Add BinaryCrossentropy loss function.

src/TensorFlowNET.Core/GlobalUsing.cs

@@ -0,0 +1,3 @@
+global using System;
+global using System.Collections.Generic;
+global using System.Text;

src/TensorFlowNET.Core/Keras/IKerasApi.cs

@@ -2,12 +2,14 @@
 using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Keras.Layers;
+using Tensorflow.Keras.Losses;
 
 namespace Tensorflow.Keras
 {
     public interface IKerasApi
     {
         public ILayersApi layers { get; }
+        public ILossesApi losses { get; }
         public IInitializersApi initializers { get; }
     }
 }

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

@@ -0,0 +1,8 @@
+namespace Tensorflow.Keras.Losses;
+
+public interface ILossFunc
+{
+    public string Reduction { get; }
+    public string Name { get; }
+    Tensor Call(Tensor y_true, Tensor y_pred, Tensor sample_weight = null);
+}

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

@@ -0,0 +1,41 @@
+namespace Tensorflow.Keras.Losses;
+
+public interface ILossesApi
+{
+    ILossFunc BinaryCrossentropy(bool from_logits = false, 
+        float label_smoothing = 0f,
+        int axis = -1,
+        string reduction = "auto",
+        string name = "binary_crossentropy");
+
+    ILossFunc SparseCategoricalCrossentropy(string reduction = null, 
+        string name = null, 
+        bool from_logits = false);
+
+    ILossFunc CategoricalCrossentropy(string reduction = null, 
+        string name = null, 
+        bool from_logits = false);
+
+    ILossFunc MeanSquaredError(string reduction = null, 
+        string name = null);
+
+    ILossFunc MeanSquaredLogarithmicError(string reduction = null, 
+        string name = null);
+
+    ILossFunc MeanAbsolutePercentageError(string reduction = null, 
+        string name = null);
+
+    ILossFunc MeanAbsoluteError(string reduction = null, 
+        string name = null);
+
+    ILossFunc CosineSimilarity(string reduction = null,
+        int axis = -1,
+        string name = null);
+
+    ILossFunc Huber(string reduction = null, 
+        string name = null, 
+        Tensor delta = null);
+
+    ILossFunc LogCosh(string reduction = null, 
+        string name = null);
+}

src/TensorFlowNET.Keras/BackendImpl.cs

@@ -276,6 +276,20 @@ namespace Tensorflow.Keras
             return -math_ops.reduce_sum(target * math_ops.log(output), new Axis(axis));
         }
 
+        public Tensor binary_crossentropy(Tensor target, Tensor output, bool from_logits = false)
+        {
+            if (from_logits)
+                return tf.nn.sigmoid_cross_entropy_with_logits(labels: target, logits: output);
+
+            var epsilon_ = constant_op.constant(epsilon(), dtype: output.dtype.as_base_dtype());
+            output = tf.clip_by_value(output, epsilon_, 1.0f - epsilon_);
+
+            // Compute cross entropy from probabilities.
+            var bce = target * tf.math.log(output + epsilon());
+            bce += (1 - target) * tf.math.log(1 - output + epsilon());
+            return -bce;
+        }
+
         /// <summary>
         /// Resizes the images contained in a 4D tensor.
         /// </summary>

src/TensorFlowNET.Keras/GlobalUsing.cs

@@ -0,0 +1,5 @@
+global using System;
+global using System.Collections.Generic;
+global using System.Text;
+global using static Tensorflow.Binding;
+global using static Tensorflow.KerasApi;

src/TensorFlowNET.Keras/KerasInterface.cs

@@ -21,7 +21,7 @@ namespace Tensorflow.Keras
         public IInitializersApi initializers { get; } = new InitializersApi();
         public Regularizers regularizers { get; } = new Regularizers();
         public ILayersApi layers { get; } = new LayersApi();
-        public LossesApi losses { get; } = new LossesApi();
+        public ILossesApi losses { get; } = new LossesApi();
         public Activations activations { get; } = new Activations();
         public Preprocessing preprocessing { get; } = new Preprocessing();
         ThreadLocal<BackendImpl> _backend = new ThreadLocal<BackendImpl>(() => new BackendImpl());

src/TensorFlowNET.Keras/Losses/BinaryCrossentropy.cs

@@ -0,0 +1,24 @@
+namespace Tensorflow.Keras.Losses;
+
+public class BinaryCrossentropy : LossFunctionWrapper, ILossFunc
+{
+    float label_smoothing;
+    public BinaryCrossentropy(
+        bool from_logits = false,
+        float label_smoothing = 0,
+        string reduction = null,
+        string name = null) :
+        base(reduction: reduction,
+             name: name == null ? "binary_crossentropy" : name, 
+             from_logits: from_logits)
+    {
+        this.label_smoothing = label_smoothing;
+    }
+
+
+    public override Tensor Apply(Tensor y_true, Tensor y_pred, bool from_logits = false, int axis = -1)
+    {
+        var sum = keras.backend.binary_crossentropy(y_true, y_pred, from_logits: from_logits);
+        return keras.backend.mean(sum, axis: axis);
+    }
+}

src/TensorFlowNET.Keras/Losses/CategoricalCrossentropy.cs

@@ -1,31 +1,24 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using static Tensorflow.Binding;
-using static Tensorflow.KerasApi;
+namespace Tensorflow.Keras.Losses;
 
-namespace Tensorflow.Keras.Losses
+public class CategoricalCrossentropy : LossFunctionWrapper, ILossFunc
 {
-    public class CategoricalCrossentropy : LossFunctionWrapper, ILossFunc
+    float label_smoothing;
+    public CategoricalCrossentropy(
+        bool from_logits = false,
+        float label_smoothing = 0,
+        string reduction = null,
+        string name = null) :
+        base(reduction: reduction,
+             name: name == null ? "categorical_crossentropy" : name, 
+             from_logits: from_logits)
     {
-        float label_smoothing;
-        public CategoricalCrossentropy(
-            bool from_logits = false,
-            float label_smoothing = 0,
-            string reduction = null,
-            string name = null) :
-            base(reduction: reduction,
-                 name: name == null ? "categorical_crossentropy" : name, 
-                 from_logits: from_logits)
-        {
-            this.label_smoothing = label_smoothing;
-        }
+        this.label_smoothing = label_smoothing;
+    }
 
 
-        public override Tensor Apply(Tensor y_true, Tensor y_pred, bool from_logits = false, int axis = -1)
-        {
-            // Try to adjust the shape so that rank of labels = rank of logits - 1.
-            return keras.backend.categorical_crossentropy(y_true, y_pred, from_logits: from_logits);
-        }
+    public override Tensor Apply(Tensor y_true, Tensor y_pred, bool from_logits = false, int axis = -1)
+    {
+        // Try to adjust the shape so that rank of labels = rank of logits - 1.
+        return keras.backend.categorical_crossentropy(y_true, y_pred, from_logits: from_logits);
     }
 }

src/TensorFlowNET.Keras/Losses/ILossFunc.cs

@@ -1,9 +0,0 @@
-namespace Tensorflow.Keras.Losses
-{
-    public interface ILossFunc
-    {
-        public string Reduction { get; }
-        public string Name { get; }
-        Tensor Call(Tensor y_true, Tensor y_pred, Tensor sample_weight = null);
-    }
-}

src/TensorFlowNET.Keras/Losses/Loss.cs

@@ -16,7 +16,7 @@ namespace Tensorflow.Keras.Losses
 
         public string Reduction => reduction;
         public string Name => name;
-        public Loss(string reduction = ReductionV2.AUTO, 
+        public Loss(string reduction = ReductionV2.AUTO,
             string name = null,
             bool from_logits = false)
         {
@@ -34,7 +34,17 @@ namespace Tensorflow.Keras.Losses
         public Tensor Call(Tensor y_true, Tensor y_pred, Tensor sample_weight = null)
         {
             var losses = Apply(y_true, y_pred, from_logits: from_logits);
-            return losses_utils.compute_weighted_loss(losses, reduction: this.reduction , sample_weight: sample_weight);
+            var reduction = GetReduction();
+            return losses_utils.compute_weighted_loss(losses, reduction: reduction, sample_weight: sample_weight);
+        }
+
+        string GetReduction()
+        {
+            return reduction switch
+            {
+                ReductionV2.AUTO => ReductionV2.SUM_OVER_BATCH_SIZE,
+                _ => reduction
+            };
         }
 
         void _set_name_scope()

src/TensorFlowNET.Keras/Losses/LossesApi.cs

@@ -1,7 +1,17 @@
 namespace Tensorflow.Keras.Losses
 {
-    public class LossesApi
+    public class LossesApi : ILossesApi
     {
+        public ILossFunc BinaryCrossentropy(bool from_logits = false,
+                float label_smoothing = 0,
+                int axis = -1,
+                string reduction = "auto",
+                string name = "binary_crossentropy")
+            => new BinaryCrossentropy(from_logits: from_logits,
+                label_smoothing: label_smoothing,
+                reduction: reduction,
+                name: name);
+
         public ILossFunc SparseCategoricalCrossentropy(string reduction = null, string name = null,bool from_logits = false)
             => new SparseCategoricalCrossentropy(reduction: reduction, name: name,from_logits: from_logits);
 
@@ -19,14 +29,13 @@
         public ILossFunc MeanAbsoluteError(string reduction = null, string name = null)
            => new MeanAbsoluteError(reduction: reduction, name: name);
 
-        public ILossFunc CosineSimilarity(string reduction = null, string name = null,int axis=-1)
-            => new CosineSimilarity(reduction: reduction, name: name, axis: axis);
+        public ILossFunc CosineSimilarity(string reduction = null, int axis = -1, string name = null)
+            => new CosineSimilarity(reduction: reduction, axis: axis, name: name);
 
         public ILossFunc Huber(string reduction = null, string name = null, Tensor delta=null)
             => new Huber(reduction: reduction, name: name, delta: delta);
 
         public ILossFunc LogCosh(string reduction = null, string name = null)
             => new LogCosh(reduction: reduction, name: name);
-
     }
 }

src/TensorFlowNET.Keras/Utils/losses_utils.cs

@@ -24,23 +24,17 @@ namespace Tensorflow.Keras.Utils
     {
         public static Tensor compute_weighted_loss(Tensor losses, Tensor sample_weight = null, string reduction = null, string name = null)
         {
-            if (sample_weight == null)
-                sample_weight = losses.dtype == TF_DataType.TF_DOUBLE ? tf.constant(1.0) : tf.constant(1.0f);
-            var weighted_losses = scale_losses_by_sample_weight(losses, sample_weight);
-            // Apply reduction function to the individual weighted losses.
-            var loss = reduce_weighted_loss(weighted_losses, reduction);
-            // Convert the result back to the input type.
-            // loss = math_ops.cast(loss, losses.dtype);
-            return loss;
-        }
-
-        public static Tensor scale_losses_by_sample_weight(Tensor losses, Tensor sample_weight)
-        {
-            // losses = math_ops.cast(losses, dtypes.float32);
-            // sample_weight = math_ops.cast(sample_weight, dtypes.float32);
-            // Update dimensions of `sample_weight` to match with `losses` if possible.
-            // (losses, sample_weight) = squeeze_or_expand_dimensions(losses, sample_weight);
-            return math_ops.multiply(losses, sample_weight);
+            return tf_with(ops.name_scope("weighted_loss"), scope =>
+            {
+                if (sample_weight == null)
+                    sample_weight = losses.dtype == TF_DataType.TF_DOUBLE ? tf.constant(1.0) : tf.constant(1.0f);
+                var weighted_losses = math_ops.multiply(losses, sample_weight);
+                // Apply reduction function to the individual weighted losses.
+                var loss = reduce_weighted_loss(weighted_losses, reduction);
+                // Convert the result back to the input type.
+                // loss = math_ops.cast(loss, losses.dtype);
+                return loss;
+            });
         }
 
         public static (Tensor, Tensor) squeeze_or_expand_dimensions(Tensor y_pred, Tensor sample_weight)

test/TensorFlowNET.Keras.UnitTest/Losses/LossesTest.cs

@@ -0,0 +1,50 @@
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using System.Threading.Tasks;
+using TensorFlowNET.Keras.UnitTest;
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+
+namespace Tensorflow.Keras.UnitTest.Losses;
+
+[TestClass]
+public class LossesTest : EagerModeTestBase
+{
+    /// <summary>
+    /// https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy
+    /// </summary>
+    [TestMethod]
+    public void BinaryCrossentropy()
+    {
+        // Example 1: (batch_size = 1, number of samples = 4)
+        var y_true = tf.constant(new float[] { 0, 1, 0, 0 });
+        var y_pred = tf.constant(new float[] { -18.6f, 0.51f, 2.94f, -12.8f });
+        var bce = tf.keras.losses.BinaryCrossentropy(from_logits: true);
+        var loss = bce.Call(y_true, y_pred);
+        Assert.AreEqual((float)loss, 0.865458f);
+
+        // Example 2: (batch_size = 2, number of samples = 4)
+        y_true = tf.constant(new float[,] { { 0, 1 }, { 0, 0 } });
+        y_pred = tf.constant(new float[,] { { -18.6f, 0.51f }, { 2.94f, -12.8f } });
+        bce = tf.keras.losses.BinaryCrossentropy(from_logits: true);
+        loss = bce.Call(y_true, y_pred);
+        Assert.AreEqual((float)loss, 0.865458f);
+
+        // Using 'sample_weight' attribute
+        loss = bce.Call(y_true, y_pred, sample_weight: tf.constant(new[] { 0.8f, 0.2f }));
+        Assert.AreEqual((float)loss, 0.2436386f);
+
+        // Using 'sum' reduction` type.
+        bce = tf.keras.losses.BinaryCrossentropy(from_logits: true, reduction: Reduction.SUM);
+        loss = bce.Call(y_true, y_pred);
+        Assert.AreEqual((float)loss, 1.730916f);
+
+        // Using 'none' reduction type.
+        bce = tf.keras.losses.BinaryCrossentropy(from_logits: true, reduction: Reduction.NONE);
+        loss = bce.Call(y_true, y_pred);
+        Assert.AreEqual(new float[] { 0.23515666f, 1.4957594f}, loss.numpy());
+    }
+}

test/TensorFlowNET.Keras.UnitTest/Tensorflow.Keras.UnitTest.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <TargetFramework>net6.0</TargetFramework>