多分类问题

分布上的性质要求 1.所有结果的概率都大于0；
2.所有结果的概率之和等于1。
普通的sigmoid函数一般不用于解决多分类的问题
因此使用softmax函数来满足其性质

import numpy as np
y=np.array([1,0,0])
z=np.array([0.2,0.1,-0.1])
y_pred=np.exp(z)/np.exp(z).sum()
loss=(-y*np.log(y_pred)).sum()
print(loss)

import torch
criterion=torch.nn.CrossEntropyLoss()
y=torch.LongTensor([2,0,1])
y_pred1=torch.Tensor([[0.1,0.2,0.9],
                   [1.1,0.1,0.2],
                   [0.2,2.1,0.1]])
y_pred2=torch.Tensor([[0.8,0.2,0.3],
                    [0.2,0.3,0.5],
                    [0.2,0.2,0.5]])
l1=criterion(y_pred1,y)
l2=criterion(y_pred2,y)
print("Batch Loss1=",l1.data,"nBatch Loss2=",l2.data)

minist数据集做多分类

import torch
from torchvision import transforms#对数据集做图像处理
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim#优化器

#Totensor 转变成c、h、w张量
#Tensor转换成w,h,c张量
batch_size=64
transforms=transforms.Compose([transforms.ToTensor(),
                              transforms.Normalize((0.1307,),(0.3081,))])
#Normalize第一个参数是均值、第二个参数是标准差

train_dataset=datasets.MNIST(root='../dataset/mnist',
                            train=True,
                            download=True,
                            transform=transforms)
train_loader=DataLoader(train_dataset,
                       shuffle=True,
                       batch_size=batch_size)
test_dataset=datasets.MNIST(root='../dataset/mnist',
                           train=False,
                           download=True,
                           transform=transforms)
test_loader=DataLoader(test_dataset,
                      shuffle=False,
                      batch_size=batch_size)

class Net(torch.nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.l1=torch.nn.Linear(784,512)
        self.l2=torch.nn.Linear(512,256)
        self.l3=torch.nn.Linear(256,128)
        self.l4=torch.nn.Linear(128,64)
        self.l5=torch.nn.Linear(64,10)
def forward(self,x):
    x=view(-1,784)#将其
    x=F.relu(self.l1(x))
    x=F.relu(self.l2(x))
    x=F.relu(self.l3(x))
    x=F.relu(self.l4(x))
    return self.l5(x)
model=Net()


criterion=torch.nn.CrossEntropyLoss()#损失函数
optimizer=optim.SGD(model.parameters(),lr=0.01,momentum=0.5)#带冲量的优化器


def train(epoch):
    running_loss=0.0
    for batch_idex,data in enumerate(train_loader,0):
        inputs,target=data
        optimizer.zero_grad()#优化器清零
        outputs=model(inputs)
        loss=criterion(outputs,target)
        loss.backward()#反馈
        optimizer.step()优化
        
        running_loss+=loss.item()
        if batch_idx%300==299:
            print('[%d,%5d] loss: %0.3f'%(epoch+1,batch_idx+1,running_loss/300))
            running_loss=0.0
 def test():
    correct=0
    total=0
    with torch.no_grad():
        for data in test_loader:
            images,labels=data
            outputs=model(images)
            _,predicted=torch.max(outputs.data,dim=1)
            total+=labels.size(0)
            correct+=(predicted==labels).sum().item()
            print('accuracy on test sst :%d %%'%(100*correct/total))
if __name__=='main':
    for epoch in range(10):
        train(epoch)
        test()