目录

• 常用函数
  • np.shape
  • np.multiply
  • np.dot
  • np.matmul
  • np.squeeze

常用函数

np.shape

W = np.random.randn(2,2,3,8)
np.shape(W) # (2,2,3,8)

np.multiply

element-wise product

https://docs.scipy.org/doc/numpy/reference/generated/numpy.multiply.html

>>> x1 = np.arange(9.0).reshape((3, 3))
>>> x1
array([[ 0.,  1.,  2.],
       [ 3.,  4.,  5.],
       [ 6.,  7.,  8.]])
>>> x2 = np.arange(3.0)
>>> x2
array([ 0.,  1.,  2.])
>>> np.multiply(x1, x2)
array([[  0.,   1.,   4.],
       [  0.,   4.,  10.],
       [  0.,   7.,  16.]])

np.dot

点乘

https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html

具体地：

• If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation).
• If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred.
• If either a or b is 0-D (scalar), it is equivalent to multiply and using numpy.multiply(a, b) or a * b is preferred.
• If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.
• If a is an N-D array and b is an M-D array (where M>=2), it is a sum product over the last axis of a and the second-to-last axis of b:

dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])

对于两个2D-array而言，就是矩阵乘法（最好使用matmul）：

>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.dot(a, b)
array([[4, 1],
       [2, 2]])

而

>>> a = np.arange(3 * 4 * 5 * 6).reshape((3,4,5,6))
>>> b = np.arange(3 * 4 * 5 * 6)[::-1].reshape((5,4,6,3))
>>> np.dot(a, b)[2,3,2,1,2,2]
499128
>>> sum(a[2,3,2,:] * b[1,2,:,2])
499128

np.matmul

矩阵乘法

https://docs.scipy.org/doc/numpy/reference/generated/numpy.matmul.html#numpy.matmul

• If both arguments are 2-D they are multiplied like conventional matrices.
• If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly.
• If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
• If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.

Multiplication by a scalar is not allowed, use * instead

matmul和dot的区别：

• Multiplication by scalars is not allowed.
• Stacks of matrices are broadcast together as if the matrices were elements.

>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.matmul(a, b)
array([[4, 1],
       [2, 2]])

np.squeeze

从数组的形状中删除单维度条目，即把shape中为1的维度去掉

参考https://blog.csdn.net/zenghaitao0128/article/details/78512715

numpy.squeeze(a,axis = None)

输入：

• a表示输入的数组；
• axis用于指定需要删除的维度，但是指定的维度必须为单维度，否则将会报错；
• axis的取值可为None 或 int 或 tuple of ints, 可选。若axis为空，则删除所有单维度的条目；

返回值：数组，不会修改原数组。

场景：在机器学习和深度学习中，通常算法的结果是可以表示向量的数组（即包含两对或以上的方括号形式[[]]），如果直接利用这个数组进行画图可能显示界面为空（见后面的示例）。我们可以利用squeeze函数将表示向量的数组转换为秩为1的数组，这样利用matplotlib库函数画图时，就可以正常的显示结果了。

例如：

import numpy as np
squares =np.array([[1,4,9,16,25]])
squares.shape
# (1, 5)
np.squeeze(squares).shape
# (5,)