Initial regularizers.

src/TensorFlowNET.Core/Keras/ArgsDefinition/Core/DenseArgs.cs

@@ -33,12 +33,12 @@ namespace Tensorflow.Keras.ArgsDefinition
         /// <summary>
         /// Regularizer function applied to the `kernel` weights matrix.
         /// </summary>
-        public IInitializer KernelRegularizer { get; set; }
+        public IRegularizer KernelRegularizer { get; set; }
 
         /// <summary>
         /// Regularizer function applied to the bias vector.
         /// </summary>
-        public IInitializer BiasRegularizer { get; set; }
+        public IRegularizer BiasRegularizer { get; set; }
 
         /// <summary>
         /// Constraint function applied to the `kernel` weights matrix.

src/TensorFlowNET.Core/Keras/Regularizers/RegularizerArgs.cs

@@ -2,5 +2,12 @@
 {
     public class RegularizerArgs
     {
+        public Tensor X { get; set; }
+
+
+        public RegularizerArgs(Tensor x)
+        {
+            X = x;          
+        }
     }
 }

src/TensorFlowNET.Keras/Engine/Layer.cs

@@ -203,7 +203,7 @@ namespace Tensorflow.Keras.Engine
 
         protected virtual void add_loss(Func<Tensor> losses)
         {
-
+            
         }
 
         /// <summary>
@@ -214,10 +214,13 @@ namespace Tensorflow.Keras.Engine
         /// <param name="regularizer"></param>
         void _handle_weight_regularization(string name, IVariableV1 variable, IRegularizer regularizer)
         {
-            add_loss(() => regularizer.Apply(new RegularizerArgs
-            {
 
-            }));
+            add_loss(() => tf_with(ops.name_scope(name + "/Regularizer"), scope => 
+                regularizer.Apply(new RegularizerArgs(variable.AsTensor())
+                {
+
+                }) 
+            ));
         }
 
         /*protected virtual void add_update(Tensor[] updates, bool inputs = false)

src/TensorFlowNET.Keras/Regularizers/L1.cs

@@ -0,0 +1,19 @@
+using System;
+
+namespace Tensorflow.Keras
+{
+    public class L1 : IRegularizer
+    {
+        float l1;
+
+        public L1(float l1 = 0.01f)
+        {
+            this.l1 = l1;
+        }
+
+        public Tensor Apply(RegularizerArgs args)
+        {
+            return l1 * math_ops.reduce_sum(math_ops.abs(args.X));
+        }
+    }
+}

src/TensorFlowNET.Keras/Regularizers/L1L2.cs

@@ -0,0 +1,24 @@
+using System;
+using static Tensorflow.Binding;
+namespace Tensorflow.Keras
+{
+    public class L1L2 : IRegularizer
+    {
+        float l1;
+        float l2;
+
+        public L1L2(float l1 = 0.0f, float l2 = 0.0f)
+        {
+            this.l1 = l1;
+            this.l2 = l2;
+
+        }
+        public Tensor Apply(RegularizerArgs args)
+        {
+            Tensor regularization = tf.constant(0.0, args.X.dtype);
+            regularization += l1 * math_ops.reduce_sum(math_ops.abs(args.X));
+            regularization += l2 * math_ops.reduce_sum(math_ops.square(args.X));
+            return regularization;
+        }
+    }
+}

src/TensorFlowNET.Keras/Regularizers/L2.cs

@@ -1,6 +1,4 @@
-using System;
-
-namespace Tensorflow.Keras
+namespace Tensorflow.Keras
 {
     public class L2 : IRegularizer
     {
@@ -13,7 +11,7 @@ namespace Tensorflow.Keras
 
         public Tensor Apply(RegularizerArgs args)
         {
-            throw new NotImplementedException();
+            return l2 * math_ops.reduce_sum(math_ops.square(args.X));
         }
     }
 }