Add keras.Concatenate.

Author: Oceania2018

src/TensorFlowNET.Core/Keras/ArgsDefinition/MergeArgs.cs

@@ -7,5 +7,6 @@ namespace Tensorflow.Keras.ArgsDefinition
     public class MergeArgs : LayerArgs
     {
         public Tensors Inputs { get; set; }
+        public int Axis { get; set; }
     }
 }

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -407,7 +407,7 @@ bool hasattr(Graph property, string attr)
 
             var ret = tensor.TensorShape.unknown_shape(shape.dims[0]);
             var value = constant_value(tensor);
-            if (value != null)
+            if (!(value is null))
             {
                 int[] d_ = { };
                 foreach (int d in value)
@@ -418,7 +418,6 @@ bool hasattr(Graph property, string attr)
                         d_[d_.Length] = -1; // None
                 }
                 ret = ret.merge_with(new TensorShape(d_));
-
             }
             return ret;
         }

src/TensorFlowNET.Keras/BackendImpl.cs

@@ -226,5 +226,25 @@ public Tensor resize_images(Tensor x, int height_factor, int width_factor,
             x.set_shape(output_shape);
             return x;
         }
+
+        /// <summary>
+        /// Concatenates a list of tensors alongside the specified axis.
+        /// </summary>
+        /// <param name="tensors">list of tensors to concatenate.</param>
+        /// <param name="axis">concatenation axis.</param>
+        /// <returns></returns>
+        public Tensor concatenate(Tensors tensors, int axis = -1)
+        {
+            if(axis < 0)
+            {
+                var rank = tensors[0].NDims;
+                if (rank > -1)
+                    axis %= rank;
+                else
+                    axis = 0;
+            }
+
+            return array_ops.concat(tensors, axis);
+        }
     }
 }

src/TensorFlowNET.Keras/Engine/Layer.cs

@@ -177,13 +177,13 @@ protected void MaybeBuild(Tensors inputs)
             tf.init_scope();
 
             tf.Context.eager_mode();
-            build(inputs.shape);
+            build(inputs);
             tf.Context.restore_mode();
 
             built = true;
         }
 
-        protected virtual void build(TensorShape input_shape)
+        protected virtual void build(Tensors inputs)
         {
             built = true;
         }

src/TensorFlowNET.Keras/Layers/BatchNormalization.cs

@@ -52,8 +52,9 @@ public BatchNormalization(BatchNormalizationArgs args) : base(args)
             axis = args.Axis.dims;
         }
 
-        protected override void build(TensorShape input_shape)
+        protected override void build(Tensors inputs)
         {
+            TensorShape input_shape = inputs.shape;
             var ndims = input_shape.ndim;
             foreach (var (idx, x) in enumerate(axis))
                 if (x < 0)

src/TensorFlowNET.Keras/Layers/Convolutional.cs

@@ -56,8 +56,9 @@ public Convolutional(ConvolutionalArgs args) : base(args)
             _tf_data_format = conv_utils.convert_data_format(data_format, rank + 2);
         }
 
-        protected override void build(TensorShape input_shape)
+        protected override void build(Tensors inputs)
         {
+            TensorShape input_shape = inputs.shape;
             int channel_axis = data_format == "channels_first" ? 1 : -1;
             int input_channel = channel_axis < 0 ?
                 input_shape.dims[input_shape.ndim + channel_axis] :

src/TensorFlowNET.Keras/Layers/Dense.cs

@@ -41,8 +41,9 @@ public Dense(DenseArgs args) :
             this.inputSpec = new InputSpec(min_ndim: 2);
         }
 
-        protected override void build(TensorShape input_shape)
+        protected override void build(Tensors inputs)
         {
+            TensorShape input_shape = inputs.shape;
             var last_dim = input_shape.dims.Last();
             var axes = new Dictionary<int, int>();
             axes[-1] = last_dim;

src/TensorFlowNET.Keras/Layers/Embedding.cs

@@ -52,7 +52,7 @@ public Embedding(EmbeddingArgs args)
             SupportsMasking = mask_zero;
         }
 
-        protected override void build(TensorShape input_shape)
+        protected override void build(Tensors inputs)
         {
             tf.Context.eager_mode();
             embeddings = add_weight(shape: (input_dim, output_dim),

src/TensorFlowNET.Keras/Layers/LayersApi.Merging.cs

@@ -0,0 +1,22 @@
+using NumSharp;
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.ArgsDefinition;
+
+namespace Tensorflow.Keras.Layers
+{
+    public partial class LayersApi
+    {
+        /// <summary>
+        /// Layer that concatenates a list of inputs.
+        /// </summary>
+        /// <param name="axis">Axis along which to concatenate.</param>
+        /// <returns></returns>
+        public Concatenate Concatenate(int axis = -1)
+            => new Concatenate(new MergeArgs
+            {
+                Axis = axis
+            });
+    }
+}

src/TensorFlowNET.Keras/Layers/Merge.cs

@@ -14,7 +14,7 @@ public Merge(MergeArgs args) : base(args)
 
         }
 
-        protected override void build(TensorShape input_shape)
+        protected override void build(Tensors inputs)
         {
             // output_shape = input_shape.dims[1^];
         }
@@ -24,7 +24,7 @@ protected override Tensors Call(Tensors inputs, Tensor state = null, bool is_tra
             return _merge_function(inputs);
         }
 
-        Tensors _merge_function(Tensors inputs)
+        protected virtual Tensors _merge_function(Tensors inputs)
         {
             var output = inputs[0];
             foreach (var i in range(1, inputs.Length))