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Chapter 4
X_train, X_test = X_train / 255.0, X_test / 255.0
# cast
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# convert class vectors to binary class matrices
y_train = tf.keras.utils.to_categorical(y_train, NB_CLASSES)
y_test = tf.keras.utils.to_categorical(y_test, NB_CLASSES)
# initialize the optimizer and model
model = build(input_shape=INPUT_SHAPE, classes=NB_CLASSES)
model.compile(loss="categorical_crossentropy", optimizer=OPTIMIZER,
metrics=["accuracy"])
model.summary()
# use TensorBoard, princess Aurora!
callbacks = [
# Write TensorBoard logs to './logs' directory
tf.keras.callbacks.TensorBoard(log_dir='./logs')
]
# fit
history = model.fit(X_train, y_train,
batch_size=BATCH_SIZE, epochs=EPOCHS,
verbose=VERBOSE, validation_split=VALIDATION_SPLIT,
callbacks=callbacks)
score = model.evaluate(X_test, y_test, verbose=VERBOSE)
print("\nTest score:", score[0])
print('Test accuracy:', score[1])
Now let's run the code. As you can see in Figure 6, the time had a significant increase
and each iteration in our DNN now takes ~28 seconds against ~1-2 seconds for the
net defined in Chapter 1, Neural Network Foundations with TensorFlow 2.0.
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