import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
# see 2c)
#   tf.keras.layers.Dense(512, activation=tf.nn.relu),
# see 2d)
#   tf.keras.layers.Dense(1, activation=tf.nn.relu),
# see 2e)
#   tf.keras.layers.Dense(512, activation=tf.nn.tanh),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)

# see 2b)
model.evaluate(x_train, y_train)

# see 2f)
model.save('mnist-tutorial.h5')
