TensorFlow：使用tf.reshape函数重塑张量
1. 代码tf.reshape(
tensor,
shape,
name=None
)
2. 作用
重塑张量. 给定tensor,这个操作返回一个张量,它与带有形状shape的tensor具有相同的值.如果shape的一个分量是特殊值-1,则计算该维度的大小,以使总大小保持不变.特别地情况为,一个[-1]维的shape变平成1维.至多能有一个shape的分量可以是-1.如果shape是1-D或更高,则操作返回形状为shape的张量,其填充为tensor的值.在这种情况下,隐含的shape元素数量必须与tensor元素数量相同.
例如：
# 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
3. 参数设置tensor：一个Tensor.
shape：一个Tensor；必须是以下类型之一：int32,int64；用于定义输出张量的形状.
name：操作的名称(可选).
返回值：该操作返回一个Tensor.与tensor具有相同的类型.