model = keras.Sequential([
    keras.layers.Flatten(input_shape=(14, 14)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(64, activation=tf.nn.relu),
    keras.layers.Dense(32, activation=tf.nn.relu),
    keras.layers.Dense(16, activation=tf.nn.relu),
    keras.layers.Dense(8, activation=tf.nn.relu),
    keras.layers.Dense(2, activation=tf.nn.softmax)
])
model.compile(optimizer=tf.train.AdamOptimizer(),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])