tensorflow: 打印内存中的变量

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

# 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

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

# 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