add InputLayer

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

@@ -1,5 +1,6 @@
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using System.Text;
 using Tensorflow.Keras;
 using Tensorflow.Keras.Engine;
@@ -12,10 +13,30 @@ namespace Tensorflow
         public static class layers
         {
             public static Embedding Embedding(int input_dim, int output_dim,
-            string embeddings_initializer = "uniform",
-            bool mask_zero = false) => new Embedding(input_dim, output_dim,
-                embeddings_initializer,
-                mask_zero);
+                IInitializer embeddings_initializer = null,
+                bool mask_zero = false) => new Embedding(input_dim, output_dim,
+                    embeddings_initializer,
+                    mask_zero);
+
+            public static InputLayer Input(int[] batch_shape = null,
+                TF_DataType dtype = TF_DataType.DtInvalid,
+                string name = null,
+                bool sparse = false,
+                Tensor tensor = null)
+            {
+                var batch_size = batch_shape[0];
+                var shape = batch_shape.Skip(1).ToArray();
+
+                var input_layer = new InputLayer(
+                    input_shape: shape,
+                    batch_size: batch_size,
+                    name: name,
+                    dtype: dtype,
+                    sparse: sparse,
+                    input_tensor: tensor);
+
+                throw new NotImplementedException("");
+            }
         }
     }
 }

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

@@ -10,11 +10,16 @@ namespace Tensorflow.Keras.Engine
         protected bool _is_compiled;
         protected bool _expects_training_arg;
         protected bool _compute_output_and_mask_jointly;
+        /// <summary>
+        /// All layers in order of horizontal graph traversal.
+        /// Entries are unique. Includes input and output layers.
+        /// </summary>
+        protected List<Layer> _layers;
 
         public Network(string name = null) 
             : base(name: name)
         {
-
+            _init_subclassed_network(name);
         }
 
         protected virtual void _init_subclassed_network(string name = null)
@@ -30,6 +35,7 @@ namespace Tensorflow.Keras.Engine
             _expects_training_arg = false;
             _compute_output_and_mask_jointly = false;
             supports_masking = false;
+            _layers = new List<Layer>();
         }
     }
 }

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

@@ -23,6 +23,18 @@ namespace Tensorflow.Keras.Engine
         {
             built = false;
             var set_inputs = false;
+            if(_layers.Count == 0)
+            {
+                var (batch_shape, dtype) = (layer._batch_input_shape, layer._dtype);
+                if(batch_shape != null)
+                {
+                    // Instantiate an input layer.
+                    var x = keras.layers.Input(
+                      batch_shape: batch_shape,
+                      dtype: dtype,
+                      name: layer._name + "_input");
+                }
+            }
         }
 
         public void __exit__()

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

@@ -12,7 +12,9 @@ namespace Tensorflow.Keras.Layers
 
         public Embedding(int input_dim, int output_dim,
             IInitializer embeddings_initializer = null,
-            bool mask_zero = false)
+            bool mask_zero = false,
+            TF_DataType dtype = TF_DataType.TF_FLOAT,
+            int[] input_shape = null) : base(dtype: dtype, input_shape: input_shape)
         {
             this.input_dim = input_dim;
             this.output_dim = output_dim;

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

@@ -0,0 +1,45 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Layers
+{
+    /// <summary>
+    /// Layer to be used as an entry point into a Network (a graph of layers).
+    /// </summary>
+    public class InputLayer : Layer
+    {
+        public bool sparse;
+        public int? batch_size;
+
+        public InputLayer(int[] input_shape = null,
+            int? batch_size = null,
+            TF_DataType dtype = TF_DataType.DtInvalid,
+            string name = null,
+            bool sparse = false,
+            Tensor input_tensor = null)
+        {
+            built = true;
+            this.sparse = sparse;
+            this.batch_size = batch_size;
+            this.supports_masking = true;
+
+            if(input_tensor == null)
+            {
+                var batch_input_shape = new int[] { batch_size.HasValue ? batch_size.Value : -1, -1 };
+
+                if (sparse)
+                {
+                    throw new NotImplementedException("InputLayer sparse is true");
+                }
+                else
+                {
+                    input_tensor = backend.placeholder(
+                          shape: batch_input_shape,
+                          dtype: dtype,
+                          name: name);
+                }
+            }
+        }
+    }
+}

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

@@ -21,7 +21,7 @@ namespace Tensorflow.Keras.Layers
         /// </summary>
         protected bool built;
         protected bool trainable;
-        protected TF_DataType _dtype;
+        public TF_DataType _dtype;
         /// <summary>
         /// A stateful layer is a layer whose updates are run during inference too,
         /// for instance stateful RNNs.
@@ -33,12 +33,16 @@ namespace Tensorflow.Keras.Layers
         protected InputSpec input_spec;
         protected bool supports_masking;
         protected List<RefVariable> _trainable_weights;
-        protected string _name;
+        public string _name;
         protected string _base_name;
         protected bool _compute_previous_mask;
         protected List<Operation> _updates;
+        public int[] _batch_input_shape;
 
-        public Layer(bool trainable = true, string name = null, TF_DataType dtype = TF_DataType.DtInvalid)
+        public Layer(bool trainable = true, 
+            string name = null, 
+            TF_DataType dtype = TF_DataType.DtInvalid,
+            int[] input_shape = null)
         {
             this.trainable = trainable;
             this._dtype = dtype;
@@ -49,6 +53,12 @@ namespace Tensorflow.Keras.Layers
             _trainable_weights = new List<RefVariable>();
             _compute_previous_mask = false;
             _updates = new List<Operation>();
+
+            // Manage input shape information if passed.
+
+            _batch_input_shape = new int[] { -1, -1 };
+
+            _dtype = dtype;
         }
 
         public Tensor __call__(Tensor inputs,

src/TensorFlowNET.Core/Keras/backend.cs

@@ -11,6 +11,22 @@ namespace Tensorflow.Keras
 
         }
 
+        public static Tensor placeholder(int[] shape = null, 
+            int ndim = -1, 
+            TF_DataType dtype = TF_DataType.DtInvalid, 
+            bool sparse = false, 
+            string name = null)
+        {
+            if(sparse)
+            {
+                throw new NotImplementedException("placeholder sparse is true");
+            }
+            else
+            {
+                return gen_array_ops.placeholder(dtype: dtype, shape: new TensorShape(shape), name: name);
+            }
+        }
+
         public static Graph get_graph()
         {
             return ops.get_default_graph();