import tensorflow as tf

update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
    train_op = optimizer.minimize(Total_loss)

correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# val_dir = '/data0/AIChallenger/ai_challenger_scene_validation_20170908/scene_validation_images_20170908/'
# annotations = '/data0/AIChallenger/ai_challenger_scene_validation_20170908/scene_validation_annotations_20170908.json'
# # a DataFlow you implement to produce [tensor1, tensor2, ..] lists from whatever sources:
