fix: fix the bug of load LSTM model and add test

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

@@ -3,7 +3,7 @@
 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 @@ public class RNNArgs : AutoSerializeLayerArgs
         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.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.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 @@ limitations under the License.
 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 @@ public static bool executing_eagerly_outside_functions()
             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.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.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 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.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 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 @@ private Tensors compute_mask(Tensors inputs, Tensors mask)
 
         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 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

@@ -1,15 +1,15 @@
 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;
 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.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.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.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.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/Assets/lstm_from_sequential/fingerprint.pb

@@ -0,0 +1 @@
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test/TensorFlowNET.Keras.UnitTest/Assets/lstm_from_sequential/keras_metadata.pb

@@ -0,0 +1,7 @@
+
+Õ&root"_tf_keras_sequential*°&{"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 5, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}}, {"class_name": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 32, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "shared_object_id": 9, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 5, 3]}, "ndim": 3, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 5, 3]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 5, 3]}, "float32", "input_1"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 5, 3]}, "float32", "input_1"]}, "keras_version": "2.12.0", "backend": "tensorflow", "model_config": {"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 5, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "shared_object_id": 0}, {"class_name": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 32, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}, "shared_object_id": 5}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 6}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 7}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 8}]}}, "training_config": {"loss": "binary_crossentropy", "metrics": [[{"class_name": "MeanMetricWrapper", "config": {"name": "accuracy", "dtype": "float32", "fn": "binary_accuracy"}, "shared_object_id": 11}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "Custom>Adam", "config": {"name": "Adam", "weight_decay": null, "clipnorm": null, "global_clipnorm": null, "clipvalue": null, "use_ema": false, "ema_momentum": 0.99, "ema_overwrite_frequency": null, "jit_compile": false, "is_legacy_optimizer": false, "learning_rate": 0.0010000000474974513, "beta_1": 0.9, "beta_2": 0.999, "epsilon": 1e-07, "amsgrad": false}}}}2

+Ý
root.layer_with_weights-0"_tf_keras_rnn_layer*¢{"name": "lstm", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 32, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}, "shared_object_id": 5, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, null, 3]}, "ndim": 3, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 12}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 5, 3]}}2

+‚root.layer_with_weights-1"_tf_keras_layer*Ë{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 6}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 7}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 8, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 32}}, "shared_object_id": 13}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32]}}2

+ìroot.layer_with_weights-0.cell"_tf_keras_layer*°{"name": "lstm_cell", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "LSTMCell", "config": {"name": "lstm_cell", "trainable": true, "dtype": "float32", "units": 32, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}, "shared_object_id": 4, "build_input_shape": {"class_name": "__tuple__", "items": [null, 3]}}2

+¹
Rroot.keras_api.metrics.0"_tf_keras_metric*‚
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 14}2

+ç
Sroot.keras_api.metrics.1"_tf_keras_metric*°
{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "binary_accuracy"}, "shared_object_id": 11}2

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

@@ -6,7 +6,7 @@
 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 +81,17 @@ public void ModelWithSelfDefinedModule()
         model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
     }
 
+    [TestMethod]
+    public void LSTMLoad()
+    {
+        var model = tf.keras.models.load_model(@"Assets/lstm_from_sequential");
+        model.summary();
+        model.compile(tf.keras.optimizers.Adam(), tf.keras.losses.MeanSquaredError(), new string[] { "accuracy" });
+        var inputs = tf.random.normal(shape: (10, 5, 3));
+        var outputs = tf.random.normal(shape: (10, 1));
+        model.fit(inputs.numpy(), outputs.numpy(), batch_size: 10, epochs: 5, workers: 16, use_multiprocessing: true);
+    }
+
     [Ignore]
     [TestMethod]
     public void VGG19()