# -*- coding: utf-8 -*-
"""
Created on Thu Apr 26 09:19:06 2018
@author: Loulch C.C
"""
import tensorflow as tf
"""
reshape(tensor, shape, name=None)
Given `tensor`, this operation returns a tensor that has the same values
as `tensor` with shape `shape`.
If one component of `shape` is the special value -1, the size of that dimension
is computed so that the total size remains constant. In particular, a `shape`
of `[-1]` flattens into 1-D. At most one component of `shape` can be -1.
If `shape` is 1-D or higher, then the operation returns a tensor with shape
`shape` filled with the values of `tensor`. In this case, the number of elements
implied by `shape` must be the same as the number of elements in `tensor`.
Args:
tensor: A `Tensor`.
shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
Defines the shape of the output tensor.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `tensor`.
"""
t = [1, 2, 3, 4, 5, 6, 7, 8, 9]
a=tf.reshape(t, [3, 3])
sess= tf.Session()
sess.run(a)
#输出：array([[1, 2, 3],
# [4, 5, 6],
# [7, 8, 9]])
t=[[[1, 1], [2, 2]],[[3, 3], [4, 4]]]
a=tf.reshape(t, [2, 4])
sess.run(a)
#输出：array([[1, 1, 2, 2],
# [3, 3, 4, 4]])
t=[[[1, 1, 1],[2, 2, 2]],[[3, 3, 3],[4, 4, 4]],[[5, 5, 5],[6, 6, 6]]]
a=tf.reshape(t, [-1])# -1 can also be used to infer the shape
sess.run(a)
#输出：array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6])
a=tf.reshape(t, [2, -1])# -1 is inferred to be 9
sess.run(a)
#输出：array([[1, 1, 1, 2, 2, 2, 3, 3, 3],
# [4, 4, 4, 5, 5, 5, 6, 6, 6]])
a=tf.reshape(t, [-1, 9])# -1 is inferred to be 2
sess.run(a)
#输出：array([[1, 1, 1, 2, 2, 2, 3, 3, 3],
# [4, 4, 4, 5, 5, 5, 6, 6, 6]])
a=tf.reshape(t, [ 2, -1, 3])# -1 is inferred to be 3
sess.run(a)
#输出：array([[[1, 1, 1],
# [2, 2, 2],
# [3, 3, 3]],
# [[4, 4, 4],
# [5, 5, 5],
# [6, 6, 6]]])
