Add alexnet loading test and check for loaded weights.

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

@@ -72,7 +72,7 @@ namespace Tensorflow.Keras.Common
             }
             if (dims is null)
             {
-                throw new ValueError("Cannot deserialize 'null' to `Shape`.");
+                return null;
             }
             long[] convertedDims = new long[dims.Length];
             for(int i = 0; i < dims.Length; i++)

src/TensorFlowNET.Keras/Utils/generic_utils.cs

@@ -19,6 +19,7 @@ using Newtonsoft.Json.Linq;
 using System;
 using System.Collections;
 using System.Collections.Generic;
+using System.Data;
 using System.Diagnostics;
 using System.Linq;
 using Tensorflow.Keras.ArgsDefinition;
@@ -65,6 +66,10 @@ namespace Tensorflow.Keras.Utils
                 "ELU" => new ELU(config.ToObject<ELUArgs>()),
                 "Dense" => new Dense(config.ToObject<DenseArgs>()),
                 "Softmax" => new Softmax(config.ToObject<SoftmaxArgs>()),
+                "Conv2D" => new Conv2D(config.ToObject<Conv2DArgs>()),
+                "BatchNormalization" => new BatchNormalization(config.ToObject<BatchNormalizationArgs>()),
+                "MaxPooling2D" => new MaxPooling2D(config.ToObject<MaxPooling2DArgs>()),
+                "Dropout" => new Dropout(config.ToObject<DropoutArgs>()),
                 _ => throw new NotImplementedException($"The deserialization of <{class_name}> has not been supported. Usually it's a miss during the development. " +
                         $"Please submit an issue to https://github.com/SciSharp/TensorFlow.NET/issues")
             };
@@ -80,6 +85,10 @@ namespace Tensorflow.Keras.Utils
                 "ELU" => new ELU(args as ELUArgs),
                 "Dense" => new Dense(args as DenseArgs),
                 "Softmax" => new Softmax(args as SoftmaxArgs),
+                "Conv2D" => new Conv2D(args as Conv2DArgs),
+                "BatchNormalization" => new BatchNormalization(args as BatchNormalizationArgs),
+                "MaxPooling2D" => new MaxPooling2D(args as MaxPooling2DArgs),
+                "Dropout" => new Dropout(args as DropoutArgs),
                 _ => throw new NotImplementedException($"The deserialization of <{class_name}> has not been supported. Usually it's a miss during the development. " +
                         $"Please submit an issue to https://github.com/SciSharp/TensorFlow.NET/issues")
             };
@@ -95,6 +104,10 @@ namespace Tensorflow.Keras.Utils
                 "ELU" => config.ToObject<ELUArgs>(),
                 "Dense" => config.ToObject<DenseArgs>(),
                 "Softmax" => config.ToObject<SoftmaxArgs>(),
+                "Conv2D" => config.ToObject<Conv2DArgs>(),
+                "BatchNormalization" => config.ToObject<BatchNormalizationArgs>(),
+                "MaxPooling2D" => config.ToObject<MaxPooling2DArgs>(),
+                "Dropout" => config.ToObject<DropoutArgs>(),
                 _ => throw new NotImplementedException($"The deserialization of <{class_name}> has not been supported. Usually it's a miss during the development. " +
                         $"Please submit an issue to https://github.com/SciSharp/TensorFlow.NET/issues")
             };

test/TensorFlowNET.Keras.UnitTest/Assets/simple_model_from_auto_compile/keras_metadata.pb

@@ -0,0 +1,9 @@
+
+´$root"_tf_keras_network*’${"name": "model", "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": "Functional", "config": {"name": "model", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "name": "input_1", "inbound_nodes": []}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "name": "flatten", "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 100, "activation": "relu", "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}, "name": "dense", "inbound_nodes": [[["flatten", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 10, "activation": "linear", "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}, "name": "dense_1", "inbound_nodes": [[["dense", 0, 0, {}]]]}, {"class_name": "Softmax", "config": {"name": "softmax", "trainable": true, "dtype": "float32", "axis": -1}, "name": "softmax", "inbound_nodes": [[["dense_1", 0, 0, {}]]]}], "input_layers": [["input_1", 0, 0]], "output_layers": [["softmax", 0, 0]]}, "shared_object_id": 9, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 28, 28, 1]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 28, 28, 1]}, "float32", "input_1"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 28, 28, 1]}, "float32", "input_1"]}, "keras_version": "2.11.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "model", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "name": "input_1", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "name": "flatten", "inbound_nodes": [[["input_1", 0, 0, {}]]], "shared_object_id": 1}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 100, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense", "inbound_nodes": [[["flatten", 0, 0, {}]]], "shared_object_id": 4}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 5}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 6}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_1", "inbound_nodes": [[["dense", 0, 0, {}]]], "shared_object_id": 7}, {"class_name": "Softmax", "config": {"name": "softmax", "trainable": true, "dtype": "float32", "axis": -1}, "name": "softmax", "inbound_nodes": [[["dense_1", 0, 0, {}]]], "shared_object_id": 8}], "input_layers": [["input_1", 0, 0]], "output_layers": [["softmax", 0, 0]]}}}2

+†
root.layer-0"_tf_keras_input_layer*Ö{"class_name": "InputLayer", "name": "input_1", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 28, 28, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}}2

+Íroot.layer-1"_tf_keras_layer*£{"name": "flatten", "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": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "inbound_nodes": [[["input_1", 0, 0, {}]]], "shared_object_id": 1, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 14}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 28, 28, 1]}}2

+¯root.layer_with_weights-0"_tf_keras_layer*ø{"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": 100, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["flatten", 0, 0, {}]]], "shared_object_id": 4, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 784}}, "shared_object_id": 15}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 784]}}2

+²root.layer_with_weights-1"_tf_keras_layer*û{"name": "dense_1", "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_1", "trainable": true, "dtype": "float32", "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 5}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 6}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense", 0, 0, {}]]], "shared_object_id": 7, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 100}}, "shared_object_id": 16}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 100]}}2

+Šroot.layer-4"_tf_keras_layer*à{"name": "softmax", "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": "Softmax", "config": {"name": "softmax", "trainable": true, "dtype": "float32", "axis": -1}, "inbound_nodes": [[["dense_1", 0, 0, {}]]], "shared_object_id": 8, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10]}}2

+¹
Troot.keras_api.metrics.0"_tf_keras_metric*‚
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 17}2

+™Uroot.keras_api.metrics.1"_tf_keras_metric*â
{"class_name": "MeanMetricWrapper", "name": "sparse_categorical_accuracy", "dtype": "float32", "config": {"name": "sparse_categorical_accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 18}2

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

@@ -12,6 +12,8 @@ using Tensorflow.Keras.Metrics;
 using Tensorflow;
 using Tensorflow.Keras.Optimizers;
 using static Tensorflow.KerasApi;
+using Tensorflow.NumPy;
+using static TensorFlowNET.Keras.UnitTest.SaveModel.SequentialModelSave;
 
 namespace TensorFlowNET.Keras.UnitTest.SaveModel;
 
@@ -19,15 +21,20 @@ namespace TensorFlowNET.Keras.UnitTest.SaveModel;
 public class SequentialModelLoad
 {
     [TestMethod]
-    public void SimpleModelFromSequential()
+    public void SimpleModelFromAutoCompile()
     {
-        //new SequentialModelSave().SimpleModelFromSequential();
-        var model = keras.models.load_model(@"D:\development\tf.net\tf_test\tf.net.simple.sequential");
-
+        var model = keras.models.load_model(@"Assets/simple_model_from_auto_compile");
         model.summary();
 
         model.compile(new Adam(0.0001f), new LossesApi().SparseCategoricalCrossentropy(), new string[] { "accuracy" });
 
+        // check the weights
+        var kernel1 = np.load(@"Assets/simple_model_from_auto_compile/kernel1.npy");
+        var bias0 = np.load(@"Assets/simple_model_from_auto_compile/bias0.npy");
+
+        Assert.IsTrue(kernel1.Zip(model.TrainableWeights[2].numpy()).All(x => x.First == x.Second));
+        Assert.IsTrue(bias0.Zip(model.TrainableWeights[1].numpy()).All(x => x.First == x.Second));
+
         var data_loader = new MnistModelLoader();
         var num_epochs = 1;
         var batch_size = 8;
@@ -40,6 +47,22 @@ public class SequentialModelLoad
         }).Result;
 
         model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
+    }
+
+    [TestMethod]
+    public void AlexnetFromSequential()
+    {
+        new SequentialModelSave().AlexnetFromSequential();
+        var model = keras.models.load_model(@"./alexnet_from_sequential");
         model.summary();
+
+        model.compile(new Adam(0.001f), new LossesApi().SparseCategoricalCrossentropy(from_logits: true), new string[] { "accuracy" });
+
+        var num_epochs = 1;
+        var batch_size = 8;
+
+        var dataset = new RandomDataSet(new Shape(227, 227, 3), 16);
+
+        model.fit(dataset.Data, dataset.Labels, batch_size, num_epochs);
     }
 }

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

@@ -78,7 +78,7 @@ public class SequentialModelSave
     }
 
     [TestMethod]
-    public void AlexModelFromSequential()
+    public void AlexnetFromSequential()
     {
         Model model = KerasApi.keras.Sequential(new List<ILayer>()
         {
@@ -121,7 +121,7 @@ public class SequentialModelSave
 
         model.fit(dataset.Data, dataset.Labels, batch_size, num_epochs);
 
-        model.save("./pb_alex_sequential", save_format: "tf");
+        model.save("./alexnet_from_sequential", save_format: "tf");
 
         // The saved model can be test with the following python code:
         #region alexnet_python_code

test/TensorFlowNET.Keras.UnitTest/Tensorflow.Keras.UnitTest.csproj

@@ -27,4 +27,28 @@
     <ProjectReference Include="..\..\src\TensorFlowNET.Keras\Tensorflow.Keras.csproj" />
   </ItemGroup>
 
+  <ItemGroup>
+    <None Update="Assets\simple_model_from_auto_compile\fingerprint.pb">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="Assets\simple_model_from_auto_compile\keras_metadata.pb">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="Assets\simple_model_from_auto_compile\saved_model.pb">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="Assets\simple_model_from_auto_compile\variables\variables.data-00000-of-00001">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="Assets\simple_model_from_auto_compile\variables\variables.index">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="Assets\simple_model_from_auto_compile\kernel1.npy">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+    <None Update="Assets\simple_model_from_auto_compile\bias0.npy">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+
 </Project>