np.pad()详解
代码示例：# pad()函数使用示例def testPad():"""np.pad()用来在numpy数组的边缘进行数值填充，例如CNN网络常用的padding操作np.pad(array，pad_width，mode，**kwargs) # 返回填充后的numpy数组参数：array：要填充的numpy数组【要对谁进行填充】pad_width：每个轴要填充的数据的数目【每个维度前、后各要填充多
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代码示例：
# pad()函数使用示例
def testPad():
"""
np.pad()用来在numpy数组的边缘进行数值填充，例如CNN网络常用的padding操作
np.pad(array，pad_width，mode，**kwargs) # 返回填充后的numpy数组
参数：
array：要填充的numpy数组【要对谁进行填充】
pad_width：每个轴要填充的数据的数目【每个维度前、后各要填充多少个数据】
mode：填充的方式【采用哪种方式填充】
"""
a = np.arange(1, 7).reshape(2, 3)
print("================a=================")
print(a)
b = np.pad(a, ((2, 4), (3, 5)), "constant")
c = np.pad(a, ((2, 4))) # 表示两个维度都按照同样的方式填充
d = np.pad(a, 2) # 表示前后填充的数值个数相同
e = np.pad(a, 2, "constant", constant_values=(8, 9))
print("================b=================")
print(b.shape) # (8, 11)
print(b)
print("================c=================")
print(c.shape)
print(c)
print("================d=================")
print(d.shape)
print(d)
print("================e=================")
print(e.shape)
print(e)
运行结果：
================a=================
[[1 2 3]
[4 5 6]]
================b=================
(8, 11)
[[0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 1 2 3 0 0 0 0 0]
[0 0 0 4 5 6 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]]
================c=================
(8, 9)
[[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 1 2 3 0 0 0 0]
[0 0 4 5 6 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]]
================d=================
(6, 7)
[[0 0 0 0 0 0 0]
[0 0 0 0 0 0 0]
[0 0 1 2 3 0 0]
[0 0 4 5 6 0 0]
[0 0 0 0 0 0 0]
[0 0 0 0 0 0 0]]
================e=================
(6, 7)
[[8 8 8 8 8 9 9]
[8 8 8 8 8 9 9]
[8 8 1 2 3 9 9]
[8 8 4 5 6 9 9]
[8 8 9 9 9 9 9]
[8 8 9 9 9 9 9]]
5.代码测试
import numpy as np
# 测试一维数组
a = np.array([1, 2, 3, 4, 5])
b = np.pad(a, 2, 'constant')
print("b = ", b)
c = np.pad(a, (2, 4), 'constant')
print("c = ", c)
# 测试二维数组
aa = np.arange(6).reshape(2, 3)
print("aa = \n", aa)
bb = np.pad(aa, (2, 4), 'constant')
print("bb = \n", bb)
cc = np.pad(aa, ((2, 4), (3, 5)), 'constant')
print("cc = \n", cc)
# 测试三维数组
aaa = np.arange(24).reshape(2, 3, 4)
print("aaa = \n", aaa)
np.set_printoptions(threshold=np.inf) # 将numpy数组完全展开
bbb = np.pad(aaa, ((2, 3), (4, 5), (6, 7)), 'constant')# 块上加了2/3，列上加了4/5，行上加了6/7
print("bbb = \n", bbb)
运行结果如下：
b = [0 0 1 2 3 4 5 0 0]
c = [0 0 1 2 3 4 5 0 0 0 0]
aa =
[[0 1 2]
[3 4 5]]
bb =
[[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 1 2 0 0 0 0]
[0 0 3 4 5 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]]
cc =
[[0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 1 2 0 0 0 0 0]
[0 0 0 3 4 5 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0]]
aaa =
[[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
[[12 13 14 15]
[16 17 18 19]
[20 21 22 23]]]
bbb =
[[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 1 2 3 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 4 5 6 7 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 8 9 10 11 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 12 13 14 15 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 16 17 18 19 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 20 21 22 23 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]]
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