Add two simple sequential test case of pb model save.

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

@@ -25,7 +25,7 @@ namespace Tensorflow.Keras.Engine
             ConcreteFunction? signatures = null,
             bool save_traces = true)
         {
-            if (save_format != "pb")
+            if (save_format != "tf")
             {
                 saver.save(this, filepath);
             }

test/TensorFlowNET.Keras.UnitTest/SaveTest.cs → test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelTest.cs

@@ -17,18 +17,13 @@ using Tensorflow.Keras.Metrics;
 using Tensorflow.Keras.Optimizers;
 using Tensorflow.Operations;
 
-namespace TensorFlowNET.Keras.UnitTest;
-
-public static class AutoGraphExtension
-{
-    
-}
+namespace TensorFlowNET.Keras.UnitTest.SaveModel;
 
 [TestClass]
-public class SaveTest
+public class SequentialModelTest
 {
     [TestMethod]
-    public void Test()
+    public void SimpleModelFromAutoCompile()
     {
         var inputs = new KerasInterface().Input((28, 28, 1));
         var x = new Flatten(new FlattenArgs()).Apply(inputs);
@@ -36,10 +31,8 @@ public class SaveTest
         x = new LayersApi().Dense(units: 10).Apply(x);
         var outputs = new LayersApi().Softmax(axis: 1).Apply(x);
         var model = new KerasInterface().Model(inputs, outputs);
-        
-        model.compile(new Adam(0.001f), new LossesApi().SparseCategoricalCrossentropy(), new  string[]{"accuracy"});
 
-        var g = ops.get_default_graph();
+        model.compile(new Adam(0.001f), new LossesApi().SparseCategoricalCrossentropy(), new string[] { "accuracy" });
 
         var data_loader = new MnistModelLoader();
         var num_epochs = 1;
@@ -49,34 +42,41 @@ public class SaveTest
         {
             TrainDir = "mnist",
             OneHot = false,
-            ValidationSize = 50000,
+            ValidationSize = 10000,
         }).Result;
-        
+
         model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
-        
-        model.save("C:\\Work\\tf.net\\tf_test\\tf.net.model", save_format:"pb");
+
+        model.save("C:\\Work\\tf.net\\tf_test\\tf.net.simple.compile", save_format: "tf");
     }
 
     [TestMethod]
-    public void Temp()
+    public void SimpleModelFromSequential()
     {
-        var graph = new Graph();
-        var g = graph.as_default();
-        //var input_tensor = array_ops.placeholder(TF_DataType.TF_FLOAT, new int[] { 1 }, "test_string_tensor");
-        var input_tensor = tf.placeholder(tf.int32, new int[] { 1 }, "aa");
-        var wrapped_func = tf.autograph.to_graph(func);
-        var res = wrapped_func(input_tensor);
-        g.Exit();
-    }
+        Model model = KerasApi.keras.Sequential(new List<ILayer>()
+        {
+            keras.layers.InputLayer((28, 28, 1)),
+            keras.layers.Flatten(),
+            keras.layers.Dense(100, "relu"),
+            keras.layers.Dense(10),
+            keras.layers.Softmax(1)
+        });
 
-    private Tensor func(Tensor tensor)
-    {
-        return gen_ops.neg(tensor);
-        //return array_ops.identity(tensor);
-        //tf.device("cpu:0");
-        //using (ops.control_dependencies(new object[] { res.op }))
-        //{
-        //    return array_ops.identity(tensor);
-        //}
+        model.compile(new Adam(0.001f), new LossesApi().SparseCategoricalCrossentropy(), new string[] { "accuracy" });
+
+        var data_loader = new MnistModelLoader();
+        var num_epochs = 1;
+        var batch_size = 50;
+
+        var dataset = data_loader.LoadAsync(new ModelLoadSetting
+        {
+            TrainDir = "mnist",
+            OneHot = false,
+            ValidationSize = 10000,
+        }).Result;
+
+        model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
+
+        model.save("C:\\Work\\tf.net\\tf_test\\tf.net.simple.sequential", save_format: "tf");
     }
 }