cifar10.py

import tensorflow as tf
from tensorflow.keras import datasets, layers, models, optimizers

# CIFAR_10 is a set of 60K images 32x32 pixels on 3 channels
IMG_CHANNELS = 3
IMG_ROWS = 32
IMG_COLS = 32

#constant
BATCH_SIZE = 128
EPOCHS = 20
CLASSES = 10
VERBOSE = 1
VALIDATION_SPLIT = 0.2
OPTIM = tf.keras.optimizers.RMSprop()

#define the convnet 
def build(input_shape, classes):
	model = models.Sequential() 
	model.add(layers.Convolution2D(32, (3, 3), activation='relu',
                        input_shape=input_shape))
	model.add(layers.MaxPooling2D(pool_size=(2, 2)))
	model.add(layers.Dropout(0.25)) 

	model.add(layers.Flatten())
	model.add(layers.Dense(512, activation='relu'))
	model.add(layers.Dropout(0.5))
	model.add(layers.Dense(classes, activation='softmax'))
	return model

# data: shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = datasets.cifar10.load_data()
# normalize
X_train, X_test = X_train / 255.0, X_test / 255.0
# convert to categorical
# convert class vectors to binary class matrices
y_train = tf.keras.utils.to_categorical(y_train, CLASSES)
y_test = tf.keras.utils.to_categorical(y_test, CLASSES)

model=build((IMG_ROWS, IMG_COLS, IMG_CHANNELS), CLASSES)
model.summary()

# use TensorBoard, princess Aurora!
callbacks = [
  # Write TensorBoard logs to `./logs` directory
  tf.keras.callbacks.TensorBoard(log_dir='./logs')
]

# train
model.compile(loss='categorical_crossentropy', optimizer=OPTIM,
	metrics=['accuracy'])
 
model.fit(X_train, y_train, batch_size=BATCH_SIZE,
	epochs=EPOCHS, validation_split=VALIDATION_SPLIT, 
	verbose=VERBOSE, callbacks=callbacks) 
score = model.evaluate(X_test, y_test,
                     batch_size=BATCH_SIZE, verbose=VERBOSE)
print("\nTest score:", score[0])
print('Test accuracy:', score[1])