np.dot()为线性代数中的乘法

import numpy as np
 
# 2-D array: 2 x 3
two_dim_matrix_one = np.array([[1, 2, 3], [4, 5, 6]])
# 2-D array: 3 x 2
two_dim_matrix_two = np.array([[1, 2], [3, 4], [5, 6]])
 
two_multi_res = np.dot(two_dim_matrix_one, two_dim_matrix_two)
print('two_multi_res: %s' %(two_multi_res))
 
# 1-D array
one_dim_vec_one = np.array([1, 2, 3])
one_dim_vec_two = np.array([4, 5, 6])
one_result_res = np.dot(one_dim_vec_one, one_dim_vec_two)
print('one_result_res: %s' %(one_result_res))

输出为：

two_multi_res: [[22 28]
 [49 64]]
one_result_res: 32

* 和 np.multiply() 两种乘法方式均为对应元素相乘，广播

import numpy as np
 
# 2-D array: 2 x 3
two_dim_matrix_one = np.array([[1, 2, 3], [4, 5, 6]])
another_two_dim_matrix_one = np.array([[7, 8, 9], [4, 7, 1]])
 
# 对应元素相乘 element-wise product
element_wise = two_dim_matrix_one * another_two_dim_matrix_one
print('element wise product: %s' %(element_wise))
 
# 对应元素相乘 element-wise product
element_wise_2 = np.multiply(two_dim_matrix_one, another_two_dim_matrix_one)
print('element wise product: %s' % (element_wise_2))

输出为：

element wise product: [[ 7 16 27]
 [16 35  6]]
element wise product: [[ 7 16 27]
 [16 35  6]]