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()