填充与复制

目录

* Outline <https://www.cnblogs.com/nickchen121/p/10852722.html#outline>
* pad <https://www.cnblogs.com/nickchen121/p/10852722.html#pad>
* Image padding
<https://www.cnblogs.com/nickchen121/p/10852722.html#image-padding>
* tile <https://www.cnblogs.com/nickchen121/p/10852722.html#tile>
* tile VS broadcast_to
<https://www.cnblogs.com/nickchen121/p/10852722.html#tile-vs-broadcast_to>
Outline

*
pad

*
tile

*
broadcast_to

pad

* [3]
* [[1,2]]
* [6]


* [2,2]
* [[0,1][1,1]] # [行,列]
* [3,4]

import tensorflow as tf a = tf.reshape(tf.range(9), [3, 3]) a <tf.Tensor:
id=17, shape=(3, 3), dtype=int32, numpy= array([[0, 1, 2], [3, 4, 5], [6, 7,
8]], dtype=int32)> tf.pad(a, [[0, 0], [0, 0]]) <tf.Tensor: id=20, shape=(3, 3),
dtype=int32, numpy= array([[0, 1, 2], [3, 4, 5], [6, 7, 8]], dtype=int32)>
tf.pad(a, [[ 1, 0, ], [0, 0]]) <tf.Tensor: id=23, shape=(4, 3), dtype=int32,
numpy= array([[0, 0, 0], [0, 1, 2], [3, 4, 5], [6, 7, 8]], dtype=int32)>
tf.pad(a, [[1, 1], [0, 0]]) <tf.Tensor: id=26, shape=(5, 3), dtype=int32,
numpy= array([[0, 0, 0], [0, 1, 2], [3, 4, 5], [6, 7, 8], [0, 0, 0]],
dtype=int32)> tf.pad(a, [[1, 1], [1, 0]]) <tf.Tensor: id=29, shape=(5, 4),
dtype=int32, numpy= array([[0, 0, 0, 0], [0, 0, 1, 2], [0, 3, 4, 5], [0, 6, 7,
8], [0, 0, 0, 0]], dtype=int32)> tf.pad(a, [[1, 1], [1, 1]]) <tf.Tensor: id=32,
shape=(5, 5), dtype=int32, numpy= array([[0, 0, 0, 0, 0], [0, 0, 1, 2, 0], [0,
3, 4, 5, 0], [0, 6, 7, 8, 0], [0, 0, 0, 0, 0]], dtype=int32)>
Image padding
a = tf.random.normal([4, 28, 28, 3]) a.shape TensorShape([4, 28, 28, 3]) #
对图片的行和列padding两行 b = tf.pad(a, [[0, 0], [2, 2], [2, 2], [0, 0]]) b.shape
TensorShape([4, 32, 32, 3])
* [1,5,5,1]
* [[0,0],[2,2],[2,2],[0,0]]
* [1,9,9,1]


tile

* repeat data along dim n times
* [a,b,c],2
* --> [a,b,c,a,b,c] a = tf.reshape(tf.range(9), [3, 3]) a <tf.Tensor: id=76,
shape=(3, 3), dtype=int32, numpy= array([[0, 1, 2], [3, 4, 5], [6, 7, 8]],
dtype=int32)> # 1表示行不复制，2表示列复制为两倍 tf.tile(a, [1, 2]) <tf.Tensor: id=79,
shape=(3, 6), dtype=int32, numpy= array([[0, 1, 2, 0, 1, 2], [3, 4, 5, 3, 4,
5], [6, 7, 8, 6, 7, 8]], dtype=int32)> tf.tile(a, [2, 1]) <tf.Tensor: id=82,
shape=(6, 3), dtype=int32, numpy= array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [0,
1, 2], [3, 4, 5], [6, 7, 8]], dtype=int32)> tf.tile(a, [2, 2]) <tf.Tensor:
id=85, shape=(6, 6), dtype=int32, numpy= array([[0, 1, 2, 0, 1, 2], [3, 4, 5,
3, 4, 5], [6, 7, 8, 6, 7, 8], [0, 1, 2, 0, 1, 2], [3, 4, 5, 3, 4, 5], [6, 7, 8,
6, 7, 8]], dtype=int32)>
tile VS broadcast_to
aa = tf.expand_dims(a, axis=0) aa <tf.Tensor: id=90, shape=(1, 3, 3),
dtype=int32, numpy= array([[[0, 1, 2], [3, 4, 5], [6, 7, 8]]], dtype=int32)>
tf.tile(aa, [2, 1, 1]) <tf.Tensor: id=93, shape=(2, 3, 3), dtype=int32, numpy=
array([[[0, 1, 2], [3, 4, 5], [6, 7, 8]], [[0, 1, 2], [3, 4, 5], [6, 7, 8]]],
dtype=int32)> # 不占用内存，性能更优 tf.broadcast_to(aa, [2, 3, 3]) <tf.Tensor: id=96,
shape=(2, 3, 3), dtype=int32, numpy= array([[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
[[0, 1, 2], [3, 4, 5], [6, 7, 8]]], dtype=int32)>

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