Question

我使用输入管道方法将数据提供给图形，并实现tf.train.shuffle_batch以生成批处理数据。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

训练时你应该只做一次sess.run。建议尝试这样的事情，希望它有所帮助：

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])

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

1 个答案: