# -*- coding: utf-8 -*-
"""
Created on Mon May 27 11:09:52 2019
@author: jiangshan
"""
import torch
import numpy
import torch.nn.functional as F
x1= torch.Tensor( [[1,2,3,4],[1,3,4,5],[3,4,5,6]])
print(x1)
import math
#将torch.Tensor转换成numpy
x1_num = x1.numpy()
print(x1_num)
r,c = x1_num.shape
Row_softmax = numpy.ndarray((r, c), dtype=numpy.float64)
Clo_softmax = numpy.ndarray((r, c), dtype=numpy.float64)
#对每一行进行softmax
for i in range(r):
sum_j = 0
for j in range(c):
ins = x1_num[i,j]
sum_j += math.exp(ins)
for j in range(c):
out_j = math.exp(x1_num[i,j])/sum_j
Row_softmax[i,j] = out_j
print(out_j)
print('=====row-%d-end====='%(i+1))
print(Row_softmax)
y12 = F.softmax(x1,dim = 1) #对每一行进行softmax --- dim = 1轴
print(y12)
y120 = F.softmax(x1,dim = -1) #对每一行进行softmax --- dim = -1
print(y120)
#对每一列进行softmax
for j in range(c):
sum_i = 0
for i in range(r):
ins = x1_num[i,j]
sum_i += math.exp(ins)
for i in range(r):
out_i = math.exp(x1_num[i,j])/sum_i
Clo_softmax[i,j] = out_i
print(out_i)
print('=====col-%d-end====='%(j+1))
print(Clo_softmax)
y11= F.softmax(x1, dim = 0) #对每一列进行softmax ---- dim = 0轴
print(y11)
print('=================================================')
# 1 维张量
x2 = torch.Tensor([1,2,3,4])
print(x2)
y2 = F.softmax(x2,dim=0) #对每一列进行softmax ---- dim = 0轴
print(y2)
y20 = F.softmax(x2,dim=-1)
print(y20)
print('=================================================')
# 2 维张量
x3 = torch.Tensor([[1],[1],[3]])
print(x3)
y3 = F.softmax(x3,dim=1)#对每一行进行softmax --- dim = 1轴
print(y3)
y31 = F.softmax(x3,dim=0) #对每一列进行softmax ---- dim = 0轴
print(y31)
y30 = F.softmax(x3,dim=-1)
print(y30)
解读如下：
# -*- coding: utf-8 -*- """ Created on Mon May 27 11:09:52 2019 @author: jiangshan """ import torch import numpy import torch.nn.functional as F x1= torch.Tensor( [[1,2,3,4],[1,3,4,5],[3,4,5,6]]) print(x1) >> tensor([[1., 2., 3., 4.], [1., 3., 4., 5.], [3., 4., 5., 6.]]) import math #将torch.Tensor转换成numpy x1_num = x1.numpy() print(x1_num) >> [[1. 2. 3. 4.] [1. 3. 4. 5.] [3. 4. 5. 6.]] r,c = x1_num.shape Row_softmax = numpy.ndarray((r, c), dtype=numpy.float64) Clo_softmax = numpy.ndarray((r, c), dtype=numpy.float64) #对每一行进行softmax for i in range(r): sum_j = 0 for j in range(c): ins = x1_num[i,j] sum_j += math.exp(ins) for j in range(c): out_j = math.exp(x1_num[i,j])/sum_j Row_softmax[i,j] = out_j print(out_j) print('=====row-%d-end====='%(i+1)) print(Row_softmax) >> 0.03205860328008499 0.08714431874203257 0.23688281808991013 0.6439142598879722 =====row-1-end===== 0.01203764271193945 0.0889468172974043 0.24178251715880075 0.6572330228318555 =====row-2-end===== 0.03205860328008499 0.08714431874203256 0.23688281808991013 0.6439142598879724 =====row-3-end===== [[0.0320586 0.08714432 0.23688282 0.64391426] [0.01203764 0.08894682 0.24178252 0.65723302] [0.0320586 0.08714432 0.23688282 0.64391426]] y12 = F.softmax(x1,dim = 1) #对每一行进行softmax --- dim = 1轴 print(y12) y120 = F.softmax(x1,dim = -1) #对每一行进行softmax --- dim = -1 print(y120) >> tensor([[0.0321, 0.0871, 0.2369, 0.6439], [0.0120, 0.0889, 0.2418, 0.6572], [0.0321, 0.0871, 0.2369, 0.6439]]) tensor([[0.0321, 0.0871, 0.2369, 0.6439], [0.0120, 0.0889, 0.2418, 0.6572], [0.0321, 0.0871, 0.2369, 0.6439]]) #对每一列进行softmax for j in range(c): sum_i = 0 for i in range(r): ins = x1_num[i,j] sum_i += math.exp(ins) for i in range(r): out_i = math.exp(x1_num[i,j])/sum_i Clo_softmax[i,j] = out_i print(out_i) print('=====col-%d-end====='%(j+1)) print(Clo_softmax) >> 0.10650697891920075 0.10650697891920075 0.7869860421615985 =====col-1-end===== 0.09003057317038046 0.24472847105479767 0.6652409557748219 =====col-2-end===== 0.09003057317038046 0.24472847105479764 0.6652409557748219 =====col-3-end===== 0.09003057317038045 0.24472847105479764 0.6652409557748219 =====col-4-end===== [[0.10650698 0.09003057 0.09003057 0.09003057] [0.10650698 0.24472847 0.24472847 0.24472847] [0.78698604 0.66524096 0.66524096 0.66524096]] y11= F.softmax(x1, dim = 0) #对每一列进行softmax ---- dim = 0轴 print(y11) print('=================================================') >> tensor([[0.1065, 0.0900, 0.0900, 0.0900], [0.1065, 0.2447, 0.2447, 0.2447], [0.7870, 0.6652, 0.6652, 0.6652]]) ================================================= # 1 维张量 x2 = torch.Tensor([1,2,3,4]) print(x2) y2 = F.softmax(x2,dim=0) #对每一列进行softmax ---- dim = 0轴 print(y2) y20 = F.softmax(x2,dim=-1) print(y20) print('=================================================') >> tensor([1., 2., 3., 4.]) tensor([0.0321, 0.0871, 0.2369, 0.6439]) tensor([0.0321, 0.0871, 0.2369, 0.6439]) ================================================= # 2 维张量 x3 = torch.Tensor([[1],[1],[3]]) print(x3) >> tensor([[1.], [1.], [3.]]) y3 = F.softmax(x3,dim=1)#对每一行进行softmax --- dim = 1轴 print(y3) y31 = F.softmax(x3,dim=0) #对每一列进行softmax ---- dim = 0轴 print(y31) y30 = F.softmax(x3,dim=-1) print(y30) >> tensor([[1.], [1.], [1.]]) tensor([[0.1065], [0.1065], [0.7870]]) tensor([[1.], [1.], [1.]])