利用利用pytorch搭建搭建lenet网络，并利用网络，并利用mnist数据集进行训练测数据集进行训练测
试试
最近在学习pytorch，手工复现了LeNet网络，并附源码如下，欢迎大家留言交流
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
class LeNet(nn.Module):
def __init__(self):
super(LeNet,self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5, 1,)
self.conv2 = nn.Conv2d(6, 16, 5, 1)
self.fc1 = nn.Linear(16*4*4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
#x:1*28*28
x = F.max_pool2d(self.conv1(x), 2, 2)
x = F.max_pool2d(self.conv2(x), 2, 2)
x = x.view(-1, 16*4*4)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return F.log_softmax(x,dim=1)
def train(model, device, train_loader, optimizer, epoch):
model.train()
for idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
pred = model(data)
loss = F.nll_loss(pred,target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if idx % 100 == 0:
print("Train Epoch: {}, iterantion: {}, Loss: {}".format(epoch, idx, loss.item()))
def test(model, device, test_loader):
model.eval()
total_loss = 0.
correct = 0.
with torch.no_grad():
for idx, (data, target) in enumerate(test_loader):
data, target = data.to(device), target.to(device)
output = model(data)
total_loss += F.nll_loss(output, target, reduction="sum").item()
pred = output.argmax(dim=1)
correct +=pred.eq(target.view_as(pred)).sum().item()
total_loss /= len(test_loader.dataset)
acc = correct/len(test_loader.dataset)*100
print("Test loss: {}, Accuracy: {}".format(total_loss, acc))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
batch_size = 32
train_dataloader = torch.utils.data.DataLoader(
datasets.MNIST("./MNIST_data", train=True, download=False,
transform = transforms.Compose([
transforms.ToTensor(),
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