Revise customized json converters.

Author: AsakusaRinne

src/TensorFlowNET.Core/Keras/Common/CustomizedAxisJsonConverter.cs

@@ -37,7 +37,16 @@ public override void WriteJson(JsonWriter writer, object? value, JsonSerializer
 
         public override object? ReadJson(JsonReader reader, Type objectType, object? existingValue, JsonSerializer serializer)
         {
-            var axis = serializer.Deserialize(reader, typeof(int[]));
+            int[]? axis;
+            if(reader.ValueType == typeof(long))
+            {
+                axis = new int[1];
+                axis[0] = (int)serializer.Deserialize(reader, typeof(int));
+            }
+            else
+            {
+                axis = serializer.Deserialize(reader, typeof(int[])) as int[];
+            }
             if (axis is null)
             {
                 throw new ValueError("Cannot deserialize 'null' to `Axis`.");

src/TensorFlowNET.Core/Keras/Common/CustomizedShapeJsonConverter.cs

@@ -51,8 +51,26 @@ public override void WriteJson(JsonWriter writer, object? value, JsonSerializer
 
         public override object? ReadJson(JsonReader reader, Type objectType, object? existingValue, JsonSerializer serializer)
         {
-            var dims = serializer.Deserialize(reader, typeof(long?[])) as long?[];
-            if(dims is null)
+            long?[] dims;
+            try
+            {
+                dims = serializer.Deserialize(reader, typeof(long?[])) as long?[];
+            }
+            catch (JsonSerializationException ex)
+            {
+                if (reader.Value.Equals("class_name"))
+                {
+                    reader.Read();
+                    reader.Read();
+                    reader.Read();
+                    dims = serializer.Deserialize(reader, typeof(long?[])) as long?[];
+                }
+                else
+                {
+                    throw ex;
+                }
+            }
+            if (dims is null)
             {
                 throw new ValueError("Cannot deserialize 'null' to `Shape`.");
             }

src/TensorFlowNET.Core/Training/Saving/SavedModel/loader.cs

@@ -11,6 +11,7 @@
 using static Tensorflow.Binding;
 using System.Runtime.CompilerServices;
 using Tensorflow.Variables;
+using Tensorflow.Functions;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Keras/Engine/Functional.cs

@@ -75,7 +75,14 @@ protected void _init_graph_network(Tensors inputs, Tensors outputs)
             this.inputs = inputs;
             this.outputs = outputs;
             built = true;
-            _buildInputShape = inputs.shape;
+            if(inputs.Length > 0)
+            {
+                _buildInputShape = inputs.shape;
+            }
+            else
+            {
+                _buildInputShape = new Saving.TensorShapeConfig();
+            }
 
             if (outputs.Any(x => x.KerasHistory == null))
                 base_layer_utils.create_keras_history(outputs);

src/TensorFlowNET.Keras/Saving/KerasObjectLoader.cs

@@ -72,6 +72,10 @@ public void load_layers(bool compile = true)
             {
                 try
                 {
+                    if (node_metadata.Identifier.Equals("_tf_keras_metric"))
+                    {
+                        continue;
+                    }
                     loaded_nodes[node_metadata.NodeId] = _load_layer(node_metadata.NodeId, node_metadata.Identifier,
                         node_metadata.Metadata);
                 }
@@ -324,7 +328,9 @@ private void _unblock_model_reconstruction(int layer_id, Layer layer)
             Trackable obj;
             if(identifier == Keras.Saving.SavedModel.Constants.METRIC_IDENTIFIER)
             {
-                throw new NotImplementedException("Not implemented, please submit an issue to https://github.com/SciSharp/TensorFlow.NET/issues.");
+                // TODO(Rinne): implement it.
+                return (null, null);
+                //throw new NotImplementedException("Not implemented, please submit an issue to https://github.com/SciSharp/TensorFlow.NET/issues.");
             }
             else
             {
@@ -343,7 +349,7 @@ private void _unblock_model_reconstruction(int layer_id, Layer layer)
 
         private (Trackable, Action<object, object, object>) _revive_custom_object(string identifier, KerasMetaData metadata)
         {
-            // TODO: implement it.
+            // TODO(Rinne): implement it.
             throw new NotImplementedException();
         }
 
@@ -367,15 +373,14 @@ Model _revive_graph_network(string identifier, KerasMetaData metadata, int node_
             }
             else if(identifier == Keras.Saving.SavedModel.Constants.SEQUENTIAL_IDENTIFIER)
             {
-                model = model = new Sequential(new SequentialArgs
+                model = new Sequential(new SequentialArgs
                 {
                     Name = class_name
                 });
             }
             else
             {
-                // TODO: implement it.
-                throw new NotImplementedException("Not implemented");
+                model = new Functional(new Tensors(), new Tensors(), config["name"].ToObject<string>());
             }
 
             // Record this model and its layers. This will later be used to reconstruct

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

@@ -21,7 +21,7 @@ public class SequentialModelLoad
     [TestMethod]
     public void SimpleModelFromSequential()
     {
-        var model = KerasLoadModelUtils.load_model(@"D:/development/tf.net/tf_test/tf.net.simple.sequential");
+        var model = KerasLoadModelUtils.load_model(@"D:/development/tf.net/tf_test/model.pb");
         Debug.Assert(model is Model);
         var m = model as Model;