深度学习实战：LeNet实现CIFAR-10图像分类
- 1.数据集介绍
- 2.LeNet网络介绍
- 3. model.py 创建
- 4. train.py创建
* - 4.1 相关包的加载
- 4.2 数据预处理
- 4.3 加载数据集
- 4.4 加载测试集
- 4.5 代码（GPU训练版本）
- 4.6 代码（CPU训练版本）
- 5. predict.py 创建
代码已上传至github(麻烦Star~)
更多Ai资讯： 公主号AiCharm
; 1.数据集介绍
利用torchvision.datasets函数可以在线导入pytorch中的数据集，包含一些常见的数据集如MNIST、CIFAR-10等。本次使用的是CIFAR10数据集，也是一个很经典的图像分类数据集，由 Hinton 的学生 Alex Krizhevsky 和 Ilya Sutskever 整理的一个用于识别普适物体的小型数据集，一共包含 10 个类别的 RGB 彩色图片。
PyTorch的CIFAR-10数据集有时下载不了，我这里将下载好的压缩包放在网盘中，需要的可以自行下载，解压后放在当前项目文件的data文件夹下。链接：https://pan.baidu.com/s/1NBHp0SxEOJ5EIyYUsDHm_g
提取码：qp3k
2.LeNet网络介绍
LeNet网络之前在我的博客详细讲解过：https://blog.csdn.net/muye_IT/article/details/123539199?spm=1001.2014.3001.5501
LeNet网络架构总览图：
; 3. model.py 创建
model.py ——定义LeNet网络模型
import torch.nn as nn
import torch.nn.functional as F
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))
x = self.pool1(x)
x = F.relu(self.conv2(x))
x = self.pool2(x)
x = x.view(-1, 32*5*5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
Conv2d、MaxPool2d、Linear在pytorch中对应的函数，以及函数参数的设置
常见的参数：
- in_channels：输入特征矩阵的深度。如输入一张RGB彩色图像，那in_channels=3
- out_channels：输入特征矩阵的深度。也等于卷积核的个数，使用n个卷积核输出的特征矩阵深度就是n
- kernel_size：卷积核的尺寸。可以是int类型，如3 代表卷积核的height=width=3，也可以是tuple类型如(3,5)代表卷积核的height=3，width=5
- stride：卷积核的步长。默认为1，和kernel_size一样输入可以是int型，也可以是tuple类型
- padding：补零操作，默认为0。可以为int型如1即补一圈0，如果输入为tuple型如(2, 1) 代表在上下补2行，左右补1列
Conv2d ['stride', 'padding', 'dilation', 'groups','padding_mode', 'output_padding', 'in_channels','out_channels', 'kernel_size']
MaxPool2d（'kernel_size', 'stride', 'padding', 'dilation','return_indices', 'ceil_mode'）
Linear('in_features', 'out_features'）
- train.py创建
train.py ——加载数据集并训练，训练集计算loss，测试集计算accuracy，保存训练好的网络参数
4.1 相关包的加载
import torch
import torchvision
import torch.nn as nn
from model import LeNet
import torch.optim as optim
import torchvision.transforms as transforms
4.2 数据预处理
由shape (H x W x C) in the range [0, 255] → shape (C x H x W) in the range [0.0, 1.0]
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
4.3 加载数据集
train_set = torchvision.datasets.CIFAR10(root='./data',
train=True,
download=True,
transform=transform)
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=50,
shuffle=False,
num_workers=0)
4.4 加载测试集
test_set = torchvision.datasets.CIFAR10(root='./data',
train=False,
download=False,transform=transform)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=10000,
shuffle=False, num_workers=0)
test_data_iter = iter(test_loader)
test_image, test_label = test_data_iter.next()
4.5 代码（GPU训练版本）
使用下面语句可以在有GPU时使用GPU，无GPU时使用CPU进行训练
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
也可以直接指定
device = torch.device("cuda")
对应的，需要用to()函数来将Tensor在CPU和GPU之间相互移动，分配到指定的device中计算
import torch
import torchvision
import torch.nn as nn
from model 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))])
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)
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()
net.to(device)
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
for epoch in range(5):
running_loss = 0.0
for step, data in enumerate(train_loader, start=0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs.to(device))
loss = loss_function(outputs, labels.to(device))
loss.backward()
optimizer.step()
running_loss += loss.item()
if step % 500 == 499:
with torch.no_grad():
outputs = net(test_image.to(device))
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()
4.6 代码（CPU训练版本）
import torch
import torchvision
import torch.nn as nn
from model 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))])
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)
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(5):
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()
- predict.py 创建
predict.py——得到训练好的网络参数后，用自己找的图像进行分类测试，自己下载一张照片保存在根目录下，命名为1.jpg
import torch
import torchvision.transforms as transforms
from PIL import Image
from model import LeNet
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()
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()
print(classes[int(predict)])
if __name__ == '__main__':
main()
代码已上传至github(麻烦Star~)
更多Ai资讯： 公主号AiCharm
Original: https://blog.csdn.net/muye_IT/article/details/123855068
Author: Jasper0420
Title: 深度学习实战（一）：LeNet实现CIFAR-10图像分类
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