106、TensorFlow变量 (二) reshape

import tensorflow as tf
rank_three_tensor = tf.ones([3, 4, 5])                 # 创建一个[3,4,5]大小的张量，3行4列，每个位置上有五个元素
matrix = tf.reshape(rank_three_tensor, [6, 10])  # 将当前变量reshape成[6,10]个大小的变量
matrixB = tf.reshape(matrix, [3, -1])                    # 将现有内容改造成3×20矩阵。-1指定这个维度的元素个数根据其他维度的元素个数来计算。
matrixAlt = tf.reshape(matrixB, [4, 3, -1])  # 将当前的矩阵reshape成 4行3列每个位置上为5个元素的 tensor
# 改变张量的形状
# The number of elements of a scalar is always
# 1、因为经常有许多不同的形状具有相同数量的元素。
#     所以改变这样的张量的形状，使得reshape前后的张量中元素的数量是相同的，

init = tf.global_variables_initializer()
sess = tf.Session()
print(sess.run(rank_three_tensor))
print(sess.run(matrix))
print(sess.run(matrixB))
print(sess.run(matrixAlt))

下边是上面代码输出的结果

2018-02-16 20:33:55.488664: I C:	f_jenkinsworkspace
el-winMwindowsPY35	ensorflowcoreplatformcpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
[[[ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]]

 [[ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]]

 [[ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]]]
[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]
[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.]]
[[[ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]]

 [[ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]]

 [[ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]]

 [[ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]
  [ 1.  1.  1.  1.  1.]]]