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add tf.summary SciSharp#248 
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‎graph/InceptionV3.meta‎

-365 KB
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‎src/TensorFlowNET.Core/APIs/tf.summary.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow.IO;
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using Tensorflow.Summaries;
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namespace Tensorflow
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{
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public static partial class tf
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{
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public static Summary summary = new Summary();
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public static Tensor scalar(string name, Tensor tensor)
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=> summary.scalar(name, tensor);
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}
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}

‎src/TensorFlowNET.Core/Operations/gen_logging_ops.cs‎

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@@ -17,5 +17,65 @@ public static Operation _assert(Tensor condition, object[] data, int? summarize
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return _op;
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}
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/// <summary>
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/// Outputs a <c>Summary</c> protocol buffer with scalar values.
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/// </summary>
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/// <param name="tags">
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/// Tags for the summary.
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/// </param>
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/// <param name="values">
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/// Same shape as <c>tags. Values for the summary.
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/// </param>
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/// <param name="name">
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/// If specified, the created operation in the graph will be this one, otherwise it will be named 'ScalarSummary'.
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/// </param>
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/// <returns>
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/// Scalar. Serialized <c>Summary</c> protocol buffer.
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/// The Operation can be fetched from the resulting Tensor, by fetching the Operation property from the result.
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/// </returns>
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/// <remarks>
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/// The input <c>tags</c> and <c>values</c> must have the same shape. The generated summary
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/// has a summary value for each tag-value pair in <c>tags</c> and <c>values</c>.
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/// </remarks>
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public static Tensor scalar_summary(string tags, Tensor values, string name = "ScalarSummary")
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{
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var dict = new Dictionary<string, object>();
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dict["tags"] = tags;
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dict["values"] = values;
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var op = _op_def_lib._apply_op_helper("ScalarSummary", name: name, keywords: dict);
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return op.output;
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}
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/// <summary>
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/// Merges summaries.
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/// </summary>
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/// <param name="inputs">
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/// Can be of any shape. Each must contain serialized <c>Summary</c> protocol
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/// buffers.
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/// </param>
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/// <param name="name">
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/// If specified, the created operation in the graph will be this one, otherwise it will be named 'MergeSummary'.
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/// </param>
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/// <returns>
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/// Scalar. Serialized <c>Summary</c> protocol buffer.
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/// The Operation can be fetched from the resulting Tensor, by fetching the Operation property from the result.
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/// </returns>
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/// <remarks>
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/// This op creates a
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/// [<c>Summary</c>](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
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/// protocol buffer that contains the union of all the values in the input
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/// summaries.
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///
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/// When the Op is run, it reports an <c>InvalidArgument</c> error if multiple values
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/// in the summaries to merge use the same tag.
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/// </remarks>
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public static Tensor merge_summary(Tensor[] inputs, string name = "MergeSummary")
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{
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var dict = new Dictionary<string, object>();
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dict["inputs"] = inputs;
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var op = _op_def_lib._apply_op_helper("MergeSummary", name: name, keywords: dict);
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return op.output;
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}
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}
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}

‎src/TensorFlowNET.Core/Summaries/Summary.cs‎

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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using static Tensorflow.Python;
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namespace Tensorflow.Summaries
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{
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public class Summary
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{
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public Tensor merge_all(string key = ops.GraphKeys.SUMMARIES, string scope= null, string name= null)
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{
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var summary_ops = ops.get_collection(key, scope: scope);
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if (summary_ops == null)
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return null;
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else
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return merge((summary_ops as List<ITensorOrOperation>).Select(x => x as Tensor).ToArray(), name: name);
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}
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/// <summary>
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/// Merges summaries.
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/// </summary>
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/// <param name="inputs"></param>
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/// <param name="collections"></param>
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/// <param name="name"></param>
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/// <returns></returns>
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public Tensor merge(Tensor[] inputs, string[] collections = null, string name = null)
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{
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return with(ops.name_scope(name, "Merge", inputs), delegate
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{
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var val = gen_logging_ops.merge_summary(inputs: inputs, name: name);
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collect(val, collections?.ToList(), new List<string>());
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return val;
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});
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}
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public Tensor scalar(string name, Tensor tensor, string[] collections = null, string family = null)
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{
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var (tag, scope) = summary_scope(name, family: family, values: new Tensor[] { tensor });
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var val = gen_logging_ops.scalar_summary(tags: tag, values: tensor, name: scope);
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collect(val, collections?.ToList(), new List<string> { ops.GraphKeys.SUMMARIES });
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return val;
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}
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/// <summary>
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/// Adds keys to a collection.
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/// </summary>
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/// <param name="val"The value to add per each key.></param>
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/// <param name="collections">A collection of keys to add.</param>
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/// <param name="default_collections">Used if collections is None.</param>
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public void collect(ITensorOrOperation val, List<string> collections, List<string> default_collections)
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{
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if (collections == null)
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collections = default_collections;
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foreach (var key in collections)
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ops.add_to_collection(key, val);
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}
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public (string, string) summary_scope(string name, string family = null, string default_name = null, Tensor[] values = null)
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{
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string scope_base_name = string.IsNullOrEmpty(family) ? name : $"{family}/{name}";
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return with(ops.name_scope(scope_base_name, default_name: default_name, values), scope =>
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{
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var tag = scope._name_scope;
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if (string.IsNullOrEmpty(family))
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tag = tag.Remove(tag.Length - 1);
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else
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tag = $"{family}/{tag.Remove(tag.Length - 1)}";
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return (tag, scope._name_scope);
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});
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}
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}
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}

‎src/TensorFlowNET.Core/ops.GraphKeys.cs‎

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@@ -47,6 +47,9 @@ public static class GraphKeys
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/// </summary>
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public static string UPDATE_OPS = "update_ops";
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// Key to collect summaries.
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public const string SUMMARIES = "summaries";
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// Used to store v2 summary names.
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public static string _SUMMARY_COLLECTION = "_SUMMARY_V2";
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‎test/TensorFlowNET.Examples/ImageProcess/RetrainImageClassifier.cs‎

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@@ -45,6 +45,8 @@ public bool Run()
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tf.train.import_meta_graph("graph/InceptionV3.meta");
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Tensor bottleneck_tensor = graph.OperationByName("module_apply_default/hub_output/feature_vector/SpatialSqueeze");
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Tensor resized_image_tensor = graph.OperationByName("Placeholder");
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Tensor final_tensor = graph.OperationByName("final_result");
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Tensor ground_truth_input = graph.OperationByName("input/GroundTruthInput");
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var sw = new Stopwatch();
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@@ -63,11 +65,45 @@ public bool Run()
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bottleneck_dir, jpeg_data_tensor,
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decoded_image_tensor, resized_image_tensor,
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bottleneck_tensor, tfhub_module);
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// Create the operations we need to evaluate the accuracy of our new layer.
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var (evaluation_step, _) = add_evaluation_step(final_tensor, ground_truth_input);
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// Merge all the summaries and write them out to the summaries_dir
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var merged = tf.summary.merge_all();
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});
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return false;
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}
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/// <summary>
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/// Inserts the operations we need to evaluate the accuracy of our results.
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/// </summary>
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/// <param name="result_tensor"></param>
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/// <param name="ground_truth_tensor"></param>
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/// <returns></returns>
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private (Tensor, Tensor) add_evaluation_step(Tensor result_tensor, Tensor ground_truth_tensor)
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{
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Tensor evaluation_step = null, correct_prediction = null, prediction = null;
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with(tf.name_scope("accuracy"), scope =>
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{
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with(tf.name_scope("correct_prediction"), delegate
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{
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prediction = tf.argmax(result_tensor, 1);
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correct_prediction = tf.equal(prediction, ground_truth_tensor);
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});
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with(tf.name_scope("accuracy"), delegate
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{
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evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32));
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});
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});
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tf.summary.scalar("accuracy", evaluation_step);
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return (evaluation_step, prediction);
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}
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/// <summary>
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/// Ensures all the training, testing, and validation bottlenecks are cached.
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/// </summary>
@@ -95,12 +131,15 @@ private void cache_bottlenecks(Session sess, Dictionary<string, Dictionary<strin
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get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir, category,
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bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
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resized_input_tensor, bottleneck_tensor, module_name);
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how_many_bottlenecks++;
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if (how_many_bottlenecks % 100 == 0)
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print($"{how_many_bottlenecks} bottleneck files created.");
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}
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}
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}
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}
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private void get_or_create_bottleneck(Session sess, Dictionary<string, Dictionary<string, string[]>> image_lists,
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private float[] get_or_create_bottleneck(Session sess, Dictionary<string, Dictionary<string, string[]>> image_lists,
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string label_name, int index, string image_dir, string category, string bottleneck_dir,
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Tensor jpeg_data_tensor, Tensor decoded_image_tensor, Tensor resized_input_tensor,
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Tensor bottleneck_tensor, string module_name)
@@ -116,6 +155,9 @@ private void get_or_create_bottleneck(Session sess, Dictionary<string, Dictionar
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image_dir, category, sess, jpeg_data_tensor,
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decoded_image_tensor, resized_input_tensor,
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bottleneck_tensor);
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var bottleneck_string = File.ReadAllText(bottleneck_path);
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var bottleneck_values = Array.ConvertAll(bottleneck_string.Split(','), x => float.Parse(x));
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return bottleneck_values;
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}
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private void create_bottleneck_file(string bottleneck_path, Dictionary<string, Dictionary<string, string[]>> image_lists,
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var bottleneck_values = run_bottleneck_on_image(
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sess, image_data, jpeg_data_tensor, decoded_image_tensor,
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resized_input_tensor, bottleneck_tensor);
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var values = bottleneck_values.Data<float>();
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var bottleneck_string = string.Join(",", values);
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File.WriteAllText(bottleneck_path, bottleneck_string);
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}
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/// <summary>
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/// Runs inference on an image to extract the 'bottleneck' summary layer.
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/// </summary>
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/// <param name="sess">Current active TensorFlow Session.</param>
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/// <param name="image_path">Path of raw JPEG data.</param>
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/// <param name="image_data">Data of raw JPEG data.</param>
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/// <param name="image_data_tensor">Input data layer in the graph.</param>
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/// <param name="decoded_image_tensor">Output of initial image resizing and preprocessing.</param>
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/// <param name="resized_input_tensor">The input node of the recognition graph.</param>

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