lenet5跑cifar_CIFAR-10数据集实战——构建LeNet5神经网络
MNIST数据集为0~9的数字，而CIFAR-10数据集为10类物品识别，包含飞机、车、鸟、猫等。照片大小为32*32的彩色图片(三通道)。每个类别大概有6000张照片，其中随机筛选出5000用来training，剩下的1000用来testing
首先引入数据集import torch
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
batch_size=32
cifar_train = datasets.CIFAR10(root='cifar', train=True, transform=transforms.Compose([
transforms.Resize([32, 32]),
transforms.ToTensor(),
]), download=True)
cifar_train = DataLoader(cifar_train, batch_size=batch_size, shuffle=True)
cifar_test = datasets.CIFAR10(root='cifar', train=False, transform=transforms.Compose([
transforms.Resize([32, 32]),
transforms.ToTensor(),
]), download=True)
cifar_test = DataLoader(cifar_test, batch_size=batch_size, shuffle=True)
x, label = iter(cifar_train).next()
print('x:', x.shape, 'label:', label.shape)
引入数据集以后，接下来开始编写经典的LeNet5神经网络import torch
from torch import nn, optim
import torch.nn.functional as F
class LeNet5(nn.Module):
"""
for CIFAR10 datasets
"""
def __init__(self):
super(LeNet5, self).__init__()
self.conv_unit = nn.Sequential(
# x: [batchsize, 3, 32, 32] => [batchsize, 6, 28, 28]
nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, stride=1, padding=0),
# [batchsize, 6, 28, 28] => [batchsize, 6, 14, 14]
nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
# [batchsize, 6, 14, 14] => [batchsize, 16, 10, 10]
nn.Conv2d(6, 16, 5, 1, 0),
# [batchsize, 16, 10, 10] => [batchsize, 16, 5, 5]
nn.AvgPool2d(2, 2, 0)
)
# fc_unit
self.fc_unit = nn.Sequential(
nn.Linear(in_features=16*5*5, out_features=120),
nn.ReLU(),
nn.Linear(120, 84),
nn.ReLU(),
nn.Linear(84, 10)
)
def forward(self, x):
batchsize = x.size(0)
# [b, 3, 32, 32] => [b, 16, 5, 5]
x = self.conv_unit(x)
# [b, 16, 5, 5] => [b, 16*5*5]
x = x.view(batchsize, -1)
# [b, 16*5*5] => [b, 10]
logits = self.fc_unit(x)
return logits
def main():
########## train ##########
#device = torch.device('cuda')
#model = LeNet5().to(device)
criteon = nn.CrossEntropyLoss()
model = LeNet5()
optimizer = optim.Adam(model.parameters(), 1e-3)
for epoch in range(1000):
model.train()
for batchidx, (x, label) in enumerate(cifar_train):
#x, label = x.to(device), label.to(device)
logits = model(x)
# logits: [b, 10]
# label: [b]
loss = criteon(logits, label)
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('train:', epoch, loss.item())
########## test ##########
model.eval()
with torch.no_grad():
total_correct = 0
total_num = 0
for x, label in cifar_test:
# x, label = x.to(device), label.to(device)
# [b]
logits = model(x)
# [b]
pred = logits.argmax(dim=1)
# [b] vs [b]
total_correct += torch.eq(pred, label).float().sum().item()
total_num += x.size(0)
acc = total_correct / total_num
print('test:', epoch, acc)
if __name__ == '__main__':
main()
完整代码如下：import torch
from torch import nn, optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
batch_size=32
cifar_train = datasets.CIFAR10(root='cifar', train=True, transform=transforms.Compose([
transforms.Resize([32, 32]),
transforms.ToTensor(),
]), download=True)
cifar_train = DataLoader(cifar_train, batch_size=batch_size, shuffle=True)
cifar_test = datasets.CIFAR10(root='cifar', train=False, transform=transforms.Compose([
transforms.Resize([32, 32]),
transforms.ToTensor(),
]), download=True)
cifar_test = DataLoader(cifar_test, batch_size=batch_size, shuffle=True)
x, label = iter(cifar_train).next()
print('x:', x.shape, 'label:', label.shape)
class LeNet5(nn.Module):
"""
for CIFAR10 datasets
"""
def __init__(self):
super(LeNet5, self).__init__()
self.conv_unit = nn.Sequential(
# x: [batchsize, 3, 32, 32] => [batchsize, 6, 28, 28]
nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, stride=1, padding=0),
# [batchsize, 6, 28, 28] => [batchsize, 6, 14, 14]
nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
# [batchsize, 6, 14, 14] => [batchsize, 16, 10, 10]
nn.Conv2d(6, 16, 5, 1, 0),
# [batchsize, 16, 10, 10] => [batchsize, 16, 5, 5]
nn.AvgPool2d(2, 2, 0)
)
# fc_unit
self.fc_unit = nn.Sequential(
nn.Linear(in_features=16*5*5, out_features=120),
nn.ReLU(),
nn.Linear(120, 84),
nn.ReLU(),
nn.Linear(84, 10)
)
def forward(self, x):
batchsize = x.size(0)
# [b, 3, 32, 32] => [b, 16, 5, 5]
x = self.conv_unit(x)
# [b, 16, 5, 5] => [b, 16*5*5]
x = x.view(batchsize, -1)
# [b, 16*5*5] => [b, 10]
logits = self.fc_unit(x)
return logits
def main():
########## train ##########
#device = torch.device('cuda')
#model = LeNet5().to(device)
criteon = nn.CrossEntropyLoss()
model = LeNet5()
optimizer = optim.Adam(model.parameters(), 1e-3)
for epoch in range(1000):
model.train()
for batchidx, (x, label) in enumerate(cifar_train):
#x, label = x.to(device), label.to(device)
logits = model(x)
# logits: [b, 10]
# label: [b]
loss = criteon(logits, label)
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('train:', epoch, loss.item())
########## test ##########
model.eval()
with torch.no_grad():
total_correct = 0
total_num = 0
for x, label in cifar_test:
# x, label = x.to(device), label.to(device)
# [b]
logits = model(x)
# [b]
pred = logits.argmax(dim=1)
# [b] vs [b]
total_correct += torch.eq(pred, label).float().sum().item()
total_num += x.size(0)
acc = total_correct / total_num
print('test:', epoch, acc)
if __name__ == '__main__':
main()
从这一部分的运行情况来看，准确率在慢慢上升，但并不稳定，读者有兴趣可以尝试自己修改网络结构，使其准确率更高
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