目录
- 1.多层神经网络的实现
- 2. 训练轮次示例
- 3. 保存并加载模型
- 4. 使用GPU加速训练
- 5. 使用上面所教，进行一次训练
摘要：今天想整理一下Pytorch常用操作，以便以后进行预习（不是）
1.多层神经网络的实现
这是常用的操作，要会
class NeuralNetwork(torch.nn.Module):def __init__(self, num_inputs, num_outputs):super().__init__()self.layers = torch.nn.Sequential(# 第一个隐藏层torch.nn.Linear(num_inputs, 30),torch.nn.ReLU(),# 第二个隐藏层torch.nn.Linear(30, 20),torch.nn.ReLU(),# 输出层torch.nn.Linear(20, num_outputs),)def forward(self, x):logits = self.layers(x)return logitsmodel = NeuralNetwork(50, 3)
print(model)NeuralNetwork(
(layers): Sequential(
(0): Linear(in_features=50, out_features=30, bias=True)
(1): ReLU()
(2): Linear(in_features=30, out_features=20, bias=True)
(3): ReLU()
(4): Linear(in_features=20, out_features=3, bias=True)
)
)
2. 训练轮次示例
import torch.nn.functional as Ftorch.manual_seed(123)
model = NeuralNetwork(num_inputs=2, num_outputs=2)
optimizer = torch.optim.SGD(model.parameters(), lr=0.5)num_epochs = 3for epoch in range(num_epochs):model.train()for batch_idx, (features, labels) in enumerate(train_loader):logits = model(features)loss = F.cross_entropy(logits, labels) # 损失函数optimizer.zero_grad()loss.backward()optimizer.step()### 日志print(f"Epoch: {epoch+1:03d}/{num_epochs:03d}"f" | Batch {batch_idx:03d}/{len(train_loader):03d}"f" | Train/Val Loss: {loss:.2f}")model.eval()# 可选的模型评估指标
Epoch: 001/003 | Batch 000/002 | Train/Val Loss: 0.75
Epoch: 001/003 | Batch 001/002 | Train/Val Loss: 0.65
Epoch: 002/003 | Batch 000/002 | Train/Val Loss: 0.44
Epoch: 002/003 | Batch 001/002 | Train/Val Loss: 0.13
Epoch: 003/003 | Batch 000/002 | Train/Val Loss: 0.03
Epoch: 003/003 | Batch 001/002 | Train/Val Loss: 0.00
3. 保存并加载模型
就一句话
torch.save(model.state_dict(), "model.pth")
4. 使用GPU加速训练
我们常常说的CUDA就是在GPU上训练
import torch
# 显示PyTorch是否支持GPU
print(torch.cuda.is_available())
如果显示True，则代表可以用GPU，否则则要用CPU
# 根据设备可用情况选择设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
5. 使用上面所教，进行一次训练
创建了一个简单的神经网络模型来对二分类问题进行训练，并且使用了 PyTorch 提供的 Dataset 和 DataLoader 类来加载数据集并进行批处理。此外，你还定义了一个函数来计算模型的准确率。
import torch
X_train = torch.tensor([[-1.2, 3.1],[-0.9, 2.9],[-0.5, 2.6],[2.3, -1.1],[2.7, -1.5]
])
y_train = torch.tensor([0, 0, 0, 1, 1])
X_test = torch.tensor([[-0.8, 2.8],[2.6, -1.6],
])
y_test = torch.tensor([0, 1])from torch.utils.data import Dataset
class ToyDataset(Dataset):def __init__(self, X, y):self.features = Xself.labels = ydef __getitem__(self, index):one_x = self.features[index]one_y = self.labels[index]return one_x, one_ydef __len__(self):return self.labels.shape[0]
train_ds = ToyDataset(X_train, y_train)
test_ds = ToyDataset(X_test, y_test)from torch.utils.data import DataLoader
torch.manual_seed(123)
train_loader = DataLoader(dataset=train_ds,batch_size=2,shuffle=True,num_workers=1,drop_last=True
)
test_loader = DataLoader(dataset=test_ds,batch_size=2,shuffle=False,num_workers=1
)class NeuralNetwork(torch.nn.Module):def __init__(self, num_inputs, num_outputs):super().__init__()self.layers = torch.nn.Sequential(# 第一个隐藏层torch.nn.Linear(num_inputs, 30),torch.nn.ReLU(),# 第二个隐藏层torch.nn.Linear(30, 20),torch.nn.ReLU(),# 输出层torch.nn.Linear(20, num_outputs),)def forward(self, x):logits = self.layers(x)return logits# 使用accuracy（准确率）作为指标
def compute_accuracy(model, dataloader, device):model = model.eval()correct = 0.0total_examples = 0for idx, (features, labels) in enumerate(dataloader):# 将数据移动到指定的设备上features, labels = features.to(device), labels.to(device) # Newwith torch.no_grad():logits = model(features)# 获取预测结果并计算准确数量predictions = torch.argmax(logits, dim=1)compare = labels == predictionscorrect += torch.sum(compare)total_examples += len(compare)# 计算并返回准确率return (correct / total_examples).item()import torch.nn.functional as F
# 设置随机数种子，以确保可复现性
torch.manual_seed(123)
# 创建神经网络模型
model = NeuralNetwork(num_inputs=2, num_outputs=2)
# 根据设备可用情况选择设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 将模型移动到所选设备上
model = model.to(device)
# 定义优化器，使用随机梯度下降 (SGD)
optimizer = torch.optim.SGD(model.parameters(), lr=0.5)
# 定义训练循环的 epoch 数量
num_epochs = 3
for epoch in range(num_epochs):model.train()for batch_idx, (features, labels) in enumerate(train_loader):features, labels = features.to(device), labels.to(device) logits = model(features)loss = F.cross_entropy(logits, labels) # 损失函数optimizer.zero_grad()loss.backward()optimizer.step()### 训练日志print(f"Epoch: {epoch+1:03d}/{num_epochs:03d}"f" | Batch {batch_idx:03d}/{len(train_loader):03d}"f" | Train/Val Loss: {loss:.2f}")model.eval()print('accuracy',str(compute_accuracy(model, train_loader, device=device)))
Epoch: 001/003 | Batch 000/002 | Train/Val Loss: 0.75
Epoch: 001/003 | Batch 001/002 | Train/Val Loss: 0.65
Epoch: 002/003 | Batch 000/002 | Train/Val Loss: 0.44
Epoch: 002/003 | Batch 001/002 | Train/Val Loss: 0.13
Epoch: 003/003 | Batch 000/002 | Train/Val Loss: 0.03
Epoch: 003/003 | Batch 001/002 | Train/Val Loss: 0.00
accuracy:1.0
