def main(args):
dataset = Dataset(batch_size)
class_num = dataset.class_num()
image_batch, label_batch = dataset.get_train_batch()
image_batch = tf.reshape(image_batch, [-1, image_size, image_size, 3])
glaph_ = Graph(batch_size, class_num)
train_op = glaph_.inference(image_batch, label_batch)
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# with sess.as_default():
# sess.run(iterator.initializer)
for epoch in range(epoch_num):
i_batch, l_batch = sess.run([image_batch, label_batch])
# logger.debug('i_batch: %s, l_batch: %s' % (i_batch, l_batch))
_, loss_t, loss_p_adv, loss_p, los_re = sess.run(train_op)
logger.debug('loss_t: %s, loss_p_adv: %s, loss_p: %s, los_re: %s' %
(loss_t, loss_p_adv, loss_p, los_re))
sess.close()
