Add InboundLayers to Node

src/TensorFlowNET.Core/Contexts/Context.ExecuteOp.cs

@@ -78,7 +78,7 @@ namespace Tensorflow.Contexts
                         if (args.GetGradientAttrs == null)
                         {
                             attrs = new Dictionary<string, object>();
-                            attrs["T"] = op.get_attr<TF_DataType>("T");
+                            attrs["T"] = op.dtype;
                         }
                         else
                         {

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

@@ -11,6 +11,7 @@ namespace Tensorflow.Keras.Engine
         ILayer Layer { get; }
         List<Tensor> KerasInputs { get; set; }
         INode[] ParentNodes { get; }
+        ILayer[] InboundLayers { get; }
         IEnumerable<(ILayer, int, int, Tensor)> iterate_inbound();
         bool is_input { get; }
         List<NodeConfig> serialize(Func<string, int, string> make_node_key, Dictionary<string, int> node_conversion_map);

src/TensorFlowNET.Core/NumPy/ShapeHelper.cs

@@ -88,6 +88,13 @@ namespace Tensorflow.NumPy
 
         public static bool Equals(Shape shape, object target)
         {
+            if (shape is null && target is null)
+                return true;
+            else if (shape is null && target is not null)
+                return false;
+            else if (shape is not null && target is null)
+                return false;
+
             switch (target)
             {
                 case Shape shape1:

src/TensorFlowNET.Core/Numpy/Shape.cs

@@ -253,5 +253,11 @@ namespace Tensorflow
         public override bool Equals(object obj) => ShapeHelper.Equals(this, obj);
 
         public override string ToString() => ShapeHelper.ToString(this);
+
+        public static bool operator ==(Shape a, Shape b) 
+            => ShapeHelper.Equals(a, b);
+
+        public static bool operator !=(Shape a, Shape b) 
+            => !ShapeHelper.Equals(a, b);
     }
 }

src/TensorFlowNET.Core/Operations/Operation.cs

@@ -55,7 +55,7 @@ namespace Tensorflow
 
         public int _id_value { get; set; }
         public Operation op => this;
-        public TF_DataType dtype => TF_DataType.DtInvalid;
+        public TF_DataType dtype => output.dtype;
         public virtual string name => _handle == IntPtr.Zero ? "" : c_api.StringPiece(c_api.TF_OperationName(_handle));
         public string OpType => _handle == IntPtr.Zero ? "" : c_api.StringPiece(c_api.TF_OperationOpType(_handle));
 

src/TensorFlowNET.Keras/Engine/Functional.cs

@@ -69,7 +69,8 @@ namespace Tensorflow.Keras.Engine
 
             NetworkNodes = nodes;
             NodesByDepth = nodes_by_depth;
-            _layers = layers;
+            if (_layers.Count == 0)
+                _layers = layers;
 
             // Build self.input_names and self.output_names.
             _set_output_names();

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

@@ -1,9 +1,13 @@
 using System.Collections.Generic;
+using System.Linq;
 
 namespace Tensorflow.Keras.Engine
 {
     public partial class Node
     {
+        public ILayer[] InboundLayers
+            => iterate_inbound().Select(x => x.Item1).ToArray();
+
         public IEnumerable<(ILayer, int, int, Tensor)> iterate_inbound()
         {
             foreach (var kt in KerasInputs)

src/TensorFlowNET.Keras/Engine/Sequential.cs

@@ -150,6 +150,9 @@ namespace Tensorflow.Keras.Engine
 
         void _build_graph_network_for_inferred_shape(Shape input_shape, TF_DataType input_dtype)
         {
+            if (_inferred_input_shape == input_shape)
+                return;
+
             ops.init_scope();
             var inputs = keras.Input(batch_input_shape: input_shape,
                 dtype: input_dtype,
@@ -157,16 +160,17 @@ namespace Tensorflow.Keras.Engine
             Tensors layer_input = inputs;
             Tensors layer_output = null;
             Tensors outputs = null;
-            
+            List<INode> created_nodes = new List<INode>();
             foreach (var layer in _layers)
             {
                 clear_previously_created_nodes(layer, _created_nodes);
                 layer_output = layer.Apply(layer_input);
                 // Keep track of nodes just created above
-                track_nodes_created_by_last_call(layer, _created_nodes);
+                track_nodes_created_by_last_call(layer, created_nodes);
                 layer_input = layer_output;
                 outputs = layer_output;
             }
+            _created_nodes = created_nodes;
             _init_graph_network(inputs, outputs);
             _graph_initialized = true;
             _inferred_input_shape = input_shape;
@@ -174,16 +178,28 @@ namespace Tensorflow.Keras.Engine
 
         void clear_previously_created_nodes(ILayer layer, List<INode> created_nodes)
         {
+            foreach(var node in layer.InboundNodes)
+            {
+                foreach(var prev_layer in node.InboundLayers)
+                {
+                    var outNodes = prev_layer.OutboundNodes.Where(x => !created_nodes.Contains(x)).ToArray();
+                    prev_layer.OutboundNodes.Clear();
+                    prev_layer.OutboundNodes.AddRange(outNodes);
+                }
+            }
 
+            var inNodes = layer.InboundNodes.Where(x => !created_nodes.Contains(x)).ToArray();
+            layer.InboundNodes.Clear();
+            layer.InboundNodes.AddRange(inNodes);
         }
 
         void track_nodes_created_by_last_call(ILayer layer, List<INode> created_nodes)
         {
             var node = layer.InboundNodes.Last();
             created_nodes.Add(node);
-            foreach(var prev_layer in node.iterate_inbound())
+            foreach(var prev_layer in node.InboundLayers)
             {
-                created_nodes.add(prev_layer.Item1.OutboundNodes.Last());
+                created_nodes.add(prev_layer.OutboundNodes.Last());
             }
         }
     }