0220627/Pytorch-Projects
/PyTorch_Basics/10-Convolution/10-Convolution.ipynb
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import time
import copy
import numpy as np
import matplotlib.pyplot as plt
# 超参数
batch_size = 64
learning_rate = 0.001
num_epochs = 5
# 数据预处理
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# 加载数据集
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)
# 定义卷积神经网络
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(32 * 7 * 7, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
out = self.pool1(F.relu(self.conv1(x)))
out = self.pool2(F.relu(self.conv2(out)))
out = out.view(out.size(0), -1)
out = F.relu(self.fc1(out))
out = self.fc2(out)
return out
# 初始化网络模型和损失函数
model = ConvNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
for epoch in range(num_epochs):
model.train()
for i, (images, labels) in enumerate(train_loader):
images = images.cuda()
labels = labels.cuda()
# 前向传播
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(train_loader)}], Loss: {loss.item():.4f}')
# 测试模型
model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.cuda()
labels = labels.cuda()
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Epoch [{epoch+1}/{num_epochs}], Accuracy: {100 * correct / total:.2f}%')
# 每轮训练结束后保存模型
torch.save(model.state_dict(), f'model_epoch_{epoch+1}.pth')
# 保存最终模型
torch.save(model.state_dict(), 'model_final.pth')
/PyTorch_Basics/11-Transformer/11-Transformer.ipynb
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.utils.data as data
from torch.utils.data import DataLoader
import time
import copy
import numpy as np
import matplotlib.pyplot as plt
# 超参数
batch_size = 64
learning_rate = 0.001
num_epochs = 10
# 数据预处理
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# 加载数据集
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
# 定义卷积神经网络
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(32 * 7 * 7, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
out