TensorFlow: Printing variables in memory

Law One:

Cycle Print Templates

for (x, y) in Zip (Tf.global_variables (), Sess.run (Tf.global_variables ())):
    print ' \ n ', X, y

Example

# 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_coll Ections=none) return bn def Main (): with TF. Graph (). As_default (): # input_x input_x = Tf.placeholder (Dtype=tf.float32, Shape=[1, 4, 4, 1]) imp ORT NumPy as NP i_p = Np.random.uniform (low=0, high=255, Size=[1, 4, 4, 1]) # outputs output = Fun C (input_x, ' my ', is_training=true) with TF. Session () as Sess:sess.run (Tf.global_variables_initializer ()) t = sess.run (output, FEED_DICT={INP
                Ut_x:i_p}) # method one: Loop print 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

Law II:

Specify the variable name print template

print ' my/batchnorm/beta:0 ', (Sess.run (' my/batchnorm/beta:0 '))

Example

# 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_coll Ections=none) return bn def Main (): with TF. Graph (). As_default (): # input_x input_x = Tf.placeholder (Dtype=tf.float32, Shape=[1, 4, 4, 1]) imp ORT NumPy as NP i_p = Np.random.uniform (low=0, high=255, Size=[1, 4, 4, 1]) # outputs output = Fun C (input_x, ' my ', is_training=true) with TF. Session () as Sess:sess.run (Tf.global_variables_initializer ()) t = sess.run (output, FEED_DICT={INP
            Ut_x:i_p}) # method Two: Specify Variable name 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/B
 Atchnorm/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 cap ability: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