Pytorch-MLP-CIFAR10

model.py

import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as initclass MLP_cls(nn.Module):def __init__(self,in_dim=3*32*32):super(MLP_cls,self).__init__()self.lin1 = nn.Linear(in_dim,128)self.lin2 = nn.Linear(128,64)self.lin3 = nn.Linear(64,10)self.relu = nn.ReLU()init.xavier_uniform_(self.lin1.weight)init.xavier_uniform_(self.lin2.weight)init.xavier_uniform_(self.lin3.weight)def forward(self,x):x = x.view(-1,3*32*32)x = self.lin1(x)x = self.relu(x)x = self.lin2(x)x = self.relu(x)x = self.lin3(x)x = self.relu(x)return x

main.py

import torch
import torch.nn as nn
import torchvision
from torch.utils.data import DataLoader
import torch.optim as optim
from model import MLP_cls,CNN_clsseed = 42
torch.manual_seed(seed)
batch_size_train = 64
batch_size_test  = 64
epochs = 10
learning_rate = 0.01
momentum = 0.5
net = MLP_cls()train_loader = torch.utils.data.DataLoader(torchvision.datasets.CIFAR10('./data/', train=True, download=True,transform=torchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.5,), (0.5,))])),batch_size=batch_size_train, shuffle=True)
test_loader = torch.utils.data.DataLoader(torchvision.datasets.CIFAR10('./data/', train=False, download=True,transform=torchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.5,), (0.5,))])),batch_size=batch_size_test, shuffle=True)optimizer = optim.SGD(net.parameters(), lr=learning_rate,momentum=momentum)
criterion = nn.CrossEntropyLoss()print("****************Begin Training****************")
net.train()
for epoch in range(epochs):run_loss = 0correct_num = 0for batch_idx, (data, target) in enumerate(train_loader):out = net(data)_,pred = torch.max(out,dim=1)optimizer.zero_grad()loss = criterion(out,target)loss.backward()run_loss += lossoptimizer.step()correct_num  += torch.sum(pred==target)print('epoch',epoch,'loss {:.2f}'.format(run_loss.item()/len(train_loader)),'accuracy {:.2f}'.format(correct_num.item()/(len(train_loader)*batch_size_train)))print("****************Begin Testing****************")
net.eval()
test_loss = 0
test_correct_num = 0
for batch_idx, (data, target) in enumerate(test_loader):out = net(data)_,pred = torch.max(out,dim=1)test_loss += criterion(out,target)test_correct_num  += torch.sum(pred==target)
print('loss {:.2f}'.format(test_loss.item()/len(test_loader)),'accuracy {:.2f}'.format(test_correct_num.item()/(len(test_loader)*batch_size_test)))

参数设置

'./data/' #数据保存路径
seed = 42 #随机种子
batch_size_train = 64
batch_size_test  = 64
epochs = 10optim --> SGD
learning_rate = 0.01
momentum = 0.5

注意事项

CIFAR10是彩色图像，单个大小为3*32*32。所以view的时候后面展平。

运行图