TensorFlow的reshape操作 tf.reshape
阿新 • • 發佈:2019-01-03
初學tensorflow,如果寫的不對的,請更正,謝謝!
tf.reshape(tensor, shape, name=None)
函式的作用是將tensor變換為引數shape的形式。
其中shape為一個列表形式,特殊的一點是列表中可以存在-1。-1代表的含義是不用我們自己指定這一維的大小,函式會自動計算,但列表中只能存在一個-1。(當然如果存在多個-1,就是一個存在多解的方程了)
好了我想說的重點還有一個就是根據shape如何變換矩陣。其實簡單的想就是,
reshape(t, shape) => reshape(t, [-1]) => reshape(t, shape)
首先將矩陣t變為一維矩陣,然後再對矩陣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