Change type of BuildInputShape to KerasShapesWrapper.

src/TensorFlowNET.Core/Extensions/JObjectExtensions.cs

@@ -0,0 +1,23 @@
+using Newtonsoft.Json.Linq;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Extensions
+{
+    public static class JObjectExtensions
+    {
+        public static T? TryGetOrReturnNull<T>(this JObject obj, string key)
+        {
+            var res = obj[key];
+            if(res is null)
+            {
+                return default(T);
+            }
+            else
+            {
+                return res.ToObject<T>();
+            }
+        }
+    }
+}

src/TensorFlowNET.Core/Framework/Models/TensorSpec.cs

@@ -7,7 +7,7 @@ namespace Tensorflow.Framework.Models
         public TensorSpec(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null) :
             base(shape, dtype, name)
         {
-
+            
         }
 
         public TensorSpec _unbatch()

src/TensorFlowNET.Core/Keras/Activations/Activations.cs

@@ -1,7 +1,7 @@
 using Newtonsoft.Json;
 using System.Reflection;
 using System.Runtime.Versioning;
-using Tensorflow.Keras.Common;
+using Tensorflow.Keras.Saving.Common;
 
 namespace Tensorflow.Keras
 {

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

@@ -2,6 +2,7 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.ArgsDefinition
 {
@@ -18,7 +19,7 @@ namespace Tensorflow.Keras.ArgsDefinition
         [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; }
+        public override KerasShapesWrapper 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/InputLayerArgs.cs

@@ -1,6 +1,6 @@
 using Newtonsoft.Json;
 using Newtonsoft.Json.Serialization;
-using Tensorflow.Keras.Common;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.ArgsDefinition
 {
@@ -17,6 +17,6 @@ namespace Tensorflow.Keras.ArgsDefinition
         [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; }
+        public override KerasShapesWrapper BatchInputShape { get => base.BatchInputShape; set => base.BatchInputShape = value; }
     }
 }

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

@@ -33,7 +33,7 @@ namespace Tensorflow.Keras.ArgsDefinition
         /// <summary>
         /// Only applicable to input layers.
         /// </summary>
-        public virtual Shape BatchInputShape { get; set; }
+        public virtual KerasShapesWrapper BatchInputShape { get; set; }
 
         public virtual int BatchSize { get; set; } = -1;
 

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

@@ -10,7 +10,7 @@ namespace Tensorflow.Keras
         string Name { get; }
         bool Trainable { get; }
         bool Built { get; }
-        void build(Shape input_shape);
+        void build(KerasShapesWrapper input_shape);
         List<ILayer> Layers { get; }
         List<INode> InboundNodes { get; }
         List<INode> OutboundNodes { get; }
@@ -22,8 +22,8 @@ namespace Tensorflow.Keras
         void set_weights(IEnumerable<NDArray> weights);
         List<NDArray> get_weights();
         Shape OutputShape { get; }
-        Shape BatchInputShape { get; }
-        TensorShapeConfig BuildInputShape { get; }
+        KerasShapesWrapper BatchInputShape { get; }
+        KerasShapesWrapper BuildInputShape { get; }
         TF_DataType DType { get; }
         int count_params();
         void adapt(Tensor data, int? batch_size = null, int? steps = null);

src/TensorFlowNET.Core/Keras/Common/CustomizedActivationJsonConverter.cs → src/TensorFlowNET.Core/Keras/Saving/Json/CustomizedActivationJsonConverter.cs

@@ -6,7 +6,7 @@ using System.Collections.Generic;
 using System.Text;
 using static Tensorflow.Binding;
 
-namespace Tensorflow.Keras.Common
+namespace Tensorflow.Keras.Saving.Common
 {
     public class CustomizedActivationJsonConverter : JsonConverter
     {

src/TensorFlowNET.Core/Keras/Common/CustomizedAxisJsonConverter.cs → src/TensorFlowNET.Core/Keras/Saving/Json/CustomizedAxisJsonConverter.cs

@@ -4,7 +4,7 @@ using System;
 using System.Collections.Generic;
 using System.Text;
 
-namespace Tensorflow.Keras.Common
+namespace Tensorflow.Keras.Saving.Common
 {
     public class CustomizedAxisJsonConverter : JsonConverter
     {
@@ -38,7 +38,7 @@ namespace Tensorflow.Keras.Common
         public override object? ReadJson(JsonReader reader, Type objectType, object? existingValue, JsonSerializer serializer)
         {
             int[]? axis;
-            if(reader.ValueType == typeof(long))
+            if (reader.ValueType == typeof(long))
             {
                 axis = new int[1];
                 axis[0] = (int)serializer.Deserialize(reader, typeof(int));
@@ -51,7 +51,7 @@ namespace Tensorflow.Keras.Common
             {
                 throw new ValueError("Cannot deserialize 'null' to `Axis`.");
             }
-            return new Axis((int[])(axis!));
+            return new Axis(axis!);
         }
     }
 }

src/TensorFlowNET.Core/Keras/Common/CustomizedDTypeJsonConverter.cs → src/TensorFlowNET.Core/Keras/Saving/Json/CustomizedDTypeJsonConverter.cs

@@ -1,7 +1,7 @@
 using Newtonsoft.Json.Linq;
 using Newtonsoft.Json;
 
-namespace Tensorflow.Keras.Common
+namespace Tensorflow.Keras.Saving.Common
 {
     public class CustomizedDTypeJsonConverter : JsonConverter
     {
@@ -16,7 +16,7 @@ namespace Tensorflow.Keras.Common
 
         public override void WriteJson(JsonWriter writer, object? value, JsonSerializer serializer)
         {
-            var token = JToken.FromObject(dtypes.as_numpy_name((TF_DataType)value));
+            var token = JToken.FromObject(((TF_DataType)value).as_numpy_name());
             token.WriteTo(writer);
         }
 

src/TensorFlowNET.Core/Keras/Common/CustomizedIInitializerJsonConverter.cs → src/TensorFlowNET.Core/Keras/Saving/Json/CustomizedIInitializerJsonConverter.cs

@@ -4,9 +4,10 @@ using System;
 using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Operations;
+
 using Tensorflow.Operations.Initializers;
 
-namespace Tensorflow.Keras.Common
+namespace Tensorflow.Keras.Saving.Common
 {
     class InitializerInfo
     {
@@ -27,7 +28,7 @@ namespace Tensorflow.Keras.Common
         public override void WriteJson(JsonWriter writer, object? value, JsonSerializer serializer)
         {
             var initializer = value as IInitializer;
-            if(initializer is null)
+            if (initializer is null)
             {
                 JToken.FromObject(null).WriteTo(writer);
                 return;
@@ -42,7 +43,7 @@ namespace Tensorflow.Keras.Common
         public override object? ReadJson(JsonReader reader, Type objectType, object? existingValue, JsonSerializer serializer)
         {
             var info = serializer.Deserialize<InitializerInfo>(reader);
-            if(info is null)
+            if (info is null)
             {
                 return null;
             }
@@ -54,8 +55,8 @@ namespace Tensorflow.Keras.Common
                 "Orthogonal" => new Orthogonal(info.config["gain"].ToObject<float>(), info.config["seed"].ToObject<int?>()),
                 "RandomNormal" => new RandomNormal(info.config["mean"].ToObject<float>(), info.config["stddev"].ToObject<float>(),
                     info.config["seed"].ToObject<int?>()),
-                "RandomUniform" => new RandomUniform(minval:info.config["minval"].ToObject<float>(), 
-                    maxval:info.config["maxval"].ToObject<float>(), seed: info.config["seed"].ToObject<int?>()),
+                "RandomUniform" => new RandomUniform(minval: info.config["minval"].ToObject<float>(),
+                    maxval: info.config["maxval"].ToObject<float>(), seed: info.config["seed"].ToObject<int?>()),
                 "TruncatedNormal" => new TruncatedNormal(info.config["mean"].ToObject<float>(), info.config["stddev"].ToObject<float>(),
                     info.config["seed"].ToObject<int?>()),
                 "VarianceScaling" => new VarianceScaling(info.config["scale"].ToObject<float>(), info.config["mode"].ToObject<string>(),

src/TensorFlowNET.Core/Keras/Saving/Json/CustomizedKerasShapesWrapperJsonConverter.cs

@@ -0,0 +1,75 @@
+using Newtonsoft.Json.Linq;
+using Newtonsoft.Json;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Saving.Json
+{
+    public class CustomizedKerasShapesWrapperJsonConverter : JsonConverter
+    {
+        public override bool CanConvert(Type objectType)
+        {
+            return objectType == typeof(KerasShapesWrapper);
+        }
+
+        public override bool CanRead => true;
+
+        public override bool CanWrite => true;
+
+        public override void WriteJson(JsonWriter writer, object? value, JsonSerializer serializer)
+        {
+            if (value is null)
+            {
+                JToken.FromObject(null).WriteTo(writer);
+                return;
+            }
+            if (value is not KerasShapesWrapper wrapper)
+            {
+                throw new TypeError($"Expected `KerasShapesWrapper` to be serialized, bug got {value.GetType()}");
+            }
+            if (wrapper.Shapes.Length == 0)
+            {
+                JToken.FromObject(null).WriteTo(writer);
+            }
+            else if (wrapper.Shapes.Length == 1)
+            {
+                JToken.FromObject(wrapper.Shapes[0]).WriteTo(writer);
+            }
+            else
+            {
+                JToken.FromObject(wrapper.Shapes).WriteTo(writer);
+            }
+        }
+
+        public override object? ReadJson(JsonReader reader, Type objectType, object? existingValue, JsonSerializer serializer)
+        {
+            if (reader.TokenType == JsonToken.StartArray)
+            {
+                TensorShapeConfig[] shapes = serializer.Deserialize<TensorShapeConfig[]>(reader);
+                if (shapes is null)
+                {
+                    return null;
+                }
+                return new KerasShapesWrapper(shapes);
+            }
+            else if (reader.TokenType == JsonToken.StartObject)
+            {
+                var shape = serializer.Deserialize<TensorShapeConfig>(reader);
+                if (shape is null)
+                {
+                    return null;
+                }
+                return new KerasShapesWrapper(shape);
+            }
+            else if (reader.TokenType == JsonToken.Null)
+            {
+                return null;
+            }
+            else
+            {
+                throw new ValueError($"Cannot deserialize the token type {reader.TokenType}");
+            }
+        }
+    }
+}

src/TensorFlowNET.Core/Keras/Common/CustomizedNodeConfigJsonConverter.cs → src/TensorFlowNET.Core/Keras/Saving/Json/CustomizedNodeConfigJsonConverter.cs

@@ -7,7 +7,7 @@ using System.Linq;
 using System.Text;
 using Tensorflow.Keras.Saving;
 
-namespace Tensorflow.Keras.Common
+namespace Tensorflow.Keras.Saving.Common
 {
     public class CustomizedNodeConfigJsonConverter : JsonConverter
     {
@@ -46,10 +46,10 @@ namespace Tensorflow.Keras.Common
             {
                 throw new ValueError("Cannot deserialize 'null' to `Shape`.");
             }
-            if(values.Length == 1)
+            if (values.Length == 1)
             {
                 var array = values[0] as JArray;
-                if(array is null)
+                if (array is null)
                 {
                     throw new ValueError($"The value ({string.Join(", ", values)}) cannot be deserialized to type `NodeConfig`.");
                 }

src/TensorFlowNET.Core/Keras/Common/CustomizedShapeJsonConverter.cs → src/TensorFlowNET.Core/Keras/Saving/Json/CustomizedShapeJsonConverter.cs

@@ -5,14 +5,14 @@ using System;
 using System.Collections.Generic;
 using System.Text;
 
-namespace Tensorflow.Keras.Common
+namespace Tensorflow.Keras.Saving.Common
 {
     class ShapeInfoFromPython
     {
         public string class_name { get; set; }
         public long?[] items { get; set; }
     }
-    public class CustomizedShapeJsonConverter: JsonConverter
+    public class CustomizedShapeJsonConverter : JsonConverter
     {
         public override bool CanConvert(Type objectType)
         {
@@ -25,12 +25,12 @@ namespace Tensorflow.Keras.Common
 
         public override void WriteJson(JsonWriter writer, object? value, JsonSerializer serializer)
         {
-            if(value is null)
+            if (value is null)
             {
                 var token = JToken.FromObject(null);
                 token.WriteTo(writer);
             }
-            else if(value is not Shape)
+            else if (value is not Shape)
             {
                 throw new TypeError($"Unable to use `CustomizedShapeJsonConverter` to serialize the type {value.GetType()}.");
             }
@@ -38,7 +38,7 @@ namespace Tensorflow.Keras.Common
             {
                 var shape = (value as Shape)!;
                 long?[] dims = new long?[shape.ndim];
-                for(int i = 0; i < dims.Length; i++)
+                for (int i = 0; i < dims.Length; i++)
                 {
                     if (shape.dims[i] == -1)
                     {
@@ -61,7 +61,7 @@ namespace Tensorflow.Keras.Common
         public override object? ReadJson(JsonReader reader, Type objectType, object? existingValue, JsonSerializer serializer)
         {
             long?[] dims;
-            try
+            if (reader.TokenType == JsonToken.StartObject)
             {
                 var shape_info_from_python = serializer.Deserialize<ShapeInfoFromPython>(reader);
                 if (shape_info_from_python is null)
@@ -70,14 +70,22 @@ namespace Tensorflow.Keras.Common
                 }
                 dims = shape_info_from_python.items;
             }
-            catch(JsonSerializationException)
+            else if (reader.TokenType == JsonToken.StartArray)
             {
                 dims = serializer.Deserialize<long?[]>(reader);
             }
+            else if (reader.TokenType == JsonToken.Null)
+            {
+                return null;
+            }
+            else
+            {
+                throw new ValueError($"Cannot deserialize the token {reader} as Shape.");
+            }
             long[] convertedDims = new long[dims.Length];
-            for(int i = 0; i < dims.Length; i++)
+            for (int i = 0; i < dims.Length; i++)
             {
-                convertedDims[i] = dims[i] ?? (-1);
+                convertedDims[i] = dims[i] ?? -1;
             }
             return new Shape(convertedDims);
         }

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

@@ -0,0 +1,60 @@
+using Newtonsoft.Json.Linq;
+using Newtonsoft.Json;
+using System;
+using System.Collections.Generic;
+using System.Text;
+using System.Diagnostics;
+using OneOf.Types;
+using Tensorflow.Keras.Saving.Json;
+
+namespace Tensorflow.Keras.Saving
+{
+    [JsonConverter(typeof(CustomizedKerasShapesWrapperJsonConverter))]
+    public class KerasShapesWrapper
+    {
+        public TensorShapeConfig[] Shapes { get; set; }
+
+        public KerasShapesWrapper(Shape shape)
+        {
+            Shapes = new TensorShapeConfig[] { shape };
+        }
+
+        public KerasShapesWrapper(TensorShapeConfig shape)
+        {
+            Shapes = new TensorShapeConfig[] { shape };
+        }
+
+        public KerasShapesWrapper(TensorShapeConfig[] shapes)
+        {
+            Shapes = shapes;
+        }
+
+        public KerasShapesWrapper(IEnumerable<Shape> shape)
+        {
+            Shapes = shape.Select(x => (TensorShapeConfig)x).ToArray();
+        }
+
+        public Shape ToSingleShape()
+        {
+            Debug.Assert(Shapes.Length == 1);
+            var shape_config = Shapes[0];
+            Debug.Assert(shape_config is not null);
+            return new Shape(shape_config.Items.Select(x => x is null ? -1 : x.Value).ToArray());
+        }
+
+        public Shape[] ToShapeArray()
+        {
+            return Shapes.Select(x => new Shape(x.Items.Select(y => y is null ? -1 : y.Value).ToArray())).ToArray();
+        }
+
+        public static implicit operator KerasShapesWrapper(Shape shape)
+        {
+            return new KerasShapesWrapper(shape);
+        }
+        public static implicit operator KerasShapesWrapper(TensorShapeConfig shape)
+        {
+            return new KerasShapesWrapper(shape);
+        }
+
+    }
+}

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

@@ -9,7 +9,7 @@ using static Google.Protobuf.Reflection.FieldDescriptorProto.Types;
 
 namespace Tensorflow.Keras.Saving
 {
-    public class ModelConfig : IKerasConfig
+    public class FunctionalConfig : IKerasConfig
     {
         [JsonProperty("name")]
         public string Name { get; set; }

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

@@ -2,7 +2,7 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
-using Tensorflow.Keras.Common;
+using Tensorflow.Keras.Saving.Common;
 
 namespace Tensorflow.Keras.Saving
 {

src/TensorFlowNET.Core/NumPy/Axis.cs

@@ -19,7 +19,7 @@ using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
-using Tensorflow.Keras.Common;
+using Tensorflow.Keras.Saving.Common;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Numpy/Shape.cs

@@ -19,7 +19,7 @@ using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
-using Tensorflow.Keras.Common;
+using Tensorflow.Keras.Saving.Common;
 using Tensorflow.NumPy;
 
 namespace Tensorflow

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

@@ -16,7 +16,7 @@
 
 using Newtonsoft.Json;
 using System.Collections.Generic;
-using Tensorflow.Keras.Common;
+using Tensorflow.Keras.Saving.Common;
 
 namespace Tensorflow
 {

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

@@ -80,9 +80,9 @@ namespace Tensorflow
 
         public Shape OutputShape => throw new NotImplementedException();
 
-        public Shape BatchInputShape => throw new NotImplementedException();
+        public KerasShapesWrapper BatchInputShape => throw new NotImplementedException();
 
-        public TensorShapeConfig BuildInputShape => throw new NotImplementedException();
+        public KerasShapesWrapper BuildInputShape => throw new NotImplementedException();
 
         public TF_DataType DType => throw new NotImplementedException();
         protected bool built = false;
@@ -162,6 +162,11 @@ namespace Tensorflow
             throw new NotImplementedException();
         }
 
+        public void build(KerasShapesWrapper input_shape)
+        {
+            throw new NotImplementedException();
+        }
+
         public Trackable GetTrackable() { throw new NotImplementedException(); }
 
         public void adapt(Tensor data, int? batch_size = null, int? steps = null)

src/TensorFlowNET.Core/Tensors/TF_DataType.cs

@@ -1,5 +1,5 @@
 using Newtonsoft.Json;
-using Tensorflow.Keras.Common;
+using Tensorflow.Keras.Saving.Common;
 
 namespace Tensorflow
 {

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

@@ -11,7 +11,7 @@ namespace Tensorflow.Keras.Engine
 {
     public partial class Functional
     {
-        public static Functional from_config(ModelConfig config)
+        public static Functional from_config(FunctionalConfig config)
         {
             var (input_tensors, output_tensors, created_layers) = reconstruct_from_config(config);
             var model = new Functional(input_tensors, output_tensors, name: config.Name);
@@ -24,7 +24,7 @@ namespace Tensorflow.Keras.Engine
         /// </summary>
         /// <param name="config"></param>
         /// <returns></returns>
-        public static (Tensors, Tensors, Dictionary<string, ILayer>) reconstruct_from_config(ModelConfig config, Dictionary<string, ILayer>? created_layers = null)
+        public static (Tensors, Tensors, Dictionary<string, ILayer>) reconstruct_from_config(FunctionalConfig config, Dictionary<string, ILayer>? created_layers = null)
         {
             // Layer instances created during the graph reconstruction process.
             created_layers = created_layers ?? new Dictionary<string, ILayer>();

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

@@ -19,9 +19,9 @@ namespace Tensorflow.Keras.Engine
         /// <summary>
         /// Builds the config, which consists of the node graph and serialized layers.
         /// </summary>
-        ModelConfig get_network_config()
+        FunctionalConfig get_network_config()
         {
-            var config = new ModelConfig
+            var config = new FunctionalConfig
             {
                 Name = name
             };

src/TensorFlowNET.Keras/Engine/Layer.cs

@@ -211,9 +211,9 @@ namespace Tensorflow.Keras.Engine
 
         protected bool computePreviousMask;
         protected List<Operation> updates;
-        public Shape BatchInputShape => args.BatchInputShape;
-        protected TensorShapeConfig _buildInputShape = null;
-        public TensorShapeConfig BuildInputShape => _buildInputShape;
+        public KerasShapesWrapper BatchInputShape => args.BatchInputShape;
+        protected KerasShapesWrapper _buildInputShape = null;
+        public KerasShapesWrapper BuildInputShape => _buildInputShape;
 
         List<INode> inboundNodes;
         public List<INode> InboundNodes => inboundNodes;
@@ -284,7 +284,7 @@ namespace Tensorflow.Keras.Engine
             // Manage input shape information if passed.
             if (args.BatchInputShape == null && args.InputShape != null)
             {
-                args.BatchInputShape = new long[] { args.BatchSize }.Concat(args.InputShape.dims).ToArray();
+                args.BatchInputShape = new KerasShapesWrapper(new long[] { args.BatchSize }.Concat(args.InputShape.dims).ToArray());
             }
         }
 
@@ -363,7 +363,7 @@ namespace Tensorflow.Keras.Engine
                 tf.Context.eager_mode(isFunc: tf.Context.is_build_function());
             }
                
-            build(inputs.shape);
+            build(new KerasShapesWrapper(inputs.shape));
 
             if (need_restore_mode)
                 tf.Context.restore_mode();
@@ -371,7 +371,7 @@ namespace Tensorflow.Keras.Engine
             built = true;
         }
 
-        public virtual void build(Shape input_shape)
+        public virtual void build(KerasShapesWrapper input_shape)
         {
             _buildInputShape = input_shape;
             built = true;

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

@@ -1,6 +1,8 @@
 using System;
 using System.Linq;
 using Tensorflow.Graphs;
+using Tensorflow.Keras.Saving;
+using Tensorflow.Keras.Utils;
 using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;
 
@@ -8,22 +10,40 @@ namespace Tensorflow.Keras.Engine
 {
     public partial class Model
     {
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
-            if (this is Functional || this is Sequential)
+            if (_is_graph_network || this is Functional || this is Sequential)
             {
                 base.build(input_shape);
                 return;
             }
 
-            var graph = tf.executing_eagerly() ? new FuncGraph("build_graph") : keras.backend.get_graph();
-
-            graph.as_default();
-
-            var x = tf.placeholder(DType, input_shape);
-            Call(x, training: false);
-
-            graph.Exit();
+            if(input_shape is not null && this.inputs is null)
+            {
+                var graph = tf.executing_eagerly() ? new FuncGraph("build_graph") : keras.backend.get_graph();
+                graph.as_default();
+                var shapes = input_shape.ToShapeArray();
+                var x = new Tensors(shapes.Select(x => base_layer_utils.generate_placeholders_from_shape(x)));
+                try
+                {
+                    Call(x, training: false);
+                }
+                catch (InvalidArgumentError)
+                {
+                    throw new ValueError("You cannot build your model by calling `build` " +
+                        "if your layers do not support float type inputs. " +
+                        "Instead, in order to instantiate and build your " +
+                        "model, `call` your model on real tensor data (of the correct dtype).");
+                }
+                catch (TypeError)
+                {
+                    throw new ValueError("You cannot build your model by calling `build` " +
+                        "if your layers do not support float type inputs. " +
+                        "Instead, in order to instantiate and build your " +
+                        "model, `call` your model on real tensor data (of the correct dtype).");
+                }
+                graph.Exit();
+            }
 
             base.build(input_shape);
         }

src/TensorFlowNET.Keras/Engine/Sequential.cs

@@ -92,7 +92,7 @@ namespace Tensorflow.Keras.Engine
                     {
                         // Instantiate an input layer.
                         var x = keras.Input(
-                              batch_input_shape: layer.BatchInputShape,
+                              batch_input_shape: layer.BatchInputShape.ToSingleShape(),
                               dtype: layer.DType,
                               name: layer.Name + "_input");
 

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

@@ -3,6 +3,7 @@ using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers {
@@ -19,7 +20,7 @@ namespace Tensorflow.Keras.Layers {
             this.args = args;
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             if (alpha < 0f)
             {

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

@@ -3,6 +3,7 @@ using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers {
@@ -12,7 +13,7 @@ namespace Tensorflow.Keras.Layers {
         {
             // Exponential has no args
         }
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             base.build(input_shape);
         }

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

@@ -3,6 +3,7 @@ using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers {
@@ -15,7 +16,7 @@ namespace Tensorflow.Keras.Layers {
             public SELU ( LayerArgs args ) : base(args) {
                   // SELU has no arguments
             }
-            public override void build(Shape input_shape) {
+            public override void build(KerasShapesWrapper input_shape) {
                 if ( alpha < 0f ) {
                     throw new ValueError("Alpha must be a number greater than 0.");
                 }

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

@@ -93,7 +93,7 @@ namespace Tensorflow.Keras.Layers
         }
 
         // Creates variable when `use_scale` is True or `score_mode` is `concat`.
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             if (this.use_scale)
                 this.scale = this.add_weight(name: "scale",

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

@@ -19,6 +19,7 @@ using static Tensorflow.Binding;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Utils;
 using static Tensorflow.KerasApi;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Layers
 {
@@ -58,13 +59,14 @@ namespace Tensorflow.Keras.Layers
             return args;
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
+            var single_shape = input_shape.ToSingleShape();
             if (len(input_shape) != 4)
                 throw new ValueError($"Inputs should have rank 4. Received input shape: {input_shape}");
 
             var channel_axis = _get_channel_axis();
-            var input_dim = input_shape[-1];
+            var input_dim = single_shape[-1];
             var kernel_shape = new Shape(kernel_size[0], kernel_size[1], filters, input_dim);
 
             kernel = add_weight(name: "kernel",

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

@@ -19,6 +19,7 @@ using System.Collections.Generic;
 using System.Linq;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using Tensorflow.Keras.Utils;
 using Tensorflow.Operations;
 using static Tensorflow.Binding;
@@ -57,12 +58,13 @@ namespace Tensorflow.Keras.Layers
             _tf_data_format = conv_utils.convert_data_format(data_format, rank + 2);
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             int channel_axis = data_format == "channels_first" ? 1 : -1;
+            var single_shape = input_shape.ToSingleShape();
             var input_channel = channel_axis < 0 ?
-                input_shape.dims[input_shape.ndim + channel_axis] :
-                input_shape.dims[channel_axis];
+                single_shape.dims[single_shape.ndim + channel_axis] :
+                single_shape.dims[channel_axis];
             Shape kernel_shape = kernel_size.dims.concat(new long[] { input_channel / args.Groups, filters });
             kernel = add_weight(name: "kernel",
                 shape: kernel_shape,

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

@@ -16,9 +16,11 @@
 
 using System;
 using System.Collections.Generic;
+using System.Diagnostics;
 using System.Linq;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers
@@ -41,10 +43,12 @@ namespace Tensorflow.Keras.Layers
             this.inputSpec = new InputSpec(min_ndim: 2);
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             _buildInputShape = input_shape;
-            var last_dim = input_shape.dims.Last();
+            Debug.Assert(input_shape.Shapes.Length <= 1);
+            var single_shape = input_shape.ToSingleShape();
+            var last_dim = single_shape.dims.Last();
             var axes = new Dictionary<int, int>();
             axes[-1] = (int)last_dim;
             inputSpec = new InputSpec(min_ndim: 2, axes: axes);

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

@@ -6,6 +6,7 @@ using System.Linq;
 using System.Text.RegularExpressions;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.ArgsDefinition.Core;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Layers
 {
@@ -119,9 +120,10 @@ namespace Tensorflow.Keras.Layers
             this.bias_constraint = args.BiasConstraint;
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
-            var shape_data = _analyze_einsum_string(this.equation, this.bias_axes, input_shape, this.partial_output_shape);
+            var shape_data = _analyze_einsum_string(this.equation, this.bias_axes, 
+                input_shape.ToSingleShape(), this.partial_output_shape);
             var kernel_shape = shape_data.Item1;
             var bias_shape = shape_data.Item2;
             this.full_output_shape = shape_data.Item3;

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

@@ -17,6 +17,7 @@
 using System.Linq;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers
@@ -48,13 +49,13 @@ namespace Tensorflow.Keras.Layers
                 args.InputShape = args.InputLength;
 
             if (args.BatchInputShape == null)
-                args.BatchInputShape = new long[] { args.BatchSize }.Concat(args.InputShape.dims).ToArray();
+                args.BatchInputShape = new KerasShapesWrapper(new long[] { args.BatchSize }.Concat(args.InputShape.dims).ToArray());
 
             embeddings_initializer = args.EmbeddingsInitializer ?? tf.random_uniform_initializer;
             SupportsMasking = mask_zero;
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             tf.Context.eager_mode();
             embeddings = add_weight(shape: (input_dim, output_dim),

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

@@ -40,10 +40,10 @@ namespace Tensorflow.Keras.Layers
             built = true;
             SupportsMasking = true;
 
-            if (BatchInputShape != null)
+            if (BatchInputShape is not null)
             {
-                args.BatchSize = (int)BatchInputShape.dims[0];
-                args.InputShape = BatchInputShape.dims.Skip(1).ToArray();
+                args.BatchSize = (int)(BatchInputShape.ToSingleShape().dims[0]);
+                args.InputShape = BatchInputShape.ToSingleShape().dims.Skip(1).ToArray();
             }
 
             // moved to base class
@@ -63,9 +63,8 @@ namespace Tensorflow.Keras.Layers
             {
                 if (args.InputShape != null)
                 {
-                    args.BatchInputShape = new long[] { args.BatchSize }
-                        .Concat(args.InputShape.dims)
-                        .ToArray();
+                    args.BatchInputShape = new Saving.KerasShapesWrapper(new long[] { args.BatchSize }
+                        .Concat(args.InputShape.dims).ToArray());
                 }
                 else
                 {
@@ -76,7 +75,7 @@ namespace Tensorflow.Keras.Layers
                 graph.as_default();
 
                 args.InputTensor = keras.backend.placeholder(
-                    shape: BatchInputShape,
+                    shape: BatchInputShape.ToSingleShape(),
                     dtype: DType,
                     name: Name,
                     sparse: args.Sparse,

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

@@ -4,6 +4,7 @@ using System.Linq;
 using System.Text;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using Tensorflow.Keras.Utils;
 using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;
@@ -23,7 +24,7 @@ namespace Tensorflow.Keras.Layers
             this.args = args;
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             /*var shape_set = new HashSet<Shape>();
             var reduced_inputs_shapes = inputs.Select(x => x.shape).ToArray();

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

@@ -4,6 +4,7 @@ using System.Text;
 using static Tensorflow.Binding;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Layers
 {
@@ -14,7 +15,7 @@ namespace Tensorflow.Keras.Layers
 
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             // output_shape = input_shape.dims[1^];
             _buildInputShape = input_shape;

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

@@ -19,6 +19,7 @@ using System.Collections.Generic;
 using System.Linq;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using Tensorflow.Keras.Utils;
 using static Tensorflow.Binding;
 
@@ -53,9 +54,10 @@ namespace Tensorflow.Keras.Layers
             axis = args.Axis.dims.Select(x => (int)x).ToArray();
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
-            var ndims = input_shape.ndim;
+            var single_shape = input_shape.ToSingleShape();
+            var ndims = single_shape.ndim;
             foreach (var (idx, x) in enumerate(axis))
                 if (x < 0)
                     args.Axis.dims[idx] = axis[idx] = ndims + x;
@@ -74,7 +76,7 @@ namespace Tensorflow.Keras.Layers
 
             var axis_to_dim = new Dictionary<int, int>();
             foreach (var x in axis)
-                axis_to_dim[x] = (int)input_shape[x];
+                axis_to_dim[x] = (int)single_shape[x];
 
             inputSpec = new InputSpec(ndim: ndims, axes: axis_to_dim);
             var param_dtype = DType == TF_DataType.DtInvalid ? TF_DataType.TF_FLOAT : DType;

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

@@ -19,6 +19,7 @@ using System.Collections.Generic;
 using System.Linq;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using Tensorflow.Keras.Utils;
 using static Tensorflow.Binding;
 
@@ -49,16 +50,17 @@ namespace Tensorflow.Keras.Layers
             axis = args.Axis.axis;
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
-            var ndims = input_shape.ndim;
+            var single_shape = input_shape.ToSingleShape();
+            var ndims = single_shape.ndim;
             foreach (var (idx, x) in enumerate(axis))
                 if (x < 0)
                     axis[idx] = ndims + x;
 
             var axis_to_dim = new Dictionary<int, int>();
             foreach (var x in axis)
-                axis_to_dim[x] = (int)input_shape[x];
+                axis_to_dim[x] = (int)single_shape[x];
 
             inputSpec = new InputSpec(ndim: ndims, axes: axis_to_dim);
             var param_dtype = DType == TF_DataType.DtInvalid ? TF_DataType.TF_FLOAT : DType;

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

@@ -15,6 +15,7 @@
 ******************************************************************************/
 
 using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Layers
 {
@@ -45,10 +46,11 @@ namespace Tensorflow.Keras.Layers
             input_variance = args.Variance;
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             base.build(input_shape);
-            var ndim = input_shape.ndim;
+            var single_shape = input_shape.ToSingleShape();
+            var ndim = single_shape.ndim;
             foreach (var (idx, x) in enumerate(axis))
                 if (x < 0)
                     axis[idx] = ndim + x;
@@ -57,8 +59,8 @@ namespace Tensorflow.Keras.Layers
             _reduce_axis = range(ndim).Where(d => !_keep_axis.Contains(d)).ToArray();
             var _reduce_axis_mask = range(ndim).Select(d => _keep_axis.Contains(d) ? 0 : 1).ToArray();
             // Broadcast any reduced axes.
-            _broadcast_shape = new Shape(range(ndim).Select(d => _keep_axis.Contains(d) ? input_shape.dims[d] : 1).ToArray());
-            var mean_and_var_shape = _keep_axis.Select(d => input_shape.dims[d]).ToArray();
+            _broadcast_shape = new Shape(range(ndim).Select(d => _keep_axis.Contains(d) ? single_shape.dims[d] : 1).ToArray());
+            var mean_and_var_shape = _keep_axis.Select(d => single_shape.dims[d]).ToArray();
 
             var param_dtype = DType == TF_DataType.DtInvalid ? TF_DataType.TF_FLOAT : DType;
             var param_shape = input_shape;

src/TensorFlowNET.Keras/Layers/Preprocessing/PreprocessingLayer.cs

@@ -77,8 +77,8 @@ namespace Tensorflow.Keras.Layers
             {
                 var data_shape = data.shape;
                 var data_shape_nones = Enumerable.Range(0, data.ndim).Select(x => -1).ToArray();
-                _args.BatchInputShape = BatchInputShape ?? new Shape(data_shape_nones);
-                build(data_shape);
+                _args.BatchInputShape = BatchInputShape ?? new Saving.KerasShapesWrapper(new Shape(data_shape_nones));
+                build(new Saving.KerasShapesWrapper(data_shape));
                 built = true;
             }
         }

src/TensorFlowNET.Keras/Layers/Preprocessing/TextVectorization.cs

@@ -3,6 +3,7 @@ using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Layers
@@ -35,12 +36,12 @@ namespace Tensorflow.Keras.Layers
             var shape = data.output_shapes[0];
             if (shape.ndim == 1)
                 data = data.map(tensor => array_ops.expand_dims(tensor, -1));
-            build(data.variant_tensor.shape);
+            build(new KerasShapesWrapper(data.variant_tensor.shape));
             var preprocessed_inputs = data.map(_preprocess);
             _index_lookup_layer.adapt(preprocessed_inputs);
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             base.build(input_shape);
         }

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

@@ -1,5 +1,6 @@
 using Tensorflow.Keras.ArgsDefinition.Reshaping;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Layers.Reshaping
 {
@@ -11,7 +12,7 @@ namespace Tensorflow.Keras.Layers.Reshaping
             this.args = args;
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             if (args.cropping.rank != 1)
             {

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

@@ -1,5 +1,6 @@
 using Tensorflow.Keras.ArgsDefinition.Reshaping;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Layers.Reshaping
 {
@@ -15,7 +16,7 @@ namespace Tensorflow.Keras.Layers.Reshaping
         {
             this.args = args;
         }
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             built = true;
             _buildInputShape = input_shape;

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

@@ -1,5 +1,6 @@
 using Tensorflow.Keras.ArgsDefinition.Reshaping;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Layers.Reshaping
 {
@@ -14,7 +15,7 @@ namespace Tensorflow.Keras.Layers.Reshaping
             this.args = args;
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             built = true;
             _buildInputShape = input_shape;

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

@@ -5,6 +5,7 @@ using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Utils;
 using static Tensorflow.Binding;
 using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Layers {
     public class Permute : Layer
@@ -14,14 +15,15 @@ namespace Tensorflow.Keras.Layers {
         {
             this.dims = args.dims;
         }
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
-            var rank = input_shape.rank;
+            var single_shape = input_shape.ToSingleShape();
+            var rank = single_shape.rank;
             if (dims.Length != rank - 1)
             {
                 throw new ValueError("Dimensions must match.");
             }
-            permute = new int[input_shape.rank];
+            permute = new int[single_shape.rank];
             dims.CopyTo(permute, 1);
             built = true;
             _buildInputShape = input_shape;

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

@@ -3,6 +3,7 @@ using System.Collections.Generic;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.ArgsDefinition.Rnn;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 // from tensorflow.python.distribute import distribution_strategy_context as ds_context;
 
 namespace Tensorflow.Keras.Layers.Rnn
@@ -36,7 +37,7 @@ namespace Tensorflow.Keras.Layers.Rnn
             //}
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
             if (!cell.Built)
             {

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

@@ -1,5 +1,6 @@
 using System.Data;
 using Tensorflow.Keras.ArgsDefinition.Rnn;
+using Tensorflow.Keras.Saving;
 using Tensorflow.Operations.Activation;
 using static HDF.PInvoke.H5Z;
 using static Tensorflow.ApiDef.Types;
@@ -14,12 +15,13 @@ namespace Tensorflow.Keras.Layers.Rnn
             this.args = args;
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
-            var input_dim = input_shape[-1];
+            var single_shape = input_shape.ToSingleShape();
+            var input_dim = single_shape[-1];
             _buildInputShape = input_shape;
 
-            kernel = add_weight("kernel", (input_shape[-1], args.Units),
+            kernel = add_weight("kernel", (single_shape[-1], args.Units),
                 initializer: args.KernelInitializer
                 //regularizer = self.kernel_regularizer,
                 //constraint = self.kernel_constraint,

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

@@ -3,6 +3,7 @@ using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Keras.ArgsDefinition.Rnn;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Layers.Rnn
 {
@@ -18,11 +19,12 @@ namespace Tensorflow.Keras.Layers.Rnn
             this.args = args;
         }
 
-        public override void build(Shape input_shape)
+        public override void build(KerasShapesWrapper input_shape)
         {
-            var input_dim = input_shape[-1];
+            var single_shape = input_shape.ToSingleShape();
+            var input_dim = single_shape[-1];
 
-            kernel = add_weight("kernel", (input_shape[-1], args.Units),
+            kernel = add_weight("kernel", (single_shape[-1], args.Units),
                 initializer: args.KernelInitializer
             );
 

src/TensorFlowNET.Keras/Models/ModelsApi.cs

@@ -11,7 +11,7 @@ namespace Tensorflow.Keras.Models
 {
     public class ModelsApi: IModelsApi
     {
-        public Functional from_config(ModelConfig config)
+        public Functional from_config(FunctionalConfig config)
             => Functional.from_config(config);
 
         public IModel load_model(string filepath, bool compile = true, LoadOptions? options = null)

src/TensorFlowNET.Keras/Saving/KerasMetaData.cs

@@ -22,16 +22,19 @@ namespace Tensorflow.Keras.Saving
         public int SharedObjectId { get; set; }
         [JsonProperty("must_restore_from_config")]
         public bool MustRestoreFromConfig { get; set; }
+        [JsonProperty("config")]
         public JObject Config { get; set; }
         [JsonProperty("build_input_shape")]
-        public TensorShapeConfig BuildInputShape { get; set; }
+        public KerasShapesWrapper BuildInputShape { get; set; }
         [JsonProperty("batch_input_shape")]
-        public TensorShapeConfig BatchInputShape { get; set; }
+        public KerasShapesWrapper BatchInputShape { get; set; }
         [JsonProperty("activity_regularizer")]
         public IRegularizer ActivityRegularizer { get; set; }
         [JsonProperty("input_spec")]
         public JToken InputSpec { get; set; }
         [JsonProperty("stateful")]
         public bool? Stateful { get; set; }
+        [JsonProperty("model_config")]
+        public KerasModelConfig? ModelConfig { get; set; }
     }
 }

src/TensorFlowNET.Keras/Saving/KerasModelConfig.cs

@@ -0,0 +1,16 @@
+using Newtonsoft.Json;
+using Newtonsoft.Json.Linq;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Saving
+{
+    public class KerasModelConfig
+    {
+        [JsonProperty("class_name")]
+        public string ClassName { get; set; }
+        [JsonProperty("config")]
+        public JObject Config { get; set; }
+    }
+}

src/TensorFlowNET.Keras/Saving/KerasObjectLoader.cs

@@ -8,6 +8,7 @@ using System.Diagnostics;
 using System.Linq;
 using System.Reflection;
 using System.Text.RegularExpressions;
+using Tensorflow.Extensions;
 using Tensorflow.Framework.Models;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
@@ -356,7 +357,7 @@ namespace Tensorflow.Keras.Saving
                 var (obj, setter) = _revive_from_config(identifier, metadata, node_id);
                 if (obj is null)
                 {
-                    (obj, setter) = _revive_custom_object(identifier, metadata);
+                    (obj, setter) = revive_custom_object(identifier, metadata);
                 }
                 if(obj is null)
                 {
@@ -398,7 +399,7 @@ namespace Tensorflow.Keras.Saving
             return (obj, setter);
         }
 
-        private (Trackable, Action<object, object, object>) _revive_custom_object(string identifier, KerasMetaData metadata)
+        private (Trackable, Action<object, object, object>) revive_custom_object(string identifier, KerasMetaData metadata)
         {
             if(identifier == SavedModel.Constants.LAYER_IDENTIFIER)
             {
@@ -437,7 +438,7 @@ namespace Tensorflow.Keras.Saving
             }
             else
             {
-                model = new Functional(new Tensors(), new Tensors(), config["name"].ToObject<string>());
+                model = new Functional(new Tensors(), new Tensors(), config.TryGetOrReturnNull<string>("name"));
             }
 
             // Record this model and its layers. This will later be used to reconstruct
@@ -619,7 +620,7 @@ namespace Tensorflow.Keras.Saving
             }
         }
 
-        private bool _try_build_layer(Layer obj, int node_id, Shape build_input_shape)
+        private bool _try_build_layer(Layer obj, int node_id, KerasShapesWrapper build_input_shape)
         {
             if (obj.Built)
                 return true;
@@ -679,10 +680,10 @@ namespace Tensorflow.Keras.Saving
             return inputs;
         }
 
-        private Shape _infer_input_shapes(int layer_node_id)
+        private KerasShapesWrapper _infer_input_shapes(int layer_node_id)
         {
             var inputs = _infer_inputs(layer_node_id);
-            return nest.map_structure(x => x.shape, inputs);
+            return new KerasShapesWrapper(nest.map_structure(x => x.shape, inputs));
         }
 
         private int? _search_for_child_node(int parent_id, IEnumerable<string> path_to_child)

src/TensorFlowNET.Keras/Utils/base_layer_utils.cs

@@ -173,6 +173,11 @@ namespace Tensorflow.Keras.Utils
                 obj is not Type;
         }
 
+        public static Tensor generate_placeholders_from_shape(Shape shape)
+        {
+            return array_ops.placeholder(keras.backend.floatx(), shape);
+        }
+
         // recusive
         static bool uses_keras_history(Tensor op_input)
         {

src/TensorFlowNET.Keras/Utils/generic_utils.cs

@@ -102,9 +102,9 @@ namespace Tensorflow.Keras.Utils
             return args as LayerArgs;
         }
 
-        public static ModelConfig deserialize_model_config(JToken json)
+        public static FunctionalConfig deserialize_model_config(JToken json)
         {
-            ModelConfig config = new ModelConfig();
+            FunctionalConfig config = new FunctionalConfig();
             config.Name = json["name"].ToObject<string>();
             config.Layers = new List<LayerConfig>();
             var layersToken = json["layers"];