Update app.py

app.py

@@ -8,4 +8,33 @@ model.add(layers.MaxPooling2D((2, 2)))
 model.add(layers.Conv2D(64, (3, 3), activation='relu'))
 model.add(layers.MaxPooling2D((2, 2)))
 model.add(layers.Conv2D(64, (3, 3), activation='relu'))
-model.summary()
+#model.summary()
+
+# add fully-connected layers at the end of the model
+model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(10, activation='softmax'))
+model.summary()
+
+from keras.datasets import mnist
+#from keras.utils import to_categorical
+from tensorflow.keras.utils import to_categorical
+
+(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
+
+train_images = train_images.reshape((60000, 28, 28, 1))
+train_images = train_images.astype('float32') / 255
+
+test_images = test_images.reshape((10000, 28, 28, 1))
+test_images = test_images.astype('float32') / 255
+
+train_labels = to_categorical(train_labels)
+test_labels = to_categorical(test_labels)
+
+# compile and train the model
+model.compile(optimizer='rmsprop',
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+model.fit(train_images, train_labels, epochs=5, batch_size=64)
+
+test_loss, test_acc = model.evaluate(test_images, test_labels)