Initializer that generates tensors with constant values with tf.constant_initializer().

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

@@ -20,6 +20,8 @@ namespace Tensorflow
 {
     public partial class tensorflow
     {
+        public IInitializer constant_initializer<T>(T value, TF_DataType dtype = TF_DataType.TF_FLOAT, bool verify_shape = false) 
+            => new Constant<T>(value, dtype: dtype, verify_shape: verify_shape);
         public IInitializer zeros_initializer => new Zeros();
         public IInitializer ones_initializer => new Ones();
         public IInitializer glorot_uniform_initializer => new GlorotUniform();

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

@@ -0,0 +1,55 @@
+/*****************************************************************************
+   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.
+******************************************************************************/
+
+namespace Tensorflow.Operations.Initializers
+{
+    public class Constant<T> : IInitializer
+    {
+        TF_DataType dtype;
+        T value;
+        bool _verify_shape;
+
+        public Constant(T value, TF_DataType dtype = TF_DataType.TF_FLOAT, bool verify_shape = false)
+        {
+            this.value = value;
+            this.dtype = dtype;
+            _verify_shape = verify_shape;
+        }
+
+        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid, bool? verify_shape = null)
+        {
+            if (dtype == TF_DataType.DtInvalid)
+                dtype = this.dtype;
+
+            if (!verify_shape.HasValue)
+                verify_shape = _verify_shape;
+
+            return constant_op._constant_impl(value, dtype, shape,
+                name: "Const",
+                verify_shape: verify_shape.Value,
+                allow_broadcast: false);
+        }
+
+        public object get_config()
+        {
+            return new
+            {
+                value,
+                dtype = dtype.name()
+            };
+        }
+    }
+}

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

@@ -18,7 +18,7 @@ namespace Tensorflow
 {
     public interface IInitializer
     {
-        Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid);
+        Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid, bool? verify_shape = null);
         object get_config();
     }
 }

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

@@ -25,7 +25,7 @@ namespace Tensorflow.Operations.Initializers
             this.dtype = dtype;
         }
 
-        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid)
+        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid, bool? verify_shape = null)
         {
             if (dtype == TF_DataType.DtInvalid)
                 dtype = this.dtype;

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

@@ -38,7 +38,7 @@ namespace Tensorflow.Operations.Initializers
             this.dtype = dtype;
         }
 
-        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid)
+        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid, bool? verify_shape = null)
         {
             if (dtype == TF_DataType.DtInvalid)
                 dtype = this.dtype;

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

@@ -28,7 +28,7 @@ namespace Tensorflow.Operations.Initializers
 
         }
 
-        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid)
+        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid, bool? verify_shape = null)
         {
             return random_ops.random_uniform(shape, 
                 minval: minval, 

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

@@ -34,7 +34,7 @@ namespace Tensorflow.Operations.Initializers
             this.dtype = dtype;
         }
 
-        public Tensor call(TensorShape shape, TF_DataType dtype)
+        public Tensor call(TensorShape shape, TF_DataType dtype, bool? verify_shape = null)
         {
             return random_ops.truncated_normal(shape, mean, stddev, dtype : dtype, seed: seed);
         }

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

@@ -45,7 +45,7 @@ namespace Tensorflow.Operations.Initializers
             _dtype = dtype;
         }
 
-        public Tensor call(TensorShape shape, TF_DataType dtype)
+        public Tensor call(TensorShape shape, TF_DataType dtype, bool? verify_shape = null)
         {
             var (fan_in, fan_out) = _compute_fans(shape);
             if (_mode == "fan_in")

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

@@ -25,7 +25,7 @@ namespace Tensorflow.Operations.Initializers
             this.dtype = dtype;
         }
 
-        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid)
+        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid, bool? verify_shape = null)
         {
             if (dtype == TF_DataType.DtInvalid)
                 dtype = this.dtype;

src/TensorFlowNET.Core/Tensors/TensorShape.cs

@@ -155,7 +155,7 @@ namespace Tensorflow
         public static implicit operator TensorShape(int[] dims) => new TensorShape(dims);
 
         public static explicit operator int(TensorShape shape) => shape.size;
-        public static explicit operator TensorShape(int dim) => new TensorShape(dim);
+        public static implicit operator TensorShape(int dim) => new TensorShape(dim);
 
         public static explicit operator (int, int)(TensorShape shape) => shape.dims.Length == 2 ? (shape.dims[0], shape.dims[1]) : (0, 0);
         public static implicit operator TensorShape((int, int) dims) => new TensorShape(dims.Item1, dims.Item2);