Add more implementations to the keras part of pb model save.

src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/SoftmaxArgs.cs

@@ -1,9 +1,18 @@
-using System;
+using Newtonsoft.Json;
+using System;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.ArgsDefinition {
-      public class SoftmaxArgs : LayerArgs {
-            public Axis axis { get; set; } = -1;
-      }
+    public class SoftmaxArgs : LayerArgs
+    {
+        [JsonProperty("axis")]
+        public Axis axis { get; set; } = -1;
+        [JsonProperty("name")]
+        public override string Name { get => base.Name; set => base.Name = value; }
+        [JsonProperty("trainable")]
+        public override bool Trainable { get => base.Trainable; set => base.Trainable = value; }
+        [JsonProperty("dtype")]
+        public override TF_DataType DType { get => base.DType; set => base.DType = value; }
+    }
 }

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

@@ -0,0 +1,19 @@
+using Newtonsoft.Json;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class AutoSerializeLayerArgs: LayerArgs
+    {
+        [JsonProperty("name")]
+        public override string Name { get => base.Name; set => base.Name = value; }
+        [JsonProperty("dtype")]
+        public override TF_DataType DType { get => base.DType; set => base.DType = value; }
+        [JsonProperty("batch_input_shape", NullValueHandling = NullValueHandling.Ignore)]
+        public override Shape BatchInputShape { get => base.BatchInputShape; set => base.BatchInputShape = value; }
+        [JsonProperty("trainable")]
+        public override bool Trainable { get => base.Trainable; set => base.Trainable = value; }
+    }
+}

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

@@ -1,13 +1,18 @@
-using System;
+using Newtonsoft.Json;
+using System;
+using System.Xml.Linq;
+using Tensorflow.Operations.Initializers;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.ArgsDefinition
 {
+    // TODO: `activity_regularizer`
     public class DenseArgs : LayerArgs
     {
         /// <summary>
         /// Positive integer, dimensionality of the output space.
         /// </summary>
+        [JsonProperty("units")]
         public int Units { get; set; }
 
         /// <summary>
@@ -15,39 +20,74 @@ namespace Tensorflow.Keras.ArgsDefinition
         /// </summary>
         public Activation Activation { get; set; }
 
+        private string _activationName;
+        [JsonProperty("activation")]
+        public string ActivationName
+        {
+            get
+            {
+                if (string.IsNullOrEmpty(_activationName))
+                {
+                    return Activation.Method.Name;
+                }
+                else
+                {
+                    return _activationName;
+                }
+            }
+            set
+            {
+                _activationName = value;
+            }
+        }
+
         /// <summary>
         /// Whether the layer uses a bias vector.
         /// </summary>
+        [JsonProperty("use_bias")]
         public bool UseBias { get; set; } = true;
 
         /// <summary>
         /// Initializer for the `kernel` weights matrix.
         /// </summary>
+        [JsonProperty("kernel_initializer")]
         public IInitializer KernelInitializer { get; set; } = tf.glorot_uniform_initializer;
 
         /// <summary>
         /// Initializer for the bias vector.
         /// </summary>
+        [JsonProperty("bias_initializer")]
         public IInitializer BiasInitializer { get; set; } = tf.zeros_initializer;
 
         /// <summary>
         /// Regularizer function applied to the `kernel` weights matrix.
         /// </summary>
+        [JsonProperty("kernel_regularizer")]
         public IRegularizer KernelRegularizer { get; set; }
 
         /// <summary>
         /// Regularizer function applied to the bias vector.
         /// </summary>
+        [JsonProperty("bias_regularizer")]
         public IRegularizer BiasRegularizer { get; set; }
 
         /// <summary>
         /// Constraint function applied to the `kernel` weights matrix.
         /// </summary>
+        [JsonProperty("kernel_constraint")]
         public Action KernelConstraint { get; set; }
 
         /// <summary>
         /// Constraint function applied to the bias vector.
         /// </summary>
+        [JsonProperty("bias_constraint")]
         public Action BiasConstraint { get; set; }
+
+        [JsonProperty("name")]
+        public override string Name { get => base.Name; set => base.Name = value; }
+        [JsonProperty("dtype")]
+        public override TF_DataType DType { get => base.DType; set => base.DType = value; }
+        [JsonProperty("trainable")]
+        public override bool Trainable { get => base.Trainable; set => base.Trainable = value; }
     }
 }

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

@@ -1,9 +1,22 @@
-namespace Tensorflow.Keras.ArgsDefinition
+using Newtonsoft.Json;
+using Newtonsoft.Json.Serialization;
+using Tensorflow.Keras.Common;
+
+namespace Tensorflow.Keras.ArgsDefinition
 {
     public class InputLayerArgs : LayerArgs
     {
+        [JsonIgnore]
         public Tensor InputTensor { get; set; }
-        public bool Sparse { get; set; }
+        [JsonProperty("sparse")]
+        public virtual bool Sparse { get; set; }
+        [JsonProperty("ragged")]
         public bool Ragged { get; set; }
+        [JsonProperty("name")]
+        public override string Name { get => base.Name; set => base.Name = value; }
+        [JsonProperty("dtype")]
+        public override TF_DataType DType { get => base.DType; set => base.DType = value; }
+        [JsonProperty("batch_input_shape", NullValueHandling = NullValueHandling.Ignore)]
+        public override Shape BatchInputShape { get => base.BatchInputShape; set => base.BatchInputShape = value; }
     }
 }

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

@@ -1,8 +1,9 @@
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.ArgsDefinition
 {
-    public class DataAdapterArgs
+    public class DataAdapterArgs: IKerasConfig
     {
         public Tensor X { get; set; }
         public Tensor Y { get; set; }

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

@@ -1,8 +1,9 @@
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.ArgsDefinition
 {
-    public class DataHandlerArgs
+    public class DataHandlerArgs: IKerasConfig
     {
         public Tensor X { get; set; }
         public Tensor Y { get; set; }

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

@@ -1,51 +1,54 @@
-namespace Tensorflow.Keras.ArgsDefinition
+using Newtonsoft.Json;
+using Tensorflow.Keras.Saving;
+
+namespace Tensorflow.Keras.ArgsDefinition
 {
-    public class LayerArgs
+    [JsonObject(MemberSerialization.OptIn)]
+    public class LayerArgs: IKerasConfig
     {
         /// <summary>
         /// Indicates whether the layer's weights are updated during training
         /// and whether the layer's updates are run during training.
         /// </summary>
-        public bool Trainable { get; set; } = true;
-
-        public string Name { get; set; }
+        public virtual bool Trainable { get; set; } = true;
+        public virtual string Name { get; set; }
 
         /// <summary>
         /// Only applicable to input layers.
         /// </summary>
-        public TF_DataType DType { get; set; } = TF_DataType.TF_FLOAT;
+        public virtual TF_DataType DType { get; set; } = TF_DataType.TF_FLOAT;
 
         /// <summary>
         /// Whether the `call` method can be used to build a TF graph without issues.
         /// This attribute has no effect if the model is created using the Functional
         /// API. Instead, `model.dynamic` is determined based on the internal layers.
         /// </summary>
-        public bool Dynamic { get; set; } = false;
+        public virtual bool Dynamic { get; set; } = false;
 
         /// <summary>
         /// Only applicable to input layers.
         /// </summary>
-        public Shape InputShape { get; set; }
+        public virtual Shape InputShape { get; set; }
 
         /// <summary>
         /// Only applicable to input layers.
         /// </summary>
-        public Shape BatchInputShape { get; set; }
+        public virtual Shape BatchInputShape { get; set; }
 
-        public int BatchSize { get; set; } = -1;
+        public virtual int BatchSize { get; set; } = -1;
 
         /// <summary>
         /// Initial weight values.
         /// </summary>
-        public float[] Weights { get; set; }
+        public virtual float[] Weights { get; set; }
 
         /// <summary>
         /// Regularizer function applied to the output of the layer(its "activation").
         /// </summary>
-        public IRegularizer ActivityRegularizer { get; set; }
+        public virtual IRegularizer ActivityRegularizer { get; set; }
 
-        public bool Autocast { get; set; }
+        public virtual bool Autocast { get; set; }
 
-        public bool IsFromConfig { get; set; }
+        public virtual bool IsFromConfig { get; set; }
     }
 }

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

@@ -1,6 +1,8 @@
-namespace Tensorflow.Keras.ArgsDefinition
+using Tensorflow.Keras.Saving;
+
+namespace Tensorflow.Keras.ArgsDefinition
 {
-    public class NodeArgs
+    public class NodeArgs: IKerasConfig
     {
         public ILayer[] InboundLayers { get; set; }
         public int[] NodeIndices { get; set; }

src/TensorFlowNET.Core/Keras/ArgsDefinition/OptimizerV2Args.cs

@@ -1,6 +1,8 @@
-namespace Tensorflow.Keras.ArgsDefinition
+using Tensorflow.Keras.Saving;
+
+namespace Tensorflow.Keras.ArgsDefinition
 {
-    public class OptimizerV2Args
+    public class OptimizerV2Args: IKerasConfig
     {
         public string Name { get; set; }
         public float LearningRate { get; set; } = 0.001f;

src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/FlattenArgs.cs

@@ -1,7 +1,10 @@
-namespace Tensorflow.Keras.ArgsDefinition
+using Newtonsoft.Json;
+
+namespace Tensorflow.Keras.ArgsDefinition
 {
-    public class FlattenArgs : LayerArgs
+    public class FlattenArgs : AutoSerializeLayerArgs
     {
+        [JsonProperty("data_format")]
         public string DataFormat { get; set; }
     }
 }

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

@@ -0,0 +1,50 @@
+using Newtonsoft.Json;
+using Newtonsoft.Json.Converters;
+using Newtonsoft.Json.Linq;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Common
+{
+    public class CustomizedActivationJsonConverter : JsonConverter
+    {
+        public override bool CanConvert(Type objectType)
+        {
+            return objectType == typeof(Activation);
+        }
+
+        public override bool CanRead => true;
+
+        public override bool CanWrite => true;
+
+        public override void WriteJson(JsonWriter writer, object? value, JsonSerializer serializer)
+        {
+            if (value is null)
+            {
+                var token = JToken.FromObject("");
+                token.WriteTo(writer);
+            }
+            else if (value is not Activation)
+            {
+                throw new TypeError($"Unable to use `CustomizedActivationJsonConverter` to serialize the type {value.GetType()}.");
+            }
+            else
+            {
+                var token = JToken.FromObject((value as Activation)!.GetType().Name);
+                token.WriteTo(writer);
+            }
+        }
+
+        public override object? ReadJson(JsonReader reader, Type objectType, object? existingValue, JsonSerializer serializer)
+        {
+            throw new NotImplementedException();
+            //var dims = serializer.Deserialize(reader, typeof(string));
+            //if (dims is null)
+            //{
+            //    throw new ValueError("Cannot deserialize 'null' to `Activation`.");
+            //}
+            //return new Shape((long[])(dims!));
+        }
+    }
+}

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

@@ -0,0 +1,48 @@
+using Newtonsoft.Json.Linq;
+using Newtonsoft.Json;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Common
+{
+    public class CustomizedAxisJsonConverter : JsonConverter
+    {
+        public override bool CanConvert(Type objectType)
+        {
+            return objectType == typeof(Axis);
+        }
+
+        public override bool CanRead => true;
+
+        public override bool CanWrite => true;
+
+        public override void WriteJson(JsonWriter writer, object? value, JsonSerializer serializer)
+        {
+            if (value is null)
+            {
+                var token = JToken.FromObject(new int[] { });
+                token.WriteTo(writer);
+            }
+            else if (value is not Axis)
+            {
+                throw new TypeError($"Unable to use `CustomizedAxisJsonConverter` to serialize the type {value.GetType()}.");
+            }
+            else
+            {
+                var token = JToken.FromObject((value as Axis)!.axis);
+                token.WriteTo(writer);
+            }
+        }
+
+        public override object? ReadJson(JsonReader reader, Type objectType, object? existingValue, JsonSerializer serializer)
+        {
+            var axis = serializer.Deserialize(reader, typeof(long[]));
+            if (axis is null)
+            {
+                throw new ValueError("Cannot deserialize 'null' to `Axis`.");
+            }
+            return new Axis((int[])(axis!));
+        }
+    }
+}

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

@@ -0,0 +1,73 @@
+using Newtonsoft.Json;
+using Newtonsoft.Json.Converters;
+using Newtonsoft.Json.Linq;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using Tensorflow.Keras.Saving;
+
+namespace Tensorflow.Keras.Common
+{
+    public class CustomizedNodeConfigJsonConverter : JsonConverter
+    {
+        public override bool CanConvert(Type objectType)
+        {
+            return objectType == typeof(NodeConfig);
+        }
+
+        public override bool CanRead => true;
+
+        public override bool CanWrite => true;
+
+        public override void WriteJson(JsonWriter writer, object? value, JsonSerializer serializer)
+        {
+            if (value is null)
+            {
+                var token = JToken.FromObject(null);
+                token.WriteTo(writer);
+            }
+            else if (value is not NodeConfig)
+            {
+                throw new TypeError($"Unable to use `CustomizedNodeConfigJsonConverter` to serialize the type {value.GetType()}.");
+            }
+            else
+            {
+                var config = value as NodeConfig;
+                var token = JToken.FromObject(new object[] { config!.Name, config.NodeIndex, config.TensorIndex });
+                token.WriteTo(writer);
+            }
+        }
+
+        public override object? ReadJson(JsonReader reader, Type objectType, object? existingValue, JsonSerializer serializer)
+        {
+            var values = serializer.Deserialize(reader, typeof(object[])) as object[];
+            if (values is null)
+            {
+                throw new ValueError("Cannot deserialize 'null' to `Shape`.");
+            }
+            if(values.Length != 3)
+            {
+                throw new ValueError($"The value ({string.Join(", ", values)}) cannot be deserialized to type `NodeConfig`.");
+            }
+            if (values[0] is not string)
+            {
+                throw new TypeError($"The first value of `NodeConfig` is expected to be `string`, but got `{values[0].GetType().Name}`");
+            }
+            if (values[1] is not int)
+            {
+                throw new TypeError($"The first value of `NodeConfig` is expected to be `int`, but got `{values[1].GetType().Name}`");
+            }
+            if (values[2] is not int)
+            {
+                throw new TypeError($"The first value of `NodeConfig` is expected to be `int`, but got `{values[2].GetType().Name}`");
+            }
+            return new NodeConfig()
+            {
+                Name = values[0] as string,
+                NodeIndex = (int)values[1],
+                TensorIndex = (int)values[2]
+            };
+        }
+    }
+}

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

@@ -0,0 +1,67 @@
+using Newtonsoft.Json;
+using Newtonsoft.Json.Converters;
+using Newtonsoft.Json.Linq;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Common
+{
+    public class CustomizedShapeJsonConverter: JsonConverter
+    {
+        public override bool CanConvert(Type objectType)
+        {
+            return objectType == typeof(Shape);
+        }
+
+        public override bool CanRead => true;
+
+        public override bool CanWrite => true;
+
+        public override void WriteJson(JsonWriter writer, object? value, JsonSerializer serializer)
+        {
+            if(value is null)
+            {
+                var token = JToken.FromObject(null);
+                token.WriteTo(writer);
+            }
+            else if(value is not Shape)
+            {
+                throw new TypeError($"Unable to use `CustomizedShapeJsonConverter` to serialize the type {value.GetType()}.");
+            }
+            else
+            {
+                var shape = (value as Shape)!;
+                long?[] dims = new long?[shape.ndim];
+                for(int i = 0; i < dims.Length; i++)
+                {
+                    if (shape.dims[i] == -1)
+                    {
+                        dims[i] = null;
+                    }
+                    else
+                    {
+                        dims[i] = shape.dims[i];
+                    }
+                }
+                var token = JToken.FromObject(dims);
+                token.WriteTo(writer);
+            }
+        }
+
+        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)
+            {
+                throw new ValueError("Cannot deserialize 'null' to `Shape`.");
+            }
+            long[] convertedDims = new long[dims.Length];
+            for(int i = 0; i < dims.Length; i++)
+            {
+                convertedDims[i] = dims[i] ?? (-1);
+            }
+            return new Shape(convertedDims);
+        }
+    }
+}

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

@@ -16,23 +16,27 @@
 
 using System.Collections.Generic;
 using System.Linq;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Engine
 {
     /// <summary>
     /// Specifies the ndim, dtype and shape of every input to a layer.
     /// </summary>
-    public class InputSpec
+    public class InputSpec: IKerasConfigable
     {
         public int? ndim;
+        public int? max_ndim;
         public int? min_ndim;
         Dictionary<int, int> axes;
         Shape shape;
+        TF_DataType dtype;
         public int[] AllAxisDim;
 
         public InputSpec(TF_DataType dtype = TF_DataType.DtInvalid,
             int? ndim = null,
             int? min_ndim = null,
+            int? max_ndim = null,
             Dictionary<int, int> axes = null,
             Shape shape = null)
         {
@@ -41,7 +45,9 @@ namespace Tensorflow.Keras.Engine
                 axes = new Dictionary<int, int>();
             this.axes = axes;
             this.min_ndim = min_ndim;
+            this.max_ndim = max_ndim;
             this.shape = shape;
+            this.dtype = dtype;
             if (ndim == null && shape != null)
                 this.ndim = shape.ndim;
 
@@ -49,7 +55,30 @@ namespace Tensorflow.Keras.Engine
                 AllAxisDim = axes.Select(x => x.Value).ToArray();
         }
 
+       public IKerasConfig get_config()
+        {
+            return new Config()
+            {
+                DType = dtype == TF_DataType.DtInvalid ? null : dtype,
+                Shape = shape,
+                Ndim = ndim,
+                MinNdim = min_ndim,
+                MaxNdim = max_ndim,
+                Axes = axes.ToDictionary(x => x.Key.ToString(), x => x.Value)
+            };
+        }
+
         public override string ToString()
             => $"ndim={ndim}, min_ndim={min_ndim}, axes={axes.Count}";
+
+        public class Config: IKerasConfig
+        {
+            public TF_DataType? DType { get; set; }
+            public Shape Shape { get; set; }
+            public int? Ndim { get; set; }
+            public int? MinNdim { get;set; }
+            public int? MaxNdim { get;set; }
+            public IDictionary<string, int> Axes { get; set; }
+        }
     }
 }

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

@@ -1,11 +1,12 @@
 using System.Collections.Generic;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using Tensorflow.Training;
 
 namespace Tensorflow.Keras
 {
-    public interface ILayer: ITrackable
+    public interface ILayer: IWithTrackable, IKerasConfigable
     {
         string Name { get; }
         bool Trainable { get; }
@@ -19,8 +20,8 @@ namespace Tensorflow.Keras
         List<IVariableV1> NonTrainableWeights { get; }
         Shape OutputShape { get; }
         Shape BatchInputShape { get; }
+        TensorShapeConfig BuildInputShape { get; }
         TF_DataType DType { get; }
         int count_params();
-        LayerArgs get_config();
     }
 }

src/TensorFlowNET.Core/Keras/Saving/IKerasConfig.cs

@@ -0,0 +1,15 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Saving
+{
+    public interface IKerasConfig
+    {
+    }
+
+    public interface IKerasConfigable
+    {
+        IKerasConfig get_config();
+    }
+}

src/TensorFlowNET.Core/Keras/Saving/LayerConfig.cs

@@ -1,4 +1,5 @@
-using System;
+using Newtonsoft.Json;
+using System;
 using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Keras.ArgsDefinition;
@@ -6,11 +7,15 @@ using Tensorflow.Keras.Engine;
 
 namespace Tensorflow.Keras.Saving
 {
-    public class LayerConfig
+    public class LayerConfig: IKerasConfig
     {
+        [JsonProperty("name")]
         public string Name { get; set; }
+        [JsonProperty("class_name")]
         public string ClassName { get; set; }
+        [JsonProperty("config")]
         public LayerArgs Config { get; set; }
+        [JsonProperty("inbound_nodes")]
         public List<NodeConfig> InboundNodes { get; set; }
     }
 }

src/TensorFlowNET.Core/Keras/Saving/ModelConfig.cs

@@ -1,15 +1,20 @@
-using System;
+using Newtonsoft.Json;
+using System;
 using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Keras.Engine;
 
 namespace Tensorflow.Keras.Saving
 {
-    public class ModelConfig
+    public class ModelConfig : IKerasConfig
     {
+        [JsonProperty("name")]
         public string Name { get; set; }
+        [JsonProperty("layers")]
         public List<LayerConfig> Layers { get; set; }
+        [JsonProperty("input_layers")]
         public List<NodeConfig> InputLayers { get; set; }
+        [JsonProperty("output_layers")]
         public List<NodeConfig> OutputLayers { get; set; }
 
         public override string ToString()

src/TensorFlowNET.Core/Keras/Saving/NodeConfig.cs

@@ -1,10 +1,13 @@
-using System;
+using Newtonsoft.Json;
+using System;
 using System.Collections.Generic;
 using System.Text;
+using Tensorflow.Keras.Common;
 
 namespace Tensorflow.Keras.Saving
 {
-    public class NodeConfig
+    [JsonConverter(typeof(CustomizedNodeConfigJsonConverter))]
+    public class NodeConfig : IKerasConfig
     {
         public string Name { get; set; }
         public int NodeIndex { get; set; }

src/TensorFlowNET.Core/Keras/Saving/TensorShapeConfig.cs

@@ -0,0 +1,21 @@
+using Newtonsoft.Json;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+
+namespace Tensorflow.Keras.Saving
+{
+    public class TensorShapeConfig
+    {
+        [JsonProperty("class_name")]
+        public string ClassName { get; set; } = "TensorShape";
+        [JsonProperty("items")]
+        public long?[] Items { get; set; }
+
+        public static implicit operator Shape(TensorShapeConfig shape)
+            => shape == null ? null : new Shape(shape.Items.Select(x => x.HasValue ? x.Value : -1).ToArray());
+
+        public static implicit operator TensorShapeConfig(Shape shape)
+            => new TensorShapeConfig() { Items = shape.dims.Select<long, long?>(x => x == -1 ? null : x).ToArray() };
+    }
+}

src/TensorFlowNET.Core/NumPy/Axis.cs

@@ -14,20 +14,29 @@
    limitations under the License.
 ******************************************************************************/
 
+using Newtonsoft.Json;
 using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
+using Tensorflow.Keras.Common;
 
 namespace Tensorflow
 {
-    public record Axis(params int[] axis)
+    [JsonConverter(typeof(CustomizedAxisJsonConverter))]
+    public class Axis
     {
+        public int[] axis { get; set; }
         public int size => axis == null ? -1 : axis.Length;
         public bool IsScalar { get; init; }
 
         public int this[int index] => axis[index];
 
+        public Axis(params int[] axis)
+        {
+            this.axis = axis;
+        }
+
         public static implicit operator int[]?(Axis axis)
             => axis?.axis;
 

src/TensorFlowNET.Core/Numpy/Shape.cs

@@ -14,14 +14,17 @@
    limitations under the License.
 ******************************************************************************/
 
+using Newtonsoft.Json;
 using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
+using Tensorflow.Keras.Common;
 using Tensorflow.NumPy;
 
 namespace Tensorflow
 {
+    [JsonConverter(typeof(CustomizedShapeJsonConverter))]
     public class Shape
     {
         public int ndim => _dims == null ? -1 : _dims.Length;

src/TensorFlowNET.Core/Operations/Initializers/Constant.cs

@@ -14,6 +14,8 @@
    limitations under the License.
 ******************************************************************************/
 
+using System.Collections.Generic;
+
 namespace Tensorflow.Operations.Initializers
 {
     public class Constant<T> : IInitializer
@@ -22,11 +24,19 @@ namespace Tensorflow.Operations.Initializers
         T value;
         bool _verify_shape;
 
+        private readonly Dictionary<string, object> _config;
+
+        public string ClassName => "Constant";
+        public IDictionary<string, object> Config => _config;
+
         public Constant(T value, TF_DataType dtype = TF_DataType.TF_FLOAT, bool verify_shape = false)
         {
             this.value = value;
             this.dtype = dtype;
             _verify_shape = verify_shape;
+
+            _config = new Dictionary<string, object>();
+            _config["value"] = this.value;
         }
 
         public Tensor Apply(InitializerArgs args)

src/TensorFlowNET.Core/Operations/Initializers/GlorotUniform.cs

@@ -14,10 +14,17 @@
    limitations under the License.
 ******************************************************************************/
 
+using System.Collections.Generic;
+
 namespace Tensorflow.Operations.Initializers
 {
     public class GlorotUniform : VarianceScaling
     {
+        private readonly Dictionary<string, object> _config;
+
+        public override string ClassName => "GlorotUniform";
+        public override IDictionary<string, object> Config => _config;
+
         public GlorotUniform(float scale = 1.0f,
             string mode = "FAN_AVG",
             bool uniform = true,
@@ -28,7 +35,8 @@ namespace Tensorflow.Operations.Initializers
                 seed: seed,
                 dtype: dtype)
         {
-
+            _config = new Dictionary<string, object>();
+            _config["seed"] = _seed;
         }
     }
 }

src/TensorFlowNET.Core/Operations/Initializers/IInitializer.cs

@@ -14,10 +14,17 @@
    limitations under the License.
 ******************************************************************************/
 
+using Newtonsoft.Json;
+using System.Collections.Generic;
+
 namespace Tensorflow
 {
     public interface IInitializer
     {
+        [JsonProperty("class_name")]
+        string ClassName { get; }
+        [JsonProperty("config")]
+        IDictionary<string, object> Config { get; }
         Tensor Apply(InitializerArgs args);
     }
 }

src/TensorFlowNET.Core/Operations/Initializers/Ones.cs

@@ -14,12 +14,19 @@
    limitations under the License.
 ******************************************************************************/
 
+using System.Collections.Generic;
+
 namespace Tensorflow.Operations.Initializers
 {
     public class Ones : IInitializer
     {
         private TF_DataType dtype;
 
+        private readonly Dictionary<string, object> _config;
+
+        public string ClassName => "Ones";
+        public IDictionary<string, object> Config => new Dictionary<string, object>();
+
         public Ones(TF_DataType dtype = TF_DataType.TF_FLOAT)
         {
             this.dtype = dtype;

src/TensorFlowNET.Core/Operations/Initializers/Orthogonal.cs

@@ -1,9 +1,14 @@
 using System;
+using System.Collections.Generic;
 
 namespace Tensorflow.Operations.Initializers
 {
     public class Orthogonal : IInitializer
     {
+        private readonly Dictionary<string, object> _config;
+
+        public string ClassName => "Orthogonal";
+        public IDictionary<string, object> Config => throw new NotImplementedException();
         public Tensor Apply(InitializerArgs args)
         {
             throw new NotImplementedException();

src/TensorFlowNET.Core/Operations/Initializers/RandomNormal.cs

@@ -14,6 +14,8 @@
    limitations under the License.
 ******************************************************************************/
 
+using System.Collections.Generic;
+
 namespace Tensorflow.Operations.Initializers
 {
     public class RandomNormal : IInitializer
@@ -23,6 +25,11 @@ namespace Tensorflow.Operations.Initializers
         private int? seed;
         private TF_DataType dtype;
 
+        private readonly Dictionary<string, object> _config;
+
+        public string ClassName => "RandomNormal";
+        public IDictionary<string, object> Config => _config;
+
         public RandomNormal(float mean = 0.0f,
             float stddev = 0.05f,
             int? seed = null,
@@ -32,6 +39,11 @@ namespace Tensorflow.Operations.Initializers
             this.stddev = stddev;
             this.seed = seed;
             this.dtype = dtype;
+
+            _config = new Dictionary<string, object>();
+            _config["mean"] = this.mean;
+            _config["stddev"] = this.stddev;
+            _config["seed"] = this.seed;
         }
 
         public Tensor Apply(InitializerArgs args)

src/TensorFlowNET.Core/Operations/Initializers/RandomUniform.cs

@@ -14,6 +14,8 @@
    limitations under the License.
 ******************************************************************************/
 
+using System.Collections.Generic;
+
 namespace Tensorflow.Operations.Initializers
 {
     public class RandomUniform : IInitializer
@@ -23,12 +25,22 @@ namespace Tensorflow.Operations.Initializers
         private float maxval;
         private TF_DataType dtype;
 
+        private readonly Dictionary<string, object> _config;
+
+        public string ClassName => "RandomUniform";
+        public IDictionary<string, object> Config => _config;
+
         public RandomUniform(TF_DataType dtype = TF_DataType.TF_FLOAT, float minval = -0.05f, float maxval = 0.05f, int? seed = null)
         {
             this.dtype = dtype;
             this.minval = minval;
             this.maxval = maxval;
             this.seed = seed;
+
+            _config = new Dictionary<string, object>();
+            _config["minval"] = this.minval;
+            _config["maxval"] = this.maxval;
+            _config["seed"] = this.seed;
         }
 
         public Tensor Apply(InitializerArgs args)

src/TensorFlowNET.Core/Operations/Initializers/TruncatedNormal.cs

@@ -14,6 +14,8 @@
    limitations under the License.
 ******************************************************************************/
 
+using System.Collections.Generic;
+
 namespace Tensorflow.Operations.Initializers
 {
     public class TruncatedNormal : IInitializer
@@ -23,6 +25,11 @@ namespace Tensorflow.Operations.Initializers
         private int? seed;
         private TF_DataType dtype;
 
+        private readonly Dictionary<string, object> _config;
+
+        public string ClassName => "TruncatedNormal";
+        public IDictionary<string, object> Config => _config;
+
         public TruncatedNormal(float mean = 0.0f,
             float stddev = 1.0f,
             int? seed = null,
@@ -32,6 +39,10 @@ namespace Tensorflow.Operations.Initializers
             this.stddev = stddev;
             this.seed = seed;
             this.dtype = dtype;
+            _config = new Dictionary<string, object>();
+            _config["mean"] = this.mean;
+            _config["stddev"] = this.stddev;
+            _config["seed"] = this.seed;
         }
 
         public Tensor Apply(InitializerArgs args)

src/TensorFlowNET.Core/Operations/Initializers/VarianceScaling.cs

@@ -15,7 +15,9 @@
 ******************************************************************************/
 
 using System;
+using System.Collections.Generic;
 using System.Linq;
+using System.Linq.Expressions;
 
 namespace Tensorflow.Operations.Initializers
 {
@@ -30,6 +32,11 @@ namespace Tensorflow.Operations.Initializers
         protected int? _seed;
         protected TF_DataType _dtype;
         protected bool _uniform;
+        private readonly Dictionary<string, object> _config;
+
+        public virtual string ClassName => "VarianceScaling";
+
+        public virtual IDictionary<string, object> Config => _config;
 
         public VarianceScaling(float factor = 2.0f,
             string mode = "FAN_IN",
@@ -50,6 +57,12 @@ namespace Tensorflow.Operations.Initializers
             _seed = seed;
             _dtype = dtype;
             _uniform = uniform;
+
+            _config = new();
+            _config["scale"] = _scale;
+            _config["mode"] = _mode;
+            _config["distribution"] = _distribution;
+            _config["seed"] = _seed;
         }
 
         public Tensor Apply(InitializerArgs args)

src/TensorFlowNET.Core/Operations/Initializers/Zeros.cs

@@ -14,6 +14,8 @@
    limitations under the License.
 ******************************************************************************/
 
+using System.Collections.Generic;
+
 namespace Tensorflow.Operations.Initializers
 {
     public class Zeros : IInitializer
@@ -21,6 +23,9 @@ namespace Tensorflow.Operations.Initializers
         Shape shape;
         TF_DataType dtype;
 
+        public string ClassName => "Zeros";
+        public IDictionary<string, object> Config => new Dictionary<string, object>();
+
         public Zeros(Shape shape = null, TF_DataType dtype = TF_DataType.TF_FLOAT)
         {
             this.shape = shape;

src/TensorFlowNET.Core/Operations/NnOps/RNNCell.cs

@@ -20,6 +20,7 @@ using Tensorflow.Keras;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.ArgsDefinition.Rnn;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using Tensorflow.Operations;
 using Tensorflow.Train;
 using Tensorflow.Util;
@@ -76,6 +77,8 @@ namespace Tensorflow
 
         public Shape BatchInputShape => throw new NotImplementedException();
 
+        public TensorShapeConfig BuildInputShape => throw new NotImplementedException();
+
         public TF_DataType DType => throw new NotImplementedException();
         protected bool built = false;
         public bool Built => built;
@@ -144,7 +147,7 @@ namespace Tensorflow
             throw new NotImplementedException();
         }
 
-        public LayerArgs get_config()
+        public IKerasConfig get_config()
         {
             throw new NotImplementedException();
         }

src/TensorFlowNET.Core/Tensorflow.Binding.csproj

@@ -108,6 +108,7 @@ https://tensorflownet.readthedocs.io</Description>
 
   <ItemGroup>
     <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.148" />
+    <PackageReference Include="Newtonsoft.Json" Version="13.0.2" />
     <PackageReference Include="Protobuf.Text" Version="0.5.0" />
     <PackageReference Include="Serilog.Sinks.Console" Version="4.1.0" />
   </ItemGroup>

src/TensorFlowNET.Core/Tensors/dtypes.cs

@@ -202,6 +202,24 @@ namespace Tensorflow
                 _ => type.ToString()
             };
 
+        public static string as_python_name(this TF_DataType type)
+            => type switch
+            {
+                TF_DataType.TF_STRING => "str",
+                TF_DataType.TF_UINT8 => "uint8",
+                TF_DataType.TF_INT8 => "int8",
+                TF_DataType.TF_UINT32 => "uint32",
+                TF_DataType.TF_INT32 => "int32",
+                TF_DataType.TF_UINT64 => "uint64",
+                TF_DataType.TF_INT64 => "int64",
+                TF_DataType.TF_FLOAT => "float32",
+                TF_DataType.TF_DOUBLE => "float64",
+                TF_DataType.TF_BOOL => "bool",
+                TF_DataType.TF_RESOURCE => "resource",
+                TF_DataType.TF_VARIANT => "variant",
+                _ => type.ToString()
+            };
+
         public static int get_datatype_size(this TF_DataType type)
             => type.as_base_dtype() switch
             {

src/TensorFlowNET.Core/Training/ITrackable.cs → src/TensorFlowNET.Core/Training/IWithTrackable.cs

@@ -5,7 +5,7 @@ using Tensorflow.Train;
 
 namespace Tensorflow.Training
 {
-    public interface ITrackable
+    public interface IWithTrackable
     {
         Trackable GetTrackable();
     }

src/TensorFlowNET.Core/Training/Trackable.cs

@@ -26,7 +26,7 @@ using static Tensorflow.Binding;
 
 namespace Tensorflow.Train
 {
-    public abstract class Trackable: ITrackable
+    public abstract class Trackable: IWithTrackable
     {
         /// <summary>
         /// Corresponding to tensorflow/python/trackable/constants.py

src/TensorFlowNET.Keras/Engine/Functional.GetConfig.cs

@@ -11,7 +11,7 @@ namespace Tensorflow.Keras.Engine
 {
     public partial class Functional
     {
-        public ModelConfig get_config()
+        public override IKerasConfig get_config()
         {
             return get_network_config();
         }
@@ -25,7 +25,7 @@ namespace Tensorflow.Keras.Engine
             {
                 Name = name
             };
-
+            
             var node_conversion_map = new Dictionary<string, int>();
             foreach (var layer in _self_tracked_trackables)
             {
@@ -42,23 +42,26 @@ namespace Tensorflow.Keras.Engine
             }
 
             var layer_configs = new List<LayerConfig>();
-            foreach (var layer in _self_tracked_trackables)
+            using (SharedObjectSavingScope.Enter())
             {
-                var filtered_inbound_nodes = new List<NodeConfig>();
-                foreach (var (original_node_index, node) in enumerate(layer.InboundNodes))
+                foreach (var layer in _self_tracked_trackables)
                 {
-                    var node_key = _make_node_key(layer.Name, original_node_index);
-                    if (NetworkNodes.Contains(node_key) && !node.is_input)
+                    var filtered_inbound_nodes = new List<NodeConfig>();
+                    foreach (var (original_node_index, node) in enumerate(layer.InboundNodes))
                     {
-                        var node_data = node.serialize(_make_node_key, node_conversion_map);
-                        filtered_inbound_nodes.append(node_data);
+                        var node_key = _make_node_key(layer.Name, original_node_index);
+                        if (NetworkNodes.Contains(node_key) && !node.is_input)
+                        {
+                            var node_data = node.serialize(_make_node_key, node_conversion_map);
+                            filtered_inbound_nodes.append(node_data);
+                        }
                     }
-                }
 
-                var layer_config = generic_utils.serialize_layer_to_config(layer);
-                layer_config.Name = layer.Name;
-                layer_config.InboundNodes = filtered_inbound_nodes;
-                layer_configs.Add(layer_config);
+                    var layer_config = generic_utils.serialize_layer_to_config(layer);
+                    layer_config.Name = layer.Name;
+                    layer_config.InboundNodes = filtered_inbound_nodes;
+                    layer_configs.Add(layer_config);
+                }
             }
             config.Layers = layer_configs;
 

src/TensorFlowNET.Keras/Engine/Functional.cs

@@ -70,6 +70,7 @@ namespace Tensorflow.Keras.Engine
             this.inputs = inputs;
             this.outputs = outputs;
             built = true;
+            _buildInputShape = inputs.shape;
 
             if (outputs.Any(x => x.KerasHistory == null))
                 base_layer_utils.create_keras_history(outputs);
@@ -357,5 +358,22 @@ namespace Tensorflow.Keras.Engine
             return LayerCheckpointDependencies.ToDictionary(x => x.Key, x => x.Value.GetTrackable()).Concat(base._trackable_children(save_type, cache))
                 .ToDictionary(x => x.Key, x => x.Value);
         }
+
+        protected override void _init_set_name(string name, bool zero_based = true)
+        {
+            if (string.IsNullOrEmpty(name))
+            {
+                string class_name = GetType().Name;
+                if (this.GetType() == typeof(Functional))
+                {
+                    class_name = "Model";
+                }
+                this.name = base_layer_utils.unique_layer_name(generic_utils.to_snake_case(class_name), zero_based: zero_based);
+            }
+            else
+            {
+                this.name = name;
+            }
+        }
     }
 }

src/TensorFlowNET.Keras/Engine/Layer.cs

@@ -61,6 +61,7 @@ namespace Tensorflow.Keras.Engine
         /// Provides information about which inputs are compatible with the layer.
         /// </summary>
         protected InputSpec inputSpec;
+        public InputSpec InputSpec => inputSpec;
         bool dynamic = true;
         public bool SupportsMasking { get; set; }
         protected List<IVariableV1> _trainable_weights;
@@ -79,6 +80,8 @@ namespace Tensorflow.Keras.Engine
         protected bool computePreviousMask;
         protected List<Operation> updates;
         public Shape BatchInputShape => args.BatchInputShape;
+        protected TensorShapeConfig _buildInputShape = null;
+        public TensorShapeConfig BuildInputShape => _buildInputShape;
 
         List<INode> inboundNodes;
         public List<INode> InboundNodes => inboundNodes;
@@ -223,6 +226,7 @@ namespace Tensorflow.Keras.Engine
 
         public virtual void build(Shape input_shape)
         {
+            _buildInputShape = input_shape;
             built = true;
         }
 
@@ -310,7 +314,7 @@ namespace Tensorflow.Keras.Engine
 
         public List<IVariableV1> Variables => weights;
 
-        public virtual LayerArgs get_config()
+        public virtual IKerasConfig get_config()
             => args;
     }
 }

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

@@ -1,6 +1,7 @@
 using System.Collections.Generic;
 using Tensorflow.Functions;
 using Tensorflow.Keras.Metrics;
+using Tensorflow.Keras.Saving;
 using Tensorflow.Keras.Saving.SavedModel;
 using Tensorflow.ModelSaving;
 
@@ -30,7 +31,10 @@ namespace Tensorflow.Keras.Engine
             }
             else
             {
-                KerasSavedModelUtils.Save(this, filepath, overwrite, include_optimizer, signatures, options, save_traces);
+                using (SharedObjectSavingScope.Enter())
+                {
+                    KerasSavedModelUtils.Save(this, filepath, overwrite, include_optimizer, signatures, options, save_traces);
+                }
             }
         }
     }

src/TensorFlowNET.Keras/Layers/Activation/ELU.cs

@@ -25,6 +25,7 @@ namespace Tensorflow.Keras.Layers {
             {
                 throw new ValueError("Alpha must be a number greater than 0.");
             }
+            _buildInputShape = input_shape;
             built = true;
         }
 

src/TensorFlowNET.Keras/Layers/Activation/Exponential.cs

@@ -14,6 +14,7 @@ namespace Tensorflow.Keras.Layers {
         }
         public override void build(Shape input_shape)
         {
+            _buildInputShape = input_shape;
             built = true;
         }
         protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)

src/TensorFlowNET.Keras/Layers/Activation/SELU.cs

@@ -16,10 +16,11 @@ namespace Tensorflow.Keras.Layers {
                   // SELU has no arguments
             }
             public override void build(Shape input_shape) {
-                  if ( alpha < 0f ) {
-                        throw new ValueError("Alpha must be a number greater than 0.");
-                  }
-                  built = true;
+                if ( alpha < 0f ) {
+                    throw new ValueError("Alpha must be a number greater than 0.");
+                }
+                _buildInputShape = input_shape;
+                built = true;
             }
             protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {
                   Tensor output = inputs;

src/TensorFlowNET.Keras/Layers/Attention/Attention.cs

@@ -4,6 +4,7 @@ using System.Collections;
 using System.Collections.Generic;
 using System.Linq;
 using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Layers
 {
@@ -146,7 +147,7 @@ namespace Tensorflow.Keras.Layers
             return scores;
         }
 
-        public override LayerArgs get_config() => this.args;
+        public override IKerasConfig get_config() => this.args;
         //var config = new Dictionary<object, object> {
         //    {
         //        "use_scale",

src/TensorFlowNET.Keras/Layers/Attention/BaseDenseAttention.cs

@@ -5,6 +5,7 @@ using static Tensorflow.KerasApi;
 using System;
 using System.Collections.Generic;
 using System.Linq;
+using Tensorflow.Keras.Saving;
 
 /// <summary>
 /// Base class for attention layers that can be used in sequence DNN/CNN models.
@@ -252,6 +253,6 @@ namespace Tensorflow.Keras.Layers
             return tf.logical_and(x, y);
         }
 
-        public override LayerArgs get_config() => this.args;
+        public override IKerasConfig get_config() => this.args;
     }
 }

src/TensorFlowNET.Keras/Layers/Convolution/Conv2DTranspose.cs

@@ -49,6 +49,7 @@ namespace Tensorflow.Keras.Layers
                 initializer: bias_initializer,
                 trainable: true);
             built = true;
+            _buildInputShape = input_shape;
         }
 
         protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)

src/TensorFlowNET.Keras/Layers/Convolution/Convolutional.cs

@@ -98,6 +98,7 @@ namespace Tensorflow.Keras.Layers
                 name: tf_op_name);
 
             built = true;
+            _buildInputShape = input_shape;
         }
 
         protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = false)

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

@@ -43,6 +43,7 @@ namespace Tensorflow.Keras.Layers
 
         public override void build(Shape input_shape)
         {
+            _buildInputShape = input_shape;
             var last_dim = input_shape.dims.Last();
             var axes = new Dictionary<int, int>();
             axes[-1] = (int)last_dim;

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

@@ -62,6 +62,7 @@ namespace Tensorflow.Keras.Layers
                 name: "embeddings");
             tf.Context.graph_mode();
             built = true;
+            _buildInputShape = input_shape;
         }
 
         protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)

src/TensorFlowNET.Keras/Layers/Cropping/Cropping1D.cs

@@ -22,6 +22,7 @@ namespace Tensorflow.Keras.Layers {
                 throw new ValueError("The `cropping` argument must be a tuple of 2 integers.");
             }
             built = true;
+            _buildInputShape = input_shape;
         }
 
         protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)

src/TensorFlowNET.Keras/Layers/Cropping/Cropping2D.cs

@@ -13,7 +13,8 @@ namespace Tensorflow.Keras.Layers {
                   this.args = args;
             }
             public override void build(Shape input_shape) {
-                  built = true;
+                built = true;
+                _buildInputShape = input_shape;
             }
             protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {
                   Tensor output = inputs;

src/TensorFlowNET.Keras/Layers/Cropping/Cropping3D.cs

@@ -12,7 +12,8 @@ namespace Tensorflow.Keras.Layers {
             }
 
             public override void build(Shape input_shape) {
-                  built = true;
+                built = true;
+                _buildInputShape = input_shape;
             }
 
             protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {

src/TensorFlowNET.Keras/Layers/LayersApi.cs

@@ -300,7 +300,8 @@ namespace Tensorflow.Keras.Layers
             => new Dense(new DenseArgs
             {
                 Units = units,
-                Activation = GetActivationByName("linear")
+                Activation = GetActivationByName("linear"),
+                ActivationName = "linear"
             });
 
         /// <summary>
@@ -321,6 +322,7 @@ namespace Tensorflow.Keras.Layers
             {
                 Units = units,
                 Activation = GetActivationByName(activation),
+                ActivationName = activation,
                 InputShape = input_shape
             });
 

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

@@ -37,6 +37,7 @@ namespace Tensorflow.Keras.Layers
                 }).ToArray();
                 shape_set.Add(shape);
             }*/
+            _buildInputShape = input_shape;
         }
 
         protected override Tensors _merge_function(Tensors inputs)

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

@@ -17,6 +17,7 @@ namespace Tensorflow.Keras.Layers
         public override void build(Shape input_shape)
         {
             // output_shape = input_shape.dims[1^];
+            _buildInputShape = input_shape;
         }
 
         protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)

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

@@ -118,6 +118,7 @@ namespace Tensorflow.Keras.Layers
                 throw new NotImplementedException("build when renorm is true");
 
             built = true;
+            _buildInputShape = input_shape;
         }
 
         public override Shape ComputeOutputShape(Shape input_shape)

src/TensorFlowNET.Keras/Layers/Normalization/LayerNormalization.cs

@@ -81,6 +81,7 @@ namespace Tensorflow.Keras.Layers
             _fused = _fused_can_be_used(ndims);
 
             built = true;
+            _buildInputShape = input_shape;
         }
 
         bool _fused_can_be_used(int ndims)

src/TensorFlowNET.Keras/Layers/Reshaping/Permute.cs

@@ -24,6 +24,7 @@ namespace Tensorflow.Keras.Layers {
             permute = new int[input_shape.rank];
             dims.CopyTo(permute, 1);
             built = true;
+            _buildInputShape = input_shape;
         }
         protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)
         {

src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNN.cs

@@ -18,6 +18,7 @@ namespace Tensorflow.Keras.Layers.Rnn
         public override void build(Shape input_shape)
         {
             var input_dim = input_shape[-1];
+            _buildInputShape = input_shape;
 
             kernel = add_weight("kernel", (input_shape[-1], args.Units),
                 initializer: args.KernelInitializer

src/TensorFlowNET.Keras/Layers/Rnn/StackedRNNCells.cs

@@ -4,6 +4,7 @@ using System.ComponentModel;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.ArgsDefinition.Rnn;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Layers.Rnn
 {
@@ -136,7 +137,7 @@ namespace Tensorflow.Keras.Layers.Rnn
             //    self.built = True
         }
 
-        public override LayerArgs get_config()
+        public override IKerasConfig get_config()
         {
             throw new NotImplementedException();
             //def get_config(self):

src/TensorFlowNET.Keras/Saving/SavedModel/Save.cs

@@ -79,7 +79,7 @@ public partial class KerasSavedModelUtils
 
             var path = node_paths[node];
             string node_path;
-            if (path is null)
+            if (path is null || path.Count() == 0)
             {
                 node_path = "root";
             }

src/TensorFlowNET.Keras/Saving/SavedModel/layer_serialization.cs

@@ -1,6 +1,7 @@
 using System.Collections.Generic;
 using Newtonsoft.Json;
 using Newtonsoft.Json.Linq;
+using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Layers;
 using Tensorflow.Keras.Utils;
@@ -85,31 +86,38 @@ public class LayerSavedModelSaver: SavedModelSaver
             JObject metadata = new JObject();
             metadata["name"] = _layer.Name;
             metadata["trainable"] = _layer.Trainable;
-            // metadata["expects_training_arg"] = _obj._expects_training_arg;
-            // metadata["dtype"] = policy.serialize(_obj._dtype_policy)
+            // TODO: implement `expects_training_arg`.
+            metadata["expects_training_arg"] = false;
+            metadata["dtype"] = _layer.DType.as_python_name();
             metadata["batch_input_shape"] = _layer.BatchInputShape is null ? null : JToken.FromObject(_layer.BatchInputShape);
             // metadata["stateful"] = _obj.stateful;
             // metadata["must_restore_from_config"] = _obj.must_restore_from_config;
             // metadata["preserve_input_structure_in_config"] = _obj.preserve_input_structure_in_config;
             metadata["autocast"] = _layer.AutoCast;
 
-            var temp = JObject.FromObject(get_serialized(_layer));
-            metadata.Merge(temp, new JsonMergeSettings
+            if(_layer.InputSpec is not null)
+            {
+                metadata["input_spec"] = generic_utils.serialize_keras_object(_layer.InputSpec);
+            }
+
+            metadata.Merge(get_serialized(_layer), new JsonMergeSettings
             {
                 // Handle conflicts by using values from obj2
                 MergeArrayHandling = MergeArrayHandling.Merge
             });
             // skip the check of `input_spec` and `build_input_shape` for the lack of members.
             // skip the check of `activity_regularizer` for the type problem.
+            if(_layer.BuildInputShape is not null)
+            {
+                metadata["build_input_shape"] = JToken.FromObject(_layer.BuildInputShape);
+            }
             return metadata.ToString();
         }
     }
 
-    public static IDictionary<string, object> get_serialized(Layer obj)
+    public static JObject get_serialized(Layer obj)
     {
-        // TODO: complete the implmentation (need to revise `get_config`).
-        return new Dictionary<string, object>();
-        //return generic_utils.serialize_keras_object(obj);
+        return generic_utils.serialize_keras_object(obj);
     }
 }
 
@@ -135,18 +143,19 @@ public class InputLayerSavedModelSaver: SavedModelSaver
     {
         get
         {
-            if(_obj is not Layer)
+            if(_obj is not InputLayer)
             {
                 throw new TypeError($"The type {_obj.GetType()} cannot be recognized as an input layer.");
             }
-            var layer = (Layer)_obj;
+            var layer = (InputLayer)_obj;
+            var config = (layer.get_config() as InputLayerArgs)!;
             var info = new
             {
                 class_name = layer.GetType().Name,
                 name = layer.Name,
                 dtype = layer.DType,
-                //sparse = layer.sparse,
-                //ragged = layer.ragged,
+                sparse = config.Sparse,
+                ragged = config.Ragged,
                 batch_input_shape = layer.BatchInputShape,
                 config = layer.get_config()
             };

src/TensorFlowNET.Keras/Saving/TensorShapeConfig.cs

@@ -1,15 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Linq;
-
-namespace Tensorflow.Keras.Saving
-{
-    public class TensorShapeConfig
-    {
-        public string ClassName { get; set; }
-        public int?[] Items { get; set; }
-
-        public static implicit operator Shape(TensorShapeConfig shape)
-            => shape == null ? null : new Shape(shape.Items.Select(x => x.HasValue ? x.Value : -1).ToArray());
-    }
-}

src/TensorFlowNET.Keras/Saving/serialization.cs

@@ -0,0 +1,125 @@
+using Newtonsoft.Json.Linq;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Reflection;
+using System.Text;
+using Tensorflow.Keras.Saving.SavedModel;
+
+namespace Tensorflow.Keras.Saving
+{
+    // TODO: make it thread safe.
+    public class SharedObjectSavingScope: IDisposable
+    {
+        private class WeakReferenceEqualityComparer: IEqualityComparer<WeakReference<object>>
+        {
+            public bool Equals(WeakReference<object> x, WeakReference<object> y)
+            {
+                if(!x.TryGetTarget(out var tx))
+                {
+                    return false;
+                }
+                if(!y.TryGetTarget(out var ty))
+                {
+                    return false;
+                }
+                return tx.Equals(ty);
+            }
+            public int GetHashCode(WeakReference<object> obj)
+            {
+                if (!obj.TryGetTarget(out var w))
+                {
+                    return 0;
+                }
+                return w.GetHashCode();
+            }
+        }
+        private static SharedObjectSavingScope? _instance = null;
+        private readonly Dictionary<WeakReference<object>, int> _shared_object_ids= new Dictionary<WeakReference<object>, int>();
+        private int _currentId = 0;
+        /// <summary>
+        /// record how many times the scope is nested.
+        /// </summary>
+        private int _nestedDepth = 0;
+        private SharedObjectSavingScope()
+        {
+
+        }
+
+        public static SharedObjectSavingScope Enter()
+        {
+            if(_instance is not null)
+            {
+                _instance._nestedDepth++;
+                return _instance;
+            }
+            else
+            {
+                _instance = new SharedObjectSavingScope();
+                _instance._nestedDepth++;
+                return _instance;
+            }
+        }
+
+        public static SharedObjectSavingScope GetScope()
+        {
+            return _instance;
+        }
+
+        public int GetId(object? obj)
+        {
+            if(obj is null)
+            {
+                return _currentId++;
+            }
+            var maybe_key = _shared_object_ids.Keys.SingleOrDefault(x => new WeakReferenceEqualityComparer().Equals(x, new WeakReference<object>(obj)));
+            if (maybe_key is not null)
+            {
+                return _shared_object_ids[maybe_key];
+            }
+            _shared_object_ids[new WeakReference<object>(obj)] = _currentId++;
+            return _currentId;
+        }
+
+        public void Dispose()
+        {
+            _nestedDepth--;
+            if(_nestedDepth== 0)
+            {
+                _instance = null;
+            }
+        }
+    }
+
+    public static class serialize_utils
+    {
+        public static readonly string SHARED_OBJECT_KEY = "shared_object_id";
+        /// <summary>
+        /// Returns the serialization of the class with the given config.
+        /// </summary>
+        /// <param name="class_name"></param>
+        /// <param name="config"></param>
+        /// <param name="obj"></param>
+        /// <param name="shared_object_id"></param>
+        /// <returns></returns>
+        public static JObject serialize_keras_class_and_config(string class_name, JToken config, object? obj = null, int? shared_object_id = null)
+        {
+            JObject res = new JObject();
+            res["class_name"] = class_name;
+            res["config"] = config;
+
+            if(shared_object_id is not null)
+            {
+                res[SHARED_OBJECT_KEY] = shared_object_id!;
+            }
+
+            var scope = SharedObjectSavingScope.GetScope();
+            if(scope is not null && obj is not null)
+            {
+                res[SHARED_OBJECT_KEY] = scope.GetId(obj);
+            }
+
+            return res;
+        }
+    }
+}

src/TensorFlowNET.Keras/Utils/base_layer_utils.cs

@@ -53,7 +53,7 @@ namespace Tensorflow.Keras.Utils
         }
 
         /// <summary>
-        /// Makes a layer name (or arbitrary string) unique within a TensorFlow graph.
+        /// Makes a layer name (or arbitrary string) unique within a TensorFlow graph. (correponding to `backend.unique_object_name` of python.)
         /// </summary>
         /// <param name="name"></param>
         /// <returns></returns>

src/TensorFlowNET.Keras/Utils/generic_utils.cs

@@ -14,10 +14,14 @@
    limitations under the License.
 ******************************************************************************/
 
+using Newtonsoft.Json;
+using Newtonsoft.Json.Linq;
 using System;
 using System.Collections;
 using System.Collections.Generic;
+using System.Diagnostics;
 using System.Linq;
+using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Utils
@@ -32,13 +36,21 @@ namespace Tensorflow.Keras.Utils
         public static LayerConfig serialize_layer_to_config(ILayer instance)
         {
             var config = instance.get_config();
+            Debug.Assert(config is LayerArgs);
             return new LayerConfig
             {
-                Config = config,
+                Config = config as LayerArgs,
                 ClassName = instance.GetType().Name
             };
         }
 
+        public static JObject serialize_keras_object(IKerasConfigable instance)
+        {
+            var config = JToken.FromObject(instance.get_config());
+            // TODO: change the class_name to registered name, instead of system class name.
+            return serialize_utils.serialize_keras_class_and_config(instance.GetType().Name, config, instance);
+        }
+
         public static string to_snake_case(string name)
         {
             return string.Concat(name.Select((x, i) =>

test/TensorFlowNET.Keras.UnitTest/Layers/ModelSaveTest.cs

@@ -1,6 +1,8 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow.Keras.Engine;
+using System.Diagnostics;
 using static Tensorflow.KerasApi;
+using Tensorflow.Keras.Saving;
 
 namespace TensorFlowNET.Keras.UnitTest
 {
@@ -15,7 +17,8 @@ namespace TensorFlowNET.Keras.UnitTest
         {
             var model = GetFunctionalModel();
             var config = model.get_config();
-            var new_model = keras.models.from_config(config);
+            Debug.Assert(config is ModelConfig);
+            var new_model = keras.models.from_config(config as ModelConfig);
             Assert.AreEqual(model.Layers.Count, new_model.Layers.Count);
         }
 

test/TensorFlowNET.Keras.UnitTest/SaveTest.cs

@@ -15,17 +15,14 @@ using Tensorflow.Keras.Layers;
 using Tensorflow.Keras.Losses;
 using Tensorflow.Keras.Metrics;
 using Tensorflow.Keras.Optimizers;
+using Tensorflow.Operations;
 
 namespace TensorFlowNET.Keras.UnitTest;
 
-// class MNISTLoader
-// {
-//     public MNISTLoader()
-//     {
-//         var mnist = new MnistModelLoader()
-//         
-//     }
-// }
+public static class AutoGraphExtension
+{
+    
+}
 
 [TestClass]
 public class SaveTest
@@ -42,6 +39,8 @@ public class SaveTest
         
         model.compile(new Adam(0.001f), new LossesApi().SparseCategoricalCrossentropy(), new  string[]{"accuracy"});
 
+        var g = ops.get_default_graph();
+
         var data_loader = new MnistModelLoader();
         var num_epochs = 1;
         var batch_size = 50;
@@ -50,11 +49,34 @@ public class SaveTest
         {
             TrainDir = "mnist",
             OneHot = false,
-            ValidationSize = 0,
+            ValidationSize = 50000,
         }).Result;
         
         model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
         
         model.save("C:\\Work\\tf.net\\tf_test\\tf.net.model", save_format:"pb");
     }
+
+    [TestMethod]
+    public void Temp()
+    {
+        var graph = new Graph();
+        var g = graph.as_default();
+        //var input_tensor = array_ops.placeholder(TF_DataType.TF_FLOAT, new int[] { 1 }, "test_string_tensor");
+        var input_tensor = tf.placeholder(tf.int32, new int[] { 1 }, "aa");
+        var wrapped_func = tf.autograph.to_graph(func);
+        var res = wrapped_func(input_tensor);
+        g.Exit();
+    }
+
+    private Tensor func(Tensor tensor)
+    {
+        return gen_ops.neg(tensor);
+        //return array_ops.identity(tensor);
+        //tf.device("cpu:0");
+        //using (ops.control_dependencies(new object[] { res.op }))
+        //{
+        //    return array_ops.identity(tensor);
+        //}
+    }
 }