fix:fix the bug of load LSTM model

src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/GRUCellArgs.cs

@@ -3,7 +3,7 @@ using System;
 using System.Collections.Generic;
 using System.Text;
 
-namespace Tensorflow.Keras.ArgsDefinition.Rnn
+namespace Tensorflow.Keras.ArgsDefinition
 {
     public class GRUCellArgs : AutoSerializeLayerArgs
     {

src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/LSTMArgs.cs

@@ -1,4 +1,4 @@
-namespace Tensorflow.Keras.ArgsDefinition.Rnn
+namespace Tensorflow.Keras.ArgsDefinition
 {
     public class LSTMArgs : RNNArgs
     {

src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/LSTMCellArgs.cs

@@ -1,7 +1,7 @@
 using Newtonsoft.Json;
 using static Tensorflow.Binding; 
 
-namespace Tensorflow.Keras.ArgsDefinition.Rnn
+namespace Tensorflow.Keras.ArgsDefinition
 {
     // TODO: complete the implementation
     public class LSTMCellArgs : AutoSerializeLayerArgs

src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/RNNArgs.cs

@@ -1,8 +1,8 @@
 using Newtonsoft.Json;
 using System.Collections.Generic;
-using Tensorflow.Keras.Layers.Rnn;
+using Tensorflow.Keras.Layers;
 
-namespace Tensorflow.Keras.ArgsDefinition.Rnn
+namespace Tensorflow.Keras.ArgsDefinition
 {
     // TODO(Rinne): add regularizers.
     public class RNNArgs : AutoSerializeLayerArgs
@@ -23,16 +23,22 @@ namespace Tensorflow.Keras.ArgsDefinition.Rnn
         public int? InputDim { get; set; }
         public int? InputLength { get; set; }
         // TODO: Add `num_constants` and `zero_output_for_mask`.
-
+        [JsonProperty("units")]
         public int Units { get; set; }
+        [JsonProperty("activation")]
         public Activation Activation { get; set; }
+        [JsonProperty("recurrent_activation")]
         public Activation RecurrentActivation { get; set; }
+        [JsonProperty("use_bias")]
         public bool UseBias { get; set; } = true;
         public IInitializer KernelInitializer { get; set; }
         public IInitializer RecurrentInitializer { get; set; }
         public IInitializer BiasInitializer { get; set; }
+        [JsonProperty("dropout")]
         public float Dropout { get; set; } = .0f;
+        [JsonProperty("zero_output_for_mask")]
         public bool ZeroOutputForMask { get; set; } = false;
+        [JsonProperty("recurrent_dropout")]
         public float RecurrentDropout { get; set; } = .0f;
     }
 }

src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/RnnOptionalArgs.cs

@@ -3,7 +3,7 @@ using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Common.Types;
 
-namespace Tensorflow.Keras.ArgsDefinition.Rnn
+namespace Tensorflow.Keras.ArgsDefinition
 {
     public class RnnOptionalArgs: IOptionalArgs
     {

src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/SimpleRNNArgs.cs

@@ -1,4 +1,4 @@
-namespace Tensorflow.Keras.ArgsDefinition.Rnn
+namespace Tensorflow.Keras.ArgsDefinition
 {
     public class SimpleRNNArgs : RNNArgs
     {

src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/SimpleRNNCellArgs.cs

@@ -1,6 +1,6 @@
 using Newtonsoft.Json;
 
-namespace Tensorflow.Keras.ArgsDefinition.Rnn
+namespace Tensorflow.Keras.ArgsDefinition
 {
     public class SimpleRNNCellArgs: AutoSerializeLayerArgs
     {

src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/StackedRNNCellsArgs.cs

@@ -1,7 +1,7 @@
 using System.Collections.Generic;
-using Tensorflow.Keras.Layers.Rnn;
+using Tensorflow.Keras.Layers;
 
-namespace Tensorflow.Keras.ArgsDefinition.Rnn
+namespace Tensorflow.Keras.ArgsDefinition
 {
     public class StackedRNNCellsArgs : LayerArgs
     {

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

@@ -1,7 +1,7 @@
 using System;
 using Tensorflow.Framework.Models;
 using Tensorflow.Keras.Engine;
-using Tensorflow.Keras.Layers.Rnn;
+using Tensorflow.Keras.Layers;
 using Tensorflow.NumPy;
 using static Google.Protobuf.Reflection.FieldDescriptorProto.Types;
 

src/TensorFlowNET.Core/Keras/Layers/Rnn/IRnnCell.cs

@@ -3,7 +3,7 @@ using System.Collections.Generic;
 using System.Text;
 using Tensorflow.Common.Types;
 
-namespace Tensorflow.Keras.Layers.Rnn
+namespace Tensorflow.Keras.Layers
 {
     public interface IRnnCell: ILayer
     {

src/TensorFlowNET.Core/Keras/Layers/Rnn/IStackedRnnCells.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using System.Text;
 
-namespace Tensorflow.Keras.Layers.Rnn
+namespace Tensorflow.Keras.Layers
 {
     public interface IStackedRnnCells : IRnnCell
     {

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

@@ -19,9 +19,8 @@ using System.Collections.Generic;
 using Tensorflow.Common.Types;
 using Tensorflow.Keras;
 using Tensorflow.Keras.ArgsDefinition;
-using Tensorflow.Keras.ArgsDefinition.Rnn;
 using Tensorflow.Keras.Engine;
-using Tensorflow.Keras.Layers.Rnn;
+using Tensorflow.Keras.Layers;
 using Tensorflow.Keras.Saving;
 using Tensorflow.NumPy;
 using Tensorflow.Operations;

src/TensorFlowNET.Core/ops.cs

@@ -571,7 +571,9 @@ namespace Tensorflow
             if (tf.Context.executing_eagerly())
                 return true;
             else
-                throw new NotImplementedException("");
+                // TODO(Wanglongzhi2001), implement the false case
+                return true;
+            //throw new NotImplementedException("");
         }
 
         public static bool inside_function()

src/TensorFlowNET.Keras/Layers/LayersApi.cs

@@ -2,9 +2,8 @@
 using Tensorflow.Framework.Models;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.ArgsDefinition.Core;
-using Tensorflow.Keras.ArgsDefinition.Rnn;
 using Tensorflow.Keras.Engine;
-using Tensorflow.Keras.Layers.Rnn;
+using Tensorflow.Keras.Layers;
 using Tensorflow.NumPy;
 using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;

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

@@ -6,7 +6,7 @@ using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Utils;
 
-namespace Tensorflow.Keras.Layers.Rnn
+namespace Tensorflow.Keras.Layers
 {
     public abstract class DropoutRNNCellMixin: Layer, IRnnCell
     {

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

@@ -3,12 +3,11 @@ using System.Collections.Generic;
 using System.Diagnostics;
 using System.Text;
 using Tensorflow.Keras.ArgsDefinition;
-using Tensorflow.Keras.ArgsDefinition.Rnn;
 using Tensorflow.Common.Extensions;
 using Tensorflow.Common.Types;
 using Tensorflow.Keras.Saving;
 
-namespace Tensorflow.Keras.Layers.Rnn
+namespace Tensorflow.Keras.Layers
 {
     /// <summary>
     /// Cell class for the GRU layer.

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

@@ -1,10 +1,10 @@
 using System.Linq;
-using Tensorflow.Keras.ArgsDefinition.Rnn;
+using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Common.Types;
 using Tensorflow.Common.Extensions;
 
-namespace Tensorflow.Keras.Layers.Rnn
+namespace Tensorflow.Keras.Layers
 {
     /// <summary>
     /// Long Short-Term Memory layer - Hochreiter 1997.

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

@@ -3,12 +3,12 @@ using Serilog.Core;
 using System.Diagnostics;
 using Tensorflow.Common.Extensions;
 using Tensorflow.Common.Types;
-using Tensorflow.Keras.ArgsDefinition.Rnn;
+using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Saving;
 using Tensorflow.Keras.Utils;
 
-namespace Tensorflow.Keras.Layers.Rnn
+namespace Tensorflow.Keras.Layers
 {
     /// <summary>
     /// Cell class for the LSTM layer.

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

@@ -3,7 +3,6 @@ using System;
 using System.Collections.Generic;
 using System.Reflection;
 using Tensorflow.Keras.ArgsDefinition;
-using Tensorflow.Keras.ArgsDefinition.Rnn;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Saving;
 using Tensorflow.Util;
@@ -14,7 +13,7 @@ using Tensorflow.Common.Types;
 using System.Runtime.CompilerServices;
 // from tensorflow.python.distribute import distribution_strategy_context as ds_context;
 
-namespace Tensorflow.Keras.Layers.Rnn
+namespace Tensorflow.Keras.Layers
 {
     /// <summary>
     /// Base class for recurrent layers.
@@ -185,6 +184,7 @@ namespace Tensorflow.Keras.Layers.Rnn
 
         public override void build(KerasShapesWrapper input_shape)
         {
+            _buildInputShape = input_shape;
             input_shape = new KerasShapesWrapper(input_shape.Shapes[0]);
 
             InputSpec get_input_spec(Shape shape)

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

@@ -4,7 +4,7 @@ using System.Text;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
 
-namespace Tensorflow.Keras.Layers.Rnn
+namespace Tensorflow.Keras.Layers
 {
     public abstract class RnnBase: Layer
     {

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

@@ -1,11 +1,11 @@
 using System.Data;
-using Tensorflow.Keras.ArgsDefinition.Rnn;
+using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Saving;
 using Tensorflow.Operations.Activation;
 using static HDF.PInvoke.H5Z;
 using static Tensorflow.ApiDef.Types;
 
-namespace Tensorflow.Keras.Layers.Rnn
+namespace Tensorflow.Keras.Layers
 {
     public class SimpleRNN : RNN
     {

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

@@ -1,7 +1,7 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
-using Tensorflow.Keras.ArgsDefinition.Rnn;
+using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Saving;
 using Tensorflow.Common.Types;
@@ -9,7 +9,7 @@ using Tensorflow.Common.Extensions;
 using Tensorflow.Keras.Utils;
 using Tensorflow.Graphs;
 
-namespace Tensorflow.Keras.Layers.Rnn
+namespace Tensorflow.Keras.Layers
 {
     /// <summary>
     /// Cell class for SimpleRNN.

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

@@ -3,12 +3,12 @@ using System.ComponentModel;
 using System.Linq;
 using Tensorflow.Common.Extensions;
 using Tensorflow.Common.Types;
-using Tensorflow.Keras.ArgsDefinition.Rnn;
+using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Saving;
 using Tensorflow.Keras.Utils;
 
-namespace Tensorflow.Keras.Layers.Rnn
+namespace Tensorflow.Keras.Layers
 {
     public class StackedRNNCells : Layer, IRnnCell
     {

src/TensorFlowNET.Keras/Saving/KerasObjectLoader.cs

@@ -13,7 +13,6 @@ using Tensorflow.Framework.Models;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Layers;
-using Tensorflow.Keras.Layers.Rnn;
 using Tensorflow.Keras.Losses;
 using Tensorflow.Keras.Metrics;
 using Tensorflow.Keras.Saving.SavedModel;

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

@@ -3,7 +3,7 @@ using System.Collections.Generic;
 using System.Linq;
 using System.Text;
 using Tensorflow.Keras.Engine;
-using Tensorflow.Keras.Layers.Rnn;
+using Tensorflow.Keras.Layers;
 using Tensorflow.Keras.Metrics;
 using Tensorflow.Train;
 

src/TensorFlowNET.Keras/Utils/RnnUtils.cs

@@ -3,7 +3,7 @@ using System.Collections.Generic;
 using System.Diagnostics;
 using System.Text;
 using Tensorflow.Common.Types;
-using Tensorflow.Keras.Layers.Rnn;
+using Tensorflow.Keras.Layers;
 using Tensorflow.Common.Extensions;
 
 namespace Tensorflow.Keras.Utils

test/TensorFlowNET.Keras.UnitTest/Layers/Rnn.Test.cs

@@ -6,7 +6,7 @@ using System.Text;
 using System.Threading.Tasks;
 using Tensorflow.Common.Types;
 using Tensorflow.Keras.Engine;
-using Tensorflow.Keras.Layers.Rnn;
+using Tensorflow.Keras.Layers;
 using Tensorflow.Keras.Saving;
 using Tensorflow.NumPy;
 using Tensorflow.Train;

test/TensorFlowNET.Keras.UnitTest/Model/ModelLoadTest.cs

@@ -1,5 +1,7 @@
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Microsoft.VisualStudio.TestPlatform.Utilities;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using System.Linq;
+using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Optimizers;
 using Tensorflow.Keras.UnitTest.Helpers;
 using Tensorflow.NumPy;
@@ -79,6 +81,31 @@ public class ModelLoadTest
         model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
     }
 
+    [TestMethod]
+    public void LSTMLoad()
+    {
+        var inputs = np.random.randn(10, 5, 3);
+        var outputs = np.random.randn(10, 1);
+        var model = keras.Sequential();
+        model.add(keras.Input(shape: (5, 3)));
+        var lstm = keras.layers.LSTM(32);
+
+        model.add(lstm);
+
+        model.add(keras.layers.Dense(1, keras.activations.Sigmoid));
+
+        model.compile(optimizer: keras.optimizers.Adam(),
+                    loss: keras.losses.MeanSquaredError(),
+        new[] { "accuracy" });
+
+        var result = model.fit(inputs.numpy(), outputs.numpy(), batch_size: 10, epochs: 3, workers: 16, use_multiprocessing: true);
+
+        model.save("LSTM_Random");
+
+        var model_loaded = keras.models.load_model("LSTM_Random");
+        model_loaded.summary();
+    }
+
     [Ignore]
     [TestMethod]
     public void VGG19()