C1W2: Implementing Callbacks in TensorFlow using the MNIST Dataset

import tensorflow as tf
from tensorflow.keras import layers, losses

MNIST dataset

• 60,000 28x28 grayscale images of the 10 digits

tf.keras.datasets.mnist.load_data

(x_train, y_train), _ = tf.keras.datasets.mnist.load_data()

class myCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if logs.get('accuracy') is not None and logs.get('accuracy') > 0.99:
            print("\nReached 99% accuracy so cancelling training!") 
            self.model.stop_training = True

def train_mnist(x_train, y_train):
    callbacks = myCallback()

    model = tf.keras.Sequential([
        layers.Rescaling(1/255, input_shape=(28, 28)),
        layers.Flatten(),
        layers.Dense(512, activation='relu'),
        layers.Dense(10)])

    model.compile(optimizer='adam',
                  loss=losses.SparseCategoricalCrossentropy(from_logits=True), 
                  metrics=['accuracy'])

    history = model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])

    return history
    

history = train_mnist(x_train, y_train)

Epoch 1/10
1875/1875 [==============================] - 10s 4ms/step - loss: 0.1972 - accuracy: 0.9423
Epoch 2/10
1875/1875 [==============================] - 13s 7ms/step - loss: 0.0796 - accuracy: 0.9759
Epoch 3/10
1875/1875 [==============================] - 13s 7ms/step - loss: 0.0510 - accuracy: 0.9843
Epoch 4/10
1875/1875 [==============================] - 13s 7ms/step - loss: 0.0361 - accuracy: 0.9888
Epoch 5/10
1868/1875 [============================>.] - ETA: 0s - loss: 0.0271 - accuracy: 0.9915
Reached 99% accuracy so cancelling training!
1875/1875 [==============================] - 13s 7ms/step - loss: 0.0270 - accuracy: 0.9915