add relu6

src/TensorFlowNET.Core/APIs/tf.nn.cs

@@ -101,6 +101,8 @@ public Tensor embedding_lookup(Tensor @params,
                     name: name);
 
             public IActivation relu() => new relu();
+
+
             public IActivation swish() => new swish();
             public IActivation tanh() => new tanh();
 
@@ -111,6 +113,9 @@ public Tensor tanh(Tensor x, string name = null)
             public Tensor relu(Tensor features, string name = null)
                 => gen_nn_ops.relu(features, name);
 
+            public Tensor relu6(Tensor features, string name = null)
+                    => gen_nn_ops.relu6(features, name);
+
             public Tensor[] fused_batch_norm(Tensor x,
                 Tensor scale,
                 Tensor offset,

src/TensorFlowNET.Core/Keras/Activations/Activations.cs

@@ -32,6 +32,7 @@ public interface IActivationsApi
         Activation Linear { get; }
 
         Activation Relu { get; }
+        Activation Relu6 { get; }
 
         Activation Sigmoid { get; }
 

src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs

@@ -180,6 +180,9 @@ public ILayer LayerNormalization(Axis? axis,
         public ILayer Normalization(Shape? input_shape = null, int? axis = -1, float? mean = null, float? variance = null, bool invert = false);
         public ILayer LeakyReLU(float alpha = 0.3f);
 
+        public ILayer ReLU6();
+
+
         public IRnnCell LSTMCell(int uints,
             string activation = "tanh",
             string recurrent_activation = "sigmoid",

src/TensorFlowNET.Keras/Activations.cs

@@ -20,6 +20,11 @@ public class Activations: IActivationsApi
             Name = "relu",
             ActivationFunction = (features, name) => tf.Context.ExecuteOp("Relu", name, new ExecuteOpArgs(features))
         };
+        private static Activation _relu6 = new Activation()
+        {
+            Name = "relu6",
+            ActivationFunction = (features, name) => tf.Context.ExecuteOp("Relu6", name, new ExecuteOpArgs(features))
+        };
         private static Activation _sigmoid = new Activation()
         {
             Name = "sigmoid",
@@ -55,6 +60,7 @@ static Activations()
             _nameActivationMap = new Dictionary<string, Activation>();
 
             RegisterActivation(_relu);
+            RegisterActivation(_relu6);
             RegisterActivation(_linear);
             RegisterActivation(_sigmoid);
             RegisterActivation(_softmax);
@@ -65,6 +71,7 @@ static Activations()
         public Activation Linear => _linear;
 
         public Activation Relu => _relu;
+        public Activation Relu6 => _relu6;
 
         public Activation Sigmoid => _sigmoid;
 

src/TensorFlowNET.Keras/Layers/Activation/ReLu6.cs

@@ -0,0 +1,25 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Engine;
+using Tensorflow.Common.Types;
+using static Tensorflow.Binding;
+
+namespace Tensorflow.Keras.Layers
+{
+    /// <summary>
+    /// Leaky version of a Rectified Linear Unit.
+    /// </summary>
+    public class ReLu6 : Layer
+    {
+        public ReLu6() : base(new LayerArgs { })
+        {
+        }
+
+        protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null)
+        {
+            return tf.nn.relu6(inputs);
+        }
+    }
+}

src/TensorFlowNET.Keras/Layers/LayersApi.cs

@@ -735,6 +735,15 @@ public ILayer LeakyReLU(float alpha = 0.3f)
             });
 
 
+        /// <summary>
+        /// Leaky version of a Rectified Linear Unit.
+        /// </summary>
+        /// <param name="alpha">Negative slope coefficient.</param>
+        /// <returns></returns>
+        public ILayer ReLU6()
+            => new ReLu6();
+
+
         public IRnnCell SimpleRNNCell(
             int units,
             string activation = "tanh",