Fix the error that loaded concrete function does not work.

src/TensorFlowNET.Core/Functions/ConcreteFunction.cs

@@ -6,6 +6,7 @@ using Tensorflow.Eager;
 using Tensorflow.Framework.Models;
 using Tensorflow.Graphs;
 using Tensorflow.Train;
+using Tensorflow.Util;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Functions
@@ -21,6 +22,7 @@ namespace Tensorflow.Functions
         protected Dictionary<string, string> _attrs;
         protected FunctionSpec _function_spec;
         protected FunctionSpec _pre_initialized_function_spec = null;
+        protected EagerDefinedFunction _inference_function;
         internal ForwardBackwardCall forward_backward;
         public Tensor[] Inputs => func_graph.Inputs;
         public Tensor[] CapturedInputs => func_graph.external_captures;
@@ -39,6 +41,7 @@ namespace Tensorflow.Functions
             _captured_inputs = func_graph.external_captures;
             _attrs= new Dictionary<string, string>();
             _delayed_rewrite_functions = new DelayedRewriteGradientFunctions(func_graph, _attrs);
+            _inference_function = _delayed_rewrite_functions.Forward();
         }
 
         public ConcreteFunction(FuncGraph graph, Dictionary<string, string> attrs = null)
@@ -49,6 +52,7 @@ namespace Tensorflow.Functions
             //ToGraph(graph.Inputs, graph.Outputs.Where(x => x != null).ToArray());
             _attrs = attrs;
             _delayed_rewrite_functions = new DelayedRewriteGradientFunctions(func_graph, _attrs);
+            _inference_function = _delayed_rewrite_functions.Forward();
         }
 
         public ConcreteFunction(Func<Tensor, Tensor> func, TF_DataType dtype)
@@ -69,6 +73,7 @@ namespace Tensorflow.Functions
             _captured_inputs = func_graph.external_captures;
             _attrs = new Dictionary<string, string>();
             _delayed_rewrite_functions = new DelayedRewriteGradientFunctions(func_graph, _attrs);
+            _inference_function = _delayed_rewrite_functions.Forward();
         }
 
         public ConcreteFunction(Func<Tensor, IDatasetV2> func, TF_DataType dtype)
@@ -92,6 +97,7 @@ namespace Tensorflow.Functions
             _captured_inputs = func_graph.external_captures;
             _attrs = new Dictionary<string, string>();
             _delayed_rewrite_functions = new DelayedRewriteGradientFunctions(func_graph, _attrs);
+            _inference_function = _delayed_rewrite_functions.Forward();
         }
 
         /*public ConcreteFunction(Func<Tensors, Tensors> func,
@@ -154,9 +160,10 @@ namespace Tensorflow.Functions
             {
                 tensor_inputs.Add(arg);
                 // If we're graph building, shape inference is on.
-                if (!executing_eagerly)
-                {
-                }
+            }
+            if (!executing_eagerly)
+            {
+
             }
             tensor_inputs.AddRange(captured_inputs);
 
@@ -166,12 +173,13 @@ namespace Tensorflow.Functions
             // No tape is watching; skip to running the function.
             if (possible_gradient_type == 0 && executing_eagerly)
             {
-                var attrs = new object[]
-                {
-                    "executor_type", "",
-                    "config_proto", tf.Context.FunctionCallOptions.config_proto_serialized()
-                };
-                return tf.Runner.Execute(tf.Context, func_graph.FuncName, func_graph.Outputs.Length, args, attrs);
+                return _build_call_outputs(_inference_function.Call(args));
+                //var attrs = new object[]
+                //{
+                //    "executor_type", "",
+                //    "config_proto", tf.Context.FunctionCallOptions.config_proto_serialized()
+                //};
+                //return tf.Runner.Execute(tf.Context, func_graph.FuncName, func_graph.Outputs.Length, args, attrs);
             }
 
             if (forward_backward == null)
@@ -184,10 +192,11 @@ namespace Tensorflow.Functions
             }
             else
             {
+                // TODO(Rinne): add `default_graph._override_gradient_function`.
                 flat_outputs = forward_function.Call(args_with_tangents);
             }
             forward_backward.Record(flat_outputs);
-            return flat_outputs;
+            return _build_call_outputs(flat_outputs);
         }
 
         public void AddTograph(Graph? g = null)
@@ -262,6 +271,13 @@ namespace Tensorflow.Functions
             };
         }
 
+        private Tensors _build_call_outputs(Tensors result)
+        {
+            // TODO(Rinne): dwal with `func_graph.structured_outputs`
+
+            return result;
+        }
+
         public override string ToString()
             => Name;
     }

src/TensorFlowNET.Keras/Engine/Sequential.cs

@@ -124,11 +124,12 @@ namespace Tensorflow.Keras.Engine
             if (set_inputs || _is_graph_network)
             {
                 _init_graph_network(inputs, outputs);
-                _is_graph_network = true;
+                _graph_initialized = true;
             }
             else
             {
                 _self_tracked_trackables.add(layer);
+                // TODO(Rinne): self._handle_deferred_layer_dependencies([layer])
             }
         }
 

src/TensorFlowNET.Keras/Saving/KerasObjectLoader.cs

@@ -189,11 +189,8 @@ namespace Tensorflow.Keras.Saving
                 }
 
                 // `model.__init__(layers, config["name"])`InitLayers(layers);
-                s = new Sequential(new SequentialArgs(){
-                    Layers = layers.Select(x => x as ILayer).ToList(), 
-                    Name = config["name"].ToObject<string>()
-                });
-                //s.Name = config["name"].ToObject<string>();
+                s.InitLayers(layers.Select(x => x as ILayer));
+                s.Name = config["name"].ToObject<string>();
                 if(s.inputs is null || s.inputs.Length == 0)
                 {
                     var first_layer = _get_child_layer_node_ids(model_id)[0];

test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelLoad.cs

@@ -62,10 +62,11 @@ public class SequentialModelLoad
     [TestMethod]
     public void Temp()
     {
-        var model = tf.keras.models.load_model(@"C:\Work\tf.net\tf_test\python_func");
+        var model = tf.keras.models.load_model(@"D:\development\tf.net\tf_test\python_func");
         model.summary();
 
         var x = tf.ones((2, 10));
         var y = model.Apply(x);
+        Console.WriteLine(y);
     }
 }