Question

import numpy as np
import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell


if __name__ == '__main__':

    np.random.seed(1234)

    X = np.array(np.array(range(1,121)).reshape(4, 6, 5),dtype=float)

    x0 = tf.placeholder(tf.float32, [4, 6, 5])
    x = tf.reshape(x0, [-1, 5])
    x = tf.split(0, 4, x)

    lstm = tf.nn.rnn_cell.BasicLSTMCell(5,state_is_tuple=True)   

    with tf.variable_scope('sen'):
        outputs, states = tf.nn.rnn(lstm, x, dtype=tf.float32)

    with tf.variable_scope('par'):
        output2, states2 = tf.nn.dynamic_rnn(lstm, x0, dtype=tf.float32,time_major = True)

    with tf.variable_scope('sen2'):
        outputs3, states3 = tf.nn.rnn(lstm, x, dtype=tf.float32)


    with tf.Session() as sess:
        for i in range(3):
            sess.run(tf.initialize_all_variables())
            result1,result2, result3 = sess.run([outputs[-1],output2[-1],outputs3[-1]],{x0:X})
            print result1
            print '---------------------------------------'
            print result2
            print '---------------------------------------'
            print result3
            print '------------------------------------------------------------------------------'

我认为result1，result2和result3应该始终相同。但他们并不相同。每次运行该函数时，result2都会发生变化。有什么问题？

Answer 1

问题是尽管您使用的是单个LSTM单元，但您在不同的变量范围内创建了3个RNN，因此它们无法共享参数。考虑打印所有可训练的变量以查看：

for var in tf.trainable_variables():
    print var.name

要明确使用相同的参数，请尝试scope.reuse_variables()其中scope是不同输出的相同范围。

我想出了以下内容：

import numpy as np
import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell


if __name__ == '__main__':

    np.random.seed(1234)

    X = np.array(np.array(range(1,121)).reshape(4, 6, 5), dtype = float)

    x0 = tf.placeholder(tf.float32, [4, 6, 5])
    x = tf.reshape(x0, [-1, 5])
    x = tf.split(0, 4, x)


    with tf.variable_scope('lstm') as scope:

        lstm = tf.nn.rnn_cell.BasicLSTMCell(5, state_is_tuple = True)   

        outputs, states = tf.nn.rnn(lstm, x, dtype = tf.float32)

        scope.reuse_variables()

        outputs2, states2 = tf.nn.dynamic_rnn(lstm, x0, dtype=tf.float32,time_major = True)

        outputs3, states3 = tf.nn.rnn(lstm, x, dtype=tf.float32)

    print(outputs3)

    with tf.Session() as sess:
        init = tf.initialize_all_variables()
        sess.run(init)

        for var in tf.trainable_variables():
            print var.name 

        for i in range(3):
            result1, result2, result3 = sess.run([outputs, outputs2, outputs3], feed_dict = {x0: X})

            print result1
            print '---------------------------------------'

            print result2
            print '---------------------------------------'

            print result3
            print '---------------------------------------'

似乎工作正常。

为什么rnn和dynamic_rnn的结果在张量流中存在不一致？

1 个答案: