Deep learning: numpy

【 Knowledge framework 】

One 、ndarray Array

1、 establish ndarray Array

array function

This function converts the list to an array

import numpy as np
a = [0,1,2,3,4]
b = np.array(a)
print(b)

arange function

Create an array , The array is a set range 、 Determine the interval 、 Incremental array

import numpy as np
a = np.arange(0,10,2)# from 0 Start to 10 end （ barring 10）, The increment interval is 2
print(a)

【 Running results 】

zeros function

Creates the full length of a shape of a specified length 0 Array

import numpy as np
a = np.zeros([3,3])# Create a 3*3 Of all the 0 Array 
print(a)

【 Running results 】

ones function

Creates a full... Of the specified length and shape 1 Array

import numpy as np
a = np.ones([3,3])# Create a 3*3 Of all the 1 Array 
print(a)

【 Running results 】

2、ndarray Properties of array

shape

The shape of the array

import numpy as np
a = np.ones([3,3])# Create a 3*3 Of all the 1 Array 
print(a.shape)

【 Running results 】

dtype

Data type of array

import numpy as np
a = np.ones([3,3])
print(a.dtype)

【 Running results 】

size

The number of elements in the array , Its size is equal to the product of each dimension

import numpy as np
a = np.ones([3,3])
print(a.size)

【 Running results 】

ndim

The dimension size of the array , Its size is equal to shape The number of elements contained

import numpy as np
a = np.ones([3,3])
print(a.ndim)

【 Running results 】

Change the shape and data type of the array

Change shape with reshape（ Slice of array ）

import numpy as np
a = np.ones([3,3])
print(' Original array shape ',a.shape)
a = a.reshape([1,9])
print(' Current array shape ',a.shape)

【 Running results 】

Change the data type with astype

import numpy as np
a = np.ones([3,3])
print(' Original array type ',a.dtype)
a = a.astype(np.int64)
print(' Current array type ',a.dtype)

【 Running results 】

3、 Scalar sum ndarray Operations between arrays

Scalar and array 4 Operations （ Add, subtract, multiply and divide ）

Scalars operate on every element in an array

import numpy as np
a = np.ones([3,3])
b = 2.0+a
c = 2.0-a
d = 2.0*a
e = 2.0/a
print(' The original array is ：\n',a)
print('2+ Array ：\n',b)
print('2- Array ：\n',c)
print('2* Array ：\n',d)
print('2/ Array ：\n',e)

【 Running results 】

Array and array operation

Operation between corresponding elements

import numpy as np
a = np.ones([3,3])
b = np.zeros([3,3])
c = a+b
d = a*b
print(' Array a:\n',a,'\n Array b:\n',b)
print('a+b:\n',c)
print('a*b:\n',d)

【 Running results 】

4、ndarray Statistical method of array

mean

Calculate the arithmetic mean

import numpy as np
a = np.ones([3,3])
# Two expressions 
print(np.mean(a))
print(a.mean())

【 Running results 】

std var

std: Standard deviation var： variance

import numpy as np
a = np.arange(0,10,2)
print(' The test array is ：\n',a)
print(' The standard deviation is ：',a.std())
print(' The variance of ：',a.var())

【 Running results 】

sum

Sum up

import numpy as np
a = np.arange(0,10,2)
print(' The test array is ：\n',a)
print(' Sum for ：',a.sum())

【 Running results 】

max min

max: Maximum min： minimum value

import numpy as np
a = np.arange(0,10,2)
print(' The test array is ：\n',a)
print(' The maximum value is ：',a.max())
print(' The minimum value is ：',a.min())

【 Running results 】

argmin argmax

minimum value 、 Maximum index

import numpy as np
a = np.arange(0,10,2)
print(' The test array is ：\n',a)
print(' The maximum index is ：',a.argmax())
print(' The minimum index is ：',a.argmin())

【 Running results 】

cumsum cumprod

Accumulation and accumulation （ Look at the results ）

import numpy as np
a = np.arange(0,10,2)
print(' The test array is ：\n',a)
print(' Add up to ：',a.cumsum())
print(' Accumulated as ：',a.cumprod())

【 Running results 】

Two 、np.random random number

1、 Random array

Uniform distribution

import numpy as np
a = np.random.rand(10)
print(a)

【 Running results 】

Specify the value range and shape

import numpy as np
a = np.random.uniform(low=-1.0,high=1.0,size=(3,3))#low Lower limit ,high ceiling ,size shape 
print(a)

【 Running results 】

Normal distribution

import numpy as np
a = np.random.randn(9)
print(a)

【 Running results 】

Specified mean loc And variance scale

import numpy as np
a = np.random.normal(loc=2.0,scale=2.0,size=(3,3))
print(a)

【 Running results 】

2、 Random disorder order

import numpy as np
a = np.arange(0,10)
print(' Original array a by ：\n',a)
np.random.shuffle(a)
print(' After randomly disrupting the order ：\n',a)

【 Running results 】

3、 Random selection of elements

import numpy as np
a = np.arange(0,10)
print(' Original array a by ：\n',a)
b = np.random.choice(a)
print(' The randomly selected elements are ：',b)

【 Running results 】

3、 ... and 、 linear algebra

diag Find diagonal elements

Returns the diagonal elements of a square array in the form of a one-dimensional array

import numpy as np
a = np.arange(1,10)
a = a.reshape([3,3])
b = np.diag(a)
print(' The test matrix is ：\n',a)
print(' The diagonal element is ：',b)

【 Running results 】

dot Matrix multiplication

Matrix multiplication

import numpy as np
a = np.arange(0,12)
b = a.reshape([3,4])
c = a.reshape([4,3])
d = b.dot(c)
print(' matrix b:\n',b)
print(' matrix c:\n',c)
print(' matrix b* matrix c:\n',d)

【 Running results 】

trace Sum of diagonal elements

Calculate the sum of diagonal elements

import numpy as np
a = np.arange(1,10)
b = a.reshape([3,3])
c = np.trace(b)
print(' The test matrix is ：\n',b)
print(' matrix b The sum of the diagonal elements of is ：',c)

【 Running results 】

det Matrix determinants

Calculate the determinant of a matrix

import numpy as np
a = np.arange(1,10)
b = a.reshape([3,3])
c = np.linalg.det(b)
print(' matrix b The determinant of is ：',c)

【 Running results 】

eig Eigenvalues and eigenvectors

Calculate the eigenvalues and eigenvectors of a square matrix

import numpy as np
a = np.arange(1,10)
b = a.reshape([3,3])
c = np.linalg.eig(b)
print(' The test matrix is ：\n',b)
print(' matrix b Eigenvalues and eigenvectors ：\n',c)

【 Running results 】

inv The inverse of the square

Calculate the inverse of the square

import numpy as np
a = np.random.rand(3,3)
b = np.linalg.inv(a)
print(' The test matrix is ：\n',a)
print(' matrix b The inverse matrix of is ：\n',b)

【 Running results 】

---

Remember the praise. 、 Focus on 、 Comment on 、 Collection 、 forward