TensorFlow 打印内存中变量的两种方法

林见深

发布时间：2026-10-13| 6人浏览过

原创

本文介绍在 TensorFlow 中查看内存变量值的两种常用方法。第一种通过 zip 函数循环遍历 tf.global_variables() 及其运行结果进行打印；第二种则通过指定具体的变量名称字符串，利用 sess.run 直接获取并输出特定变量的值。

方法一：

循环打印

模板

for (x, y) in zip(tf.global_variables(), sess.run(tf.global_variables())):
 print '\n', x, y

实例

# coding=utf-8

import tensorflow as tf


def func(in_put, layer_name, is_training=True):
 with tf.variable_scope(layer_name, reuse=tf.AUTO_REUSE):
  bn = tf.contrib.layers.batch_norm(inputs=in_put,
           decay=0.9,
           is_training=is_training,
           updates_collections=None)
 return bn

def main():

 with tf.Graph().as_default():
  # input_x
  input_x = tf.placeholder(dtype=tf.float32, shape=[1, 4, 4, 1])
  import numpy as np
  i_p = np.random.uniform(low=0, high=255, size=[1, 4, 4, 1])
  # outputs
  output = func(input_x, 'my', is_training=True)
  with tf.Session() as sess:
   sess.run(tf.global_variables_initializer())
   t = sess.run(output, feed_dict={input_x:i_p})

   # 法一： 循环打印
   for (x, y) in zip(tf.global_variables(), sess.run(tf.global_variables())):
    print '\n', x, y

if __name__ == "__main__":
 main()

2017-09-29 10:10:22.714213: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1052] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)

<tf.Variable 'my/BatchNorm/beta:0' shape=(1,) dtype=float32_ref> [ 0.]

<tf.Variable 'my/BatchNorm/moving_mean:0' shape=(1,) dtype=float32_ref> [ 13.46412563]

<tf.Variable 'my/BatchNorm/moving_variance:0' shape=(1,) dtype=float32_ref> [ 452.62246704]

Process finished with exit code 0

方法二：

指定变量名打印

模板

print 'my/BatchNorm/beta:0', (sess.run('my/BatchNorm/beta:0'))

实例

# coding=utf-8

import tensorflow as tf


def func(in_put, layer_name, is_training=True):
 with tf.variable_scope(layer_name, reuse=tf.AUTO_REUSE):
  bn = tf.contrib.layers.batch_norm(inputs=in_put,
           decay=0.9,
           is_training=is_training,
           updates_collections=None)
 return bn

def main():

 with tf.Graph().as_default():
  # input_x
  input_x = tf.placeholder(dtype=tf.float32, shape=[1, 4, 4, 1])
  import numpy as np
  i_p = np.random.uniform(low=0, high=255, size=[1, 4, 4, 1])
  # outputs
  output = func(input_x, 'my', is_training=True)
  with tf.Session() as sess:
   sess.run(tf.global_variables_initializer())
   t = sess.run(output, feed_dict={input_x:i_p})

   # 法二： 指定变量名打印
   print 'my/BatchNorm/beta:0', (sess.run('my/BatchNorm/beta:0'))
   print 'my/BatchNorm/moving_mean:0', (sess.run('my/BatchNorm/moving_mean:0'))
   print 'my/BatchNorm/moving_variance:0', (sess.run('my/BatchNorm/moving_variance:0'))

if __name__ == "__main__":
 main()

2017-09-29 10:12:41.374055: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1052] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)

my/BatchNorm/beta:0 [ 0.]
my/BatchNorm/moving_mean:0 [ 8.08649635]
my/BatchNorm/moving_variance:0 [ 368.03442383]

Process finished with exit code 0

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