Excercise 3 Solution.py

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

    # YOUR CODE STARTS HERE
    class myCallback(tf.keras.callbacks.Callback):
        def on_epoch_end(self, epoch, logs={}):
            if(logs.get('acc')>0.998):
                print("/n Reached 99.8% accuracy so cancelling training!")
                self.model.stop_training = True
    # YOUR CODE ENDS HERE

    mnist = tf.keras.datasets.mnist
    (training_images, training_labels), (test_images, test_labels) = mnist.load_data()
    # YOUR CODE STARTS HERE
    callbacks = myCallback()
    
    training_images=training_images.reshape(60000, 28, 28, 1)
    test_images=test_images.reshape(10000, 28, 28, 1)
    training_images = training_images / 255.0
    test_images = test_images / 255.0
    # YOUR CODE ENDS HERE

    model = tf.keras.models.Sequential([
            # YOUR CODE STARTS HERE
            tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)),
            tf.keras.layers.MaxPooling2D(2, 2),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(256, activation='relu'),
            tf.keras.layers.Dense(10, activation='softmax')
            # YOUR CODE ENDS HERE
    ])

    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
    # model fitting
    history = model.fit(
        # YOUR CODE STARTS HERE
        training_images,
        training_labels,
        epochs = 20,
        callbacks=[callbacks]
        # YOUR CODE ENDS HERE
    )
    # model fitting
    return history.epoch, history.history['acc'][-1]