目录
1、环境条件
- pycharm编译器
- pytorch依赖
- matplotlib依赖
- numpy依赖等等
2、代码实现
python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
# 设置设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 定义数据变换
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# 加载 MNIST 数据集
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)
# 定义 LeNet-5 模型
class LeNet5(nn.Module):
def __init__(self):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=2)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# 初始化模型、损失函数和优化器
model = LeNet5().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
epochs = 5
for epoch in range(epochs):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print(f'[Epoch {epoch + 1}, Batch {i + 1}] loss: {running_loss / 100:.3f}')
running_loss = 0.0
print('Finished Training')
# 保存模型
torch.save(model.state_dict(), 'lenet5.pth')
print('Model saved to lenet5.pth')
# 加载模型
model = LeNet5()
model.load_state_dict(torch.load('lenet5.pth'))
model.to(device)
model.eval()
# 在测试集上评估模型
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Accuracy on the test set: {100 * correct / total:.2f}%')
# 加载并预处理本地图片进行预测
from PIL import Image
def load_and_preprocess_image(image_path):
img = Image.open(image_path).convert('L') # 转为灰度图
img = img.resize((28, 28))
img = np.array(img, dtype=np.float32)
img = (img / 255.0 - 0.5) / 0.5 # 归一化到[-1, 1]
img = torch.tensor(img).unsqueeze(0).unsqueeze(0) # 添加批次和通道维度
return img.to(device)
# 预测本地图片
image_path = '4.png' # 替换为你的本地图片路径
img = load_and_preprocess_image(image_path)
# 使用加载的模型进行预测
model.eval()
with torch.no_grad():
outputs = model(img)
_, predicted = torch.max(outputs, 1)
# 打印预测结果
predicted_label = predicted.item()
print(f'预测结果: {predicted_label}')
# 显示图片及预测结果
img_np = img.cpu().numpy().squeeze()
plt.imshow(img_np, cmap='gray')
plt.title(f'预测结果: {predicted_label}')
plt.show()解释：torch.save（）方法完成模型的保存，image_path为本地图片，用于测试
3、总结
安装环境是比较难的点，均使用pip install 。。指令进行依赖环境的安装，其他的比较简单。
学习之所以会想睡觉，是因为那是梦开始的地方。
ଘ(੭ˊᵕˋ)੭ （开心） ଘ(੭ˊᵕˋ)੭ （开心）ଘ(੭ˊᵕˋ)੭ （开心）ଘ(੭ˊᵕˋ)੭ （开心）ଘ(੭ˊᵕˋ)੭ （开心）
------不写代码不会凸的小刘