normalize_tuple, InputSpec

TensorFlow.NET.sln

@@ -9,7 +9,7 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Examples", "t
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Core", "src\TensorFlowNET.Core\TensorFlowNET.Core.csproj", "{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}"
 EndProject
-Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Visualization", "TensorFlowNET.Visualization\TensorFlowNET.Visualization.csproj", "{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}"
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Visualization", "src\TensorFlowNET.Visualization\TensorFlowNET.Visualization.csproj", "{0254BFF9-453C-4FE0-9609-3644559A79CE}"
 EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
@@ -29,10 +29,10 @@ Global
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Release|Any CPU.Build.0 = Release|Any CPU
-		{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}.Release|Any CPU.Build.0 = Release|Any CPU
+		{0254BFF9-453C-4FE0-9609-3644559A79CE}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{0254BFF9-453C-4FE0-9609-3644559A79CE}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{0254BFF9-453C-4FE0-9609-3644559A79CE}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{0254BFF9-453C-4FE0-9609-3644559A79CE}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE

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

@@ -19,14 +19,30 @@ namespace Tensorflow
                 int[] dilation_rate = null,
                 bool use_bias = true,
                 IActivation activation = null,
-                IInitializer kernel_initializer = null)
+                IInitializer kernel_initializer = null,
+                IInitializer bias_initializer = null,
+                bool trainable = true,
+                string name = null)
             {
                 if (strides == null)
                     strides = new int[] { 1, 1 };
                 if (dilation_rate == null)
                     dilation_rate = new int[] { 1, 1 };
+                if (bias_initializer == null)
+                    bias_initializer = tf.zeros_initializer;
 
-                var layer = new Conv2D(filters, kernel_size);
+                var layer = new Conv2D(filters,
+                    kernel_size: kernel_size,
+                    strides: strides,
+                    padding: padding,
+                    data_format: data_format,
+                    dilation_rate: dilation_rate,
+                    activation: activation,
+                    use_bias: use_bias,
+                    kernel_initializer: kernel_initializer,
+                    bias_initializer: bias_initializer,
+                    trainable: trainable,
+                    name: name);
 
                 return layer.apply(inputs);
             }

src/TensorFlowNET.Core/Keras/Engine/InputSpec.cs

@@ -0,0 +1,17 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Engine
+{
+    /// <summary>
+    /// Specifies the ndim, dtype and shape of every input to a layer.
+    /// </summary>
+    public class InputSpec
+    {
+        public InputSpec(TF_DataType dtype = TF_DataType.DtInvalid)
+        {
+
+        }
+    }
+}

src/TensorFlowNET.Core/Keras/Engine/Layer.cs

@@ -12,6 +12,77 @@ namespace Tensorflow.Keras.Engine
     /// </summary>
     public class Layer : CheckpointableBase
     {
+        protected bool trainable;
+        protected string _name;
+        protected TF_DataType _dtype;
+        protected Graph _graph;
+        protected string _base_name;
+        protected VariableScope _scope;
+        /// <summary>
+        /// A stateful layer is a layer whose updates are run during inference too,
+        /// for instance stateful RNNs.
+        /// </summary>
+        protected bool stateful;
+        /// <summary>
+        /// Indicates whether `build` needs to be called upon layer call, to create
+        /// the layer's weights.
+        /// </summary>
+        protected bool built;
+        /// <summary>
+        /// Provides information about which inputs are compatible with the layer.
+        /// </summary>
+        protected InputSpec input_spec;
+        protected bool supports_masking;
 
+        public Layer(bool trainable = true, 
+            string name = null, 
+            TF_DataType dtype = TF_DataType.DtInvalid)
+        {
+            this.trainable = trainable;
+            this.stateful = false;
+            this.built = false;
+            this.supports_masking = false;
+            _init_set_name(name);
+        }
+
+        public Tensor apply(Tensor inputs)
+        {
+            return __call__(inputs);
+        }
+
+        public Tensor __call__(Tensor inputs,
+            VariableScope scope = null)
+        {
+            _set_scope(scope);
+            _graph = ops._get_graph_from_inputs(new List<Tensor> { inputs }, graph: _graph);
+            var scope_context_manager = tf.variable_scope(_scope);
+
+            throw new NotImplementedException("");
+        }
+
+        private void _init_set_name(string name)
+        {
+            if (string.IsNullOrEmpty(name))
+                (_name, _base_name) = _make_unique_name();
+        }
+
+        private (string, string) _make_unique_name()
+        {
+            string base_name = "conv2d";
+            string name = base_layer_utils.unique_layer_name(base_name);
+            return (name, base_name);
+        }
+
+        private void _set_scope(VariableScope scope = null)
+        {
+            if (_scope == null)
+            {
+                Python.with(tf.variable_scope(scope, default_name: _base_name), captured_scope =>
+                {
+                    _scope = captured_scope;
+                });
+            }
+                
+        }
     }
 }

src/TensorFlowNET.Core/Keras/Engine/base_layer_utils.cs

@@ -0,0 +1,26 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Engine
+{
+    public class base_layer_utils
+    {
+        /// <summary>
+        /// Makes a layer name (or arbitrary string) unique within a TensorFlow graph.
+        /// </summary>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static string unique_layer_name(string name)
+        {
+            int number = get_default_graph_uid_map();
+            return $"{name}_{number}";
+        }
+
+        public static int get_default_graph_uid_map()
+        {
+            var graph = ops.get_default_graph();
+            return graph._next_id();
+        }
+    }
+}

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

@@ -1,10 +1,50 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
+using Tensorflow.Keras.Engine;
+using Tensorflow.Operations.Activation;
 
 namespace Tensorflow.Keras.Layers
 {
-    public class Conv
+    public class Conv : Layer
     {
+        protected int rank;
+        protected int filters;
+        protected int[] kernel_size;
+        protected int[] strides;
+        protected string padding;
+        protected string data_format;
+        protected int[] dilation_rate;
+        protected IActivation activation;
+        protected bool use_bias;
+        protected IInitializer kernel_initializer;
+        protected IInitializer bias_initializer;
+
+        public Conv(int rank, 
+            int filters,
+            int[] kernel_size,
+            int[] strides = null,
+            string padding = "valid",
+            string data_format = null,
+            int[] dilation_rate = null,
+            IActivation activation = null,
+            bool use_bias = true,
+            IInitializer kernel_initializer = null,
+            IInitializer bias_initializer = null,
+            bool trainable = true, 
+            string name = null) : base(trainable: trainable, name: name)
+        {
+            this.rank = rank;
+            this.filters = filters;
+            this.kernel_size = kernel_size;
+            this.strides = strides;
+            this.padding = padding;
+            this.data_format = data_format;
+            this.dilation_rate = dilation_rate;
+            this.activation = activation;
+            this.use_bias = use_bias;
+            this.kernel_initializer = kernel_initializer;
+            this.bias_initializer = bias_initializer;
+        }
     }
 }

src/TensorFlowNET.Core/Keras/Layers/Conv2D.cs

@@ -7,10 +7,6 @@ namespace Tensorflow.Keras.Layers
 {
     public class Conv2D : Conv
     {
-        private int filters;
-        private int[] kernel_size;
-        private int[] strides;
-
         public Conv2D(int filters,
             int[] kernel_size,
             int[] strides = null,
@@ -22,14 +18,21 @@ namespace Tensorflow.Keras.Layers
             IInitializer kernel_initializer = null,
             IInitializer bias_initializer = null,
             bool trainable = true,
-            string name = null)
+            string name = null) : base(2, 
+                filters,
+                kernel_size,
+                strides: strides,
+                padding: padding,
+                data_format: data_format,
+                dilation_rate: dilation_rate,
+                activation: activation,
+                use_bias: use_bias,
+                kernel_initializer: kernel_initializer,
+                bias_initializer: bias_initializer,
+                trainable: trainable, 
+                name: name)
         {
 
         }
-
-        public Tensor apply(Tensor inputs)
-        {
-            throw new NotImplementedException("apply");
-        }
     }
 }