法一：
循环打印
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for (x, y) in zip(tf.global_variables(), sess.run(tf.global_variables())): print '\n', x, y |
实例
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# coding=utf-8import tensorflow as tfdef 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 bndef 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, yif __name__ == "__main__": main() |
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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
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print 'my/BatchNorm/beta:0', (sess.run('my/BatchNorm/beta:0')) |
实例
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# coding=utf-8import tensorflow as tfdef 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 bndef 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() |
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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 |
以上这篇tensorflow 打印内存中的变量方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/JNingWei/article/details/78131214
