UnitTests

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

@@ -16,6 +16,7 @@ namespace Tensorflow.Keras
 
   public interface IRegularizerApi
   {
+    IRegularizer GetRegularizerFromName(string name);
     IRegularizer L1 { get; }
     IRegularizer L2 { get; }
     IRegularizer L1L2 { get; }

src/TensorFlowNET.Core/Operations/Regularizers/L1.cs

@@ -9,7 +9,7 @@ namespace Tensorflow.Operations.Regularizers
     float _l1;
     private readonly Dictionary<string, object> _config;
 
-    public string ClassName => "L2";
+    public string ClassName => "L1";
     public virtual IDictionary<string, object> Config => _config;
 
     public L1(float l1 = 0.01f)

src/TensorFlowNET.Keras/Regularizers.cs

@@ -1,23 +1,51 @@
-namespace Tensorflow.Keras
+using Tensorflow.Operations.Regularizers;
+
+namespace Tensorflow.Keras
 {
   public class Regularizers: IRegularizerApi
   {
+    private static Dictionary<string, IRegularizer> _nameActivationMap;
+
     public IRegularizer l1(float l1 = 0.01f)
-        => new Tensorflow.Operations.Regularizers.L1(l1);
+        => new L1(l1);
     public IRegularizer l2(float l2 = 0.01f)
-        => new Tensorflow.Operations.Regularizers.L2(l2);
+        => new L2(l2);
 
     //From TF source
     //# The default value for l1 and l2 are different from the value in l1_l2
     //# for backward compatibility reason. Eg, L1L2(l2=0.1) will only have l2
     //# and no l1 penalty.
     public IRegularizer l1l2(float l1 = 0.00f, float l2 = 0.00f)
-        => new Tensorflow.Operations.Regularizers.L1L2(l1, l2);
+        => new L1L2(l1, l2);
+
+    static Regularizers()
+    {
+      _nameActivationMap = new Dictionary<string, IRegularizer>();
+      _nameActivationMap["L1"] = new L1();
+      _nameActivationMap["L1"] = new L2();
+      _nameActivationMap["L1"] = new L1L2();
+    }
 
     public IRegularizer L1 => l1();
 
     public IRegularizer L2 => l2();
 
     public IRegularizer L1L2 => l1l2();
+
+    public IRegularizer GetRegularizerFromName(string name)
+    {
+      if (name == null)
+      {
+        throw new Exception($"Regularizer name cannot be null");
+      }
+      if (!_nameActivationMap.TryGetValue(name, out var res))
+      {
+        throw new Exception($"Regularizer {name} not found");
+      }
+      else
+      {
+        return res;
+      }
+    }
   }
 }

test/TensorFlowNET.Keras.UnitTest/Model/ModelSaveTest.cs

@@ -2,6 +2,7 @@
 using System.Collections.Generic;
 using System.Diagnostics;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Layers;
 using Tensorflow.Keras.Models;
 using Tensorflow.Keras.Optimizers;
 using Tensorflow.Keras.Saving;
@@ -108,7 +109,13 @@ namespace Tensorflow.Keras.UnitTest.Model
                 tf.keras.layers.BatchNormalization(),
                 tf.keras.layers.MaxPooling2D((3, 3), strides:(2, 2)),
 
-                tf.keras.layers.Conv2D(256, (5, 5), (1, 1), "same", activation: "relu"),
+                tf.keras.layers.Conv2D(256, (5, 5), (1, 1), "same", activation: keras.activations.Relu, bias_regularizer:keras.regularizers.L1L2),
+                tf.keras.layers.BatchNormalization(),
+
+                tf.keras.layers.Conv2D(256, (5, 5), (1, 1), "same", activation: keras.activations.Relu, bias_regularizer:keras.regularizers.L2),
+                tf.keras.layers.BatchNormalization(),
+
+                tf.keras.layers.Conv2D(256, (5, 5), (1, 1), "same", activation: keras.activations.Relu, bias_regularizer:keras.regularizers.L1),
                 tf.keras.layers.BatchNormalization(),
                 tf.keras.layers.MaxPooling2D((3, 3), (2, 2)),