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Add TimeSeries example.
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‎README.md‎

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# SciSharp STACK Examples
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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.
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[![Join the chat at https://gitter.im/publiclab/publiclab](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/sci-sharp/community)
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Requirements:
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* [.NET Core 3.1](https://dotnet.microsoft.com/download/dotnet-core/3.1)
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* [.NET Core 5.0](https://dotnet.microsoft.com/download/dotnet-core/5.0)
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* [Visual Studio 2019](https://visualstudio.microsoft.com/vs/) or [Visual Studio Code](https://code.visualstudio.com/)
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Run specific example in shell:
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#### C#
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dotnet TensorFlowNET.Examples.FSharp.dll -ex "MNIST CNN (Eager)"
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```
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Example runner will download all the required files like training data and model pb files.
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#### Basic Model
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* CNN In Your Own Dataset [C#](src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs), [F#](src/TensorFlowNET.Examples.FSharp/ImageProcessing/CnnInYourOwnData.fs)
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#### Natural Language Processing
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* Binary Text Classification [C#](src/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs)
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* CNN Text Classification [C#](src/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs)
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* Named Entity Recognition [C#](src/TensorFlowNET.Examples/TextProcessing/NER)
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#### Time Series
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* Weather Prediction [C#](src/TensorFlowNET.Examples/TimeSeries/WeatherPrediction.cs)
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#### Welcome to PR your example to us.
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Your contribution will make .NET community better than ever.
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<br>
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<a href="http://scisharpstack.org"><img src="https://github.com/SciSharp/SciSharp/blob/master/art/scisharp-stack.png" width="391" height="100" /></a>

‎src/SharpCV.Examples/SharpCV.Examples.csproj‎

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<ItemGroup>
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<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.4.0.20200915" />
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.6.0-rc0" />
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<PackageReference Include="SharpCV" Version="0.10.0" />
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.7.0" />
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<PackageReference Include="SharpCV" Version="0.11.0" />
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</ItemGroup>
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</Project>

‎src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs‎

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// var tf.summary.histogram("weight", W);
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var b = bias_variable("b", new[] { num_filters });
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// tf.summary.histogram("bias", b);
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var layer = tf.nn.conv2d(x, W,
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var layer = tf.nn.conv2d(x, W.AsTensor(),
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strides: new int[] { 1, stride, stride, 1 },
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padding: "SAME");
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layer += b.AsTensor();

‎src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs‎

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var result = task.Predict(input);
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long output = np.argmax(y_test[0]);
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Debug.Assert(result.Label == output.ToString());
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input = x_test["1:2"];
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result = task.Predict(input);
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output = np.argmax(y_test[1]);
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Debug.Assert(result.Label == output.ToString());
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}
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public override void PrepareData()

‎src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs‎

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tf.enable_eager_execution();
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PrepareData();
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Train();
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Test();
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return accuracy_test >= 0.80;
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}
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// Store layers weight & bias
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public override void Train()
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{
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// A random value generator to initialize weights.
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var random_normal = tf.initializers.random_normal_initializer();
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print($"step: {step}, loss: {(float)loss}, accuracy: {(float)acc}");
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}
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}
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}
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public override void Test()
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{
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// Test model on validation set.
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{
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x_test = x_test["::100"];
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y_test = y_test["::100"];
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var pred = conv_net(x_test);
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accuracy_test = (float)accuracy(pred, y_test);
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print($"Test Accuracy: {accuracy_test}");
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}
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return accuracy_test >= 0.80;
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x_test = x_test["::100"];
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y_test = y_test["::100"];
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var pred = conv_net(x_test);
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accuracy_test = (float)accuracy(pred, y_test);
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print($"Test Accuracy: {accuracy_test}");
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}
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void run_optimization(OptimizerV2 optimizer, Tensor x, Tensor y)
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Tensor conv2d(Tensor x, IVariableV1 W, IVariableV1 b, int strides = 1)
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{
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x = tf.nn.conv2d(x, W, new int[] { 1, strides, strides, 1 }, padding: "SAME");
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x = tf.nn.conv2d(x, W.AsTensor(), new int[] { 1, strides, strides, 1 }, padding: "SAME");
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x = tf.nn.bias_add(x, b);
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return tf.nn.relu(x);
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}

‎src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs‎

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train_ds = train_ds.shuffle(1000).prefetch(buffer_size: -1);
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val_ds = val_ds.prefetch(buffer_size: -1);
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foreach (var (img, label) in train_ds)
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{
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print($"images: {img.shape}");
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print($"labels: {label.numpy()}");
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}
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}
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}
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}

‎src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass.cs‎

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Tensor conv2d(Tensor x, IVariableV1 W, IVariableV1 b, int strides = 1)
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{
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x = tf.nn.conv2d(x, W, new int[] { 1, strides, strides, 1 }, padding: "SAME");
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x = tf.nn.conv2d(x, W.AsTensor(), new int[] { 1, strides, strides, 1 }, padding: "SAME");
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x = tf.nn.bias_add(x, b);
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return tf.nn.relu(x);
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}

‎src/TensorFlowNET.Examples/ObjectDetection/MnistInYOLOv3.cs‎

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/*****************************************************************************
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Copyright 2021 Haiping Chen. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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******************************************************************************/
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using SciSharp.Models;
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using SciSharp.Models.ObjectDetection;
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using System;
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using System.IO;
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namespace TensorFlowNET.Examples
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{
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public class MnistInYOLOv3 : SciSharpExample, IExample
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{
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YoloConfig cfg;
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float accuracy_test = 0f;
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YoloDataset trainingData, testingData;
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public ExampleConfig InitConfig()
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=> Config = new ExampleConfig
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{
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Name = "MNIST in YOLOv3",
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Enabled = false
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};
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public bool Run()
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{
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cfg = new YoloConfig("YOLOv3");
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(trainingData, testingData) = PrepareData();
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Train();
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return true;
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}
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public override void Train()
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{
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// using wizard to train model
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var wizard = new ModelWizard();
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var task = wizard.AddObjectDetectionTask<YOLOv3>(new TaskOptions
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{
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InputShape = (28, 28, 1),
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NumberOfClass = 10,
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});
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task.SetModelArgs(cfg);
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task.Train(new YoloTrainingOptions
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{
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TrainingData = trainingData,
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TestingData = testingData
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});
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}
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public override void Test()
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{
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var wizard = new ModelWizard();
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var task = wizard.AddObjectDetectionTask<YOLOv3>(new TaskOptions
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{
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ModelPath = @"./YOLOv3/yolov3.h5"
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});
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var result = task.Test(new TestingOptions
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{
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});
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accuracy_test = result.Accuracy;
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}
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public (YoloDataset, YoloDataset) PrepareData()
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{
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string dataDir = Path.Combine("YOLOv3", "data");
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Directory.CreateDirectory(dataDir);
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var trainset = new YoloDataset("train", cfg);
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var testset = new YoloDataset("test", cfg);
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return (trainset, testset);
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}
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}
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}

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