为什么添加不可用的张量会改变tensorflow中RNN单元的结果?

下面是可以重现问题的最简单代码:

import numpy as np
import random
import tensorflow as tf

tf.set_random_seed(12345)
np.random.seed(12345)
random.seed(12345)

unusable1 = tf.constant(1e-3, tf.float32)
unusable2 = tf.constant(1e-3, tf.float32)
unusable3 = tf.constant(1e-3, tf.float32)

X = tf.placeholder(tf.float32, shape=[2, 3])

cell = tf.contrib.rnn.BasicRNNCell(5)

changed_data = tf.reduce_sum(cell(X, state = tf.zeros((2, 5)))[0])

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    output = sess.run(changed_data, feed_dict={X: np.ones((2, 3))})
    print(output)   # = -1.46618

上述代码的结果是 -1.46618 在我的机器上。

然而,如果我注释掉三个不可用的常数张量声明,结果就会变成 1.76918 !

import numpy as np
import random
import tensorflow as tf

tf.set_random_seed(12345)
np.random.seed(12345)
random.seed(12345)

# unusable1 = tf.constant(1e-3, tf.float32)
# unusable2 = tf.constant(1e-3, tf.float32)
# unusable3 = tf.constant(1e-3, tf.float32)

X = tf.placeholder(tf.float32, shape=[2, 3])

cell = tf.contrib.rnn.BasicRNNCell(5)

changed_data = tf.reduce_sum(cell(X, state = tf.zeros((2, 5)))[0])

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    output = sess.run(changed_data, feed_dict={X: np.ones((2, 3))})
    print(output)   # = 1.76918

实际上,您可以随意添加/删除/修改常数张量声明,结果会有很大不同!

有什么问题?