C1W3: Improve MNIST with Convolutions

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') > 0.995:
            print('\nReached 99.5% accuracy so cancelling training!')
            self.model.stop_training = True

def convolutional_model():
    
    model = tf.keras.Sequential([
        layers.Lambda(lambda x: tf.expand_dims(x, axis=-1), input_shape=(28, 28)),
        layers.Rescaling(1/255),
        layers.Conv2D(32, 3, activation='relu'),
        layers.MaxPooling2D(),
        
        layers.Flatten(),
        layers.Dense(128, activation='relu'),
        layers.Dense(10)])
    
    model.compile(optimizer='adam', 
                  loss=losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])

    return model

model = convolutional_model()

callbacks = myCallback()

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

Epoch 1/10
1875/1875 [==============================] - 12s 5ms/step - loss: 0.1582 - accuracy: 0.9529
Epoch 2/10
1875/1875 [==============================] - 10s 6ms/step - loss: 0.0561 - accuracy: 0.9833
Epoch 3/10
1875/1875 [==============================] - 10s 5ms/step - loss: 0.0367 - accuracy: 0.9883
Epoch 4/10
1875/1875 [==============================] - 11s 6ms/step - loss: 0.0249 - accuracy: 0.9923
Epoch 5/10
1875/1875 [==============================] - 10s 6ms/step - loss: 0.0182 - accuracy: 0.9942
Epoch 6/10
1873/1875 [============================>.] - ETA: 0s - loss: 0.0124 - accuracy: 0.9959
Reached 99.5% accuracy so cancelling training!
1875/1875 [==============================] - 11s 6ms/step - loss: 0.0124 - accuracy: 0.9959