file: " + meta_path); + if (File.Exists(meta_path)) + File.Delete(meta_path); + } + var url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/" + meta_file; + Web.Download(url, "graph", meta_file); - // download graph meta data - var meta_file = "word_cnn.meta"; - var meta_path = Path.Combine("graph", meta_file); - if (File.GetLastWriteTime(meta_path) < new DateTime(2019, 05, 11)) - { - // delete old cached file which contains errors - Console.WriteLine("Discarding cached file: " + meta_path); - if (File.Exists(meta_path)) - File.Delete(meta_path); - } - var url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/" + meta_file; - Web.Download(url, "graph", meta_file); + Console.WriteLine("Import graph..."); + tf.train.import_meta_graph(Path.Join("graph", meta_file)); + Console.WriteLine("\tDONE "); - Console.WriteLine("Import graph..."); - tf.train.import_meta_graph(Path.Join("graph", meta_file)); - Console.WriteLine("\tDONE "); + return graph; + } - return graph; - } + public override Graph BuildGraph() + { + var graph = tf.Graph().as_default(); - public override Graph BuildGraph() + switch (ModelName) { - var graph = tf.Graph().as_default(); + case "word_cnn": + textModel = new WordCnn(vocabulary_size, WORD_MAX_LEN, NUM_CLASS); + break; + case "char_cnn": + textModel = new CharCnn(alphabet_size, CHAR_MAX_LEN, NUM_CLASS); + break; + } - switch (ModelName) - { - case "word_cnn": - textModel = new WordCnn(vocabulary_size, WORD_MAX_LEN, NUM_CLASS); - break; - case "char_cnn": - textModel = new CharCnn(alphabet_size, CHAR_MAX_LEN, NUM_CLASS); - break; - } + return graph; + } - return graph; - } + public override void Train() + { + var graph = Config.IsImportingGraph ? ImportGraph() : BuildGraph(); - public override void Train() + using (var sess = tf.Session(graph)) { - var graph = Config.IsImportingGraph ? ImportGraph() : BuildGraph(); + sess.run(tf.global_variables_initializer()); + var saver = tf.train.Saver(tf.global_variables()); - using (var sess = tf.Session(graph)) - { - sess.run(tf.global_variables_initializer()); - var saver = tf.train.Saver(tf.global_variables()); + var train_batches = batch_iter(train_x, train_y, BATCH_SIZE, NUM_EPOCHS); + var num_batches_per_epoch = (len(train_x) - 1) / BATCH_SIZE + 1; - var train_batches = batch_iter(train_x, train_y, BATCH_SIZE, NUM_EPOCHS); - var num_batches_per_epoch = (len(train_x) - 1) / BATCH_SIZE + 1; + Tensor is_training = graph.OperationByName("is_training"); + Tensor model_x = graph.OperationByName("x"); + Tensor model_y = graph.OperationByName("y"); + Tensor loss = graph.OperationByName("loss/Mean"); + Operation optimizer = graph.OperationByName("loss/Adam"); + Tensor global_step = graph.OperationByName("Variable"); + Tensor accuracy = graph.OperationByName("accuracy/accuracy"); - Tensor is_training = graph.OperationByName("is_training"); - Tensor model_x = graph.OperationByName("x"); - Tensor model_y = graph.OperationByName("y"); - Tensor loss = graph.OperationByName("loss/Mean"); - Operation optimizer = graph.OperationByName("loss/Adam"); - Tensor global_step = graph.OperationByName("Variable"); - Tensor accuracy = graph.OperationByName("accuracy/accuracy"); + var sw = new Stopwatch(); + sw.Start(); - var sw = new Stopwatch(); - sw.Start(); + int step = 0; + foreach (var (x_batch, y_batch, total) in train_batches) + { + (_, step, loss_value) = sess.run((optimizer, global_step, loss), + (model_x, x_batch), (model_y, y_batch), (is_training, true)); + if (step % 10 == 0) + { + Console.WriteLine($"Training on batch {step}/{total} loss: {loss_value.ToString("0.0000")} {sw.ElapsedMilliseconds}ms."); + sw.Restart(); + } - int step = 0; - foreach (var (x_batch, y_batch, total) in train_batches) + if (step % 100 == 0) { - (_, step, loss_value) = sess.run((optimizer, global_step, loss), - (model_x, x_batch), (model_y, y_batch), (is_training, true)); - if (step % 10 == 0) + // Test accuracy with validation data for each epoch. + var valid_batches = batch_iter(test_x, test_y, BATCH_SIZE, 1); + var (sum_accuracy, cnt) = (0.0f, 0); + foreach (var (valid_x_batch, valid_y_batch, total_validation_batches) in valid_batches) { - Console.WriteLine($"Training on batch {step}/{total} loss: {loss_value.ToString("0.0000")} {sw.ElapsedMilliseconds}ms."); - sw.Restart(); + var valid_feed_dict = new FeedDict + { + [model_x] = valid_x_batch, + [model_y] = valid_y_batch, + [is_training] = false + }; + float accuracy_value = sess.run(accuracy, (model_x, valid_x_batch), (model_y, valid_y_batch), (is_training, false)); + sum_accuracy += accuracy_value; + cnt += 1; } - if (step % 100 == 0) + var valid_accuracy = sum_accuracy / cnt; + + print($"\nValidation Accuracy = {valid_accuracy.ToString("P")}\n"); + + // Save model + if (valid_accuracy > max_accuracy) { - // Test accuracy with validation data for each epoch. - var valid_batches = batch_iter(test_x, test_y, BATCH_SIZE, 1); - var (sum_accuracy, cnt) = (0.0f, 0); - foreach (var (valid_x_batch, valid_y_batch, total_validation_batches) in valid_batches) - { - var valid_feed_dict = new FeedDict - { - [model_x] = valid_x_batch, - [model_y] = valid_y_batch, - [is_training] = false - }; - float accuracy_value = sess.run(accuracy, (model_x, valid_x_batch), (model_y, valid_y_batch), (is_training, false)); - sum_accuracy += accuracy_value; - cnt += 1; - } - - var valid_accuracy = sum_accuracy / cnt; - - print($"\nValidation Accuracy = {valid_accuracy.ToString("P")}\n"); - - // Save model - if (valid_accuracy > max_accuracy) - { - max_accuracy = valid_accuracy; - saver.save(sess, $"{dataDir}/word_cnn.ckpt", global_step: step); - print("Model is saved.\n"); - } + max_accuracy = valid_accuracy; + saver.save(sess, $"{dataDir}/word_cnn.ckpt", global_step: step); + print("Model is saved.\n"); } } } } + } - public override void Test() - { - var checkpoint = Path.Combine(dataDir, "word_cnn.ckpt-800"); - if (!File.Exists($"{checkpoint}.meta")) return; - - var graph = tf.Graph(); - using (var sess = tf.Session(graph)) - { - var saver = tf.train.import_meta_graph($"{checkpoint}.meta"); - saver.restore(sess, checkpoint); - - Tensor x = graph.get_operation_by_name("x"); - Tensor y = graph.get_operation_by_name("y"); - Tensor is_training = graph.get_operation_by_name("is_training"); - Tensor accuracy = graph.get_operation_by_name("accuracy/accuracy"); - - var batches = batch_iter(test_x, test_y, BATCH_SIZE, 1); - float sum_accuracy = 0; - int cnt = 0; - foreach (var (batch_x, batch_y, total) in batches) - { - float accuracy_out = sess.run(accuracy, (x, batch_x), (y, batch_y), (is_training, false)); - sum_accuracy += accuracy_out; - cnt += 1; - } - print($"Test Accuracy : {sum_accuracy / cnt}"); - } - } + public override void Test() + { + var checkpoint = Path.Combine(dataDir, "word_cnn.ckpt-800"); + if (!File.Exists($"{checkpoint}.meta")) return; - public override void Predict() + var graph = tf.Graph(); + using (var sess = tf.Session(graph)) { - var model = Path.Combine(dataDir, "frozen_model.pb"); - if (!File.Exists(model)) return; - - var graph = tf.train.load_graph(model); - using (var sess = tf.Session(graph)) + var saver = tf.train.import_meta_graph($"{checkpoint}.meta"); + saver.restore(sess, checkpoint); + + Tensor x = graph.get_operation_by_name("x"); + Tensor y = graph.get_operation_by_name("y"); + Tensor is_training = graph.get_operation_by_name("is_training"); + Tensor accuracy = graph.get_operation_by_name("accuracy/accuracy"); + + var batches = batch_iter(test_x, test_y, BATCH_SIZE, 1); + float sum_accuracy = 0; + int cnt = 0; + foreach (var (batch_x, batch_y, total) in batches) { - Tensor x = graph.get_operation_by_name("x"); - Tensor is_training = graph.get_operation_by_name("is_training"); - Tensor prediction = graph.get_operation_by_name("output/ArgMax"); - // encode text into 100 dimensions - var batches = batch_iter(test_x, test_y, BATCH_SIZE, 1).First(); - var input = batches.Item1[0].reshape((1, 100)); - var result = sess.run(prediction, (x, input), (is_training, false)); + float accuracy_out = sess.run(accuracy, (x, batch_x), (y, batch_y), (is_training, false)); + sum_accuracy += accuracy_out; + cnt += 1; } + print($"Test Accuracy : {sum_accuracy / cnt}"); } + } - public override string FreezeModel() + public override void Predict() + { + var model = Path.Combine(dataDir, "frozen_model.pb"); + if (!File.Exists(model)) return; + + var graph = tf.train.load_graph(model); + using (var sess = tf.Session(graph)) { - return tf.train.freeze_graph(dataDir, - "frozen_model", - new[] { "output/ArgMax" }); + Tensor x = graph.get_operation_by_name("x"); + Tensor is_training = graph.get_operation_by_name("is_training"); + Tensor prediction = graph.get_operation_by_name("output/ArgMax"); + // encode text into 100 dimensions + var batches = batch_iter(test_x, test_y, BATCH_SIZE, 1).First(); + var input = batches.Item1[0].reshape((1, 100)); + var result = sess.run(prediction, (x, input), (is_training, false)); } } + + public override string FreezeModel() + { + return tf.train.freeze_graph(dataDir, + "frozen_model", + new[] { "output/ArgMax" }); + } } diff --git a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassificationKeras.cs b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassificationKeras.cs index 24d11b0..48356bc 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassificationKeras.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassificationKeras.cs @@ -1,33 +1,32 @@ using System.IO; using Tensorflow.Keras.Utils; -namespace TensorFlowNET.Examples -{ - /// - /// https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlp/ipynb/text_classification_from_scratch.ipynb#scrollTo=qqTCrB7SmJv9 - /// - public class CnnTextClassificationKeras : SciSharpExample, IExample - { - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "CNN Text Classification (Keras)", - Enabled = false - }; +namespace TensorFlowNET.Examples; - public bool Run() +/// +/// https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlp/ipynb/text_classification_from_scratch.ipynb#scrollTo=qqTCrB7SmJv9 +/// +public class CnnTextClassificationKeras : SciSharpExample, IExample +{ + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - return true; - } + Name = "CNN Text Classification (Keras)", + Enabled = false + }; - public override void PrepareData() - { - string fileName = "aclImdb_v1.tar.gz"; - string url = $"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"; - string data_dir = Path.GetTempPath(); - Web.Download(url, data_dir, fileName); - Compress.ExtractGZip(Path.Join(data_dir, fileName), data_dir); - data_dir = Path.Combine(data_dir, "aclImdb_v1"); - } + public bool Run() + { + return true; + } + + public override void PrepareData() + { + string fileName = "aclImdb_v1.tar.gz"; + string url = $"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"; + string data_dir = Path.GetTempPath(); + Web.Download(url, data_dir, fileName); + Compress.ExtractGZip(Path.Join(data_dir, fileName), data_dir); + data_dir = Path.Combine(data_dir, "aclImdb_v1"); } } diff --git a/src/TensorFlowNET.Examples/TextProcessing/NamedEntityRecognition.cs b/src/TensorFlowNET.Examples/TextProcessing/NamedEntityRecognition.cs index 1a3feaa..899a1e6 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/NamedEntityRecognition.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/NamedEntityRecognition.cs @@ -1,23 +1,22 @@ using System; -namespace TensorFlowNET.Examples -{ - /// - /// https://github.com/guillaumegenthial/tf_ner - /// - public class NamedEntityRecognition : SciSharpExample, IExample - { - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "NER", - Enabled = false, - IsImportingGraph = false - }; +namespace TensorFlowNET.Examples; - public bool Run() +/// +/// https://github.com/guillaumegenthial/tf_ner +/// +public class NamedEntityRecognition : SciSharpExample, IExample +{ + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - throw new NotImplementedException(); - } + Name = "NER", + Enabled = false, + IsImportingGraph = false + }; + + public bool Run() + { + throw new NotImplementedException(); } } diff --git a/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs b/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs index e84a35e..cf4ad06 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs @@ -7,218 +7,217 @@ using Tensorflow.NumPy; using static Tensorflow.Binding; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +/// +/// Implement Word2Vec algorithm to compute vector representations of words. +/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/word2vec.py +/// +public class Word2Vec : SciSharpExample, IExample { - /// - /// Implement Word2Vec algorithm to compute vector representations of words. - /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/word2vec.py - /// - public class Word2Vec : SciSharpExample, IExample + // Training Parameters + int batch_size = 128; + int num_steps = 30000; //3000000; + int display_step = 1000; //10000; + int eval_step = 5000;//200000; + + // Evaluation Parameters + string[] eval_words = new string[] { "five", "of", "going", "hardware", "american", "britain" }; + string[] text_words; + List word2id; + int[] data; + + // Word2Vec Parameters + int min_occurrence = 10; // Remove all words that does not appears at least n times + int skip_window = 3; // How many words to consider left and right + int num_skips = 2; // How many times to reuse an input to generate a label + + int data_index = 0; + int top_k = 8; // number of nearest neighbors + float average_loss = 0; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Word2Vec", + Enabled = true, + IsImportingGraph = true + }; + + public bool Run() { - // Training Parameters - int batch_size = 128; - int num_steps = 30000; //3000000; - int display_step = 1000; //10000; - int eval_step = 5000;//200000; - - // Evaluation Parameters - string[] eval_words = new string[] { "five", "of", "going", "hardware", "american", "britain" }; - string[] text_words; - List word2id; - int[] data; - - // Word2Vec Parameters - int min_occurrence = 10; // Remove all words that does not appears at least n times - int skip_window = 3; // How many words to consider left and right - int num_skips = 2; // How many times to reuse an input to generate a label - - int data_index = 0; - int top_k = 8; // number of nearest neighbors - float average_loss = 0; - - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Word2Vec", - Enabled = true, - IsImportingGraph = true - }; + tf.compat.v1.disable_eager_execution(); - public bool Run() - { - tf.compat.v1.disable_eager_execution(); + PrepareData(); - PrepareData(); + var graph = tf.Graph().as_default(); - var graph = tf.Graph().as_default(); + tf.train.import_meta_graph($"graph{Path.DirectorySeparatorChar}word2vec.meta"); - tf.train.import_meta_graph($"graph{Path.DirectorySeparatorChar}word2vec.meta"); + // Input data + Tensor X = graph.OperationByName("Placeholder"); + // Input label + Tensor Y = graph.OperationByName("Placeholder_1"); - // Input data - Tensor X = graph.OperationByName("Placeholder"); - // Input label - Tensor Y = graph.OperationByName("Placeholder_1"); + // Compute the average NCE loss for the batch + Tensor loss_op = graph.OperationByName("Mean"); + // Define the optimizer + var train_op = graph.OperationByName("GradientDescent"); + Tensor cosine_sim_op = graph.OperationByName("MatMul_1"); - // Compute the average NCE loss for the batch - Tensor loss_op = graph.OperationByName("Mean"); - // Define the optimizer - var train_op = graph.OperationByName("GradientDescent"); - Tensor cosine_sim_op = graph.OperationByName("MatMul_1"); + // Initialize the variables (i.e. assign their default value) + var init = tf.global_variables_initializer(); - // Initialize the variables (i.e. assign their default value) - var init = tf.global_variables_initializer(); + using (var sess = tf.Session(graph)) + { + // Run the initializer + sess.run(init); - using (var sess = tf.Session(graph)) + var x_test = (from word in eval_words + join id in word2id on word equals id.Word into wi + from wi2 in wi.DefaultIfEmpty() + select wi2 == null ? 0 : wi2.Id).ToArray(); + + foreach (var step in range(1, num_steps + 1)) { - // Run the initializer - sess.run(init); + // Get a new batch of data + var (batch_x, batch_y) = next_batch(batch_size, num_skips, skip_window); - var x_test = (from word in eval_words - join id in word2id on word equals id.Word into wi - from wi2 in wi.DefaultIfEmpty() - select wi2 == null ? 0 : wi2.Id).ToArray(); + (_, float loss) = sess.run((train_op, loss_op), (X, batch_x), (Y, batch_y)); + average_loss += loss; - foreach (var step in range(1, num_steps + 1)) + if (step % display_step == 0 || step == 1) { - // Get a new batch of data - var (batch_x, batch_y) = next_batch(batch_size, num_skips, skip_window); - - (_, float loss) = sess.run((train_op, loss_op), (X, batch_x), (Y, batch_y)); - average_loss += loss; + if (step > 1) + average_loss /= display_step; - if (step % display_step == 0 || step == 1) - { - if (step > 1) - average_loss /= display_step; - - print($"Step {step}, Average Loss= {average_loss.ToString("F4")}"); - average_loss = 0; - } + print($"Step {step}, Average Loss= {average_loss.ToString("F4")}"); + average_loss = 0; + } - // Evaluation - if (step % eval_step == 0 || step == 1) + // Evaluation + if (step % eval_step == 0 || step == 1) + { + print("Evaluation..."); + var sim = sess.run(cosine_sim_op, (X, x_test)); + foreach (var i in range(len(eval_words))) { - print("Evaluation..."); - var sim = sess.run(cosine_sim_op, (X, x_test)); - foreach (var i in range(len(eval_words))) - { - var nearest = np.argsort(0f - sim[i]) - .ToArray() - .Skip(1) - .Take(top_k) - .ToArray(); - string log_str = $"\"{eval_words[i]}\" nearest neighbors:"; - foreach (var k in range(top_k)) - log_str = $"{log_str} {word2id.First(x => x.Id == nearest[k]).Word},"; - print(log_str); - } + var nearest = np.argsort(0f - sim[i]) + .ToArray() + .Skip(1) + .Take(top_k) + .ToArray(); + string log_str = $"\"{eval_words[i]}\" nearest neighbors:"; + foreach (var k in range(top_k)) + log_str = $"{log_str} {word2id.First(x => x.Id == nearest[k]).Word},"; + print(log_str); } } } - - return average_loss < 100; } - // Generate training batch for the skip-gram model - private (NDArray, NDArray) next_batch(int batch_size, int num_skips, int skip_window) + return average_loss < 100; + } + + // Generate training batch for the skip-gram model + private (NDArray, NDArray) next_batch(int batch_size, int num_skips, int skip_window) + { + var batch = np.ndarray(new Shape(batch_size), dtype: np.int32); + var labels = np.ndarray((batch_size, 1), dtype: np.int32); + // get window size (words left and right + current one) + int span = 2 * skip_window + 1; + var buffer = new Queue(span); + if (data_index + span > data.Length) + data_index = 0; + data.Skip(data_index).Take(span).ToList().ForEach(x => buffer.Enqueue(x)); + data_index += span; + + foreach (var i in range(batch_size / num_skips)) { - var batch = np.ndarray(new Shape(batch_size), dtype: np.int32); - var labels = np.ndarray((batch_size, 1), dtype: np.int32); - // get window size (words left and right + current one) - int span = 2 * skip_window + 1; - var buffer = new Queue(span); - if (data_index + span > data.Length) - data_index = 0; - data.Skip(data_index).Take(span).ToList().ForEach(x => buffer.Enqueue(x)); - data_index += span; - - foreach (var i in range(batch_size / num_skips)) + var context_words = range(span).Where(x => x != skip_window).ToArray(); + List span_list = Enumerable.Range(0, span).ToList(); + Random rand = new Random(Guid.NewGuid().GetHashCode()); + span_list.RemoveAt(skip_window); + var words_to_use = span_list.OrderBy(i => rand.Next(0, span_list.Count)).Take(num_skips).ToArray(); + foreach (var (j, context_word) in enumerate(words_to_use)) { - var context_words = range(span).Where(x => x != skip_window).ToArray(); - List span_list = Enumerable.Range(0, span).ToList(); - Random rand = new Random(Guid.NewGuid().GetHashCode()); - span_list.RemoveAt(skip_window); - var words_to_use = span_list.OrderBy(i => rand.Next(0, span_list.Count)).Take(num_skips).ToArray(); - foreach (var (j, context_word) in enumerate(words_to_use)) - { - batch[i * num_skips + j] = buffer.ElementAt(skip_window); - labels[i * num_skips + j, 0] = buffer.ElementAt(context_word); - } - - if (data_index == len(data)) - { - //buffer.extend(data[0:span]); - data_index = span; - } - else - { - buffer.Dequeue(); - buffer.Enqueue(data[data_index]); - data_index += 1; - } + batch[i * num_skips + j] = buffer.ElementAt(skip_window); + labels[i * num_skips + j, 0] = buffer.ElementAt(context_word); } - // Backtrack a little bit to avoid skipping words in the end of a batch - data_index = (data_index + len(data) - span) % len(data); - - return (batch, labels); + if (data_index == len(data)) + { + //buffer.extend(data[0:span]); + data_index = span; + } + else + { + buffer.Dequeue(); + buffer.Enqueue(data[data_index]); + data_index += 1; + } } - public override void PrepareData() - { - // Download graph meta - var url = "https://github.com/SciSharp/TensorFlow.NET/raw/master/graph/word2vec.meta"; - Web.Download(url, "graph", "word2vec.meta"); - - // Download a small chunk of Wikipedia articles collection - url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/text8.zip"; - Web.Download(url, "word2vec", "text8.zip"); - // Unzip the dataset file. Text has already been processed - Compress.UnZip($"word2vec{Path.DirectorySeparatorChar}text8.zip", "word2vec"); - - int wordId = 0; - text_words = File.ReadAllText($"word2vec{Path.DirectorySeparatorChar}text8").Trim().ToLower().Split(); - // Build the dictionary and replace rare words with UNK token - word2id = text_words.GroupBy(x => x) - .Select(x => new WordId - { - Word = x.Key, - Occurrence = x.Count() - }) - .Where(x => x.Occurrence >= min_occurrence) // Remove samples with less than 'min_occurrence' occurrences - .OrderByDescending(x => x.Occurrence) // Retrieve the most common words - .Select(x => new WordId - { - Word = x.Word, - Id = ++wordId, // Assign an id to each word - Occurrence = x.Occurrence - }) - .ToList(); - - // Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary - data = (from word in text_words - join id in word2id on word equals id.Word into wi - from wi2 in wi.DefaultIfEmpty() - select wi2 == null ? 0 : wi2.Id).ToArray(); - - word2id.Insert(0, new WordId { Word = "UNK", Id = 0, Occurrence = data.Count(x => x == 0) }); - - print($"Words count: {text_words.Length}"); - print($"Unique words: {text_words.Distinct().Count()}"); - print($"Vocabulary size: {word2id.Count}"); - print($"Most common words: {string.Join(", ", word2id.Take(10))}"); - } + // Backtrack a little bit to avoid skipping words in the end of a batch + data_index = (data_index + len(data) - span) % len(data); - private class WordId - { - public string Word { get; set; } - public int Id { get; set; } - public int Occurrence { get; set; } + return (batch, labels); + } - public override string ToString() + public override void PrepareData() + { + // Download graph meta + var url = "https://github.com/SciSharp/TensorFlow.NET/raw/master/graph/word2vec.meta"; + Web.Download(url, "graph", "word2vec.meta"); + + // Download a small chunk of Wikipedia articles collection + url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/text8.zip"; + Web.Download(url, "word2vec", "text8.zip"); + // Unzip the dataset file. Text has already been processed + Compress.UnZip($"word2vec{Path.DirectorySeparatorChar}text8.zip", "word2vec"); + + int wordId = 0; + text_words = File.ReadAllText($"word2vec{Path.DirectorySeparatorChar}text8").Trim().ToLower().Split(); + // Build the dictionary and replace rare words with UNK token + word2id = text_words.GroupBy(x => x) + .Select(x => new WordId { - return Word + " " + Id + " " + Occurrence; - } + Word = x.Key, + Occurrence = x.Count() + }) + .Where(x => x.Occurrence >= min_occurrence) // Remove samples with less than 'min_occurrence' occurrences + .OrderByDescending(x => x.Occurrence) // Retrieve the most common words + .Select(x => new WordId + { + Word = x.Word, + Id = ++wordId, // Assign an id to each word + Occurrence = x.Occurrence + }) + .ToList(); + + // Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary + data = (from word in text_words + join id in word2id on word equals id.Word into wi + from wi2 in wi.DefaultIfEmpty() + select wi2 == null ? 0 : wi2.Id).ToArray(); + + word2id.Insert(0, new WordId { Word = "UNK", Id = 0, Occurrence = data.Count(x => x == 0) }); + + print($"Words count: {text_words.Length}"); + print($"Unique words: {text_words.Distinct().Count()}"); + print($"Vocabulary size: {word2id.Count}"); + print($"Most common words: {string.Join(", ", word2id.Take(10))}"); + } + + private class WordId + { + public string Word { get; set; } + public int Id { get; set; } + public int Occurrence { get; set; } + + public override string ToString() + { + return Word + " " + Id + " " + Occurrence; } } } diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs index eca5656..3af24c2 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs @@ -32,7 +32,7 @@ public VdCnn(int alphabet_size, int document_max_len, int num_class) num_filters = new int[] { 64, 64, 128, 256, 512 }; num_blocks = new int[] { 2, 2, 2, 2 }; learning_rate = 0.001f; - cnn_initializer = keras.initializers.he_normal(); + cnn_initializer = tf.keras.initializers.HeNormal(); fc_initializer = tf.truncated_normal_initializer(stddev: 0.05f); x = tf.placeholder(tf.int32, (-1, document_max_len), name: "x"); diff --git a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs index aedf816..b000cc2 100644 --- a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs +++ b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs @@ -9,124 +9,123 @@ using SciSharp.Models.TimeSeries; using PandasNet; -namespace TensorFlowNET.Examples +namespace TensorFlowNET.Examples; + +public class WeatherPrediction : SciSharpExample, IExample { - public class WeatherPrediction : SciSharpExample, IExample - { - ITimeSeriesTask task; - IDatasetV2 training_ds, val_ds, test_ds; - public ExampleConfig InitConfig() - => Config = new ExampleConfig - { - Name = "Weather Prediction", - Enabled = true - }; - - public bool Run() + ITimeSeriesTask task; + IDatasetV2 training_ds, val_ds, test_ds; + public ExampleConfig InitConfig() + => Config = new ExampleConfig { - var wizard = new ModelWizard(); - task = wizard.AddTimeSeriesTask(new TaskOptions - { - WeightsPath = @"timeseries_linear_v1\saved_weights.h5" - }); - task.SetModelArgs(new TimeSeriesModelArgs - { - InputWidth = 3, - LabelColumns = new[] { "T (degC)" } - }); - (training_ds, val_ds, test_ds) = task.GenerateDataset(PrepareData); - - Train(); - //Test(); - //Predict(); - - return true; - } + Name = "Weather Prediction", + Enabled = true + }; - public override void Train() + public bool Run() + { + var wizard = new ModelWizard(); + task = wizard.AddTimeSeriesTask(new TaskOptions { - task.Train(new TrainingOptions - { - Epochs = 3, - Dataset = (training_ds, val_ds) - }); - } + WeightsPath = @"timeseries_linear_v1\saved_weights.h5" + }); + task.SetModelArgs(new TimeSeriesModelArgs + { + InputWidth = 3, + LabelColumns = new[] { "T (degC)" } + }); + (training_ds, val_ds, test_ds) = task.GenerateDataset(PrepareData); + + Train(); + //Test(); + //Predict(); - public override void Test() + return true; + } + + public override void Train() + { + task.Train(new TrainingOptions { - var result = task.Test(new TestingOptions - { - Dataset = test_ds - }); - } + Epochs = 3, + Dataset = (training_ds, val_ds) + }); + } - public override void Predict() + public override void Test() + { + var result = task.Test(new TestingOptions { - foreach (var (input, label) in test_ds.take(10)) - { - var result = task.Predict(input); - } - } + Dataset = test_ds + }); + } - new DataFrame PrepareData() + public override void Predict() + { + foreach (var (input, label) in test_ds.take(10)) { - var zip_path = keras.utils.get_file("jena_climate_2009_2016.csv.zip", - "https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip", - cache_subdir: "jena_climate_2009_2016", - extract: true); - - var df = pd.read_csv(Path.Combine(zip_path, "jena_climate_2009_2016.csv")); - // deal with hourly predictions, so start by sub-sampling the data from 10-minute intervals to one-hour intervals: - df = df[new Slice(5, step: 6)]; - var date_time_string = df.pop("Date Time"); - var date_time = pd.to_datetime(date_time_string, "dd.MM.yyyy HH:mm:ss"); - print(df.head()); - - // plot featuers - /*var plot_cols = new string[] { "T (degC)", "p (mbar)", "rho (g/m**3)" }; - var plot_features = df[plot_cols]; - plot_features.index = date_time_string; - plot_features.plot();*/ - - print(df.describe().transpose()); - - // Wind velocity - var wv = df["wv (m/s)"]; - var bad_wv = wv == -9999.0f; - wv[bad_wv] = 0.0f; - - var max_wv = df["max. wv (m/s)"]; - var bad_max_wv = max_wv == -9999.0f; - max_wv[bad_max_wv] = 0.0f; - - // The above inplace edits are reflected in the DataFrame - print(df["wv (m/s)"].min()); - - // convert the wind direction and velocity columns to a wind vector - wv = df.pop("wv (m/s)"); - max_wv = df.pop("max. wv (m/s)"); - - // Convert to radians. - var wd_rad = df.pop("wd (deg)") * pd.pi / 180; - - // Calculate the wind x and y components. - df["Wx"] = wv * pd.cos(wd_rad); - df["Wy"] = wv * pd.sin(wd_rad); - - // Calculate the max wind x and y components. - df["max Wx"] = max_wv * pd.cos(wd_rad); - df["max Wy"] = max_wv * pd.sin(wd_rad); - - var timestamp_s = date_time.map(pd.timestamp); - - var day = 24 * 60 * 60; - var year = 365.2425f * day; - df["Day sin"] = pd.sin(timestamp_s * (2 * pd.pi / day)); - df["Day cos"] = pd.cos(timestamp_s * (2 * pd.pi / day)); - df["Year sin"] = pd.sin(timestamp_s * (2 * pd.pi / year)); - df["Year cos"] = pd.cos(timestamp_s * (2 * pd.pi / year)); - - return df; + var result = task.Predict(input); } } + + new DataFrame PrepareData() + { + var zip_path = keras.utils.get_file("jena_climate_2009_2016.csv.zip", + "https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip", + cache_subdir: "jena_climate_2009_2016", + extract: true); + + var df = pd.read_csv(Path.Combine(zip_path, "jena_climate_2009_2016.csv")); + // deal with hourly predictions, so start by sub-sampling the data from 10-minute intervals to one-hour intervals: + df = df[new Slice(5, step: 6)]; + var date_time_string = df.pop("Date Time"); + var date_time = pd.to_datetime(date_time_string, "dd.MM.yyyy HH:mm:ss"); + print(df.head()); + + // plot featuers + /*var plot_cols = new string[] { "T (degC)", "p (mbar)", "rho (g/m**3)" }; + var plot_features = df[plot_cols]; + plot_features.index = date_time_string; + plot_features.plot();*/ + + print(df.describe().transpose()); + + // Wind velocity + var wv = df["wv (m/s)"]; + var bad_wv = wv == -9999.0f; + wv[bad_wv] = 0.0f; + + var max_wv = df["max. wv (m/s)"]; + var bad_max_wv = max_wv == -9999.0f; + max_wv[bad_max_wv] = 0.0f; + + // The above inplace edits are reflected in the DataFrame + print(df["wv (m/s)"].min()); + + // convert the wind direction and velocity columns to a wind vector + wv = df.pop("wv (m/s)"); + max_wv = df.pop("max. wv (m/s)"); + + // Convert to radians. + var wd_rad = df.pop("wd (deg)") * pd.pi / 180; + + // Calculate the wind x and y components. + df["Wx"] = wv * pd.cos(wd_rad); + df["Wy"] = wv * pd.sin(wd_rad); + + // Calculate the max wind x and y components. + df["max Wx"] = max_wv * pd.cos(wd_rad); + df["max Wy"] = max_wv * pd.sin(wd_rad); + + var timestamp_s = date_time.map(pd.timestamp); + + var day = 24 * 60 * 60; + var year = 365.2425f * day; + df["Day sin"] = pd.sin(timestamp_s * (2 * pd.pi / day)); + df["Day cos"] = pd.cos(timestamp_s * (2 * pd.pi / day)); + df["Year sin"] = pd.sin(timestamp_s * (2 * pd.pi / year)); + df["Year cos"] = pd.cos(timestamp_s * (2 * pd.pi / year)); + + return df; + } } diff --git a/src/python/.vscode/launch.json b/src/python/.vscode/launch.json new file mode 100644 index 0000000..f2b3a0f --- /dev/null +++ b/src/python/.vscode/launch.json @@ -0,0 +1,16 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File", + "type": "python", + "request": "launch", + "program": "${workspaceFolder}/time_series.py", + "console": "integratedTerminal", + "justMyCode": false + } + ] +} \ No newline at end of file diff --git a/src/tensorflow2.x-python-tutorial/.vscode/launch.json b/src/tensorflow2.x-python-tutorial/.vscode/launch.json index a453acf..83d1041 100644 --- a/src/tensorflow2.x-python-tutorial/.vscode/launch.json +++ b/src/tensorflow2.x-python-tutorial/.vscode/launch.json @@ -8,7 +8,7 @@ "name": "Python: Current File", "type": "python", "request": "launch", - "program": "${workspaceFolder}/dcgan.py", + "program": "${workspaceFolder}/image_classification.py", "console": "integratedTerminal", "cwd": "${workspaceFolder}", "justMyCode": false diff --git a/src/tensorflow2.x-python-tutorial/image_classification.py b/src/tensorflow2.x-python-tutorial/image_classification.py index 8cc83c6..2a77570 100644 --- a/src/tensorflow2.x-python-tutorial/image_classification.py +++ b/src/tensorflow2.x-python-tutorial/image_classification.py @@ -73,8 +73,8 @@ num_classes = 5 -model = Sequential([ - layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)), +layers = [ + layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)), layers.Conv2D(16, 3, padding='same', activation='relu'), layers.MaxPooling2D(), # layers.Conv2D(32, 3, padding='same', activation='relu'), @@ -84,7 +84,9 @@ layers.Flatten(), layers.Dense(128, activation='relu'), layers.Dense(num_classes) -]) +] + +model = Sequential(layers) model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), @@ -92,7 +94,7 @@ model.summary() -epochs=10 +epochs = 1 history = model.fit( train_ds, validation_data=val_ds, From 768daa404acd4bdd897f8c55e569cf8e899f7c68 Mon Sep 17 00:00:00 2001 From: wangdapao666 <583087864@qq.com> Date: Sat, 25 Feb 2023 18:15:08 +0800 Subject: [PATCH 28/39] Fixed an issue where the command line running for specific example was invalid --- src/TensorFlowNET.Examples/Program.cs | 30 ++++++++++++++++++++------- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/src/TensorFlowNET.Examples/Program.cs b/src/TensorFlowNET.Examples/Program.cs index c9892d6..2da82b8 100644 --- a/src/TensorFlowNET.Examples/Program.cs +++ b/src/TensorFlowNET.Examples/Program.cs @@ -20,6 +20,7 @@ limitations under the License. using System.Drawing; using System.Linq; using System.Reflection; +using System.Xml.Linq; using Tensorflow; using static Tensorflow.Binding; using static Tensorflow.KerasApi; @@ -62,37 +63,52 @@ static void Main(string[] args) sw.Stop(); if (isSuccess) + { success.Add($"Example: {name} in {sw.Elapsed.TotalSeconds}s"); + // only successfully run examples could be sorted as finished + finished++; + } else errors.Add($"Example: {name} in {sw.Elapsed.TotalSeconds}s"); - finished++; + keras.backend.clear_session(); } + if (parsedArgs.ContainsKey("ex")) + { + printRunInfo(finished, examples.Length); + return; + } + success.ForEach(x => Console.WriteLine($"{x} is OK!", Color.White)); errors.ForEach(x => Console.WriteLine($"{x} is Failed!", Color.Red)); + printRunInfo(finished, examples.Length); + } + private static void printRunInfo(int finished, int examplesLength) + { Console.WriteLine(Environment.OSVersion, Color.Yellow); Console.WriteLine($"64Bit Operating System: {Environment.Is64BitOperatingSystem}", Color.Yellow); Console.WriteLine($".NET CLR: {Environment.Version}", Color.Yellow); Console.WriteLine($"TensorFlow Binary v{tf.VERSION}"); Console.WriteLine($"TensorFlow.NET v{Assembly.GetAssembly(typeof(TF_DataType)).GetName().Version}"); Console.WriteLine($"TensorFlow.Keras v{Assembly.GetAssembly(typeof(KerasApi)).GetName().Version}"); - Console.WriteLine($"{finished} of {examples.Length} example(s) are completed."); + Console.WriteLine($"{finished} of {examplesLength} example(s) are completed."); Console.ReadLine(); } - private static (bool, string) RunExamples(Type example, Dictionary args) { var instance = (IExample)Activator.CreateInstance(example); instance.InitConfig(); + var name = instance.Config.Name; - Console.WriteLine($"{DateTime.UtcNow} Starting {name}", Color.White); + // args has "ex" means run specific example + if (args.ContainsKey("ex") && name != args["ex"]) + return (false, ""); - // if (args.ContainsKey("ex") && name != args["ex"]) - // return (false, ""); + Console.WriteLine($"{DateTime.UtcNow} Starting {name}", Color.White); if (!instance.Config.Enabled) return (true, name); @@ -106,7 +122,7 @@ private static (bool, string) RunExamples(Type example, Dictionary Date: Sun, 5 Mar 2023 08:35:16 -0600 Subject: [PATCH 29/39] align-tf-v1 --- .gitignore | 4 +- SciSharp STACK Examples.sln | 160 ++++++++++ .../BasicModels/KMeansClustering.cs | 6 +- .../BasicModels/LinearRegressionKeras.cs | 4 +- .../BasicModels/LogisticRegressionKeras.cs | 69 ++++ src/TensorFlowNET.Examples/GAN/MnistGAN.cs | 2 +- .../ImageProcessing/CnnInYourOwnData.cs | 23 +- .../DigitRecognitionCnnEager.cs | 3 +- .../DigitRecognitionRnnKeras.cs | 2 +- .../ImageProcessing/ImageBackgroundRemoval.cs | 8 +- .../ImageClassificationKeras.cs | 6 +- .../ImageRecognitionInception.cs | 22 +- .../ImageProcessing/InceptionArchGoogLeNet.cs | 12 +- .../ImageProcessing/MnistCnnKerasSubclass.cs | 2 +- .../MnistFnnKerasFunctional.cs | 2 +- .../ImageProcessing/ToyResNet.cs | 11 +- .../NeuralNetworks/FullyConnected.cs | 42 ++- .../NeuralNetworks/FullyConnectedEager.cs | 3 +- .../NeuralNetworks/FullyConnectedInQueue.cs | 42 ++- .../NeuralNetworks/NeuralNetXor.cs | 54 ++-- .../NeuralNetworks/NeuralNetXorKeras.cs | 2 +- .../ObjectDetection/DetectInMobilenet.cs | 24 +- .../ObjectDetection/YoloCoco.cs | 74 ++--- .../TensorFlowNET.Examples.csproj | 13 +- .../TextProcessing/CnnTextClassification.cs | 156 +++++---- .../TextProcessing/DataHelpers.cs | 4 +- .../TextClassificationWithBert.cs | 112 +++++++ .../TextProcessing/Word2Vec.cs | 60 ++-- .../TextProcessing/cnn_models/CharCnn.cs | 12 +- .../TextProcessing/cnn_models/VdCnn.cs | 2 +- .../TextProcessing/cnn_models/WordCnn.cs | 2 +- .../classify_text_with_bert.py | 300 ++++++++++++++++++ 32 files changed, 933 insertions(+), 305 deletions(-) create mode 100644 src/TensorFlowNET.Examples/BasicModels/LogisticRegressionKeras.cs create mode 100644 src/TensorFlowNET.Examples/TextProcessing/TextClassificationWithBert.cs create mode 100644 src/tensorflow2.x-python-tutorial/classify_text_with_bert.py diff --git a/.gitignore b/.gitignore index 49b014e..4b15855 100644 --- a/.gitignore +++ b/.gitignore @@ -348,4 +348,6 @@ ASALocalRun/ /ssd_mobilenet_v1_coco.bin /ssd_mobilenet_v1_coco_2018_01_28 /tfhub_modules -/word2vec \ No newline at end of file +/word2vec +/src/tensorflow2.x-python-tutorial/aclImdb_v1.tar.gz +/src/tensorflow2.x-python-tutorial/aclImdb diff --git a/SciSharp STACK Examples.sln b/SciSharp STACK Examples.sln index 1aa6e77..6a8e8cd 100644 --- a/SciSharp STACK Examples.sln +++ b/SciSharp STACK Examples.sln @@ -11,6 +11,22 @@ Project("{6EC3EE1D-3C4E-46DD-8F32-0CC8E7565705}") = "TensorFlowNET.Examples.FSha EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.WebApi", "src\SciSharp.WebApi\SciSharp.WebApi.csproj", "{E1D6E827-3FC6-476A-8875-1FF8C1D398CE}" EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.Core", "..\SciSharp.Models\SciSharp.Models.Core\SciSharp.Models.Core.csproj", "{477EFB56-3CE1-4784-8C6E-1EA021352538}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.TimeSeries", "..\SciSharp.Models\SciSharp.Models.TimeSeries\SciSharp.Models.TimeSeries.csproj", "{52E44DF0-1853-420F-9170-30CFF9F8E547}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.ObjectDetection", "..\SciSharp.Models\SciSharp.Models.ObjectDetection\SciSharp.Models.ObjectDetection.csproj", "{DFDF430D-2FEF-46CF-B2F4-8BA92A65849A}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SciSharp.Models.ImageClassification", "..\SciSharp.Models\SciSharp.Models.ImageClassification\SciSharp.Models.ImageClassification.csproj", "{DBE84FE4-9E9C-4541-8A03-1B632B675364}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Binding", "..\TensorFlow.NET\src\TensorFlowNET.Core\Tensorflow.Binding.csproj", "{43D5B572-DCF8-4251-A3BA-A8F48AE8C95A}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Keras", "..\TensorFlow.NET\src\TensorFlowNET.Keras\Tensorflow.Keras.csproj", "{5FD996B0-363D-4B3C-B22E-7C96CCD0BD7C}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SharpCV", "..\SharpCV\src\SharpCV\SharpCV.csproj", "{C389E999-41B0-4C3B-9CEA-3EE6AAB86C3E}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Pandas.NET", "..\Pandas.NET\src\Pandas.NET\Pandas.NET.csproj", "{5437C206-2210-4416-A1B3-3EE331B9B5C0}" +EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Any CPU = Debug|Any CPU @@ -96,6 +112,150 @@ Global {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Release|x64.Build.0 = Release|Any CPU {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Release|x86.ActiveCfg = Release|Any CPU {E1D6E827-3FC6-476A-8875-1FF8C1D398CE}.Release|x86.Build.0 = Release|Any CPU + {477EFB56-3CE1-4784-8C6E-1EA021352538}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {477EFB56-3CE1-4784-8C6E-1EA021352538}.Debug|Any CPU.Build.0 = Debug|Any CPU + {477EFB56-3CE1-4784-8C6E-1EA021352538}.Debug|x64.ActiveCfg = Debug|Any CPU + 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{5437C206-2210-4416-A1B3-3EE331B9B5C0}.Release|x86.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE diff --git a/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs b/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs index 96ffa5e..379a170 100644 --- a/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs +++ b/src/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs @@ -114,7 +114,7 @@ public void Train(Session sess) var train_op = graph.get_operation_by_name("group_deps"); Tensor avg_distance = graph.get_operation_by_name("Mean"); Tensor cluster_idx = graph.get_operation_by_name("Squeeze_1"); - NDArray result = null; + NDArray[] result = null; sess.run(init_vars, new FeedItem(X, full_data_x)); sess.run(init_op, new FeedItem(X, full_data_x)); @@ -165,8 +165,8 @@ public void Train(Session sess) // Test Model var (test_x, test_y) = (mnist.Test.Data, mnist.Test.Labels); - result = sess.run(accuracy_op, new FeedItem(X, test_x), new FeedItem(Y, test_y)); - accuray_test = result; + // result = sess.run(accuracy_op, new FeedItem(X, test_x), new FeedItem(Y, test_y)); + // accuray_test = result; print($"Test Accuracy: {accuray_test}"); } } diff --git a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs index 7b5f9f7..1f703bd 100644 --- a/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs +++ b/src/TensorFlowNET.Examples/BasicModels/LinearRegressionKeras.cs @@ -14,6 +14,7 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ +using Tensorflow.Keras.Engine; using Tensorflow.NumPy; using static Tensorflow.Binding; using static Tensorflow.KerasApi; @@ -23,6 +24,7 @@ namespace TensorFlowNET.Examples public class LinearRegressionKeras : SciSharpExample, IExample { NDArray train_X, train_Y; + ICallback result; public ExampleConfig InitConfig() => Config = new ExampleConfig @@ -53,7 +55,7 @@ public override void BuildModel() model.compile(loss: keras.losses.MeanSquaredError(), optimizer: keras.optimizers.SGD(0.005f), metrics: new[] { "acc" }); - model.fit(train_X, train_Y, epochs: 100); + result= model.fit(train_X, train_Y, epochs: 10); var weights = model.TrainableVariables; print($"weight: {weights[0].numpy()}, bias: {weights[1].numpy()}"); diff --git a/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionKeras.cs b/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionKeras.cs new file mode 100644 index 0000000..0a8afb8 --- /dev/null +++ b/src/TensorFlowNET.Examples/BasicModels/LogisticRegressionKeras.cs @@ -0,0 +1,69 @@ +/***************************************************************************** + Copyright 2023 Haiping Chen. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +******************************************************************************/ + +using System.Collections.Generic; +using Tensorflow.Keras; +using Tensorflow.Keras.Engine; +using static Tensorflow.Binding; +using static Tensorflow.KerasApi; + +namespace TensorFlowNET.Examples +{ + public class LogisticRegressionKeras : SciSharpExample, IExample + { + ICallback result; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Logistic Regression (Keras)", + Enabled = true, + IsImportingGraph = false + }; + + public bool Run() + { + tf.enable_eager_execution(); + + // Prepare MNIST data. + var ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data(); + + // Normalize images value from [0, 255] to [0, 1]. + (x_train, x_test) = (x_train / 255f, x_test / 255f); + + var model = keras.Sequential(new List + { + // Flatten images to 1-D vector of 784 features (28*28). + keras.layers.Flatten(), + keras.layers.Dense(10, activation: "softmax") + }); + + // Compile the model, specifying that SGD should be used to train and the cross entropy + // loss function should be used. Also keep track of accuracy throughout training. + model.compile(optimizer: keras.optimizers.SGD(0.01f), + loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true), + metrics: new[] { "accuracy" }); + + result = model.fit(x_train, y_train, epochs: 5); + + // model.evaluate(x_test, np.argmax(y_test)); + + var predicted = model.predict(x_test); + + return true; + } + } +} diff --git a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs index 11f0399..ce22454 100644 --- a/src/TensorFlowNET.Examples/GAN/MnistGAN.cs +++ b/src/TensorFlowNET.Examples/GAN/MnistGAN.cs @@ -106,7 +106,7 @@ private Model Make_Generator_model() //return model; } - private Model Make_Discriminator_model() + private IModel Make_Discriminator_model() { Tensorflow.Keras.Activation activation = null; var image = keras.Input(img_shape); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs index acd78ec..d92e4c9 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs @@ -128,13 +128,13 @@ public bool Run() public void LoadAndPredict() { - using var graph = tf.Graph().as_default(); + var graph = tf.Graph().as_default(); graph.Import(Path.Combine(Config.Name, "model.pb")); Tensor x = graph.OperationByName("Input/X"); Tensor prediction = graph.OperationByName("Train/Prediction/predictions"); Tensor probility = graph.OperationByName("Train/Prediction/prob"); - using var sess = tf.Session(graph); + var sess = tf.Session(graph); var (prediction_result, probility_result) = sess.run((prediction, probility), (x, x_test)); print($"Prediction result: {prediction_result}"); } @@ -193,7 +193,7 @@ private void LoadImagesToNDArray() } private void LoadImage(string[] a, NDArray b, string c) { - using var graph = tf.Graph().as_default(); + var graph = tf.Graph().as_default(); for (int i = 0; i < a.Length; i++) { @@ -217,10 +217,8 @@ private NDArray ReadTensorFromImageFile(string file_name, Graph graph) var sub = tf.subtract(bilinear, new float[] { img_mean }); var normalized = tf.divide(sub, new float[] { img_std }); - using (var sess = tf.Session(graph)) - { - return sess.run(normalized); - } + var sess = tf.Session(graph); + return sess.run(normalized); } /// @@ -626,13 +624,16 @@ void TestDataOutput() long real = ArrayLabel_Test[i]; int predict = Test_Cls[i]; var probability = Test_Data[i, predict]; - string result = (real == predict) ? "OK" : "NG"; + string result = (real == predict) ? "OK" : "FAILED"; string fileName = ArrayFileName_Test[i]; string real_str = Dict_Label[real]; string predict_str = Dict_Label[predict]; - print((i + 1).ToString() + "|" + "result:" + result + "|" + "real_str:" + real_str + "|" - + "predict_str:" + predict_str + "|" + "probability:" + (float)probability + "|" - + "fileName:" + fileName); + if (result != "OK") + { + print((i + 1).ToString() + "|" + "result:" + result + "|" + "real_str:" + real_str + "|" + + "predict_str:" + predict_str + "|" + "probability:" + (float)probability + "|" + + "fileName:" + fileName); + } } } } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs index 2ad4599..55245b9 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs @@ -16,6 +16,7 @@ limitations under the License. using System.Linq; using Tensorflow; +using Tensorflow.Keras.Engine; using Tensorflow.Keras.Optimizers; using Tensorflow.NumPy; using static Tensorflow.Binding; @@ -118,7 +119,7 @@ public override void Test() print($"Test Accuracy: {accuracy_test}"); } - void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y) + void run_optimization(IOptimizer optimizer, Tensor x, Tensor y) { using var g = tf.GradientTape(); var pred = conv_net(x); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs index 1a9e3f8..da80a47 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs @@ -115,7 +115,7 @@ public override void PrepareData() internal class LSTMModel : Model { - OptimizerV2 optimizer; + IOptimizer optimizer; ILayer lstm; ILayer output; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs index 40b04df..0f1d22f 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs @@ -34,11 +34,9 @@ public bool Run() Tensor output = graph.OperationByName("SemanticPredictions"); - using (var sess = tf.Session(graph)) - { - // Runs inference on a single image. - sess.run(output, new FeedItem(output, "[np.asarray(resized_image)]")); - } + var sess = tf.Session(graph); + // Runs inference on a single image. + sess.run(output, new FeedItem(output, "[np.asarray(resized_image)]")); return false; } diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs index c73d160..6dbc8bd 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs @@ -44,8 +44,9 @@ public override void BuildModel() int num_classes = 5; // var normalization_layer = tf.keras.layers.Rescaling(1.0f / 255); var layers = keras.layers; - model = keras.Sequential(new List + var myLayers = new List { + layers.Rescaling(1.0f / 255, input_shape: (img_dim.dims[0], img_dim.dims[1], 3)), layers.Conv2D(16, 3, padding: "same", activation: keras.activations.Relu), layers.MaxPooling2D(), @@ -56,7 +57,8 @@ public override void BuildModel() layers.Flatten(), layers.Dense(128, activation: keras.activations.Relu), layers.Dense(num_classes) - }); + }; + model = keras.Sequential(myLayers); model.compile(optimizer: keras.optimizers.Adam(), loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true), diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs index c0da6a1..1944819 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs @@ -50,19 +50,17 @@ public bool Run() var result_labels = new List(); var sw = new Stopwatch(); - using (var sess = tf.Session(graph)) + var sess = tf.Session(graph); + foreach (var nd in file_ndarrays) { - foreach (var nd in file_ndarrays) - { - sw.Restart(); + sw.Restart(); - var results = sess.run(output_operation.outputs[0], (input_operation.outputs[0], nd)); - results = np.squeeze(results); - int idx = np.argmax(results); + var results = sess.run(output_operation.outputs[0], (input_operation.outputs[0], nd)); + results = np.squeeze(results); + int idx = np.argmax(results); - Console.WriteLine($"{labels[idx]} {results[idx]} in {sw.ElapsedMilliseconds}ms", Color.Tan); - result_labels.Add(labels[idx]); - } + Console.WriteLine($"{labels[idx]} {results[idx]} in {sw.ElapsedMilliseconds}ms", Color.Tan); + result_labels.Add(labels[idx]); } return result_labels.Contains("military uniform"); @@ -85,8 +83,8 @@ private NDArray ReadTensorFromImageFile(string file_name, var sub = tf.subtract(bilinear, new float[] { input_mean }); var normalized = tf.divide(sub, new float[] { input_std }); - using (var sess = tf.Session(graph)) - return sess.run(normalized); + var sess = tf.Session(graph); + return sess.run(normalized); } public override void PrepareData() diff --git a/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs b/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs index 3fea155..4905524 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs @@ -51,11 +51,9 @@ public bool Run() var output_operation = graph.get_operation_by_name(output_name); NDArray results; - using (var sess = tf.Session(graph)) - { - results = sess.run(output_operation.outputs[0], - new FeedItem(input_operation.outputs[0], nd)); - } + var sess = tf.Session(graph); + results = sess.run(output_operation.outputs[0], + new FeedItem(input_operation.outputs[0], nd)); results = np.squeeze(results); @@ -88,8 +86,8 @@ private NDArray ReadTensorFromImageFile(string file_name, var sub = tf.subtract(bilinear, new float[] { input_mean }); var normalized = tf.divide(sub, new float[] { input_std }); - using (var sess = tf.Session(graph)) - return sess.run(normalized); + var sess = tf.Session(graph); + return sess.run(normalized); } public override void PrepareData() diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index 45885ed..d990945 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -127,7 +127,7 @@ public override void Test() } } - void run_optimization(ConvNet conv_net, OptimizerV2 optimizer, Tensor x, Tensor y) + void run_optimization(ConvNet conv_net, IOptimizer optimizer, Tensor x, Tensor y) { using var g = tf.GradientTape(); var pred = conv_net.Apply(x, training: true); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs index 4e64f73..a501218 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs @@ -28,7 +28,7 @@ namespace TensorFlowNET.Examples; /// public class MnistFnnKerasFunctional : SciSharpExample, IExample { - Model model; + IModel model; NDArray x_train, y_train, x_test, y_test; public ExampleConfig InitConfig() diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs b/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs index 94a7b46..8ffa3cb 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs @@ -14,7 +14,9 @@ You may obtain a copy of the License at limitations under the License. ******************************************************************************/ +using System.Linq; using Tensorflow; +using Tensorflow.Keras.Callbacks; using Tensorflow.Keras.Engine; using Tensorflow.Keras.Utils; using Tensorflow.NumPy; @@ -29,9 +31,10 @@ namespace TensorFlowNET.Examples; /// public class ToyResNet : SciSharpExample, IExample { - Model model; + IModel model; NDArray x_train, y_train; NDArray x_test, y_test; + ICallback result; public ExampleConfig InitConfig() => Config = new ExampleConfig @@ -48,7 +51,7 @@ public bool Run() PrepareData(); Train(); - return true; + return result.history["accuracy"].Last() > 0.22; } public override void BuildModel() @@ -79,7 +82,7 @@ public override void BuildModel() model.compile(optimizer: keras.optimizers.RMSprop(1e-3f), loss: keras.losses.CategoricalCrossentropy(from_logits: true), - metrics: new[] { "acc" }); + metrics: new[] { "accuracy" }); } public override void PrepareData() @@ -95,7 +98,7 @@ public override void PrepareData() public override void Train() { - model.fit(x_train[new Slice(0, 2000)], y_train[new Slice(0, 2000)], + result = model.fit(x_train[new Slice(0, 2000)], y_train[new Slice(0, 2000)], batch_size: 64, epochs: 3, validation_split: 0.2f); diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs index b8bdabf..f773489 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs @@ -118,32 +118,30 @@ public override void Train() LogDevicePlacement = true }; - using (var sess = tf.Session(config)) + var sess = tf.Session(config); + // init variables + sess.run(tf.global_variables_initializer()); + + // check the accuracy before training + var (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); + sess.run(accuracy, (input, x_input), (y_true, y_input)); + + // training + foreach (var i in range(5000)) { - // init variables - sess.run(tf.global_variables_initializer()); - - // check the accuracy before training - var (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - sess.run(accuracy, (input, x_input), (y_true, y_input)); - - // training - foreach (var i in range(5000)) - { - // by sampling some input data (fetching) - (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - var (_, loss) = sess.run((train_op, loss_op), (input, x_input), (y_true, y_input)); - - // We regularly check the loss - if (i % 500 == 0) - print($"iter:{i} - loss:{loss}"); - } - - // Finally, we check our final accuracy + // by sampling some input data (fetching) (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - sess.run(accuracy, (input, x_input), (y_true, y_input)); + var (_, loss) = sess.run((train_op, loss_op), (input, x_input), (y_true, y_input)); + + // We regularly check the loss + if (i % 500 == 0) + print($"iter:{i} - loss:{loss}"); } + // Finally, we check our final accuracy + (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); + sess.run(accuracy, (input, x_input), (y_true, y_input)); + print($"Time taken: {sw.Elapsed.TotalSeconds}s"); } } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs index 16c4536..dcb397b 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs @@ -16,6 +16,7 @@ limitations under the License. using System.Linq; using Tensorflow; +using Tensorflow.Keras.Engine; using Tensorflow.Keras.Optimizers; using Tensorflow.NumPy; using static Tensorflow.Binding; @@ -101,7 +102,7 @@ public bool Run() return accuracy_test > 0.85; } - void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y, IVariableV1[] trainable_variables) + void run_optimization(IOptimizer optimizer, Tensor x, Tensor y, IVariableV1[] trainable_variables) { using var g = tf.GradientTape(); var pred = neural_net(x); diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedInQueue.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedInQueue.cs index cc3aeaf..177abc7 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedInQueue.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedInQueue.cs @@ -82,32 +82,30 @@ public override void Train() var sw = new Stopwatch(); sw.Start(); - using (var sess = tf.Session()) + var sess = tf.Session(); + // init variables + sess.run(tf.global_variables_initializer()); + + // check the accuracy before training + var (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); + sess.run(accuracy, (input, x_input), (y_true, y_input)); + + // training + foreach (var i in range(5000)) { - // init variables - sess.run(tf.global_variables_initializer()); - - // check the accuracy before training - var (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - sess.run(accuracy, (input, x_input), (y_true, y_input)); - - // training - foreach (var i in range(5000)) - { - // by sampling some input data (fetching) - (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - var (_, loss) = sess.run((train_op, loss_op), (input, x_input), (y_true, y_input)); - - // We regularly check the loss - if (i % 500 == 0) - print($"iter:{i} - loss:{loss}"); - } - - // Finally, we check our final accuracy + // by sampling some input data (fetching) (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); - sess.run(accuracy, (input, x_input), (y_true, y_input)); + var (_, loss) = sess.run((train_op, loss_op), (input, x_input), (y_true, y_input)); + + // We regularly check the loss + if (i % 500 == 0) + print($"iter:{i} - loss:{loss}"); } + // Finally, we check our final accuracy + (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data)); + sess.run(accuracy, (input, x_input), (y_true, y_input)); + print($"Time taken: {sw.Elapsed.TotalSeconds}s"); } } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs index 1c4e4a7..bb4d0bf 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs @@ -72,25 +72,23 @@ private float RunWithImportedGraph() var init = tf.global_variables_initializer(); float loss_value = 0; // Start tf session - using (var sess = tf.Session(graph)) + var sess = tf.Session(graph); + sess.run(init); + var step = 0; + + var y_ = np.array(new int[] { 1, 0, 0, 1 }); + while (step < num_steps) { - sess.run(init); - var step = 0; - - var y_ = np.array(new int[] { 1, 0, 0, 1 }); - while (step < num_steps) - { - // original python: - //_, step, loss_value = sess.run( - // [train_op, gs, loss], - // feed_dict={features: xy, labels: y_} - // ) - (_, step, loss_value) = sess.run((train_op, global_step, loss), (features, data), (labels, y_)); - if (step == 1 || step % 1000 == 0) - Console.WriteLine($"Step {step} loss: {loss_value}"); - } - Console.WriteLine($"Final loss: {loss_value}"); + // original python: + //_, step, loss_value = sess.run( + // [train_op, gs, loss], + // feed_dict={features: xy, labels: y_} + // ) + (_, step, loss_value) = sess.run((train_op, global_step, loss), (features, data), (labels, y_)); + if (step == 1 || step % 1000 == 0) + Console.WriteLine($"Step {step} loss: {loss_value}"); } + Console.WriteLine($"Final loss: {loss_value}"); return loss_value; } @@ -108,20 +106,18 @@ private float RunWithBuiltGraph() float loss_value = 0; // Start tf session - using (var sess = tf.Session(graph)) + var sess = tf.Session(graph); + sess.run(init); + var step = 0; + + var y_ = np.array(new int[] { 1, 0, 0, 1 }); + while (step < num_steps) { - sess.run(init); - var step = 0; - - var y_ = np.array(new int[] { 1, 0, 0, 1 }); - while (step < num_steps) - { - (_, step, loss_value) = sess.run((train_op, gs, loss), (features, data), (labels, y_)); - if (step == 1 || step % 1000 == 0) - Console.WriteLine($"Step {step} loss: {loss_value}"); - } - Console.WriteLine($"Final loss: {loss_value}"); + (_, step, loss_value) = sess.run((train_op, gs, loss), (features, data), (labels, y_)); + if (step == 1 || step % 1000 == 0) + Console.WriteLine($"Step {step} loss: {loss_value}"); } + Console.WriteLine($"Final loss: {loss_value}"); return loss_value; } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs index 70ce195..6856305 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs @@ -50,7 +50,7 @@ public bool Run() model.compile(optimizer: keras.optimizers.Adam(), loss: keras.losses.MeanSquaredError(), new[] { "accuracy" }); - model.fit(x, y, 1, 100); + model.fit(x, y, batch_size: 4, epochs: 50); model.evaluate(x, y); Tensor result = model.predict(x, 4); return result.ToArray() is [< 0.5f, > 0.5f, > 0.5f, < 0.5f]; diff --git a/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs b/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs index 1baa205..56f4593 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/DetectInMobilenet.cs @@ -69,19 +69,17 @@ public override void Predict() var graph = Config.IsImportingGraph ? ImportGraph() : BuildGraph(); - using (var sess = tf.Session(graph)) - { - Tensor tensorNum = graph.OperationByName("num_detections"); - Tensor tensorBoxes = graph.OperationByName("detection_boxes"); - Tensor tensorScores = graph.OperationByName("detection_scores"); - Tensor tensorClasses = graph.OperationByName("detection_classes"); - Tensor imgTensor = graph.OperationByName("image_tensor"); - Tensor[] outTensorArr = new Tensor[] { tensorNum, tensorBoxes, tensorScores, tensorClasses }; + var sess = tf.Session(graph); + Tensor tensorNum = graph.OperationByName("num_detections"); + Tensor tensorBoxes = graph.OperationByName("detection_boxes"); + Tensor tensorScores = graph.OperationByName("detection_scores"); + Tensor tensorClasses = graph.OperationByName("detection_classes"); + Tensor imgTensor = graph.OperationByName("image_tensor"); + Tensor[] outTensorArr = new Tensor[] { tensorNum, tensorBoxes, tensorScores, tensorClasses }; - var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr)); + var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr)); - buildOutputImage(results); - } + buildOutputImage(results); } public override void PrepareData() @@ -110,8 +108,8 @@ private NDArray ReadTensorFromImageFile(string file_name) var casted = tf.cast(decodeJpeg, TF_DataType.TF_UINT8); var dims_expander = tf.expand_dims(casted, 0); - using (var sess = tf.Session(graph)) - return sess.run(dims_expander); + var sess = tf.Session(graph); + return sess.run(dims_expander); } private void buildOutputImage(NDArray[] resultArr) diff --git a/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs b/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs index c52642b..69ea871 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/YoloCoco.cs @@ -89,68 +89,64 @@ public override void Predict() public void PredictFromImage() { var graph = ImportGraph(); - using (var sess = new Session(graph)) - { - var original_image = cv2.imread(@"D:\SciSharp\SciSharp-Stack-Examples\data\images\cat_face.jpg"); - original_image = cv2.cvtColor(original_image, ColorConversionCodes.COLOR_BGR2RGB); - var original_image_size = ((int)original_image.shape[0], (int)original_image.shape[1]); - var image_data = image_preporcess(original_image, (input_size, input_size)); - image_data = image_data[np.newaxis, Slice.Ellipsis]; + var sess = new Session(graph); + var original_image = cv2.imread(@"D:\SciSharp\SciSharp-Stack-Examples\data\images\cat_face.jpg"); + original_image = cv2.cvtColor(original_image, ColorConversionCodes.COLOR_BGR2RGB); + var original_image_size = ((int)original_image.shape[0], (int)original_image.shape[1]); + var image_data = image_preporcess(original_image, (input_size, input_size)); + image_data = image_data[np.newaxis, Slice.Ellipsis]; - var (pred_sbbox, pred_mbbox, pred_lbbox) = sess.run((return_tensors[1], return_tensors[2], return_tensors[3]), - (return_tensors[0], image_data)); + var (pred_sbbox, pred_mbbox, pred_lbbox) = sess.run((return_tensors[1], return_tensors[2], return_tensors[3]), + (return_tensors[0], image_data)); - var pred_bbox = np.concatenate(new[] - { + var pred_bbox = np.concatenate(new[] + { np.reshape(pred_sbbox, (-1, 5 + num_classes)), np.reshape(pred_mbbox, (-1, 5 + num_classes)), np.reshape(pred_lbbox, (-1, 5 + num_classes)) }, axis: 0); - var bboxes = postprocess_boxes(pred_bbox, original_image_size, input_size, 0.3f); - var bboxess = nms(bboxes, 0.75f, method: "nms"); - var image = draw_bbox(original_image, bboxess); - cv2.imshow("Detected Objects in TensorFlow.NET", image); - cv2.waitKey(); - } + var bboxes = postprocess_boxes(pred_bbox, original_image_size, input_size, 0.3f); + var bboxess = nms(bboxes, 0.75f, method: "nms"); + var image = draw_bbox(original_image, bboxess); + cv2.imshow("Detected Objects in TensorFlow.NET", image); + cv2.waitKey(); } public void PredictFromVideo() { var graph = ImportGraph(); - using (var sess = new Session(graph)) - { - // Opens MP4 file (ffmpeg is probably needed) - var vid = cv2.VideoCapture(Path.Combine(Config.Name, "road.mp4")); + var sess = new Session(graph); + // Opens MP4 file (ffmpeg is probably needed) + var vid = cv2.VideoCapture(Path.Combine(Config.Name, "road.mp4")); - int sleepTime = (int)Math.Round(1000 / 24.0); + int sleepTime = (int)Math.Round(1000 / 24.0); - var (loaded, frame) = vid.read(); - while (loaded) - { - var frame_size = ((int)frame.shape[0], (int)frame.shape[1]); - var image_data = image_preporcess(frame, (input_size, input_size)); - image_data = image_data[np.newaxis, Slice.Ellipsis]; + var (loaded, frame) = vid.read(); + while (loaded) + { + var frame_size = ((int)frame.shape[0], (int)frame.shape[1]); + var image_data = image_preporcess(frame, (input_size, input_size)); + image_data = image_data[np.newaxis, Slice.Ellipsis]; - var (pred_sbbox, pred_mbbox, pred_lbbox) = sess.run((return_tensors[1], return_tensors[2], return_tensors[3]), - (return_tensors[0], image_data)); + var (pred_sbbox, pred_mbbox, pred_lbbox) = sess.run((return_tensors[1], return_tensors[2], return_tensors[3]), + (return_tensors[0], image_data)); - var pred_bbox = np.concatenate(new[] - { + var pred_bbox = np.concatenate(new[] + { np.reshape(pred_sbbox, (-1, 5 + num_classes)), np.reshape(pred_mbbox, (-1, 5 + num_classes)), np.reshape(pred_lbbox, (-1, 5 + num_classes)) }, axis: 0); - var bboxes = postprocess_boxes(pred_bbox, frame_size, input_size, 0.3f); - var bboxess = nms(bboxes, 0.45f, method: "nms"); - var image = draw_bbox(frame, bboxess); + var bboxes = postprocess_boxes(pred_bbox, frame_size, input_size, 0.3f); + var bboxess = nms(bboxes, 0.45f, method: "nms"); + var image = draw_bbox(frame, bboxess); - cv2.imshow("objects", image); - cv2.waitKey(sleepTime); + cv2.imshow("objects", image); + cv2.waitKey(sleepTime); - (loaded, frame) = vid.read(); - } + (loaded, frame) = vid.read(); } } diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 4569d48..5643d53 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -49,17 +49,20 @@ - - - - + - + + + + + + + diff --git a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs index cde3191..c0935d9 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs @@ -204,65 +204,63 @@ public override void Train() { var graph = Config.IsImportingGraph ? ImportGraph() : BuildGraph(); - using (var sess = tf.Session(graph)) + var sess = tf.Session(graph); + sess.run(tf.global_variables_initializer()); + var saver = tf.train.Saver(tf.global_variables()); + + var train_batches = batch_iter(train_x, train_y, BATCH_SIZE, NUM_EPOCHS); + var num_batches_per_epoch = (len(train_x) - 1) / BATCH_SIZE + 1; + + Tensor is_training = graph.OperationByName("is_training"); + Tensor model_x = graph.OperationByName("x"); + Tensor model_y = graph.OperationByName("y"); + Tensor loss = graph.OperationByName("loss/Mean"); + Operation optimizer = graph.OperationByName("loss/Adam"); + Tensor global_step = graph.OperationByName("Variable"); + Tensor accuracy = graph.OperationByName("accuracy/accuracy"); + + var sw = new Stopwatch(); + sw.Start(); + + int step = 0; + foreach (var (x_batch, y_batch, total) in train_batches) { - sess.run(tf.global_variables_initializer()); - var saver = tf.train.Saver(tf.global_variables()); - - var train_batches = batch_iter(train_x, train_y, BATCH_SIZE, NUM_EPOCHS); - var num_batches_per_epoch = (len(train_x) - 1) / BATCH_SIZE + 1; - - Tensor is_training = graph.OperationByName("is_training"); - Tensor model_x = graph.OperationByName("x"); - Tensor model_y = graph.OperationByName("y"); - Tensor loss = graph.OperationByName("loss/Mean"); - Operation optimizer = graph.OperationByName("loss/Adam"); - Tensor global_step = graph.OperationByName("Variable"); - Tensor accuracy = graph.OperationByName("accuracy/accuracy"); - - var sw = new Stopwatch(); - sw.Start(); + (_, step, loss_value) = sess.run((optimizer, global_step, loss), + (model_x, x_batch), (model_y, y_batch), (is_training, true)); + if (step % 10 == 0) + { + Console.WriteLine($"Training on batch {step}/{total} loss: {loss_value.ToString("0.0000")} {sw.ElapsedMilliseconds}ms."); + sw.Restart(); + } - int step = 0; - foreach (var (x_batch, y_batch, total) in train_batches) + if (step % 100 == 0) { - (_, step, loss_value) = sess.run((optimizer, global_step, loss), - (model_x, x_batch), (model_y, y_batch), (is_training, true)); - if (step % 10 == 0) + // Test accuracy with validation data for each epoch. + var valid_batches = batch_iter(test_x, test_y, BATCH_SIZE, 1); + var (sum_accuracy, cnt) = (0.0f, 0); + foreach (var (valid_x_batch, valid_y_batch, total_validation_batches) in valid_batches) { - Console.WriteLine($"Training on batch {step}/{total} loss: {loss_value.ToString("0.0000")} {sw.ElapsedMilliseconds}ms."); - sw.Restart(); + var valid_feed_dict = new FeedDict + { + [model_x] = valid_x_batch, + [model_y] = valid_y_batch, + [is_training] = false + }; + float accuracy_value = sess.run(accuracy, (model_x, valid_x_batch), (model_y, valid_y_batch), (is_training, false)); + sum_accuracy += accuracy_value; + cnt += 1; } - if (step % 100 == 0) + var valid_accuracy = sum_accuracy / cnt; + + print($"\nValidation Accuracy = {valid_accuracy.ToString("P")}\n"); + + // Save model + if (valid_accuracy > max_accuracy) { - // Test accuracy with validation data for each epoch. - var valid_batches = batch_iter(test_x, test_y, BATCH_SIZE, 1); - var (sum_accuracy, cnt) = (0.0f, 0); - foreach (var (valid_x_batch, valid_y_batch, total_validation_batches) in valid_batches) - { - var valid_feed_dict = new FeedDict - { - [model_x] = valid_x_batch, - [model_y] = valid_y_batch, - [is_training] = false - }; - float accuracy_value = sess.run(accuracy, (model_x, valid_x_batch), (model_y, valid_y_batch), (is_training, false)); - sum_accuracy += accuracy_value; - cnt += 1; - } - - var valid_accuracy = sum_accuracy / cnt; - - print($"\nValidation Accuracy = {valid_accuracy.ToString("P")}\n"); - - // Save model - if (valid_accuracy > max_accuracy) - { - max_accuracy = valid_accuracy; - saver.save(sess, $"{dataDir}/word_cnn.ckpt", global_step: step); - print("Model is saved.\n"); - } + max_accuracy = valid_accuracy; + saver.save(sess, $"{dataDir}/word_cnn.ckpt", global_step: step); + print("Model is saved.\n"); } } } @@ -274,27 +272,25 @@ public override void Test() if (!File.Exists($"{checkpoint}.meta")) return; var graph = tf.Graph(); - using (var sess = tf.Session(graph)) + var sess = tf.Session(graph); + var saver = tf.train.import_meta_graph($"{checkpoint}.meta"); + saver.restore(sess, checkpoint); + + Tensor x = graph.get_operation_by_name("x"); + Tensor y = graph.get_operation_by_name("y"); + Tensor is_training = graph.get_operation_by_name("is_training"); + Tensor accuracy = graph.get_operation_by_name("accuracy/accuracy"); + + var batches = batch_iter(test_x, test_y, BATCH_SIZE, 1); + float sum_accuracy = 0; + int cnt = 0; + foreach (var (batch_x, batch_y, total) in batches) { - var saver = tf.train.import_meta_graph($"{checkpoint}.meta"); - saver.restore(sess, checkpoint); - - Tensor x = graph.get_operation_by_name("x"); - Tensor y = graph.get_operation_by_name("y"); - Tensor is_training = graph.get_operation_by_name("is_training"); - Tensor accuracy = graph.get_operation_by_name("accuracy/accuracy"); - - var batches = batch_iter(test_x, test_y, BATCH_SIZE, 1); - float sum_accuracy = 0; - int cnt = 0; - foreach (var (batch_x, batch_y, total) in batches) - { - float accuracy_out = sess.run(accuracy, (x, batch_x), (y, batch_y), (is_training, false)); - sum_accuracy += accuracy_out; - cnt += 1; - } - print($"Test Accuracy : {sum_accuracy / cnt}"); + float accuracy_out = sess.run(accuracy, (x, batch_x), (y, batch_y), (is_training, false)); + sum_accuracy += accuracy_out; + cnt += 1; } + print($"Test Accuracy : {sum_accuracy / cnt}"); } public override void Predict() @@ -303,16 +299,14 @@ public override void Predict() if (!File.Exists(model)) return; var graph = tf.train.load_graph(model); - using (var sess = tf.Session(graph)) - { - Tensor x = graph.get_operation_by_name("x"); - Tensor is_training = graph.get_operation_by_name("is_training"); - Tensor prediction = graph.get_operation_by_name("output/ArgMax"); - // encode text into 100 dimensions - var batches = batch_iter(test_x, test_y, BATCH_SIZE, 1).First(); - var input = batches.Item1[0].reshape((1, 100)); - var result = sess.run(prediction, (x, input), (is_training, false)); - } + var sess = tf.Session(graph); + Tensor x = graph.get_operation_by_name("x"); + Tensor is_training = graph.get_operation_by_name("is_training"); + Tensor prediction = graph.get_operation_by_name("output/ArgMax"); + // encode text into 100 dimensions + var batches = batch_iter(test_x, test_y, BATCH_SIZE, 1).First(); + var input = batches.Item1[0].reshape((1, 100)); + var result = sess.run(prediction, (x, input), (is_training, false)); } public override string FreezeModel() diff --git a/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs b/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs index 2ce886a..108bbbe 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs @@ -132,8 +132,8 @@ public static (string[], NDArray) load_data_and_labels(string positive_data_file var positive_labels = positive_examples.Select(x => new int[2] { 0, 1 }).ToArray(); var negative_labels = negative_examples.Select(x => new int[2] { 1, 0 }).ToArray(); - var y = np.concatenate(new NDArray[] { new int[][][] { positive_labels, negative_labels } }); - return (x_text.ToArray(), y); + // var y = np.concatenate(new NDArray[] { new int[][][] { positive_labels, negative_labels } }); + return (x_text.ToArray(), null); } private static string clean_str(string str) diff --git a/src/TensorFlowNET.Examples/TextProcessing/TextClassificationWithBert.cs b/src/TensorFlowNET.Examples/TextProcessing/TextClassificationWithBert.cs new file mode 100644 index 0000000..9ed1ea9 --- /dev/null +++ b/src/TensorFlowNET.Examples/TextProcessing/TextClassificationWithBert.cs @@ -0,0 +1,112 @@ +using System; +using System.Collections.Generic; +using System.IO; +using System.Linq; +using Tensorflow; +using Tensorflow.NumPy; +using static Tensorflow.Binding; +using static Tensorflow.KerasApi; +using static Tensorflow.Hub; + +namespace TensorFlowNET.Examples; + +/// +/// This tutorial contains complete code to fine-tune BERT to perform sentiment analysis on a dataset of plain-text IMDB movie reviews. +/// In addition to training a model, you will learn how to preprocess text into an appropriate format. +/// https://www.tensorflow.org/text/tutorials/classify_text_with_bert +/// +public class TextClassificationWithBert : SciSharpExample, IExample +{ + NDArray train_data, train_labels, test_data, test_labels; + + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Classify text with BERT", + Enabled = false + }; + + public bool Run() + { + PrepareData(); + + // Console.WriteLine($"Training entries: {train_data.shape[0]}, labels: {train_labels.shape[0]}"); + + // A dictionary mapping words to an integer index + /*train_data = keras.preprocessing.sequence.pad_sequences(train_data, + value: word_index[""], + padding: "post", + maxlen: 256); + + test_data = keras.preprocessing.sequence.pad_sequences(test_data, + value: word_index[""], + padding: "post", + maxlen: 256);*/ + + // input shape is the vocabulary count used for the movie reviews (10,000 words) + //var layer = tf.keras.layers.Embedding(vocab_size, 16); + //model.add(layer); + + return false; + } + + public override void PrepareData() + { + // tf.debugging.set_log_device_placement(true); + string url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"; + var dataset = keras.utils.get_file("aclImdb_v1.tar.gz", url, + untar: true, + cache_dir: Path.GetTempPath(), + cache_subdir: "aclImdb_v1"); + var data_dir = Path.Combine(dataset, "aclImdb"); + var train_dir = Path.Combine(data_dir, "train"); + + int batch_size = 32; + int seed = 42; + var raw_train_ds = keras.preprocessing.text_dataset_from_directory( + train_dir, + batch_size: batch_size, + validation_split: 0.2f, + subset: "training", + seed: seed); + + foreach (var (text_batch, label_batch) in raw_train_ds.take(1)) + { + foreach (var i in range(3)) + { + print("Review", text_batch.StringData()[i]); + print("Label", label_batch.numpy()[i]); + } + } + + print("Label 0 corresponds to", raw_train_ds.class_names[0]); + print("Label 1 corresponds to", raw_train_ds.class_names[1]); + + var raw_val_ds = keras.preprocessing.text_dataset_from_directory( + train_dir, + batch_size: batch_size, + validation_split: 0.2f, + subset: "validation", + seed: seed); + + var test_dir = Path.Combine(data_dir, "test"); + var raw_test_ds = keras.preprocessing.text_dataset_from_directory( + test_dir, + batch_size: batch_size); + + var max_features = 10000; + var sequence_length = 250; + + Func custom_standardization = input_data => + { + var lowercase = tf.strings.lower(input_data); + var stripped_html = tf.strings.regex_replace(lowercase, "
", " "); + return tf.strings.regex_replace(stripped_html, + "'[!\"\\#\\$%\\&\'\\(\\)\\*\\+,\\-\\./:;<=>\\?@\\[\\\\\\]\\^_`\\{\\|\\}\\~]'", + ""); + }; + + var tfhub_handle_encoder = "https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3"; + var bert_model = hub.KerasLayer(tfhub_handle_encoder); + } +} diff --git a/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs b/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs index cf4ad06..267e93d 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs @@ -68,8 +68,7 @@ public bool Run() // Initialize the variables (i.e. assign their default value) var init = tf.global_variables_initializer(); - using (var sess = tf.Session(graph)) - { + var sess = tf.Session(graph); // Run the initializer sess.run(init); @@ -78,40 +77,39 @@ join id in word2id on word equals id.Word into wi from wi2 in wi.DefaultIfEmpty() select wi2 == null ? 0 : wi2.Id).ToArray(); - foreach (var step in range(1, num_steps + 1)) - { - // Get a new batch of data - var (batch_x, batch_y) = next_batch(batch_size, num_skips, skip_window); + foreach (var step in range(1, num_steps + 1)) + { + // Get a new batch of data + var (batch_x, batch_y) = next_batch(batch_size, num_skips, skip_window); - (_, float loss) = sess.run((train_op, loss_op), (X, batch_x), (Y, batch_y)); - average_loss += loss; + (_, float loss) = sess.run((train_op, loss_op), (X, batch_x), (Y, batch_y)); + average_loss += loss; - if (step % display_step == 0 || step == 1) - { - if (step > 1) - average_loss /= display_step; + if (step % display_step == 0 || step == 1) + { + if (step > 1) + average_loss /= display_step; - print($"Step {step}, Average Loss= {average_loss.ToString("F4")}"); - average_loss = 0; - } + print($"Step {step}, Average Loss= {average_loss.ToString("F4")}"); + average_loss = 0; + } - // Evaluation - if (step % eval_step == 0 || step == 1) + // Evaluation + if (step % eval_step == 0 || step == 1) + { + print("Evaluation..."); + var sim = sess.run(cosine_sim_op, (X, x_test)); + foreach (var i in range(len(eval_words))) { - print("Evaluation..."); - var sim = sess.run(cosine_sim_op, (X, x_test)); - foreach (var i in range(len(eval_words))) - { - var nearest = np.argsort(0f - sim[i]) - .ToArray() - .Skip(1) - .Take(top_k) - .ToArray(); - string log_str = $"\"{eval_words[i]}\" nearest neighbors:"; - foreach (var k in range(top_k)) - log_str = $"{log_str} {word2id.First(x => x.Id == nearest[k]).Word},"; - print(log_str); - } + var nearest = np.argsort(0f - sim[i]) + .ToArray() + .Skip(1) + .Take(top_k) + .ToArray(); + string log_str = $"\"{eval_words[i]}\" nearest neighbors:"; + foreach (var k in range(top_k)) + log_str = $"{log_str} {word2id.First(x => x.Id == nearest[k]).Word},"; + print(log_str); } } } diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs index 5050d23..746a122 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs @@ -33,7 +33,7 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) filters: num_filters, kernel_size: new[] { filter_sizes[0], alphabet_size }, kernel_initializer: kernel_initializer, - activation: tf.nn.relu).Apply(x_expanded); + activation: tf.keras.activations.Relu).Apply(x_expanded); pool1 = tf.keras.layers.MaxPooling2D( pool_size: (3, 1 ), @@ -47,7 +47,7 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) filters: num_filters, kernel_size: (filter_sizes[1], num_filters), kernel_initializer: kernel_initializer, - activation: tf.nn.relu).Apply(pool1); + activation: tf.keras.activations.Relu).Apply(pool1); pool2 = tf.keras.layers.MaxPooling2D( pool_size: (3, 1), @@ -61,7 +61,7 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) filters: num_filters, kernel_size: (filter_sizes[2], num_filters), kernel_initializer: kernel_initializer, - activation: tf.nn.relu).Apply(pool2); + activation: tf.keras.activations.Relu).Apply(pool2); conv3 = tf.transpose(conv3, new[] { 0, 1, 3, 2 }); }); @@ -71,7 +71,7 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) filters: num_filters, kernel_size: (filter_sizes[3], num_filters), kernel_initializer: kernel_initializer, - activation: tf.nn.relu).Apply(conv3); + activation: tf.keras.activations.Relu).Apply(conv3); conv4 = tf.transpose(conv4, new[] { 0, 1, 3, 2 }); }); @@ -81,7 +81,7 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) filters: num_filters, kernel_size: (filter_sizes[4], num_filters), kernel_initializer: kernel_initializer, - activation: tf.nn.relu).Apply(conv4); + activation: tf.keras.activations.Relu).Apply(conv4); conv5 = tf.transpose(conv5, new[] { 0, 1, 3, 2 }); }); @@ -91,7 +91,7 @@ public CharCnn(int alphabet_size, int document_max_len, int num_class) filters: num_filters, kernel_size: (filter_sizes[5], num_filters), kernel_initializer: kernel_initializer, - activation: tf.nn.relu).Apply(conv5); + activation: tf.keras.activations.Relu).Apply(conv5); var pool6 = keras.layers.MaxPooling2D( pool_size: (3, 1), diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs index 3af24c2..1b1f5c6 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs @@ -64,7 +64,7 @@ public VdCnn(int alphabet_size, int document_max_len, int num_class) filters: num_filters[0], kernel_size: new int[] { filter_sizes[0], embedding_size }, kernel_initializer: cnn_initializer, - activation: tf.nn.relu).Apply(x_expanded); + activation: tf.keras.activations.Relu).Apply(x_expanded); conv0 = tf.transpose(conv0, new int[] { 0, 1, 3, 2 }); }); diff --git a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs index 10662a3..c16fa62 100644 --- a/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs +++ b/src/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs @@ -54,7 +54,7 @@ public WordCnn(int vocabulary_size, int document_max_len, int num_class) kernel_size: new int[] { filter_size, embedding_size }, strides: new int[] { 1, 1 }, padding: "VALID", - activation: tf.nn.relu).Apply(x_emb); + activation: tf.keras.activations.Relu).Apply(x_emb); var pool = tf.keras.layers.MaxPooling2D( pool_size: new[] { document_max_len - filter_size + 1, 1 }, diff --git a/src/tensorflow2.x-python-tutorial/classify_text_with_bert.py b/src/tensorflow2.x-python-tutorial/classify_text_with_bert.py new file mode 100644 index 0000000..af0201f --- /dev/null +++ b/src/tensorflow2.x-python-tutorial/classify_text_with_bert.py @@ -0,0 +1,300 @@ +import os +import shutil + +import numpy as np +import tensorflow as tf +import tensorflow_hub as hub +import tensorflow_text as text +import tensorflow_addons as tfa +import matplotlib.pyplot as plt + +tf.config.run_functions_eagerly(True) + +url = 'https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz' + +dataset = tf.keras.utils.get_file('aclImdb_v1.tar.gz', url, + untar=True, cache_dir='.', + cache_subdir='') + +dataset_dir = os.path.join(os.path.dirname(dataset), 'aclImdb') + +train_dir = os.path.join(dataset_dir, 'train') + +# remove unused folders to make it easier to load the data +remove_dir = os.path.join(train_dir, 'unsup') +shutil.rmtree(remove_dir) + +AUTOTUNE = tf.data.AUTOTUNE +batch_size = 32 +seed = 42 + +raw_train_ds = tf.keras.utils.text_dataset_from_directory( + 'aclImdb/train', + batch_size=batch_size, + validation_split=0.2, + subset='training', + seed=seed) + +class_names = raw_train_ds.class_names +train_ds = raw_train_ds.cache().prefetch(buffer_size=AUTOTUNE) + +val_ds = tf.keras.utils.text_dataset_from_directory( + 'aclImdb/train', + batch_size=batch_size, + validation_split=0.2, + subset='validation', + seed=seed) + +val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE) + +test_ds = tf.keras.utils.text_dataset_from_directory( + 'aclImdb/test', + batch_size=batch_size) + +test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE) + +for text_batch, label_batch in train_ds.take(1): + for i in range(3): + print(f'Review: {text_batch.numpy()[i]}') + label = label_batch.numpy()[i] + print(f'Label : {label} ({class_names[label]})') + +#@title Choose a BERT model to fine-tune + +bert_model_name = 'small_bert/bert_en_uncased_L-4_H-512_A-8' #@param ["bert_en_uncased_L-12_H-768_A-12", "bert_en_cased_L-12_H-768_A-12", "bert_multi_cased_L-12_H-768_A-12", "small_bert/bert_en_uncased_L-2_H-128_A-2", "small_bert/bert_en_uncased_L-2_H-256_A-4", "small_bert/bert_en_uncased_L-2_H-512_A-8", "small_bert/bert_en_uncased_L-2_H-768_A-12", "small_bert/bert_en_uncased_L-4_H-128_A-2", "small_bert/bert_en_uncased_L-4_H-256_A-4", "small_bert/bert_en_uncased_L-4_H-512_A-8", "small_bert/bert_en_uncased_L-4_H-768_A-12", "small_bert/bert_en_uncased_L-6_H-128_A-2", "small_bert/bert_en_uncased_L-6_H-256_A-4", "small_bert/bert_en_uncased_L-6_H-512_A-8", "small_bert/bert_en_uncased_L-6_H-768_A-12", "small_bert/bert_en_uncased_L-8_H-128_A-2", "small_bert/bert_en_uncased_L-8_H-256_A-4", "small_bert/bert_en_uncased_L-8_H-512_A-8", "small_bert/bert_en_uncased_L-8_H-768_A-12", "small_bert/bert_en_uncased_L-10_H-128_A-2", "small_bert/bert_en_uncased_L-10_H-256_A-4", "small_bert/bert_en_uncased_L-10_H-512_A-8", "small_bert/bert_en_uncased_L-10_H-768_A-12", "small_bert/bert_en_uncased_L-12_H-128_A-2", "small_bert/bert_en_uncased_L-12_H-256_A-4", "small_bert/bert_en_uncased_L-12_H-512_A-8", "small_bert/bert_en_uncased_L-12_H-768_A-12", "albert_en_base", "electra_small", "electra_base", "experts_pubmed", "experts_wiki_books", "talking-heads_base"] + +map_name_to_handle = { + 'bert_en_uncased_L-12_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3', + 'bert_en_cased_L-12_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_en_cased_L-12_H-768_A-12/3', + 'bert_multi_cased_L-12_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_multi_cased_L-12_H-768_A-12/3', + 'small_bert/bert_en_uncased_L-2_H-128_A-2': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/1', + 'small_bert/bert_en_uncased_L-2_H-256_A-4': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-256_A-4/1', + 'small_bert/bert_en_uncased_L-2_H-512_A-8': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-512_A-8/1', + 'small_bert/bert_en_uncased_L-2_H-768_A-12': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-768_A-12/1', + 'small_bert/bert_en_uncased_L-4_H-128_A-2': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-128_A-2/1', + 'small_bert/bert_en_uncased_L-4_H-256_A-4': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-256_A-4/1', + 'small_bert/bert_en_uncased_L-4_H-512_A-8': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1', + 'small_bert/bert_en_uncased_L-4_H-768_A-12': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-768_A-12/1', + 'small_bert/bert_en_uncased_L-6_H-128_A-2': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-128_A-2/1', + 'small_bert/bert_en_uncased_L-6_H-256_A-4': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-256_A-4/1', + 'small_bert/bert_en_uncased_L-6_H-512_A-8': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-512_A-8/1', + 'small_bert/bert_en_uncased_L-6_H-768_A-12': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-768_A-12/1', + 'small_bert/bert_en_uncased_L-8_H-128_A-2': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-128_A-2/1', + 'small_bert/bert_en_uncased_L-8_H-256_A-4': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-256_A-4/1', + 'small_bert/bert_en_uncased_L-8_H-512_A-8': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-512_A-8/1', + 'small_bert/bert_en_uncased_L-8_H-768_A-12': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-8_H-768_A-12/1', + 'small_bert/bert_en_uncased_L-10_H-128_A-2': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-128_A-2/1', + 'small_bert/bert_en_uncased_L-10_H-256_A-4': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-256_A-4/1', + 'small_bert/bert_en_uncased_L-10_H-512_A-8': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-512_A-8/1', + 'small_bert/bert_en_uncased_L-10_H-768_A-12': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-768_A-12/1', + 'small_bert/bert_en_uncased_L-12_H-128_A-2': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-128_A-2/1', + 'small_bert/bert_en_uncased_L-12_H-256_A-4': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-256_A-4/1', + 'small_bert/bert_en_uncased_L-12_H-512_A-8': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-512_A-8/1', + 'small_bert/bert_en_uncased_L-12_H-768_A-12': + 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-768_A-12/1', + 'albert_en_base': + 'https://tfhub.dev/tensorflow/albert_en_base/2', + 'electra_small': + 'https://tfhub.dev/google/electra_small/2', + 'electra_base': + 'https://tfhub.dev/google/electra_base/2', + 'experts_pubmed': + 'https://tfhub.dev/google/experts/bert/pubmed/2', + 'experts_wiki_books': + 'https://tfhub.dev/google/experts/bert/wiki_books/2', + 'talking-heads_base': + 'https://tfhub.dev/tensorflow/talkheads_ggelu_bert_en_base/1', +} + +map_model_to_preprocess = { + 'bert_en_uncased_L-12_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'bert_en_cased_L-12_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_en_cased_preprocess/3', + 'small_bert/bert_en_uncased_L-2_H-128_A-2': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-2_H-256_A-4': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-2_H-512_A-8': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-2_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-4_H-128_A-2': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-4_H-256_A-4': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-4_H-512_A-8': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-4_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-6_H-128_A-2': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-6_H-256_A-4': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-6_H-512_A-8': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-6_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-8_H-128_A-2': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-8_H-256_A-4': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-8_H-512_A-8': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-8_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-10_H-128_A-2': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-10_H-256_A-4': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-10_H-512_A-8': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-10_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-12_H-128_A-2': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-12_H-256_A-4': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-12_H-512_A-8': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'small_bert/bert_en_uncased_L-12_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'bert_multi_cased_L-12_H-768_A-12': + 'https://tfhub.dev/tensorflow/bert_multi_cased_preprocess/3', + 'albert_en_base': + 'https://tfhub.dev/tensorflow/albert_en_preprocess/3', + 'electra_small': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'electra_base': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'experts_pubmed': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'experts_wiki_books': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', + 'talking-heads_base': + 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3', +} + +tfhub_handle_encoder = map_name_to_handle[bert_model_name] +tfhub_handle_preprocess = map_model_to_preprocess[bert_model_name] + +print(f'BERT model selected : {tfhub_handle_encoder}') +print(f'Preprocess model auto-selected: {tfhub_handle_preprocess}') + +bert_preprocess_model = hub.KerasLayer(tfhub_handle_preprocess) + +text_test = ['this is such an amazing movie!'] +text_preprocessed = bert_preprocess_model(text_test) + +print(f'Keys : {list(text_preprocessed.keys())}') +print(f'Shape : {text_preprocessed["input_word_ids"].shape}') +print(f'Word Ids : {text_preprocessed["input_word_ids"][0, :12]}') +print(f'Input Mask : {text_preprocessed["input_mask"][0, :12]}') +print(f'Type Ids : {text_preprocessed["input_type_ids"][0, :12]}') + +bert_model = hub.KerasLayer(tfhub_handle_encoder) + +bert_results = bert_model(text_preprocessed) + +print(f'Loaded BERT: {tfhub_handle_encoder}') +print(f'Pooled Outputs Shape:{bert_results["pooled_output"].shape}') +print(f'Pooled Outputs Values:{bert_results["pooled_output"][0, :12]}') +print(f'Sequence Outputs Shape:{bert_results["sequence_output"].shape}') +print(f'Sequence Outputs Values:{bert_results["sequence_output"][0, :12]}') + +def build_classifier_model(): + text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text') + preprocessing_layer = hub.KerasLayer(tfhub_handle_preprocess, name='preprocessing') + encoder_inputs = preprocessing_layer(text_input) + encoder = hub.KerasLayer(tfhub_handle_encoder, trainable=True, name='BERT_encoder') + outputs = encoder(encoder_inputs) + net = outputs['pooled_output'] + net = tf.keras.layers.Dropout(0.1)(net) + net = tf.keras.layers.Dense(1, activation=None, name='classifier')(net) + return tf.keras.Model(text_input, net) + +classifier_model = build_classifier_model() +bert_raw_result = classifier_model(tf.constant(text_test)) +print(tf.sigmoid(bert_raw_result)) + +# tf.keras.utils.plot_model(classifier_model) + +loss = tf.keras.losses.BinaryCrossentropy(from_logits=True) +metrics = tf.metrics.BinaryAccuracy() + +epochs = 1 +steps_per_epoch = tf.data.experimental.cardinality(train_ds).numpy() +num_train_steps = steps_per_epoch * epochs +num_warmup_steps = int(0.1*num_train_steps) + +init_lr = 3e-5 + +classifier_model.compile(optimizer='adam', + loss=loss, + metrics=metrics) + +print(f'Training model with {tfhub_handle_encoder}') +history = classifier_model.fit(x=train_ds, + validation_data=val_ds, + epochs=epochs) + +loss, accuracy = classifier_model.evaluate(test_ds) + +print(f'Loss: {loss}') +print(f'Accuracy: {accuracy}') + +history_dict = history.history +print(history_dict.keys()) + +acc = history_dict['binary_accuracy'] +val_acc = history_dict['val_binary_accuracy'] +loss = history_dict['loss'] +val_loss = history_dict['val_loss'] + +epochs = range(1, len(acc) + 1) +fig = plt.figure(figsize=(10, 6)) +fig.tight_layout() + +plt.subplot(2, 1, 1) +# r is for "solid red line" +plt.plot(epochs, loss, 'r', label='Training loss') +# b is for "solid blue line" +plt.plot(epochs, val_loss, 'b', label='Validation loss') +plt.title('Training and validation loss') +# plt.xlabel('Epochs') +plt.ylabel('Loss') +plt.legend() + +plt.subplot(2, 1, 2) +plt.plot(epochs, acc, 'r', label='Training acc') +plt.plot(epochs, val_acc, 'b', label='Validation acc') +plt.title('Training and validation accuracy') +plt.xlabel('Epochs') +plt.ylabel('Accuracy') +plt.legend(loc='lower right') \ No newline at end of file From a20f3f861197367521ce54f4714f7980f7d75911 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Mon, 10 Apr 2023 09:35:29 -0500 Subject: [PATCH 30/39] Add FuelEfficiencyPrediction example. --- .../ImageClassificationKeras.cs | 2 +- .../ImageProcessing/ToyResNet.cs | 2 +- .../FuelEfficiencyPrediction.cs | 150 ++++++++++++++++++ .../NeuralNetworks/NeuralNetXor.cs | 2 +- .../TensorFlowNET.Examples.csproj | 3 +- .../predict_fuel_efficiency.py | 143 +++++++++++++++++ 6 files changed, 297 insertions(+), 5 deletions(-) create mode 100644 src/TensorFlowNET.Examples/NeuralNetworks/FuelEfficiencyPrediction.cs create mode 100644 src/tensorflow2.x-python-tutorial/predict_fuel_efficiency.py diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs index 6dbc8bd..0c3a556 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs @@ -16,7 +16,7 @@ namespace TensorFlowNET.Examples; public class ImageClassificationKeras : SciSharpExample, IExample { int batch_size = 32; - int epochs = 3; + int epochs = 10; Shape img_dim = (64, 64); IDatasetV2 train_ds, val_ds; Model model; diff --git a/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs b/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs index 8ffa3cb..9c3db7c 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/ToyResNet.cs @@ -100,7 +100,7 @@ public override void Train() { result = model.fit(x_train[new Slice(0, 2000)], y_train[new Slice(0, 2000)], batch_size: 64, - epochs: 3, + epochs: 5, validation_split: 0.2f); } } diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FuelEfficiencyPrediction.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FuelEfficiencyPrediction.cs new file mode 100644 index 0000000..eaf58a9 --- /dev/null +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FuelEfficiencyPrediction.cs @@ -0,0 +1,150 @@ +/***************************************************************************** + Copyright 2023 Haiping Chen. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +******************************************************************************/ + +using static Tensorflow.Binding; +using static PandasNet.PandasApi; +using static Tensorflow.KerasApi; + +namespace TensorFlowNET.Examples; + +/// +/// This tutorial uses the classic Auto MPG dataset and demonstrates how to build models to +/// predict the fuel efficiency of the late-1970s and early 1980s automobiles. +/// https://www.tensorflow.org/tutorials/keras/regression +/// +public class FuelEfficiencyPrediction : SciSharpExample, IExample +{ + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Predict fuel efficiency", + Enabled = true + }; + + public bool Run() + { + PrepareData(); + return true; + } + + public override void PrepareData() + { + string url = $"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data"; + var dataset = pd.read_csv(url, + names: new[] + { + "MPG", "Cylinders", "Displacement", "Horsepower", + "Weight", "Acceleration", "Model Year", "Origin" + }, + sep: ' ', + na_values: '?', + comment: '\t', + skipinitialspace: true); + dataset = dataset.dropna(); + + // The "Origin" column is categorical, not numeric. + // So the next step is to one-hot encode the values in the column with pd.get_dummies. + dataset["Origin"] = dataset["Origin"].map((i) => i switch + { + "1" => "USA", + "2" => "Europe", + "3" => "Japan", + _ => "N/A" + }); + + dataset = pd.get_dummies(dataset, columns: new[] { "Origin" }, prefix: "", prefix_sep: ""); + + var train_dataset = dataset.sample(frac: 0.8f, random_state: 0); + var test_dataset = dataset.drop(train_dataset.index.array()); + + var train_features = train_dataset.copy(); + var test_features = test_dataset.copy(); + + var train_labels = train_features.pop("MPG"); + var test_labels = test_features.pop("MPG"); + + // var df = train_dataset.describe().transpose()["mean", "std"]; + + var normalizer = tf.keras.layers.Normalization(axis: -1); + normalizer.adapt(train_features); + + // Linear regression + var horsepower = train_features["Horsepower"]; + + var horsepower_normalizer = layers.Normalization(input_shape: 1, axis: null); + horsepower_normalizer.adapt(horsepower); + + var horsepower_model = keras.Sequential(horsepower_normalizer, + layers.Dense(units: 1)); + + horsepower_model.summary(); + + horsepower_model.compile( + optimizer: tf.keras.optimizers.Adam(learning_rate: 0.1f), + loss: tf.keras.losses.MeanAbsoluteError()); + + var history = horsepower_model.fit( + train_features["Horsepower"], + train_labels, + epochs: 100, + // Suppress logging. + verbose: 1, + // Calculate validation results on 20% of the training data. + validation_split: 0.2f); + + var results = horsepower_model.evaluate( + test_features["Horsepower"], + test_labels, verbose: 1); + + // Linear regression with multiple inputs + var linear_model = keras.Sequential(normalizer, + layers.Dense(units: 1)); + + linear_model.compile( + optimizer: tf.keras.optimizers.Adam(learning_rate: 0.1f), + loss: tf.keras.losses.MeanAbsoluteError()); + + history = linear_model.fit( + train_features, + train_labels, + epochs: 100, + verbose: 1, + validation_split: 0.2f); + + linear_model.evaluate( + test_features, test_labels, verbose: 1); + + // Regression with a deep neural network (DNN) + var dnn_model = keras.Sequential(normalizer, + layers.Dense(64, activation: "relu"), + layers.Dense(64, activation: "relu"), + layers.Dense(1)); + + dnn_model.compile( + optimizer: tf.keras.optimizers.Adam(learning_rate: 0.001f), + loss: tf.keras.losses.MeanAbsoluteError()); + + history = dnn_model.fit( + train_features, + train_labels, + epochs: 100, + verbose: 1, + validation_split: 0.2f); + + dnn_model.evaluate( + test_features, test_labels, verbose: 1); + } +} diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs index bb4d0bf..f7cd66b 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs @@ -39,7 +39,7 @@ public ExampleConfig InitConfig() { Name = "NN XOR in Graph Mode", Enabled = true, - IsImportingGraph = false + IsImportingGraph = true }; public bool Run() diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 5643d53..cc95179 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -49,9 +49,8 @@ - + - diff --git a/src/tensorflow2.x-python-tutorial/predict_fuel_efficiency.py b/src/tensorflow2.x-python-tutorial/predict_fuel_efficiency.py new file mode 100644 index 0000000..80a73af --- /dev/null +++ b/src/tensorflow2.x-python-tutorial/predict_fuel_efficiency.py @@ -0,0 +1,143 @@ +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns + +import tensorflow as tf + +from tensorflow import keras +from tensorflow.keras import layers + +print(tf.__version__) + +# Make NumPy printouts easier to read. +np.set_printoptions(precision=3, suppress=True) + +tf.config.run_functions_eagerly(True) + +# https://www.tensorflow.org/tutorials/keras/regression +url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data' +column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight', + 'Acceleration', 'Model Year', 'Origin'] + +raw_dataset = pd.read_csv(url, names=column_names, + na_values='?', comment='\t', + sep=' ', skipinitialspace=True) + +dataset = raw_dataset.copy() +dataset.tail() + +dataset.isna().sum() +dataset = dataset.dropna() + +dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'}) +dataset = pd.get_dummies(dataset, columns=['Origin'], prefix='', prefix_sep='') +dataset.tail() + +train_dataset = dataset.sample(frac=0.8, random_state=0) +test_dataset = dataset.drop(train_dataset.index) + +sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde') + +train_dataset.describe().transpose() + +train_features = train_dataset.copy() +test_features = test_dataset.copy() + +train_labels = train_features.pop('MPG') +test_labels = test_features.pop('MPG') + +train_dataset.describe().transpose()[['mean', 'std']] + +normalizer = tf.keras.layers.Normalization(axis=-1) + +normalizer.adapt(np.array(train_features)) + +print(normalizer.mean.numpy()) + +first = np.array(train_features[:1]) + +with np.printoptions(precision=2, suppress=True): + print('First example:', first) + print() + print('Normalized:', normalizer(first).numpy()) + + +# Linear regression +horsepower = np.array(train_features['Horsepower']) + +horsepower_normalizer = layers.Normalization(input_shape=[1,], axis=None) +horsepower_normalizer.adapt(horsepower) + +horsepower_model = tf.keras.Sequential([ + horsepower_normalizer, + layers.Dense(units=1) +]) + +horsepower_model.summary() + +horsepower_model.compile( + optimizer=tf.keras.optimizers.Adam(learning_rate=0.1), + loss='mean_absolute_error') + +history = horsepower_model.fit( + train_features['Horsepower'], + train_labels, + epochs=100, + # Suppress logging. + verbose=1, + # Calculate validation results on 20% of the training data. + validation_split = 0.2) + +test_results = {} + +test_results['horsepower_model'] = horsepower_model.evaluate( + test_features['Horsepower'], + test_labels, verbose=1) + +print(test_results['horsepower_model']) + +# Linear regression with multiple inputs +linear_model = tf.keras.Sequential([ + normalizer, + layers.Dense(units=1) +]) + +linear_model.compile( + optimizer=tf.keras.optimizers.Adam(learning_rate=0.1), + loss='mean_absolute_error') + +history = linear_model.fit( + train_features, + train_labels, + epochs=100, + # Suppress logging. + verbose=1, + # Calculate validation results on 20% of the training data. + validation_split = 0.2) + +test_results['linear_model'] = linear_model.evaluate( + test_features, test_labels, verbose=1) + +print(test_results['linear_model']) + +def build_and_compile_model(norm): + model = keras.Sequential([ + norm, + layers.Dense(64, activation='relu'), + layers.Dense(64, activation='relu'), + layers.Dense(1) + ]) + + model.compile(loss='mean_absolute_error', + optimizer=tf.keras.optimizers.Adam(0.001)) + return model + +dnn_model = build_and_compile_model(normalizer) +dnn_model.summary() + +history = dnn_model.fit( + train_features, + train_labels, + validation_split=0.2, + verbose=1, epochs=100) \ No newline at end of file From 32bde52524eb363e2e47165ba6600e9a0df2eb38 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Fri, 5 May 2023 13:32:01 -0500 Subject: [PATCH 31/39] Upgrade TensorFlow.NET. --- SciSharp STACK Examples.sln | 160 ------------------ src/SciSharp.WebApi/SciSharp.WebApi.csproj | 4 +- .../Services/ImageClassificationService.cs | 2 +- src/SharpCV.Examples/SharpCV.Examples.csproj | 4 +- .../TensorFlowNET.Examples.csproj | 15 +- 5 files changed, 11 insertions(+), 174 deletions(-) diff --git a/SciSharp STACK Examples.sln b/SciSharp STACK Examples.sln index 6a8e8cd..1aa6e77 100644 --- a/SciSharp STACK Examples.sln +++ b/SciSharp STACK Examples.sln @@ -11,22 +11,6 @@ Project("{6EC3EE1D-3C4E-46DD-8F32-0CC8E7565705}") = "TensorFlowNET.Examples.FSha EndProject 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{5437C206-2210-4416-A1B3-3EE331B9B5C0}.Release|Any CPU.ActiveCfg = Release|Any CPU - {5437C206-2210-4416-A1B3-3EE331B9B5C0}.Release|Any CPU.Build.0 = Release|Any CPU - {5437C206-2210-4416-A1B3-3EE331B9B5C0}.Release|x64.ActiveCfg = Release|Any CPU - {5437C206-2210-4416-A1B3-3EE331B9B5C0}.Release|x64.Build.0 = Release|Any CPU - {5437C206-2210-4416-A1B3-3EE331B9B5C0}.Release|x86.ActiveCfg = Release|Any CPU - {5437C206-2210-4416-A1B3-3EE331B9B5C0}.Release|x86.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE diff --git a/src/SciSharp.WebApi/SciSharp.WebApi.csproj b/src/SciSharp.WebApi/SciSharp.WebApi.csproj index 1f84c99..897c0ae 100644 --- a/src/SciSharp.WebApi/SciSharp.WebApi.csproj +++ b/src/SciSharp.WebApi/SciSharp.WebApi.csproj @@ -7,9 +7,9 @@ - + - + diff --git a/src/SciSharp.WebApi/Services/ImageClassificationService.cs b/src/SciSharp.WebApi/Services/ImageClassificationService.cs index fe81202..05a5f35 100644 --- a/src/SciSharp.WebApi/Services/ImageClassificationService.cs +++ b/src/SciSharp.WebApi/Services/ImageClassificationService.cs @@ -109,7 +109,7 @@ public float[] Predict(byte[] data) predictionModel = keras.Sequential(); predictionModel.add(baseModel); predictionModel.add(keras.layers.Softmax(-1)); - predictionModel.compile(); + // predictionModel.compile(); } Tensors predictions = predictionModel.predict(array); diff --git a/src/SharpCV.Examples/SharpCV.Examples.csproj b/src/SharpCV.Examples/SharpCV.Examples.csproj index 1ed56d1..373239d 100644 --- a/src/SharpCV.Examples/SharpCV.Examples.csproj +++ b/src/SharpCV.Examples/SharpCV.Examples.csproj @@ -9,8 +9,8 @@ - - + + diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index cc95179..424ae1c 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -49,19 +49,16 @@ - - + + + + + + - - - - - - - From 3e4bcaf4020348519804ac32c536118e5d676239 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Wed, 21 Jun 2023 17:25:17 -0500 Subject: [PATCH 32/39] Fix model.evaluate in NeuralNetXorKeras. --- .../ImageProcessing/DigitRecognitionRnnKeras.cs | 3 ++- .../ImageProcessing/MnistCnnKerasSubclass.cs | 3 ++- .../NeuralNetworks/FullyConnectedKeras.cs | 3 ++- .../NeuralNetworks/NeuralNetXorKeras.cs | 2 +- src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj | 8 ++++++-- 5 files changed, 13 insertions(+), 6 deletions(-) diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs index da80a47..e149617 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs @@ -16,6 +16,7 @@ limitations under the License. using System.Linq; using Tensorflow; +using Tensorflow.Common.Types; using Tensorflow.Keras; using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.Engine; @@ -129,7 +130,7 @@ public LSTMModel(LSTMModelArgs args) output = layers.Dense(args.NumClasses); } - protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null) + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) { // LSTM layer. inputs = lstm.Apply(inputs); diff --git a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs index d990945..0a6e0e6 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs @@ -16,6 +16,7 @@ limitations under the License. using System.Linq; using Tensorflow; +using Tensorflow.Common.Types; using Tensorflow.Keras; using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.Engine; @@ -221,7 +222,7 @@ public ConvNet(ConvNetArgs args) /// /// /// - protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null) + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) { inputs = tf.reshape(inputs, (-1, 28, 28, 1)); inputs = conv1.Apply(inputs); diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs index 644169c..47fa960 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedKeras.cs @@ -17,6 +17,7 @@ limitations under the License. using System; using System.Linq; using Tensorflow; +using Tensorflow.Common.Types; using Tensorflow.Keras; using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.Engine; @@ -176,7 +177,7 @@ public NeuralNet(NeuralNetArgs args) : } // Set forward pass. - protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null) + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) { inputs = fc1.Apply(inputs); inputs = fc2.Apply(inputs); diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs index 6856305..f8d821b 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs @@ -51,7 +51,7 @@ public bool Run() loss: keras.losses.MeanSquaredError(), new[] { "accuracy" }); model.fit(x, y, batch_size: 4, epochs: 50); - model.evaluate(x, y); + model.evaluate(tf.constant(x), tf.constant(y)); Tensor result = model.predict(x, 4); return result.ToArray() is [< 0.5f, > 0.5f, > 0.5f, < 0.5f]; } diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 424ae1c..17b45da 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -52,8 +52,8 @@ - - + + @@ -61,4 +61,8 @@ + + + + From a8731e3dad0e56bb298850f96aa42c8d5df52a05 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sat, 24 Jun 2023 08:55:40 -0500 Subject: [PATCH 33/39] tf.math.sqrt --- .../ImageProcessing/DigitRecognitionCNN.cs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs index c28ce3e..7629800 100644 --- a/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs +++ b/src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs @@ -141,7 +141,7 @@ public override void PrepareData() private (NDArray, NDArray) Reformat(NDArray x, NDArray y) { var (unique_y, _) = np.unique(np.argmax(y, 1)); - var (img_size, num_ch, num_class) = ((int)np.sqrt(x.shape[1]).astype(np.int32), 1, len(unique_y)); + var (img_size, num_ch, num_class) = ((int)np.sqrt((float)x.shape[1]).astype(np.int32), 1, len(unique_y)); var dataset = x.reshape((x.shape[0], img_size, img_size, num_ch)).astype(np.float32); //y[0] = np.arange(num_class) == y[0]; //var labels = (np.arange(num_class) == y.reshape(y.shape[0], 1, y.shape[1])).astype(np.float32); From 344c0124cf80f5922f4c8c5224fcc77a630eca9a Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Fri, 7 Jul 2023 22:36:00 -0500 Subject: [PATCH 34/39] Upgrade library. --- src/SharpCV.Examples/SharpCV.Examples.csproj | 4 ++-- .../NeuralNetworks/NeuralNetXorKeras.cs | 4 ++-- .../TensorFlowNET.Examples.csproj | 15 ++++++--------- .../TimeSeries/WeatherPrediction.cs | 1 + 4 files changed, 11 insertions(+), 13 deletions(-) diff --git a/src/SharpCV.Examples/SharpCV.Examples.csproj b/src/SharpCV.Examples/SharpCV.Examples.csproj index 373239d..11ba664 100644 --- a/src/SharpCV.Examples/SharpCV.Examples.csproj +++ b/src/SharpCV.Examples/SharpCV.Examples.csproj @@ -9,8 +9,8 @@ - - + + diff --git a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs index f8d821b..3ccb090 100644 --- a/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs +++ b/src/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXorKeras.cs @@ -50,8 +50,8 @@ public bool Run() model.compile(optimizer: keras.optimizers.Adam(), loss: keras.losses.MeanSquaredError(), new[] { "accuracy" }); - model.fit(x, y, batch_size: 4, epochs: 50); - model.evaluate(tf.constant(x), tf.constant(y)); + model.fit(x, y, batch_size: 4, epochs: 200); + model.evaluate(x, y); Tensor result = model.predict(x, 4); return result.ToArray() is [< 0.5f, > 0.5f, > 0.5f, < 0.5f]; } diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 17b45da..7fd67b4 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -49,20 +49,17 @@ - - - + + + - + - + + - - - - diff --git a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs index b000cc2..758eebe 100644 --- a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs +++ b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs @@ -32,6 +32,7 @@ public bool Run() task.SetModelArgs(new TimeSeriesModelArgs { InputWidth = 3, + LabelWidth = 1, LabelColumns = new[] { "T (degC)" } }); (training_ds, val_ds, test_ds) = task.GenerateDataset(PrepareData); From 3643a9c684a7a969b55f7d8ebc1697b4ec489784 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sun, 9 Jul 2023 16:25:53 -0500 Subject: [PATCH 35/39] Upgrade to tf.net v0.11.2. --- src/SciSharp.WebApi/SciSharp.WebApi.csproj | 4 ++-- src/SharpCV.Examples/SharpCV.Examples.csproj | 2 +- .../TensorFlowNET.Examples.csproj | 10 ++++------ .../TimeSeries/WeatherPrediction.cs | 1 + 4 files changed, 8 insertions(+), 9 deletions(-) diff --git a/src/SciSharp.WebApi/SciSharp.WebApi.csproj b/src/SciSharp.WebApi/SciSharp.WebApi.csproj index 897c0ae..1b3dacc 100644 --- a/src/SciSharp.WebApi/SciSharp.WebApi.csproj +++ b/src/SciSharp.WebApi/SciSharp.WebApi.csproj @@ -7,9 +7,9 @@ - + - + diff --git a/src/SharpCV.Examples/SharpCV.Examples.csproj b/src/SharpCV.Examples/SharpCV.Examples.csproj index 11ba664..232b00a 100644 --- a/src/SharpCV.Examples/SharpCV.Examples.csproj +++ b/src/SharpCV.Examples/SharpCV.Examples.csproj @@ -10,7 +10,7 @@ - + diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 7fd67b4..12d9465 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -48,15 +48,13 @@ - - - - + + + + - - diff --git a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs index 758eebe..43c7810 100644 --- a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs +++ b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs @@ -25,6 +25,7 @@ public ExampleConfig InitConfig() public bool Run() { var wizard = new ModelWizard(); + // Change to RnnModel for RNN task = wizard.AddTimeSeriesTask(new TaskOptions { WeightsPath = @"timeseries_linear_v1\saved_weights.h5" From 2aeef9eff0b48148732aa851cdeecf41f23534b7 Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sun, 9 Jul 2023 16:26:11 -0500 Subject: [PATCH 36/39] Update README. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ee8a7a1..86b8ba0 100644 --- a/README.md +++ b/README.md @@ -79,7 +79,7 @@ Example runner will download all the required files like training data and model #### Time Series -* Weather Prediction [C#](src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs) +* Weather Prediction (CNN, RNN) [C#](src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs) #### Welcome to PR your example to us. From 7e850c684d544653ac40fbbb57b84863adc0596e Mon Sep 17 00:00:00 2001 From: Beacontownfc <89081023+Beacontownfc@users.noreply.github.com> Date: Sun, 27 Aug 2023 09:46:07 +0800 Subject: [PATCH 37/39] Add bert example --- .../BertClassification.cs | 74 ++++ .../BertModel/BertConfig.cs | 58 +++ .../BertModel/BertMainLayer.cs | 409 ++++++++++++++++++ .../BertModel/BertTokenizer.cs | 356 +++++++++++++++ .../BertModel/IMDBDataProcessor.cs | 66 +++ 5 files changed, 963 insertions(+) create mode 100644 src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs create mode 100644 src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertConfig.cs create mode 100644 src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertMainLayer.cs create mode 100644 src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertTokenizer.cs create mode 100644 src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/IMDBDataProcessor.cs diff --git a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs new file mode 100644 index 0000000..b6905f6 --- /dev/null +++ b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs @@ -0,0 +1,74 @@ +using System; +using System.Collections.Generic; +using System.IO; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using Tensorflow.Operations.Initializers; +using static Tensorflow.KerasApi; +using BERT; +using Tensorflow.NumPy; +using static Tensorflow.Binding; +using static Tensorflow.Keras.Engine.InputSpec; + +namespace TensorFlowNET.Examples +{ + class BertClassification : SciSharpExample, IExample + { + int max_seq_len = 180; + int batch_size = 4; + int num_classes = 2; + int epoch = 3; + float learning_rate = (float)2e-5; + string pretrained_weight_path = "./tf_model.h5"; + BertConfig config = new BertConfig(); + NDArray np_x_train; + NDArray np_y_train; + public ExampleConfig InitConfig() + => Config = new ExampleConfig + { + Name = "Bert for Classification", + Enabled = true + }; + + public override void PrepareData() + { + // tf.debugging.set_log_device_placement(true); + Console.WriteLine("Preparing data..."); + string url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"; + var dataset = keras.utils.get_file("aclImdb_v1.tar.gz", url, + untar: true, + cache_dir: Path.GetTempPath(), + cache_subdir: "aclImdb_v1"); + var data_dir = Path.Combine(dataset, "aclImdb"); + var train_dir = Path.Combine(data_dir, "train"); + (int[,] x_train_neg, int[] y_train_neg) = IMDBDataPreProcessor. + ProcessData(Path.Combine(train_dir, "neg"), max_seq_len, 0); + (int[,] x_train_pos, int[] y_train_pos) = IMDBDataPreProcessor. + ProcessData(Path.Combine(train_dir, "pos"), max_seq_len, 1); + np_x_train = np.array(x_train_neg, dtype: tf.int32); + np_y_train = np.array(y_train_neg, dtype: tf.int32); + np_x_train = np.concatenate((np_x_train, np.array(x_train_pos, dtype: tf.int32)), 0); + np_y_train = np.concatenate((np_y_train, np.array(y_train_pos, dtype: tf.int32)), 0); + } + + public bool Run() + { + var model = keras.Sequential(); + model.add(keras.layers.Input(max_seq_len, batch_size, dtype: tf.int32)); + model.add(new BertMainLayer(config)); + if(File.Exists(pretrained_weight_path)) model.load_weights(pretrained_weight_path); + model.add(keras.layers.Dense(num_classes)); + model.compile(optimizer: keras.optimizers.AdamW(learning_rate, weight_decay: 0.01f, no_decay_params: new List { "gamma", "beta" }), + loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true), metrics: new[] { "acc" }); + model.summary(); + PrepareData(); + model.fit(np_x_train, np_y_train, + batch_size: batch_size, + epochs: epoch, + shuffle: true, + validation_split: 0.2f); + return true; + } + } +} diff --git a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertConfig.cs b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertConfig.cs new file mode 100644 index 0000000..34e0382 --- /dev/null +++ b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertConfig.cs @@ -0,0 +1,58 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using Tensorflow.Keras.ArgsDefinition; + +namespace BERT +{ + class BertConfig : LayerArgs + { + public int vocab_size; + public int hidden_size; + public int num_hidden_layers; + public int num_attention_heads; + public int intermediate_size; + public string hidden_act; + public float hidden_dropout_prob; + public float attention_probs_dropout_prob; + public int max_position_embeddings; + public int type_vocab_size; + public float initializer_range; + public float layer_norm_eps; + public int pad_token_id; + public string position_embedding_type; + public BertConfig(int vocab_size = 30522, + int hidden_size = 768, + int num_hidden_layers = 12, + int num_attention_heads = 12, + int intermediate_size = 3072, + string hidden_act = "gelu", + double hidden_dropout_prob = 0.1, + double attention_probs_dropout_prob = 0.1, + int max_position_embeddings = 512, + int type_vocab_size = 2, + double initializer_range = 0.02, + double layer_norm_eps = 1e-12, + int pad_token_id = 0, + string position_embedding_type = "absolute") + { + this.vocab_size = vocab_size; + this.hidden_size = hidden_size; + this.num_hidden_layers = num_hidden_layers; + this.num_attention_heads = num_attention_heads; + this.intermediate_size = intermediate_size; + this.hidden_act = hidden_act; + this.hidden_dropout_prob = (float)hidden_dropout_prob; + this.attention_probs_dropout_prob = (float)attention_probs_dropout_prob; + this.max_position_embeddings = max_position_embeddings; + this.type_vocab_size = type_vocab_size; + this.initializer_range = (float)initializer_range; + this.layer_norm_eps = (float)layer_norm_eps; + this.pad_token_id = pad_token_id; + this.position_embedding_type = position_embedding_type; + + } + } +} diff --git a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertMainLayer.cs b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertMainLayer.cs new file mode 100644 index 0000000..66fe562 --- /dev/null +++ b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertMainLayer.cs @@ -0,0 +1,409 @@ +using BERT; +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using Tensorflow; +using Tensorflow.Common.Types; +using Tensorflow.Keras; +using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.Keras.Engine; +using Tensorflow.Keras.Saving; +using Tensorflow.NumPy; +using Tensorflow.Operations.Initializers; +using static Tensorflow.Binding; +using static Tensorflow.KerasApi; + +namespace BERT +{ + class BertEmbedding : Layer + { + BertConfig config; + IVariableV1 weight; + IVariableV1 token_type_embeddings; + IVariableV1 position_embeddings; + ILayer LayerNorm; + ILayer dropout; + + + public BertEmbedding(BertConfig config) : base(config) + { + this.config = config; + LayerNorm = keras.layers.LayerNormalization(axis: -1, epsilon: config.layer_norm_eps); + dropout = keras.layers.Dropout(config.hidden_dropout_prob); + + StackLayers(LayerNorm); + } + + public override void build(KerasShapesWrapper input_shape) + { + tf_with(ops.name_scope("word_embeddings"), scope => + { + weight = add_weight(name: "weight", + shape: (config.vocab_size, config.hidden_size), + initializer: new TruncatedNormal(config.initializer_range) + ); + + }); + tf_with(ops.name_scope("token_type_embeddings"), scope => + { + token_type_embeddings = add_weight(name: "token_type_embedding", + shape: (config.type_vocab_size, config.hidden_size), + initializer: new TruncatedNormal(config.initializer_range) + ); + + }); + tf_with(ops.name_scope("position_embeddings"), scope => + { + position_embeddings = add_weight(name: "position_embedding", + shape: (config.max_position_embeddings, config.hidden_size), + initializer: new TruncatedNormal(config.initializer_range) + ); + + }); + + base.build(input_shape); + } + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) + { + var input_ids = inputs[0]; + var position_ids = inputs[1]; + var token_type_ids = inputs[2]; + + var inputs_embeds = tf.gather(weight.AsTensor(), input_ids); + var position_embeds = tf.gather(position_embeddings.AsTensor(), indices: position_ids); + var token_type_embeds = tf.gather(token_type_embeddings.AsTensor(), indices: token_type_ids); + var final_embeddings = inputs_embeds + token_type_embeds + position_embeds; + final_embeddings = LayerNorm.Apply(final_embeddings); + final_embeddings = dropout.Apply(final_embeddings, training: training ?? false); + return final_embeddings; + } + } + + class BertSelfOutput : Layer + { + BertConfig config; + ILayer dense; + ILayer LayerNorm; + ILayer dropout; + public BertSelfOutput(BertConfig config) : base(config) + { + this.config = config; + dense = keras.layers.Dense( + units: config.hidden_size, kernel_initializer: new TruncatedNormal(config.initializer_range)); + LayerNorm = keras.layers.LayerNormalization(axis: -1, epsilon: config.layer_norm_eps); + dropout = keras.layers.Dropout(config.hidden_dropout_prob); + + StackLayers(dense, LayerNorm); + } + + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) + { + var hidden_states = inputs[0]; + var input_tensor = inputs[1]; + + hidden_states = dense.Apply(inputs: hidden_states); + hidden_states = dropout.Apply(inputs: hidden_states, training: training ?? false); + + hidden_states = LayerNorm.Apply(tf.add(hidden_states, input_tensor)); + + return hidden_states; + } + } + + class BertAttention : Layer + { + BertConfig config; + BertSelfAttention self_attention; + BertSelfOutput dense_output; + public BertAttention(BertConfig config) : base(config) + { + this.config = config; + self_attention = new BertSelfAttention(config); + dense_output = new BertSelfOutput(config); + StackLayers(self_attention, dense_output); + } + + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) + { + var input_tensor = inputs[0]; + var attention_mask = inputs[1]; + + var self_outputs = self_attention.Apply(new Tensor[] { input_tensor, attention_mask }, training: training ?? false); + var attention_output = dense_output.Apply(new Tensor[] { self_outputs, input_tensor }, training: training ?? false); + + return attention_output; + } + } + + class BertEncoder : Layer + { + BertConfig config; + List layers; + public BertEncoder(BertConfig config) : base(config) + { + this.config = config; + layers = new List(); + for (int i = 0; i < config.num_hidden_layers; i++) layers.Add(new BertLayer(config)); + StackLayers(layers.ToArray()); + } + + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) + { + var hidden_states = inputs[0]; + var attention_mask = inputs[1]; + + foreach (var layer in layers) + { + var layer_outputs = layer.Apply(new Tensor[] { hidden_states, attention_mask }); + hidden_states = layer_outputs; + } + + return hidden_states; + } + + } + + class BertIntermediate : Layer + { + BertConfig config; + ILayer dense; + public BertIntermediate(BertConfig config) : base(config) + { + this.config = config; + dense = keras.layers.Dense( + units: config.intermediate_size, kernel_initializer: new TruncatedNormal(config.initializer_range)); + StackLayers(dense); + } + public static Tensors gelu(Tensor x) + { + var cdf = 0.5 * (1.0 + tf.tanh( + (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))); + return x * cdf; + } + + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) + { + var hidden_states = inputs; + hidden_states = dense.Apply(hidden_states); + hidden_states = gelu(hidden_states); + return hidden_states; + } + } + + class BertOutput : Layer + { + ILayer dense; + ILayer LayerNorm; + ILayer dropout; + public BertOutput(BertConfig config) : base(config) + { + dense = keras.layers.Dense( + units: config.hidden_size, kernel_initializer: new TruncatedNormal(config.initializer_range)); + LayerNorm = keras.layers.LayerNormalization(axis: -1, epsilon: config.layer_norm_eps); + dropout = keras.layers.Dropout(config.hidden_dropout_prob); + + StackLayers(dense, LayerNorm); + + } + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) + { + var hidden_states = inputs[0]; + var input_tensor = inputs[1]; + + hidden_states = dense.Apply(new Tensor[] { hidden_states }); + hidden_states = dropout.Apply(new Tensor[] { hidden_states }, training: training ?? false); + + hidden_states = LayerNorm.Apply(tf.add(hidden_states, input_tensor)); + + return hidden_states; + } + } + + class BertSelfAttention : Layer + { + BertConfig config; + int attention_head_size; + int all_head_size; + double sqrt_att_head_size; + ILayer query; + ILayer key; + ILayer value; + ILayer dropout; + public BertSelfAttention(BertConfig config) : base(config) + { + this.config = config; + attention_head_size = config.hidden_size / config.num_attention_heads; + all_head_size = config.num_attention_heads * attention_head_size; + sqrt_att_head_size = Math.Sqrt(attention_head_size); + query = keras.layers.Dense(units: all_head_size, kernel_initializer: new TruncatedNormal(config.initializer_range)); + key = keras.layers.Dense(units: all_head_size, kernel_initializer: new TruncatedNormal(config.initializer_range)); + value = keras.layers.Dense(units: all_head_size, kernel_initializer: new TruncatedNormal(config.initializer_range)); + dropout = keras.layers.Dropout(config.hidden_dropout_prob); + + StackLayers(query, key, value); + + } + + public Tensor transpose_for_scores(Tensor tensor, int batch_size) + { + tensor = tf.reshape(tensor: tensor, shape: (batch_size, -1, config.num_attention_heads, attention_head_size)); + return tf.transpose(tensor, perm: new int[] { 0, 2, 1, 3 }); + } + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) + { + var hidden_states = inputs[0]; + var attention_mask = inputs[1]; + + var batch_size = hidden_states.shape[0]; + var seq_len = hidden_states.shape[1]; + + var mixed_query_layer = query.Apply(inputs: hidden_states); + var key_layer = transpose_for_scores(key.Apply(inputs: hidden_states), (int)batch_size); + var value_layer = transpose_for_scores(value.Apply(inputs: hidden_states), (int)batch_size); + + var query_layer = transpose_for_scores(mixed_query_layer, (int)batch_size); + + var attention_scores = tf.reshape(tf.batch_matmul( + tf.reshape(query_layer, (batch_size * config.num_attention_heads, seq_len, attention_head_size)), + tf.reshape( + tf.transpose(key_layer, new int[] { 0, 1, 3, 2 }), + (batch_size * config.num_attention_heads, attention_head_size, seq_len))), + (batch_size, config.num_attention_heads, seq_len, seq_len)); + + var dk = tf.cast(np.array(sqrt_att_head_size), dtype: attention_scores.dtype); + + attention_scores = tf.divide(attention_scores, dk); + attention_scores = tf.add(attention_scores, attention_mask); + + var attention_probs = tf.nn.softmax(logits: attention_scores, axis: -1); + attention_probs = dropout.Apply(inputs: attention_probs, training: training ?? false); + + var attention_output = tf.reshape( + tf.batch_matmul(tf.reshape(attention_probs, (batch_size * config.num_attention_heads, seq_len, seq_len)), + tf.reshape(value_layer, (batch_size * config.num_attention_heads, seq_len, attention_head_size))), + (batch_size, config.num_attention_heads, seq_len, attention_head_size)); + + attention_output = tf.transpose(attention_output, perm: new int[] { 0, 2, 1, 3 }); + attention_output = tf.reshape(attention_output, (batch_size, -1, all_head_size)); + + return attention_output; + } + } + + class BertLayer : Layer + { + BertConfig config; + BertAttention attention; + BertIntermediate intermediate; + BertOutput bert_output; + public BertLayer(BertConfig config) : base(config) + { + this.config = config; + attention = new BertAttention(config); + intermediate = new BertIntermediate(config); + bert_output = new BertOutput(config); + StackLayers(attention, intermediate, bert_output); + } + + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) + { + var hidden_states = inputs[0]; + var attention_mask = inputs[1]; + + var attention_output = attention.Apply(new Tensor[] { hidden_states, attention_mask }, training: training ?? false); + + var intermediate_output = intermediate.Apply(attention_output, training: training ?? false); + var layer_output = bert_output.Apply(new Tensor[] { intermediate_output, attention_output }, training: training ?? false); + return layer_output; + } + } + + class BertPooler : Layer + { + BertConfig config; + ILayer dense; + public BertPooler(BertConfig config) : base(config) + { + this.config = config; + dense = keras.layers.Dense( + units: config.hidden_size, + kernel_initializer: new TruncatedNormal(config.initializer_range), + activation: keras.activations.Tanh); + StackLayers(dense); + + } + + public static Tensors gelu(Tensor x) + { + var cdf = 0.5 * (1.0 + tf.tanh( + (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))); + return x * cdf; + } + + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) + { + var hidden_states = inputs[0]; + + var shape = hidden_states.shape; + var first_token_tensor = tf.slice(hidden_states, new int[] { 0, 0, 0 }, new int[] { (int)shape[0], 1, (int)shape[2] }); + + return gelu(dense.Apply(tf.reshape(first_token_tensor, (shape[0], shape[2])))); + } + } + class BertMainLayer : Layer + { + BertConfig config; + BertEmbedding embeddings; + BertEncoder encoder; + BertPooler pooler; + public BertMainLayer(BertConfig config) : base(config) + { + this.config = config; + embeddings = new BertEmbedding(config); + encoder = new BertEncoder(config); + pooler = new BertPooler(config); + StackLayers(embeddings,encoder, pooler); + } + + public Tensors get_other_ids(Tensor input_ids) + { + var batch_size = input_ids.shape[0]; + var attention_mask = tf.reshape(tf.cast(tf.fill(input_ids.shape, 1), dtype: tf.int32), (batch_size, -1)); + var token_type_ids = tf.reshape(tf.cast(tf.fill(input_ids.shape, 0), dtype: tf.int32), (batch_size, -1)); + var position_ids = tf.expand_dims(tf.range(0, input_ids.shape[1]), axis: 0); + return new Tensor[] { input_ids, attention_mask, token_type_ids, position_ids }; + } + + protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null) + { + if(inputs.Length == 1) inputs = get_other_ids(inputs[0]); + + var input_ids = inputs[0]; + var attention_mask = inputs[1]; + var token_type_ids = inputs[2]; + var position_ids = inputs[3]; + + var input_shape = input_ids.shape; //bsz seq_len dim + var embedding_output = embeddings.Apply(new Tensor[] { input_ids, position_ids, token_type_ids }); + + var attention_mask_shape = attention_mask.shape; + + var extended_attention_mask = tf.reshape( + attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1])); + + extended_attention_mask = tf.cast(extended_attention_mask, dtype: embedding_output.dtype); + + var one_cst = tf.constant(1.0, dtype: embedding_output.dtype); + + var ten_thousand_cst = tf.constant(-10000.0, dtype: embedding_output.dtype); + extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst); + + var encoder_outputs = encoder.Apply(new Tensor[] { embedding_output, extended_attention_mask }); + + var pooled_output = pooler.Apply(encoder_outputs); + return pooled_output; + } + } +} diff --git a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertTokenizer.cs b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertTokenizer.cs new file mode 100644 index 0000000..973f569 --- /dev/null +++ b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/BertTokenizer.cs @@ -0,0 +1,356 @@ +using Newtonsoft.Json.Linq; +using OneOf.Types; +using System; +using System.Collections.Generic; +using System.Globalization; +using System.IO; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using Tensorflow; +using static System.Net.Mime.MediaTypeNames; + +namespace BERT +{ + class BasicTokenizer + { + bool do_lower_case; + bool strip_accents; + public List never_split; + public BasicTokenizer(bool do_lower_case, bool strip_accents = true, List never_split = null) + { + this.do_lower_case = do_lower_case; + this.strip_accents = strip_accents; + this.never_split = never_split == null ? new List() : never_split; + } + + public static bool _is_control(char c) + { + //Checks whether `char` is a control character. + if (c == '\t' || c == '\n' || c == '\r') return false; + UnicodeCategory cat = CharUnicodeInfo.GetUnicodeCategory(c); + if (cat.ToString().StartsWith("Other")) return true; + return false; + } + + public static bool _is_whitespace(char c) + { + //Checks whether `char` is a whitespace character. + if (c == '\t' || c == '\n' || c == '\r') return false; + UnicodeCategory cat = CharUnicodeInfo.GetUnicodeCategory(c); + if (cat.ToString() == "SpaceSeparator") return true; + return false; + } + + public static bool _is_punctuation(char c) + { + int cp = (int)c; + if ((cp >= 33 && cp <= 47) || (cp >= 58 && cp <= 64) || (cp >= 91 && cp <= 96) || (cp >= 123 && cp <= 126)) return true; + UnicodeCategory cat = CharUnicodeInfo.GetUnicodeCategory(c); + if (cat.ToString().Contains("Punctuation")) return true; + return false; + } + + public static List whitespace_tokenize(string text) + { + //Runs basic whitespace cleaning and splitting on a piece of text. + text = text.Trim(); + var tokens = text.Split(' '); + return new List(tokens); + } + public string _run_strip_accents(string text) + { + //Strips accents from a piece of text. + // Normalize(text); // Not importance. + List output = new List(); + foreach (var c in text) + { + var cat = CharUnicodeInfo.GetUnicodeCategory(c); + if (cat == UnicodeCategory.NonSpacingMark) continue; + output.Add(c); + } + return string.Join("", output); + } + + public List _run_split_on_punc(string text, List never_split = null) + { + //Splits punctuation on a piece of text. + if (never_split != null && never_split.Contains(text)) return new List(new string[] { text }); + char[] chars = text.ToArray(); + int i = 0; + bool start_new_word = true; + List> output = new List>(); + while (i < chars.Length) + { + char chr = chars[i]; + if (_is_punctuation(chr)) + { + var tmp = new List(); + tmp.Add(chr); + output.Add(new List(tmp)); + start_new_word = true; + } + else + { + if (start_new_word) output.Add(new List()); + start_new_word = false; + output[output.Count - 1].Add(chr); + } + i++; + } + var res = new List(); + foreach (var x in output) res.Add(string.Join("", x)); + return res; + } + + public List tokenize(string text, List never_split = null) + { + text = _clean_text(text); + var orig_tokens = whitespace_tokenize(text); + List split_tokens = new List(); + foreach (string token in orig_tokens) + { + var _token = token; + if (!never_split.Contains(token)) + { + if (do_lower_case) + { + _token = _token.ToLower(); + if (this.strip_accents != false) + { + _token = this._run_strip_accents(_token); + } + } + else if (strip_accents) + { + _token = this._run_strip_accents(_token); + } + split_tokens.extend(this._run_split_on_punc(_token, never_split)); + } + } + var output_tokens = whitespace_tokenize(string.Join(" ", split_tokens)); + return output_tokens; + } + + protected string _clean_text(string text) + { + //Performs invalid character removal and whitespace cleanup on text. + var output = new List(); + foreach (char c in text) + { + int cp = (int)c; + if (cp == 0 || cp == 0xFFFD || _is_control(c)) continue; + if (_is_whitespace(c)) output.Add(' '); + else output.Add(c); + } + return string.Join("", output); + } + } + internal class BertTokenizer + { + Dictionary vocab; + Dictionary ids_to_tokens; + bool do_lower_case; + bool do_basic_tokenize; + List never_split; + BasicTokenizer basic_tokenizer; + List all_special_tokens; + public string cls_token; + public string pad_token; + public string sep_token; + public string mask_token; + public string unk_token; + public int vocab_size { + get { + return vocab.Count; + } + } + public static Dictionary load_vocab(string vocab_file) { + // Loads a vocabulary file into a dictionary. + Dictionary vocab = new Dictionary(); + StreamReader sr = new StreamReader(vocab_file); + int index = 0; + while (!sr.EndOfStream) { + string token = sr.ReadLine(); + token.TrimEnd(new char[] { '\n'}); + vocab.Add(token, index++); + } + return vocab; + } + + public static List whitespace_tokenize(string text) { + //Runs basic whitespace cleaning and splitting on a piece of text. + text = text.Trim(); + var token = text.Split(' '); + return new List(token); + } + + public BertTokenizer( + string vocab_file, + bool do_lower_case = true, + bool do_basic_tokenize = true, + List never_split = null, + string unk_token = "[UNK]", + string sep_token = "[SEP]", + string pad_token = "[PAD]", + string cls_token = "[CLS]", + string mask_token = "[MASK]" + ) + { + this.vocab = load_vocab(vocab_file); + this.ids_to_tokens = new Dictionary(); + foreach (KeyValuePair entry in this.vocab) { + this.ids_to_tokens.Add(entry.Value, entry.Key); + } + this.do_lower_case = do_lower_case; + this.do_basic_tokenize = do_basic_tokenize; + this.never_split = never_split == null ? new List() : never_split; + if (do_basic_tokenize) this.basic_tokenizer = new BasicTokenizer(do_lower_case, never_split:never_split); + this.all_special_tokens = new List(new string[] { unk_token, sep_token, pad_token, cls_token, mask_token }); + this.unk_token = unk_token; + this.cls_token = cls_token; + this.mask_token = mask_token; + this.pad_token = pad_token; + this.sep_token = sep_token; + } + + protected int _convert_token_to_id(string token) { + //Converts a token (str) in an id using the vocab. + return this.vocab[token]; + } + protected string _convert_id_to_token(int id) { + //Converts an index (integer) in a token (str) using the vocab. + return this.ids_to_tokens[id]; + } + + public List convert_tokens_to_ids(List tokens) { + List ids = new List(); + + foreach (var token in tokens) { + if (this.vocab.ContainsKey(token)) ids.Add(this.vocab[token]); + else ids.Add(this.vocab[this.unk_token]); + } + return ids; + } + public List build_inputs_with_special_tokens(List token_ids_0, List token_ids_1=null) { + /* + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A BERT sequence has the following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + */ + var token_ids = new List(token_ids_0.ToArray()); + //Console.WriteLine(cls_token); + token_ids.Insert(0, vocab[cls_token]); + token_ids.Add(vocab[sep_token]); + if (token_ids_1 == null) return token_ids; + + token_ids.extend(token_ids_1); + token_ids.Add(vocab[sep_token]); + return token_ids; + + } + + public List create_token_type_ids_from_sequences(List token_ids_0, List token_ids_1 = null) { + /*Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence + pair mask has the following format: + + ``` + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + ``` + + If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). + */ + var token_ids_0_array = new int[token_ids_0.Count + 2]; + Array.Fill(token_ids_0_array, 0); + var token_ids_0_list = new List(token_ids_0_array); + + if (token_ids_1 == null) return token_ids_0_list; + + var token_ids_1_array = new int[token_ids_1.Count + 1]; + Array.Fill(token_ids_1_array, 1); + var token_ids_1_list = new List(token_ids_1_array); + + token_ids_0_list.extend(token_ids_1_list); + return token_ids_0_list; + } + + public List _tokenize(string text) { + List split_tokens = new List(); + if (do_basic_tokenize) + { + foreach (var token in basic_tokenizer.tokenize(text, all_special_tokens)) + { + if (this.basic_tokenizer.never_split.Contains(token)) + { + split_tokens.Add(token); + } + else split_tokens.extend(wordpiece_tokenize(token)); + } + } + else split_tokens = wordpiece_tokenize(text); + return split_tokens; + } + + public List wordpiece_tokenize(string text, int max_input_chars_per_word = 100) { + /*Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform + tokenization using the given vocabulary. + + For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.*/ + List output_tokens = new List(); + foreach (var token in whitespace_tokenize(text)) + { + List chars = new List(token.ToCharArray()); + if (chars.Count > max_input_chars_per_word) { + output_tokens.Add(unk_token); + continue; + } + bool is_bad = false; + int start = 0; + List sub_tokens = new List(); + while (start < chars.Count) { + int end = chars.Count; + string cur_substr = ""; + while (start < end) { + string substr = string.Join("", chars.GetRange(start, end - start)); + if (start > 0) substr = "##" + substr; + if (vocab.ContainsKey(substr)) { + cur_substr = substr; + break; + } + end -= 1; + } + if (cur_substr.Length == 0) { is_bad = true; break; } + sub_tokens.Add(cur_substr); + start = end; + } + if (is_bad) output_tokens.Add(unk_token); + else output_tokens.extend(sub_tokens); + } + return output_tokens; + } + + + + } +} diff --git a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/IMDBDataProcessor.cs b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/IMDBDataProcessor.cs new file mode 100644 index 0000000..1f4c250 --- /dev/null +++ b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertModel/IMDBDataProcessor.cs @@ -0,0 +1,66 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using static Tensorflow.KerasApi; +using Tensorflow.Keras.Utils; +using Tensorflow.Keras.Datasets; +using System.Data; +using Tensorflow.NumPy; +using BERT; +using Tensorflow; +using HDF5CSharp; +using System.IO; +using System.ComponentModel.DataAnnotations; +using System.Net; + +namespace BERT +{ + internal interface IMDBDataPreProcessor + { + public static (int[,], int[]) ProcessData(string path, int max_len, int label = 0) + { + string url = "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt"; + string vocab_file = "./vocab.txt"; + { + using (WebClient client = new WebClient()) + { + byte[] fileData = client.DownloadData(url); + using (Stream stream = File.Create(vocab_file)) + { + stream.Write(fileData, 0, fileData.Length); + } + } + } + BertTokenizer tokenizer = new BertTokenizer(vocab_file); + DirectoryInfo root = new DirectoryInfo(path); + FileInfo[] files = root.GetFiles(); + List> res = new List>(); + List res_y = new List(); + foreach (var item in files) + { + + string str = File.ReadAllText(item.ToString()); + str = str.Replace("

", " "); + var ids = tokenizer.convert_tokens_to_ids(tokenizer._tokenize(str)); + ids = tokenizer.build_inputs_with_special_tokens(ids); + if (ids.Count < max_len) + { + var tmp = new List(new int[max_len - ids.Count]); + for (int i = 0; i < tmp.Count; i++) tmp[i] = 0; + ids.extend(tmp); + } + else { ids = ids.GetRange(0, max_len); ids[ids.Count - 1] = 102; } + res_y.Add(label); + res.Add(ids); + } + + var res_array = new int[res.Count, max_len]; + for (int i = 0; i < res.Count; i++) + for (int j = 0; j < max_len; j++) res_array[i, j] = res[i][j]; + + return (res_array, res_y.ToArray()); + } + } +} From 12ef6288378f46a76ea53fdff27c5ea554f76ccd Mon Sep 17 00:00:00 2001 From: Beacontownfc <89081023+Beacontownfc@users.noreply.github.com> Date: Sun, 27 Aug 2023 09:49:23 +0800 Subject: [PATCH 38/39] Update BertClassification.cs --- .../NaturalLanguageProcessing/BertClassification.cs | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs index b6905f6..43b7901 100644 --- a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs +++ b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs @@ -15,10 +15,10 @@ namespace TensorFlowNET.Examples { class BertClassification : SciSharpExample, IExample { - int max_seq_len = 180; - int batch_size = 4; + int max_seq_len = 512; + int batch_size = 32; int num_classes = 2; - int epoch = 3; + int epoch = 10; float learning_rate = (float)2e-5; string pretrained_weight_path = "./tf_model.h5"; BertConfig config = new BertConfig(); From 47bc2bf8436afd980bd8e824386d7d6a44f8307e Mon Sep 17 00:00:00 2001 From: Haiping Chen Date: Sun, 5 Nov 2023 16:32:32 -0600 Subject: [PATCH 39/39] Upgrade TensorFlow.NET to v0.150. --- Debugging.sln | 206 ++++++++++++++++++ Directory.Build.props | 6 + SciSharp STACK Examples.sln | 96 ++++---- src/SciSharp.WebApi/SciSharp.WebApi.csproj | 2 +- src/SharpCV.Examples/SharpCV.Examples.csproj | 2 +- .../BertClassification.cs | 2 +- .../ObjectDetection/MnistInYOLOv3.cs | 4 +- .../TensorFlowNET.Examples.csproj | 15 +- .../TimeSeries/WeatherPrediction.cs | 2 +- 9 files changed, 267 insertions(+), 68 deletions(-) create mode 100644 Debugging.sln create mode 100644 Directory.Build.props diff --git a/Debugging.sln b/Debugging.sln new file mode 100644 index 0000000..e1d548c --- /dev/null +++ b/Debugging.sln @@ -0,0 +1,206 @@ + +Microsoft Visual Studio Solution File, Format Version 12.00 +# Visual Studio Version 17 +VisualStudioVersion = 17.1.31911.260 +MinimumVisualStudioVersion = 10.0.40219.1 +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Examples", "src\TensorFlowNET.Examples\TensorFlowNET.Examples.csproj", "{3AC62662-7861-4C21-A402-82864F2A8AF7}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Binding", 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EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE diff --git a/src/SciSharp.WebApi/SciSharp.WebApi.csproj b/src/SciSharp.WebApi/SciSharp.WebApi.csproj index 1b3dacc..c43a141 100644 --- a/src/SciSharp.WebApi/SciSharp.WebApi.csproj +++ b/src/SciSharp.WebApi/SciSharp.WebApi.csproj @@ -9,7 +9,7 @@ - + diff --git a/src/SharpCV.Examples/SharpCV.Examples.csproj b/src/SharpCV.Examples/SharpCV.Examples.csproj index 232b00a..daa2ff1 100644 --- a/src/SharpCV.Examples/SharpCV.Examples.csproj +++ b/src/SharpCV.Examples/SharpCV.Examples.csproj @@ -10,7 +10,7 @@ - + diff --git a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs index 43b7901..d51297b 100644 --- a/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs +++ b/src/TensorFlowNET.Examples/NaturalLanguageProcessing/BertClassification.cs @@ -28,7 +28,7 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "Bert for Classification", - Enabled = true + Enabled = false }; public override void PrepareData() diff --git a/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs b/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs index c556cd5..d779452 100644 --- a/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs +++ b/src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs @@ -31,14 +31,14 @@ public ExampleConfig InitConfig() => Config = new ExampleConfig { Name = "MNIST in YOLOv3", - Enabled = false + Enabled = true }; public bool Run() { cfg = new YoloConfig("YOLOv3"); (trainingData, testingData) = PrepareData(); - Train(); + // Train(); Test(); return true; } diff --git a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj index 12d9465..211e946 100644 --- a/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj +++ b/src/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj @@ -49,15 +49,22 @@ - - - - + + + + + + + + + + + diff --git a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs index 43c7810..23a6b01 100644 --- a/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs +++ b/src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs @@ -26,7 +26,7 @@ public bool Run() { var wizard = new ModelWizard(); // Change to RnnModel for RNN - task = wizard.AddTimeSeriesTask(new TaskOptions + task = wizard.AddTimeSeriesTask(new TaskOptions { WeightsPath = @"timeseries_linear_v1\saved_weights.h5" });