当迭代次数超过10,000时,Tensorflow训练变得越来越慢.为什么？

Question

我使用输入管道方法将数据提供给图形,并tf.train.shuffle_batch实现生成批处理数据.然而,随着训练的进行,张量流变得越来越慢,以后的迭代.我很困惑导致它的根本原因是什么？非常感谢!我的代码片段是:

def main(argv=None):

# define network parameters
# weights
# bias

# define graph
# graph network

# define loss and optimization method
# data = inputpipeline('*')
# loss 
# optimizer

# Initializaing the variables
init = tf.initialize_all_variables()

# 'Saver' op to save and restore all the variables
saver = tf.train.Saver()

# Running session
print "Starting session... "
with tf.Session() as sess:

    # initialize the variables
    sess.run(init)

    # initialize the queue threads to start to shovel data
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    print "from the train set:"
    for i in range(train_set_size * epoch):
        _, d, pre = sess.run([optimizer, depth_loss, prediction])

    print "Training Finished!"

    # Save the variables to disk.
    save_path = saver.save(sess, model_path)
    print("Model saved in file: %s" % save_path)

    # stop our queue threads and properly close the session
    coord.request_stop()
    coord.join(threads)
    sess.close()

Answer 1

训练时，您应该只执行一次 sess.run 。建议尝试这样的方法，希望有帮助：

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  for i in range(train_set_size * epoch):
    train_step.run([optimizer, depth_loss, prediction])