Introduction-to-TensorFlow-for-Artificial-Intelligence-Machine-Learning-and-Deep

GRADED FUNCTION: train_mnist

def train_mnist(): # Please write your code only where you are indicated. # please do not remove # model fitting inline comments.

# YOUR CODE SHOULD START HERE
class myCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if(logs.get('acc')>0.99):
            print("/nReached 99% accuracy so cancelling training!")
            self.model.stop_training = True
# YOUR CODE SHOULD END HERE

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
# YOUR CODE SHOULD START HERE
x_train, x_test = x_train / 255.0, x_test / 255.0


callbacks = myCallback()
# YOUR CODE SHOULD END HERE
model = tf.keras.models.Sequential([
    # YOUR CODE SHOULD START HERE
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation=tf.nn.relu),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
    # YOUR CODE SHOULD END HERE
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# model fitting
history = model.fit(# YOUR CODE SHOULD START HERE
    x_train,
    y_train,
    epochs=10,
    callbacks=[callbacks]
          # YOUR CODE SHOULD END HERE
)
# model fitting
return history.epoch, history.history['acc'][-1]