tensorflow: 打印内存中的变量

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-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()

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

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

实例

# 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()

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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