更新Mse

src/TensorFlowNET.Keras/Losses/ILossFunc.cs

@@ -3,6 +3,6 @@
     public interface ILossFunc
     {
         string Reduction { get; }
-        Tensor Call(Tensor y_true, Tensor y_pred);
+        Tensor Call(Tensor y_true, Tensor y_pred, Tensor sample_weight = null);
     }
 }

src/TensorFlowNET.Keras/Losses/Loss.cs

@@ -31,10 +31,11 @@ namespace Tensorflow.Keras.Losses
             throw new NotImplementedException("");
         }
 
-        public Tensor Call(Tensor y_true, Tensor y_pred)
+        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: ReductionV2.SUM_OVER_BATCH_SIZE);
+
+            return losses_utils.compute_weighted_loss(losses, reduction: this.reduction == null?ReductionV2.SUM_OVER_BATCH_SIZE : this.reduction, sample_weight: sample_weight);
         }
 
         void _set_name_scope()

src/TensorFlowNET.Keras/Losses/LossesApi.cs

@@ -7,5 +7,8 @@
 
         public ILossFunc CategoricalCrossentropy(bool from_logits = false)
             => new CategoricalCrossentropy(from_logits: from_logits);
+        
+        public ILossFunc MeanSquaredError(string reduction = null)
+          => new MeanSquaredError(reduction: reduction);
     }
 }

src/TensorFlowNET.Keras/Losses/MeanSquaredError.cs

@@ -0,0 +1,26 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+
+namespace Tensorflow.Keras.Losses
+{
+    public class MeanSquaredError : LossFunctionWrapper, ILossFunc
+    {
+        public MeanSquaredError(
+            string reduction = ReductionV2.AUTO,
+            string name = "mean_squared_error") :
+            base(reduction: reduction, 
+                name: name)
+        {
+        }
+
+        public override Tensor Apply(Tensor y_true = null, Tensor y_pred =null, bool from_logits = false, int axis = -1)
+        {
+            Tensor y_pred_dispatch = ops.convert_to_tensor(y_pred);
+            Tensor y_true_cast = gen_math_ops.cast(y_true, y_pred_dispatch.dtype);
+            return  gen_math_ops.mean(gen_math_ops.squared_difference(y_pred_dispatch, y_true_cast), axis: -1);
+        }
+    }
+}