add tf.random_normal_initializer

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

@@ -52,5 +52,13 @@ namespace Tensorflow
                 stddev: stddev,
                 seed: seed,
                 dtype: dtype);
+
+        public IInitializer random_normal_initializer(float mean = 0.0f,
+            float stddev = 1.0f,
+            int? seed = null,
+            TF_DataType dtype = TF_DataType.DtInvalid) => new RandomNormal(mean: mean,
+                stddev: stddev,
+                seed: seed,
+                dtype: dtype);
     }
 }

src/TensorFlowNET.Core/Operations/Initializers/RandomNormal.cs

@@ -0,0 +1,51 @@
+/*****************************************************************************
+   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+******************************************************************************/
+
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Operations.Initializers
+{
+    public class RandomNormal : IInitializer
+    {
+        private float mean;
+        private float stddev;
+        private int? seed;
+        private TF_DataType dtype;
+
+        public RandomNormal(float mean = 0.0f,
+            float stddev = 1.0f,
+            int? seed = null,
+            TF_DataType dtype = TF_DataType.TF_FLOAT)
+        {
+            this.mean = mean;
+            this.stddev = stddev;
+            this.seed = seed;
+            this.dtype = dtype;
+        }
+
+        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid)
+        {
+            throw new NotImplementedException();
+        }
+
+        public object get_config()
+        {
+            throw new NotImplementedException();
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/nn_ops.cs

@@ -116,6 +116,19 @@ namespace Tensorflow
             return _softmax(logits, gen_nn_ops.log_softmax, axis, name);
         }
 
+        public static Tensor leaky_relu(Tensor features, float alpha = 0.2f, string name = null)
+        {
+            return tf_with(ops.name_scope(name, "LeakyRelu", new { features, alpha }), scope =>
+            {
+                name = scope;
+                features = ops.convert_to_tensor(features, name: "features");
+                if (features.dtype.is_integer())
+                    features = math_ops.cast(features, dtypes.float32);
+                return gen_nn_ops.leaky_relu(features, alpha: alpha, name: name);
+                //return math_ops.maximum(alpha * features, features, name: name);
+            });
+        }
+
         /// <summary>
         /// Performs the max pooling on the input.
         /// </summary>