add Embedding layer

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

@@ -0,0 +1,21 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras;
+using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Layers;
+
+namespace Tensorflow
+{
+    public static partial class keras
+    {
+        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);
+        }
+    }
+}

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

@@ -10,7 +10,8 @@ namespace Tensorflow
         public static IInitializer zeros_initializer => new Zeros();
         public static IInitializer ones_initializer => new Ones();
         public static IInitializer glorot_uniform_initializer => new GlorotUniform();
-        
+        public static IInitializer uniform_initializer => new RandomUniform();
+
         public static variable_scope variable_scope(string name,
                string default_name = null,
                object values = null,

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

@@ -4,7 +4,12 @@ using System.Text;
 
 namespace Tensorflow.Keras.Engine
 {
-    internal class Model : Network
+    public class Model : Network
     {
+        public Model(string name = null) 
+            : base(name: name)
+        {
+
+        }
     }
 }

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

@@ -7,5 +7,29 @@ namespace Tensorflow.Keras.Engine
 {
     public class Network : Layer
     {
+        protected bool _is_compiled;
+        protected bool _expects_training_arg;
+        protected bool _compute_output_and_mask_jointly;
+
+        public Network(string name = null) 
+            : base(name: name)
+        {
+
+        }
+
+        protected virtual void _init_subclassed_network(string name = null)
+        {
+            _base_init(name: name);
+        }
+
+        protected virtual void _base_init(string name = null)
+        {
+            _init_set_name(name);
+            trainable = true;
+            _is_compiled = false;
+            _expects_training_arg = false;
+            _compute_output_and_mask_jointly = false;
+            supports_masking = false;
+        }
     }
 }

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

@@ -1,24 +1,38 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
+using Tensorflow.Keras.Layers;
 
 namespace Tensorflow.Keras.Engine
 {
-    public class Sequential : Network, IPython
+    public class Sequential : Model, IPython
     {
-        public void Dispose()
+        public Sequential(string name = null) 
+            : base(name: name)
         {
-            throw new NotImplementedException();
+            supports_masking = true;
+            _compute_output_and_mask_jointly = true;
         }
 
         public void __enter__()
         {
-            throw new NotImplementedException();
+            
+        }
+
+        public void add(Layer layer)
+        {
+            built = false;
+            var set_inputs = false;
         }
 
         public void __exit__()
         {
-            throw new NotImplementedException();
+            
+        }
+
+        public void Dispose()
+        {
+
         }
     }
 }

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

@@ -0,0 +1,25 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Layers
+{
+    public class Embedding : Layer
+    {
+        private int input_dim;
+        private int output_dim;
+        private bool mask_zero;
+
+        public Embedding(int input_dim, int output_dim,
+            IInitializer embeddings_initializer = null,
+            bool mask_zero = false)
+        {
+            this.input_dim = input_dim;
+            this.output_dim = output_dim;
+            if (embeddings_initializer == null)
+                embeddings_initializer = tf.uniform_initializer;
+            this.mask_zero = mask_zero;
+            supports_masking = mask_zero;
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/ControlFlows/CondContext.cs

@@ -17,11 +17,6 @@ namespace Tensorflow.Operations
         private Tensor _pred;
         public Tensor pred => _pred;
 
-        /// <summary>
-        /// The predicate tensor in this branch
-        /// </summary>
-        private Tensor _pivot;
-
         /// <summary>
         /// 0 or 1 representing this branch
         /// </summary>

src/TensorFlowNET.Core/Operations/ControlFlows/ControlFlowContext.cs

@@ -6,6 +6,11 @@ namespace Tensorflow.Operations
 {
     public abstract class ControlFlowContext : IPython, IControlFlowContext
     {
+        /// <summary>
+        /// The predicate tensor in this branch
+        /// </summary>
+        protected Tensor _pivot;
+
         protected Stack<IControlFlowContext> _context_stack;
         public ControlFlowContext()
         {
@@ -28,6 +33,29 @@ namespace Tensorflow.Operations
             graph._set_control_flow_context(this);
         }
 
+        public void AddOp(Operation op)
+        {
+            _AddOpInternal(op);
+        }
+
+        protected virtual void _AddOpInternal(Operation op)
+        {
+            if(op.inputs.Length == 0)
+            {
+                _RemoveExternalControlEdges(op);
+                op._add_control_input(_pivot.op);
+            }
+            else
+            {
+
+            }
+        }
+
+        protected virtual void _RemoveExternalControlEdges(Operation op)
+        {
+            var internal_control_inputs = op.control_inputs;
+        }
+
         public void Exit()
         {
             var graph = ops.get_default_graph();

src/TensorFlowNET.Core/Operations/ControlFlows/IControlFlowContext.cs

@@ -6,5 +6,6 @@ namespace Tensorflow
 {
     public interface IControlFlowContext
     {
+        void AddOp(Operation op);
     }
 }

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

@@ -0,0 +1,38 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Operations.Initializers
+{
+    public class RandomUniform : IInitializer
+    {
+        private int? seed;
+        private float minval;
+        private float maxval;
+        private TF_DataType dtype;
+
+        public RandomUniform()
+        {
+
+        }
+
+        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid)
+        {
+            return random_ops.random_uniform(shape, 
+                minval: minval, 
+                maxval: maxval, 
+                dtype: dtype, 
+                seed: seed);
+        }
+
+        public object get_config()
+        {
+            return new {
+                minval,
+                maxval,
+                seed,
+                dtype
+            };
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/Operation.Control.cs

@@ -18,14 +18,20 @@ namespace Tensorflow
             {
 
             }
+
+            if (_control_flow_context != null)
+                _control_flow_context.AddOp(this);
+        }
+
+        public void _add_control_input(Operation op)
+        {
+            c_api.TF_AddControlInput(_handle, op);
         }
 
         public void _add_control_inputs(Operation[] ops)
         {
-            foreach(var op in ops)
-            {
-                c_api.TF_AddControlInput(graph, op);
-            }
+            foreach (var op in ops)
+                _add_control_input(op);
         }
 
         public void _set_control_flow_context(IControlFlowContext ctx)

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -54,4 +54,8 @@ Docs: https://tensorflownet.readthedocs.io</Description>
     <ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
   </ItemGroup>
 
+  <ItemGroup>
+    <Folder Include="Keras\Initializers\" />
+  </ItemGroup>
+
 </Project>

test/TensorFlowNET.Examples/TextClassificationWithMovieReviews.cs

@@ -37,6 +37,7 @@ namespace TensorFlowNET.Examples
             int vocab_size = 10000;
 
             var model = keras.Sequential();
+            model.add(keras.layers.Embedding(vocab_size, 16));
         }
 
         private ((NDArray, NDArray), (NDArray, NDArray)) PrepareData()