excercise2_solution.py

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]