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Chapter 4
Congratulations! You have defined a deeper network. Let us run the code for
40 iterations reaching an accuracy of 82%! Let's add the remaining part of the
code for the sake of completeness. The first part is to load and normalize the data:
import tensorflow as tf
from tensorflow.keras import datasets, layers, models, regularizers,
optimizers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
EPOCHS=50
NUM_CLASSES = 10
def load_data():
(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# normalize
mean = np.mean(x_train,axis=(0,1,2,3))
std = np.std(x_train,axis=(0,1,2,3))
x_train = (x_train-mean)/(std+1e-7)
x_test = (x_test-mean)/(std+1e-7)
y_train = tf.keras.utils.to_categorical(y_train,NUM_CLASSES)
y_test = tf.keras.utils.to_categorical(y_test,NUM_CLASSES)
return x_train, y_train, x_test, y_test
Then we need to have a part to train the network:
(x_train, y_train, x_test, y_test) = load_data()
model = build_model()
model.compile(loss='categorical_crossentropy',
optimizer='RMSprop',
metrics=['accuracy'])
# train
batch_size = 64
model.fit(x_train, y_train, batch_size=batch_size,
epochs=EPOCHS, validation_data=(x_test,y_test))
score = model.evaluate(x_test, y_test, batch_size=batch_size)
print("\nTest score:", score[0])
print('Test accuracy:', score[1])
[ 127 ]