def get_data(path, size, dataset):
# Retrieves all preprocessed images and labels using a tensorflow queue.
# This only uses the cpu.
os.environ["CUDA_VISIBLE_DEVICES"] = ""
with tf.device("/cpu:0"):
dataset = cifar_input.build_data(path, size, dataset)
sess = tf.Session()
images, labels = sess.run(dataset)
sess.close()
return images, labels
def get_data(path, size, dataset):
# Retrieves all preprocessed images and labels using a tensorflow queue.
# This only uses the cpu.
os.environ["CUDA_VISIBLE_DEVICES"] = ""
with tf.device("/cpu:0"):
dataset = cifar_input.build_data(path, size, dataset)
sess = tf.Session()
images, labels = sess.run(dataset)
sess.close()
return images, labels
def get_data(path, size):
os.environ['CUDA_VISIBLE_DEVICES'] = ''
with tf.device('/cpu:0'):
queue = cifar_input.build_data(path, size)
sess = tf.Session()
coord = tf.train.Coordinator()
tf.train.start_queue_runners(sess, coord=coord)
images, labels = sess.run(queue)
coord.request_stop()
sess.close()
return (images[:int(size / 3), :],
images[int(size / 3):int(2 * size / 3), :],
images[int(2 * size / 3):, :], labels)
def get_data(path, size, dataset):
# Retrieves all preprocessed images and labels using a tensorflow queue.
# This only uses the cpu.
os.environ["CUDA_VISIBLE_DEVICES"] = ""
with tf.device("/cpu:0"):
queue = cifar_input.build_data(path, size, dataset)
sess = tf.Session()
coord = tf.train.Coordinator()
tf.train.start_queue_runners(sess, coord=coord)
images, labels = sess.run(queue)
coord.request_stop()
sess.close()
return images, labels