pytorch 入门学习多分类问题

运行结果

 [1,  300] loss: 2.287[1,  600] loss: 2.137[1,  900] loss: 1.192
Accuracy on test set: 78 % [2,  300] loss: 0.560[2,  600] loss: 0.422[2,  900] loss: 0.361
Accuracy on test set: 90 % [3,  300] loss: 0.307[3,  600] loss: 0.292[3,  900] loss: 0.258
Accuracy on test set: 93 % [4,  300] loss: 0.228[4,  600] loss: 0.221[4,  900] loss: 0.201
Accuracy on test set: 94 % [5,  300] loss: 0.178[5,  600] loss: 0.178[5,  900] loss: 0.158
Accuracy on test set: 95 % [6,  300] loss: 0.141[6,  600] loss: 0.139[6,  900] loss: 0.144
Accuracy on test set: 96 % [7,  300] loss: 0.129[7,  600] loss: 0.116[7,  900] loss: 0.114
Accuracy on test set: 96 % [8,  300] loss: 0.107[8,  600] loss: 0.100[8,  900] loss: 0.106
Accuracy on test set: 96 % [9,  300] loss: 0.091[9,  600] loss: 0.088[9,  900] loss: 0.089
Accuracy on test set: 96 % [10,  300] loss: 0.079[10,  600] loss: 0.074[10,  900] loss: 0.080
Accuracy on test set: 96 % Process finished with exit code 0

import  torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim#step1 准备数据集batch_size = 64
transform = transforms.Compose([transforms.ToTensor(), #对图像数据进行处理，归一化到0到1transforms.Normalize((0.137,),(0.3081,)) #平均值和标准差，mnist数据集提前算出来的
])train_dataset = datasets.MNIST(root='../dataset/mnist',train=True,download=True,transform=transform)train_loder = DataLoader(train_dataset,shuffle=True,batch_size=batch_size)test_dataset = datasets.MNIST(root='../dataset/mnist',train=False,download=True,transform=transform)test_loder = DataLoader(test_dataset,shuffle=False,batch_size=batch_size)#step2 搭建网络
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 = x.view(-1,784)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()
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)#step3 训练
def train(epoch):running_loss = 0.0for batch_idx,data in enumerate(train_loder,0):inputs,target = dataoptimizer.zero_grad()    #梯度清零#forward + backward + updateoutputs = model(inputs)loss = criterion(outputs,target)loss.backward()optimizer.step()running_loss += loss.item()if batch_idx % 300 == 299:print(' [%d,%5d] loss: %.3f' % (epoch + 1,batch_idx + 1, running_loss / 300))running_loss = 0.0def test():correct = 0total = 0with torch.no_grad():      #不计算梯度for data in test_loder:images,labels = dataoutputs = model(images)_,predicted = torch.max(outputs.data,dim=1)total += labels.size(0)correct += (predicted == labels).sum().item()print('Accuracy on test set: %d %% '%(100 * correct / total))if __name__ == '__main__':for epoch in range(10):train(epoch)test()

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pytorch 入门学习多分类问题-9

pytorch 入门学习多分类问题

运行结果

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