Gets an existing variable with these parameters or create a new one.

單獨用tf.get_variable()只能建立新的變數，並不能得到已定義的變數。

import numpy as np
import tensorflow as tf


Y,X = np.mgrid[1:10,60:69]

Z = X + 1j*Y

xs = tf.constant(Z.astype('complex64'))

zs = tf.Variable(xs,name='zs')

haha = tf.get_variable("zs",[9,9])

print('xs name:')
print(xs.name)
print('zs name:')
print(zs.name)
print('haha name')
print(haha.name)


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print('zs:')
    print(sess.run(zs))
    print('haha:')
    print(sess.run(haha))
 

xs name:
Const:0
zs name:
zs:0
haha name
zs_1:0

zs:
[[ 60.+1.j  61.+1.j  62.+1.j  63.+1.j  64.+1.j  65.+1.j  66.+1.j  67.+1.j
   68.+1.j]
 [ 60.+2.j  61.+2.j  62.+2.j  63.+2.j  64.+2.j  65.+2.j  66.+2.j  67.+2.j
   68.+2.j]
 [ 60.+3.j  61.+3.j  62.+3.j  63.+3.j  64.+3.j  65.+3.j  66.+3.j  67.+3.j
   68.+3.j]
 [ 60.+4.j  61.+4.j  62.+4.j  63.+4.j  64.+4.j  65.+4.j  66.+4.j  67.+4.j
   68.+4.j]
 [ 60.+5.j  61.+5.j  62.+5.j  63.+5.j  64.+5.j  65.+5.j  66.+5.j  67.+5.j
   68.+5.j]
 [ 60.+6.j  61.+6.j  62.+6.j  63.+6.j  64.+6.j  65.+6.j  66.+6.j  67.+6.j
   68.+6.j]
 [ 60.+7.j  61.+7.j  62.+7.j  63.+7.j  64.+7.j  65.+7.j  66.+7.j  67.+7.j
   68.+7.j]
 [ 60.+8.j  61.+8.j  62.+8.j  63.+8.j  64.+8.j  65.+8.j  66.+8.j  67.+8.j
   68.+8.j]
 [ 60.+9.j  61.+9.j  62.+9.j  63.+9.j  64.+9.j  65.+9.j  66.+9.j  67.+9.j
   68.+9.j]]
haha:
[[-0.14533952 -0.14523381  0.14145613 -0.22598141  0.1617533   0.19500619
  -0.3357884   0.37150401 -0.37969798]
 [ 0.0509094  -0.0470084  -0.49762926 -0.51874632 -0.29320604 -0.16176665
  -0.5291841  -0.29577848 -0.45970225]
 [-0.25813478 -0.54057223  0.05018246  0.03825295  0.57038844  0.48767948
  -0.4359569   0.1867137   0.310839  ]
 [-0.52079606 -0.01381797 -0.47396937 -0.49792162 -0.3253502   0.50621891
  -0.233078    0.32882768  0.09004182]
 [-0.16246468  0.50816798  0.4791249  -0.36204988  0.13710362  0.01671994
   0.47486234 -0.23209643  0.19463843]
 [-0.12628189 -0.08157283  0.05564255 -0.51489758  0.05933517  0.11145258
  -0.30569348 -0.25055888 -0.11803272]
 [ 0.20007056 -0.2579872   0.36695904 -0.23011178 -0.08050397  0.41955733
   0.42810726 -0.53095597  0.08598411]
 [-0.4530935  -0.30864996  0.56224608  0.5119139   0.33313245  0.09980142
  -0.22323224  0.36909598  0.05093253]
 [-0.42787349  0.26984555 -0.28011185 -0.09457535  0.23706919  0.5402844
  -0.16451114  0.14855558  0.09051406]]