Added unit test for multi input model training using the dataset

test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs

@@ -2,6 +2,7 @@
 using System;
 using Tensorflow.Keras.Optimizers;
 using Tensorflow.NumPy;
+using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;
 
 namespace Tensorflow.Keras.UnitTest
@@ -66,5 +67,79 @@ public void LeNetModel()
             var pred = model.predict((x1, x2));
             Console.WriteLine(pred);
         }
+
+        [TestMethod]
+        public void LeNetModelDataset()
+        {
+            var inputs = keras.Input((28, 28, 1));
+            var conv1 = keras.layers.Conv2D(16, (3, 3), activation: "relu", padding: "same").Apply(inputs);
+            var pool1 = keras.layers.MaxPooling2D((2, 2), 2).Apply(conv1);
+            var conv2 = keras.layers.Conv2D(32, (3, 3), activation: "relu", padding: "same").Apply(pool1);
+            var pool2 = keras.layers.MaxPooling2D((2, 2), 2).Apply(conv2);
+            var flat1 = keras.layers.Flatten().Apply(pool2);
+
+            var inputs_2 = keras.Input((28, 28, 1));
+            var conv1_2 = keras.layers.Conv2D(16, (3, 3), activation: "relu", padding: "same").Apply(inputs_2);
+            var pool1_2 = keras.layers.MaxPooling2D((4, 4), 4).Apply(conv1_2);
+            var conv2_2 = keras.layers.Conv2D(32, (1, 1), activation: "relu", padding: "same").Apply(pool1_2);
+            var pool2_2 = keras.layers.MaxPooling2D((2, 2), 2).Apply(conv2_2);
+            var flat1_2 = keras.layers.Flatten().Apply(pool2_2);
+
+            var concat = keras.layers.Concatenate().Apply((flat1, flat1_2));
+            var dense1 = keras.layers.Dense(512, activation: "relu").Apply(concat);
+            var dense2 = keras.layers.Dense(128, activation: "relu").Apply(dense1);
+            var dense3 = keras.layers.Dense(10, activation: "relu").Apply(dense2);
+            var output = keras.layers.Softmax(-1).Apply(dense3);
+
+            var model = keras.Model((inputs, inputs_2), output);
+            model.summary();
+
+            var data_loader = new MnistModelLoader();
+
+            var dataset = data_loader.LoadAsync(new ModelLoadSetting
+            {
+                TrainDir = "mnist",
+                OneHot = false,
+                ValidationSize = 59900,
+            }).Result;
+
+            var loss = keras.losses.SparseCategoricalCrossentropy();
+            var optimizer = new Adam(0.001f);
+            model.compile(optimizer, loss, new string[] { "accuracy" });
+
+            NDArray x1 = np.reshape(dataset.Train.Data, (dataset.Train.Data.shape[0], 28, 28, 1));
+
+            var multiInputDataset = tf.data.Dataset.zip(
+                tf.data.Dataset.from_tensor_slices(x1),
+                tf.data.Dataset.from_tensor_slices(x1),
+                tf.data.Dataset.from_tensor_slices(dataset.Train.Labels)
+            ).batch(8);
+            multiInputDataset.FirstInputTensorCount = 2;
+
+            model.fit(multiInputDataset, epochs: 3);
+
+            x1 = x1["0:8"];
+
+            multiInputDataset = tf.data.Dataset.zip(
+                tf.data.Dataset.from_tensor_slices(x1),
+                tf.data.Dataset.from_tensor_slices(x1),
+                tf.data.Dataset.from_tensor_slices(dataset.Train.Labels["0:8"])
+            ).batch(8);
+            multiInputDataset.FirstInputTensorCount = 2;
+
+            (model as Engine.Model).evaluate(multiInputDataset);
+
+            x1 = np.ones((1, 28, 28, 1), TF_DataType.TF_FLOAT);
+            var x2 = np.zeros((1, 28, 28, 1), TF_DataType.TF_FLOAT);
+
+            multiInputDataset = tf.data.Dataset.zip(
+                tf.data.Dataset.from_tensor_slices(x1),
+                tf.data.Dataset.from_tensor_slices(x2)
+            ).batch(8);
+            multiInputDataset.FirstInputTensorCount = 2;
+
+            var pred = model.predict(multiInputDataset);
+            Console.WriteLine(pred);
+        }
     }
 }