squeeze 压缩维度为1的numpy向量
argmax 获取最大值的下标
np.random.shuffle(index)
reshape是从低维度到高维度。max,sum等函数都是注意axis，不选择就是全体计算。
swapaxes 转换轴，将两个选择的轴对调，在CNN中X乘W有的时候需要拉伸，如果轴不同结果不对。
看print 出来的np.array，最后在一维的是最后的维度。
可以看下面的示例。
import numpy as np
a=np.arange(3*4*5).reshape(3,4,5)
# array([[[ 0, 1, 2, 3, 4],
# [ 5, 6, 7, 8, 9],
# [10, 11, 12, 13, 14],
# [15, 16, 17, 18, 19]],
#
# [[20, 21, 22, 23, 24],
# [25, 26, 27, 28, 29],
# [30, 31, 32, 33, 34],
# [35, 36, 37, 38, 39]],
#
# [[40, 41, 42, 43, 44],
# [45, 46, 47, 48, 49],
# [50, 51, 52, 53, 54],
# [55, 56, 57, 58, 59]]])
b=a.reshape(6,10)
# array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
# [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
# [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
# [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
# [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
# [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]])
b.swapaxes(1,2)
# array([[[ 0, 5, 10, 15],
# [ 1, 6, 11, 16],
# [ 2, 7, 12, 17],
# [ 3, 8, 13, 18],
# [ 4, 9, 14, 19]],
#
# [[20, 25, 30, 35],
# [21, 26, 31, 36],
# [22, 27, 32, 37],
# [23, 28, 33, 38],
# [24, 29, 34, 39]],
#
# [[40, 45, 50, 55],
# [41, 46, 51, 56],
# [42, 47, 52, 57],
# [43, 48, 53, 58],
# [44, 49, 54, 59]]])
random.randn(n) n这里指的是获取n个独立高斯分布的变量，使用元组就是帮你reshape一下。
因此如果直接用n=1000，那么方差就是1000而不是1，这里要注意。
np.random.randn(100).reshape(10,5,2)