tensorflow 打印内存中的变量方法_Python

tensorflow 打印内存中的变量方法

2021-03-24 00:07JNingWei Python

今天小编就为大家分享一篇tensorflow 打印内存中的变量方法，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

法一：

模板

1 2	`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

法二：

指定变量名打印

模板

1	`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