softmax代码实现，代码原理供参考

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我是靠谱客的博主野性可乐，最近开发中收集的这篇文章主要介绍softmax代码实现，代码原理供参考，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

import torch
from IPython import display
from d2l import torch as d2l
import torch.utils.data.dataloader
from matplotlib import pyplot as plt
# import pylab
batch_size=64
train_iter,test_iter=d2l.load_data_fashion_mnist(batch_size)
#训练集和测试集的迭代器
num_inputs=784
num_outputs=10
w=torch.normal(0,0.1,size=(num_inputs,num_outputs),requires_grad=True)#定义权重，高斯初始化参数
b=torch.zeros(num_outputs,requires_grad=True)#偏移量，需计算梯度
# x=torch.tensor([[1.0,2.0,3.0],[4.0,5.0,6.0]])
# x.sum(0,keepdim=True)
# x.sum(1,keepdim=True)
# 给定一个对所有元素求和的矩阵X
def softmax(x):
x_exp=torch.exp(x)#对每个元素做指数计算
partition=x_exp.sum(1,keepdim=True)#按照维度为一把每一行求和
return
x_exp/partition#应用广播机制，消除量纲
# softmax实现
def net(x):
return softmax(torch.matmul(x.reshape((-1,w.shape[0])),w)+b)#-1是自动计算的意思，w。shape0是批量大小
#把wx+b放入spftmax里面
y=torch.tensor([0,2])
y_hat=torch.tensor([[0.1,0.3,0.6],[0.3,0.2,0.5]])
y_hat[[0,1],y]
def cross_entropy(y_hat,y):#交叉熵损失函数
return -torch.log(y_hat[range(len(y_hat)),y])
cross_entropy(y_hat,y)
def accuracy(y_hat,y):#正确率
if len(y_hat.shape)>1 and y_hat.shape[1]>1:
y_hat=y_hat.argmax(axis=1)
cmp=y_hat.type(y.dtype)==y
return float(cmp.type(y.dtype).sum())
# accuracy(y_hat,y)/len(y)
def evaluate_accuracy(net,data_iter):
if isinstance(net,torch.nn.Module):
net.eval()
metric=Accumulator(2)
for x,y in data_iter:
metric.add(accuracy(net(x),y),y.numel())
return
metric[0]/metric[1]
class Accumulator:
def __init__(self,n):
self.data=[0.0]*n
def add(self,*args):
self.data=[a+float(b) for a,b in zip(self.data,args)]
def reset(self):
self.data = [0.0]*len(self.data)
def __getitem__(self,idx):
return self.data[idx]
def train_epoch_ch3(net,train_iter,loss,updater):
if isinstance(net,torch.nn.Module):
net.train()
metric=Accumulator(3)#长度为3的迭代器累积信息
for x ,y in train_iter:
Y_hat=net(x)
l=loss(Y_hat,y)
if isinstance(updater,torch.optim.Optimizer):
updater.zero_grad()
l.backward()
updater.step()
metric.add(float(l)*len(y),
accuracy(Y_hat,y),
y.size().numel())#样本数、正确率等放进累加器
else:
l.sum().backward()
updater(x.shape[0])
metric.add(float(l.sum()),accuracy(Y_hat,y),y.size().numel())
return metric[0]/metric[2],metric[1]/metric[2]
# 可视化
class Animator:
def __init__(self,xlabel=None,ylabel=None,legend=None,xlim=None,
ylim=None,xscale='linear',yscale='linear',
fmts=('_','m--','g-.','r:'),nrows=1,ncols=1,
figsize=(3.5,2.5)):
if legend is None:
legend=[]
d2l.use_svg_display()
self.fig,self.axes=d2l.plt.subplots(nrows,ncols,figsize=figsize)
if nrows*ncols==1:
self.axes=[self.axes,]
self.config_axes=lambda:d2l.set_axes(self.axes[0],xlabel,ylabel,xlim,ylim,xscale,yscale,legend)
self.x,self.y,self.fmts=None,None,fmts
def add(self,x,y):
if not hasattr(y,'__len__'):
y=[y]
n=len(y)
# 训练函数
def train_ch3(net,train_iter,test_iter,loss,num_epochs,updater):
animator=Animator(xlabel='epoch',xlim=[1,num_epochs],ylim=[0.3],legend=['train loss','train acc','test acc'])
for epoch in range(num_epochs):#训练开始
train_metrics=train_epoch_ch3(net,train_iter,loss,updater)
test_acc=evaluate_accuracy(net,test_iter)
animator.add(epoch+1,train_metrics+(test_acc,))
train_loss,train_acc=train_metrics
lr=0.1
def updater(batch_size):
return d2l.sgd([w,b],lr,batch_size)
num_epochs=10
train_ch3(net,train_iter,test_iter,cross_entropy,num_epochs,updater)
d2l.plt.show()