RegularizerAPI and UnitTest

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

@@ -12,5 +12,14 @@ namespace Tensorflow.Keras
     [JsonProperty("config")]
     IDictionary<string, object> Config { get; }
     Tensor Apply(RegularizerArgs args);
-    }
+  }
+
+  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

@@ -1,17 +1,51 @@
-namespace Tensorflow.Keras
+using Tensorflow.Operations.Regularizers;
+
+namespace Tensorflow.Keras
 {
-  public class Regularizers
+  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/ModelLoadTest.cs

@@ -1,6 +1,7 @@
 using Microsoft.VisualStudio.TestPlatform.Utilities;
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Newtonsoft.Json.Linq;
+using System.Collections.Generic;
 using System.Linq;
 using System.Xml.Linq;
 using Tensorflow.Keras.Engine;
@@ -129,6 +130,53 @@ public class ModelLoadTest
     }
 
 
+    [TestMethod]
+    public void BiasRegularizerSaveAndLoad()
+    {
+        var savemodel = keras.Sequential(new List<ILayer>()
+            {
+                tf.keras.layers.InputLayer((227, 227, 3)),
+                tf.keras.layers.Conv2D(96, (11, 11), (4, 4), activation:"relu", padding:"valid"),
+                tf.keras.layers.BatchNormalization(),
+                tf.keras.layers.MaxPooling2D((3, 3), strides:(2, 2)),
+
+                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)),
+
+                tf.keras.layers.Flatten(),
+
+                tf.keras.layers.Dense(1000, activation: "linear"),
+                tf.keras.layers.Softmax(1)
+            });
+
+        savemodel.compile(tf.keras.optimizers.Adam(), tf.keras.losses.SparseCategoricalCrossentropy(from_logits: true), new string[] { "accuracy" });
+
+        var num_epochs = 1;
+        var batch_size = 8;
+
+        var trainDataset = new RandomDataSet(new Shape(227, 227, 3), 16);
+
+        savemodel.fit(trainDataset.Data, trainDataset.Labels, batch_size, num_epochs);
+
+        savemodel.save(@"./bias_regularizer_save_and_load", save_format: "tf");
+
+        var loadModel = tf.keras.models.load_model(@"./bias_regularizer_save_and_load");
+        loadModel.summary();
+
+        loadModel.compile(tf.keras.optimizers.Adam(), tf.keras.losses.SparseCategoricalCrossentropy(from_logits: true), new string[] { "accuracy" });
+
+        var fitDataset = new RandomDataSet(new Shape(227, 227, 3), 16);
+
+        loadModel.fit(fitDataset.Data, fitDataset.Labels, batch_size, num_epochs);
+    }
+
 
     [TestMethod]
     public void CreateConcatenateModelSaveAndLoad()