python - tensorflow 无法初始化某些变量

Question

我使用以下代码来初始化 tensorflow 中的某些变量。令我惊讶的是，我得到的变量“V”是一个空列表，而其他变量是合理的数字。仅当我注释 weights_W 的代码时，我才能获取“V”的值。

我是 tensorflow 新手。我搞砸了什么吗？

代码:

import math
import tensorflow as tf

layer_sizes = [4, 5, 3]
shapes = zip(layer_sizes[:-1], layer_sizes[1:])
L = len(layer_sizes)-1

def bi(inits, size, name):
    return tf.Variable(inits * tf.ones([size]), name=name)

def wi(shape, name):
    return tf.Variable(tf.random_normal(shape, name=name)) / math.sqrt(shape[0])

weights_W = {'W': [wi(s, "W") for s in shapes]}
weights_V = {'V': [wi(s[::-1], "V") for s in shapes]}
bias_b = {'beta': [bi(0.0, layer_sizes[l+1], "beta") for l in range(L)]}
bias_g = {'gamma': [bi(1.0, layer_sizes[l+1], "gamma") for l in range(L)]}

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print(sess.run(weights_W))
    print(sess.run(weights_V))
    print(sess.run(bias_b))
    print(sess.run(bias_g))

输出:

{'W': [array([[ 0.87929118, -1.52028453, -0.25481933, -0.0707642 ,  0.21771625],
   [-0.46657208, -0.08346261,  0.18036443, -0.75888193,  0.41950777],
   [-0.7241388 ,  0.08610565, -0.6172654 , -0.40768555,  0.24912448],
   [ 0.38304791, -0.16632535,  1.0700382 , -0.06679908, -0.68657762]], dtype=float32), array([[-0.37793589, -0.14964254, -0.56092912],
   [-0.18630502, -0.31269881,  0.25770813],
   [-0.12167504, -0.20703614, -0.06239036],
   [ 0.35287923, -0.67617333, -0.01133266],
   [ 0.24189886, -0.34389392,  0.0007165 ]], dtype=float32)]}
{'V': []}
{'beta': [array([ 0.,  0.,  0.,  0.,  0.], dtype=float32), array([ 0.,  0.,  0.], dtype=float32)]}
{'gamma': [array([ 1.,  1.,  1.,  1.,  1.], dtype=float32), array([ 1.,  1.,  1.], dtype=float32)]}

我在 python 3.5.4 中使用 tensorflow 1.2.1，没有 GPU。

感谢您的帮助。

Answer 1

这是因为 zip 返回一个迭代器，并且您只能迭代它一次。

您可以制作一个 zip 列表并使用它。

shapes = list(zip(layer_sizes[:-1], layer_sizes[1:]))