TensorFlow学习笔记（五）：tf.reshape用法

tf.reshape(tensor,shape, name=None) 
函数的作用是将tensor变换为参数shape的形式。 
其中shape为一个列表形式，特殊的一点是列表中可以存在-1。-1代表的含义是不用我们自己指定这一维的大小，函数会自动计算，但列表中只能存在一个-1。（当然如果存在多个-1，就是一个存在多解的方程了）

好了我想说的重点还有一个就是根据shape如何变换矩阵。其实简单的想就是，

reshape（t, shape） => reshape(t, [-1]) =>reshape(t, shape)

首先将矩阵t变为一维矩阵，然后再对矩阵的形式更改就可以了。

官方的例子：

# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]

# tensor 't' has shape [9]

reshape(t, [3, 3]) ==> [[1, 2, 3],

                        [4, 5, 6],

                        [7, 8, 9]]

# tensor 't' is [[[1, 1], [2, 2]],

#                [[3, 3], [4, 4]]]

# tensor 't' has shape [2, 2, 2]

reshape(t, [2, 4]) ==> [[1, 1, 2, 2],

                        [3, 3, 4, 4]]

# tensor 't' is [[[1, 1, 1],

#                 [2, 2, 2]],

#                [[3, 3, 3],

#                 [4, 4, 4]],

#                [[5, 5, 5],

#                 [6, 6, 6]]]

# tensor 't' has shape [3, 2, 3]

# pass '[-1]' to flatten 't'

reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]

# -1 can also be used to infer the shape

# -1 is inferred to be 9:

reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],

                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]

# -1 is inferred to be 2:

reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],

                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]

# -1 is inferred to be 3:

reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],

                              [2, 2, 2],

                              [3, 3, 3]],

                             [[4, 4, 4],

                              [5, 5, 5],

                              [6, 6, 6]]]

# tensor 't' is [7]

# shape `[]` reshapes to a scalar

reshape(t, []) ==> 7