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Add BinaryCrossentropy loss function. · feelsyt/TensorFlow.NET@ccda2c3 · GitHub

  1. feelsyt
  2. TensorFlow.NET

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Commit ccda2c3

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Oceania2018
committed
Add BinaryCrossentropy loss function.
1 parent 8a21ad2 commit ccda2c3

15 files changed

Lines changed: 202 additions & 58 deletions

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‎src/TensorFlowNET.Core/GlobalUsing.cs‎

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global using System;
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global using System.Collections.Generic;
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global using System.Text;

‎src/TensorFlowNET.Core/Keras/IKerasApi.cs‎

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using System.Collections.Generic;
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using System.Text;
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using Tensorflow.Keras.Layers;
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using Tensorflow.Keras.Losses;
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namespace Tensorflow.Keras
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{
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public interface IKerasApi
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{
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public ILayersApi layers { get; }
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public ILossesApi losses { get; }
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public IInitializersApi initializers { get; }
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}
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}

‎src/TensorFlowNET.Core/Keras/Losses/ILossFunc.cs‎

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namespace Tensorflow.Keras.Losses;
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public interface ILossFunc
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{
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public string Reduction { get; }
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public string Name { get; }
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Tensor Call(Tensor y_true, Tensor y_pred, Tensor sample_weight = null);
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}

‎src/TensorFlowNET.Core/Keras/Losses/ILossesApi.cs‎

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namespace Tensorflow.Keras.Losses;
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public interface ILossesApi
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{
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ILossFunc BinaryCrossentropy(bool from_logits = false,
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float label_smoothing = 0f,
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int axis = -1,
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string reduction = "auto",
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string name = "binary_crossentropy");
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ILossFunc SparseCategoricalCrossentropy(string reduction = null,
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string name = null,
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bool from_logits = false);
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ILossFunc CategoricalCrossentropy(string reduction = null,
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string name = null,
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bool from_logits = false);
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ILossFunc MeanSquaredError(string reduction = null,
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string name = null);
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ILossFunc MeanSquaredLogarithmicError(string reduction = null,
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string name = null);
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ILossFunc MeanAbsolutePercentageError(string reduction = null,
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string name = null);
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ILossFunc MeanAbsoluteError(string reduction = null,
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string name = null);
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ILossFunc CosineSimilarity(string reduction = null,
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int axis = -1,
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string name = null);
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ILossFunc Huber(string reduction = null,
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string name = null,
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Tensor delta = null);
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ILossFunc LogCosh(string reduction = null,
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string name = null);
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}

‎src/TensorFlowNET.Keras/BackendImpl.cs‎

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return -math_ops.reduce_sum(target * math_ops.log(output), new Axis(axis));
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}
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public Tensor binary_crossentropy(Tensor target, Tensor output, bool from_logits = false)
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{
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if (from_logits)
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return tf.nn.sigmoid_cross_entropy_with_logits(labels: target, logits: output);
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var epsilon_ = constant_op.constant(epsilon(), dtype: output.dtype.as_base_dtype());
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output = tf.clip_by_value(output, epsilon_, 1.0f - epsilon_);
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// Compute cross entropy from probabilities.
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var bce = target * tf.math.log(output + epsilon());
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bce += (1 - target) * tf.math.log(1 - output + epsilon());
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return -bce;
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}
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/// <summary>
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/// Resizes the images contained in a 4D tensor.
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/// </summary>

‎src/TensorFlowNET.Keras/GlobalUsing.cs‎

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global using System;
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global using System.Collections.Generic;
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global using System.Text;
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global using static Tensorflow.Binding;
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global using static Tensorflow.KerasApi;

‎src/TensorFlowNET.Keras/KerasInterface.cs‎

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public IInitializersApi initializers { get; } = new InitializersApi();
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public Regularizers regularizers { get; } = new Regularizers();
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public ILayersApi layers { get; } = new LayersApi();
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public LossesApi losses { get; } = new LossesApi();
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public ILossesApi losses { get; } = new LossesApi();
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public Activations activations { get; } = new Activations();
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public Preprocessing preprocessing { get; } = new Preprocessing();
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ThreadLocal<BackendImpl> _backend = new ThreadLocal<BackendImpl>(() => new BackendImpl());

‎src/TensorFlowNET.Keras/Losses/BinaryCrossentropy.cs‎

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namespace Tensorflow.Keras.Losses;
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public class BinaryCrossentropy : LossFunctionWrapper, ILossFunc
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{
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float label_smoothing;
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public BinaryCrossentropy(
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bool from_logits = false,
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float label_smoothing = 0,
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string reduction = null,
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string name = null) :
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base(reduction: reduction,
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name: name == null ? "binary_crossentropy" : name,
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from_logits: from_logits)
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{
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this.label_smoothing = label_smoothing;
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}
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public override Tensor Apply(Tensor y_true, Tensor y_pred, bool from_logits = false, int axis = -1)
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{
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var sum = keras.backend.binary_crossentropy(y_true, y_pred, from_logits: from_logits);
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return keras.backend.mean(sum, axis: axis);
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}
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}

‎src/TensorFlowNET.Keras/Losses/CategoricalCrossentropy.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 static Tensorflow.Binding;
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using static Tensorflow.KerasApi;
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namespace Tensorflow.Keras.Losses;
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namespace Tensorflow.Keras.Losses
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public class CategoricalCrossentropy : LossFunctionWrapper, ILossFunc
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{
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public class CategoricalCrossentropy : LossFunctionWrapper, ILossFunc
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float label_smoothing;
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public CategoricalCrossentropy(
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bool from_logits = false,
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float label_smoothing = 0,
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string reduction = null,
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string name = null) :
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base(reduction: reduction,
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name: name == null ? "categorical_crossentropy" : name,
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from_logits: from_logits)
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{
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float label_smoothing;
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public CategoricalCrossentropy(
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bool from_logits = false,
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float label_smoothing = 0,
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string reduction = null,
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string name = null) :
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base(reduction: reduction,
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name: name == null ? "categorical_crossentropy" : name,
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from_logits: from_logits)
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{
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this.label_smoothing = label_smoothing;
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}
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this.label_smoothing = label_smoothing;
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}
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public override Tensor Apply(Tensor y_true, Tensor y_pred, bool from_logits = false, int axis = -1)
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{
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// Try to adjust the shape so that rank of labels = rank of logits - 1.
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return keras.backend.categorical_crossentropy(y_true, y_pred, from_logits: from_logits);
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}
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public override Tensor Apply(Tensor y_true, Tensor y_pred, bool from_logits = false, int axis = -1)
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{
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// Try to adjust the shape so that rank of labels = rank of logits - 1.
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return keras.backend.categorical_crossentropy(y_true, y_pred, from_logits: from_logits);
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}
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}

‎src/TensorFlowNET.Keras/Losses/ILossFunc.cs‎

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