mnist_test.py

import tensorflow as tf
from time import time

start = time()

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(),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  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)

end = time()

def get_time_format(elapsed):
    hour = (elapsed) // (60*60)
    min = (elapsed - hour * 60*60) // (60)
    sec = elapsed % 60
    return '{:02.0f}:{:02.0f}:{:02.0f}'.format(hour, min, sec)

print("The script ran for ", get_time_format(end - start))