Add Tensors class to adapt Tensor and Tensor[].

.gitignore

@@ -337,3 +337,5 @@ test/TensorFlowNET.Examples/mnist
 # training model resources
 .resources
 /redist
+*.xml
+*.xsd

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

@@ -18,7 +18,7 @@ namespace Tensorflow
 {
     public partial class tensorflow
     {
-        public Tensor reshape<T>(T tensor,
+        public Tensor reshape(Tensor tensor,
             TensorShape shape,
             string name = null) => gen_array_ops.reshape(tensor, shape, name);
 

src/TensorFlowNET.Core/Framework/sparse_tensor.py.cs

@@ -1,4 +1,5 @@
-using System;
+using NumSharp;
+using System;
 using System.Linq;
 using static Tensorflow.Binding;
 
@@ -42,8 +43,8 @@ public SparseTensor(long[,] indices_, T[] values_, long[] dense_shape_)
             var values_shape = values.TensorShape.with_rank(1);
             var dense_shape_shape = dense_shape.TensorShape.with_rank(1);
 
-            indices_shape[0].merge_with(values_shape.dims[0]);
-            indices_shape[1].merge_with(dense_shape_shape.dims[0]);
+            indices_shape["0"].merge_with(values_shape[0]);
+            indices_shape["1"].merge_with(dense_shape_shape[0]);
 
             _shape = new TensorShape(_dense_shape.Select(x => Convert.ToInt32(x)).ToArray());
         }

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

@@ -6,5 +6,7 @@ namespace Tensorflow.Keras.ArgsDefinition
 {
     public class ModelArgs : LayerArgs
     {
+        public Tensor[] Inputs { get; set; }
+        public Tensor[] Outputs { get; set; }
     }
 }

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

@@ -12,6 +12,6 @@ public class NodeArgs
         public int[] NodeIndices { get; set; }
         public int[] TensorIndices { get; set; }
         public Tensor InputTensors { get; set; }
-        public Tensor Outputs { get; set; }
+        public Tensors Outputs { get; set; }
     }
 }

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

@@ -21,7 +21,7 @@ public Flatten(FlattenArgs args)
             _channels_first = args.DataFormat == "channels_first";
         }
 
-        protected override Tensor call(Tensor inputs, bool is_training = false)
+        protected override Tensors call(Tensors inputs, Tensor state = null, bool is_training = false)
         {
             if (_channels_first)
             {

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

@@ -0,0 +1,29 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Engine
+{
+    /// <summary>
+    /// Tracks the Layer call that created a Tensor, for Keras Graph Networks.
+    /// </summary>
+    public class KerasHistory
+    {
+        Layer layer;
+        int node_index;
+        int tensor_index;
+
+        public KerasHistory(Layer layer, int node_index, int tensor_index)
+        {
+            this.layer = layer;
+            this.node_index = node_index;
+            this.tensor_index = tensor_index;
+        }
+
+        public static implicit operator Layer(KerasHistory history)
+            => history.layer;
+
+        public static implicit operator (Layer, int, int)(KerasHistory history)
+            => (history.layer, history.node_index, history.tensor_index);
+    }
+}

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

@@ -119,11 +119,12 @@ public Layer(LayerArgs args)
         /// Wraps `call`, applying pre- and post-processing steps.
         /// </summary>
         /// <param name="input"></param>
+        /// <param name="state"></param>
         /// <param name="is_training"></param>
         /// <returns></returns>
-        public Tensor Apply(Tensor inputs, bool is_training = false)
+        public Tensors Apply(Tensors inputs, Tensor state = null, bool is_training = false)
         {
-            Tensor outputs = null;
+            Tensors outputs = null;
 
             callContext = callContext ?? new ThreadLocal<CallContext>()
             {
@@ -148,7 +149,7 @@ public Tensor Apply(Tensor inputs, bool is_training = false)
                 if (!built)
                     MaybeBuild(inputs);
 
-                outputs = call(inputs, is_training: is_training);
+                outputs = call(inputs, state: state, is_training: is_training);
 
                 outputs = _set_connectivity_metadata_(inputs, outputs);
                 _handle_activity_regularization(inputs, outputs);
@@ -161,36 +162,7 @@ public Tensor Apply(Tensor inputs, bool is_training = false)
             return outputs;
         }
 
-        public Tensor[] Apply(Tensor[] inputs, Tensor state, bool is_training = false)
-        {
-            Tensor[] outputs = null;
-
-            callContext = callContext ?? new ThreadLocal<CallContext>()
-            {
-                Value = new CallContext()
-            };
-
-            var eager = tf.executing_eagerly();
-            using var ctxManager = CallContext.enter();
-
-            string nameScope = "";
-            if (eager)
-                nameScope = name;
-            else
-                nameScope = _name_scope();
-
-            tf_with(ops.name_scope(nameScope), scope =>
-            {
-                if (!built)
-                    MaybeBuild(inputs[0]);
-
-                outputs = call(inputs, is_training: is_training, state: state);
-            });
-
-            return outputs;
-        }
-
-        private Tensor _set_connectivity_metadata_(Tensor inputs, Tensor outputs)
+        private Tensors _set_connectivity_metadata_(Tensors inputs, Tensors outputs)
         {
             /*var returnOutputs = new List<Tensor>();
             foreach(var x in outputs)
@@ -211,15 +183,15 @@ private Tensor _set_connectivity_metadata_(Tensor inputs, Tensor outputs)
             return outputs;
         }
 
-        private void _handle_activity_regularization(Tensor inputs, Tensor outputs)
+        private void _handle_activity_regularization(Tensors inputs, Tensors outputs)
         {
             //if(_activity_regularizer != null)
             {
 
             }
         }
 
-        private void _set_mask_metadata(Tensor inputs, Tensor outputs, Tensor previous_mask)
+        private void _set_mask_metadata(Tensors inputs, Tensors outputs, Tensors previous_mask)
         {
 
         }
@@ -229,12 +201,7 @@ private Tensor compute_mask(Tensor inputs, Tensor mask = null)
             return null;
         }
 
-        protected virtual Tensor call(Tensor inputs, bool is_training = false)
-        {
-            throw new NotImplementedException("");
-        }
-
-        protected virtual Tensor[] call(Tensor[] inputs, Tensor state, bool is_training = false)
+        protected virtual Tensors call(Tensors inputs, Tensor state = null, bool is_training = false)
         {
             throw new NotImplementedException("");
         }
@@ -244,15 +211,15 @@ protected virtual string _name_scope()
             return Name;
         }
 
-        protected void MaybeBuild(Tensor inputs)
+        protected void MaybeBuild(Tensors inputs)
         {
             // Check input assumptions set before layer building, e.g. input rank.
             if (built)
                 return;
             if (DType == TF_DataType.DtInvalid)
                 args.DType = inputs.dtype;
 
-            var input_shapes = inputs.TensorShape;
+            var input_shapes = inputs.shape;
             build(input_shapes);
             built = true;
         }

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

@@ -27,7 +27,11 @@ public class Model : Layer
         public Model(ModelArgs args) 
             : base(args)
         {
-
+            // Build _output_layers
+            /*foreach(var x in args.Outputs)
+            {
+                var layer = x.KerasHistory;
+            }*/
         }
 
         public void compile(string optimizerName, string lossName)

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

@@ -35,8 +35,8 @@ public class Node
 
         public int[] node_indices;
         public int[] tensor_indices;
-        public Tensor input_tensors;
-        public Tensor Outputs => args.Outputs;
+        public Tensors input_tensors;
+        public Tensors Outputs => args.Outputs;
         public TensorShape[] input_shapes;
         public TensorShape[] output_shapes;
         List<Layer> kerasInputs;
@@ -57,7 +57,8 @@ public Node(Layer layer, NodeArgs args)
 
             // Set metadata on outputs.
             var node_index = layer.InboundNodes.Count - 1;
-            args.Outputs.KerasHistory.Add(layer);
+            foreach (var (i, tensor) in enumerate(Outputs))
+                tensor.KerasHistory = new KerasHistory(layer, node_index, i);
         }
     }
 }

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

@@ -60,7 +60,7 @@ public Sequential(SequentialArgs args)
 
         public void add(Tensor tensor)
         {
-            var layer = tensor.KerasHistory[0];
+            Layer layer = tensor.KerasHistory;
             add(layer);
         }
 
@@ -129,7 +129,7 @@ void _init_graph_network(Tensor inputs, Tensor outputs)
 
         void _map_graph_network(Tensor inputs, Tensor outputs)
         {
-            layers.add(outputs.KerasHistory[0]);
+            layers.add(outputs.KerasHistory);
         }
     }
 }

src/TensorFlowNET.Core/Keras/KerasApi.cs

@@ -30,6 +30,19 @@ public Sequential Sequential(List<Layer> layers = null,
                 Name = name
             });
 
+        /// <summary>
+        /// `Model` groups layers into an object with training and inference features.
+        /// </summary>
+        /// <param name="input"></param>
+        /// <param name="output"></param>
+        /// <returns></returns>
+        public Model Model(Tensor input, Tensor output)
+            => new Model(new ModelArgs
+            {
+                Inputs = new[] { input },
+                Outputs = new[] { output }
+            });
+
         /// <summary>
         /// Instantiate a Keras tensor.
         /// </summary>

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

@@ -143,7 +143,7 @@ protected override void build(TensorShape input_shape)
             built = true;
         }
 
-        protected override Tensor call(Tensor inputs, bool is_training = false)
+        protected override Tensors call(Tensors inputs, Tensor state = null, bool is_training = false)
         {
             Tensor outputs = null;
 

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

@@ -95,7 +95,7 @@ protected override void build(TensorShape input_shape)
             built = true;
         }
 
-        protected override Tensor call(Tensor inputs, bool training = false)
+        protected override Tensors call(Tensors inputs, Tensor state = null, bool training = false)
         {
             var outputs = _convolution_op.__call__(inputs, kernel);
             if (use_bias)

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

@@ -65,7 +65,7 @@ protected override void build(TensorShape input_shape)
             built = true;
         }
 
-        protected override Tensor call(Tensor inputs, bool training = false)
+        protected override Tensors call(Tensors inputs, Tensor state = null, bool training = false)
         {
             Tensor outputs = null;
             var rank = inputs.rank;

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

@@ -18,7 +18,7 @@ public Dropout(DropoutArgs args)
             this.args = args;
         }
 
-        protected override Tensor call(Tensor inputs, bool is_training = false)
+        protected override Tensors call(Tensors inputs, Tensor state = null, bool is_training = false)
         {
             var output = tf_utils.smart_cond(is_training,
                 () => tf.nn.dropout(inputs,

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

@@ -62,7 +62,7 @@ protected override void build(TensorShape input_shape)
             built = true;
         }
 
-        protected override Tensor call(Tensor inputs, bool is_training = false)
+        protected override Tensors call(Tensors inputs, Tensor state = null, bool is_training = false)
         {
             var dtype = inputs.dtype;
             if (dtype != tf.int32 && dtype != tf.int64)

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

@@ -29,9 +29,9 @@ public LSTM(LSTMArgs args) :
                 .ToArray();
         }
 
-        protected override Tensor call(Tensor inputs, bool is_training = false)
+        protected override Tensors call(Tensors inputs, Tensor state = null, bool is_training = false)
         {
-            return base.call(inputs, is_training);
+            return base.call(inputs, state: state, is_training: is_training);
         }
     }
 }

src/TensorFlowNET.Core/Keras/Layers/Pooling2D.cs

@@ -36,7 +36,7 @@ public Pooling2D(Pooling2DArgs args)
             input_spec = new InputSpec(ndim: 4);
         }
 
-        protected override Tensor call(Tensor inputs, bool is_training = false)
+        protected override Tensors call(Tensors inputs, Tensor state = null, bool is_training = false)
         {
             int[] pool_shape;
             int[] strides;

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

@@ -20,7 +20,7 @@ public Rescaling(RescalingArgs args) : base(args)
             this.args = args;
         }
 
-        protected override Tensor call(Tensor inputs, bool is_training = false)
+        protected override Tensors call(Tensors inputs, Tensor state = null, bool is_training = false)
         {
             scale = math_ops.cast(args.Scale, args.DType);
             offset = math_ops.cast(args.Offset, args.DType);