softmax的实现，使用 numpy

实现了一维和二维矩阵的softmax

import numpy as np
def softmax(x):
    orig_shape=x.shape
    print("orig_shape",orig_shape)
    
    if len(x.shape)>1:
        #矩阵
        tmp=np.max(x,axis=1)
        x-=tmp.reshape((x.shape[0],1))
        x=np.exp(x)
        tmp=np.sum(x,axis=1)
        x/=tmp.reshape((x.shape[0],1))
        print("matrix")
    else:
        #向量
        tmp=np.max(x)
        x-=tmp
        x=np.exp(x)
        tmp=np.sum(x)
        x/=tmp
        print("vector")
    return x

x=np.array([[1,2,3,4],[1,2,3,4]])
x1=np.array([1,2,3,4])

print(x)
print(x1)

#
orig_shape (2, 4)
orig_shape (4,)
#

print(np.max(x,axis=1))
print(np.sum(x,axis=1))

#
[4 4]
[10 10]
#

print(softmax(x))
print(softmax(x1))

#
[[0.0320586  0.08714432 0.23688282 0.64391426]
 [0.0320586  0.08714432 0.23688282 0.64391426]]

[0.0320586  0.08714432 0.23688282 0.64391426]
#

numpy

列表转numpy
test=[1,2,3]
print("test",test)
print(np.array(test))


输出：
test [1, 2, 3]
[1 2 3]

numpy矩阵
x=np.array([[1,2,3,4],[1,2,3,4]])  #注意这里是矩阵，有两个中括号
print(x)

输出:
[[1 2 3 4]
 [1 2 3 4]]