LeNet是一个较为简单的卷积神经网络。通过巧妙的设计,利用卷积、参数共享、池化等操作提取特征,避免了大量的计算成本,最后使用全连接神经网络进行分类识别。
现在大多数的卷积神经网络都是基于LeNet的框架(卷积层、池化层、全连接层)。
采用PyTorch框架,使用CIFAR10训练集,训练100个epoch。
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 5)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(16, 32, 5)
self.pool2 = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(32*5*5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = F.relu(self.conv1(x)) # input(3, 32, 32) output(16, 28, 28)
x = self.pool1(x) # output(16, 14, 14)
x = F.relu(self.conv2(x)) # output(32, 10, 10)
x = self.pool2(x) # output(32, 5, 5)
x = x.view(-1, 32*5*5) # output(32*5*5)
x = F.relu(self.fc1(x)) # output(120)
x = F.relu(self.fc2(x)) # output(84)
x = self.fc3(x) # output(10)
return x
import torch
import torchvision
import torch.nn as nn
from LeNet import LeNet
import torch.optim as optim
import torchvision.transforms as transforms
def main():
transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
)
#50000
#
train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=False, transform=transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=36, shuffle=True, num_workers=0)
#10000
val_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=False, transform=transform)
val_loader = torch.utils.data.DataLoader(val_set, batch_size=5000, shuffle=False, num_workers=0)
val_data_iter = iter(val_loader)
val_image, val_label = val_data_iter.next()
net = LeNet()
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr = 0.001)
for epoch in range(100):
running_loss = 0.0
for step, data in enumerate(train_loader, start=0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if step % 500 == 499:
with torch.no_grad():
outputs = net(val_image)
predict_y = torch.max(outputs, dim=1)[1]
accuracy = torch.eq(predict_y, val_label).sum().item() / val_label.size(0)
print('[%d, %5d] train_loss: %.3f test_accuracy: %.3f' %
(epoch + 1, step + 1, running_loss / 500, accuracy))
running_loss = 0.0
print('Finished Training')
save_path = './LeNEt.pth'
torch.save(net.state_dict(), save_path)
if __name__ == '__main__':
main()
import torch
import torchvision.transforms as transforms
from PIL import Image
from LeNet import LeNet
import onnx
def main():
transform = transforms.Compose(
[
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
net = LeNet()
print(net)
net.load_state_dict(torch.load('Lenet.pth'))
im = Image.open('./1.jpg')
im = transform(im)
im = torch.unsqueeze(im, dim =0)
with torch.no_grad():
outputs = net(im)
predict = torch.max(outputs, dim=1)[1].data.numpy()
torch.onnx.export(net, im, "lenet.onnx")
print(classes[int(predict)])
if __name__ == '__main__':
main()