输入是427*2801,共427个数据,每个数据2801个特征,输出为0或1,用pytorch一维卷积神经网络对数据进行分类,模型应该如何搭建?每层的卷积应该如何设置?以及训练和测试以及保存该网络。帮我写出代码
时间: 2023-03-28 18:02:27 浏览: 51
很高兴回答你的问题。对于这个问题,我可以给你一些建议。
首先,你需要定义一个数据集类,用于加载和处理数据。然后,你需要定义一个模型类,用于搭建卷积神经网络。接下来,你需要定义一个训练函数和一个测试函数,用于训练和测试模型。最后,你需要定义一个保存函数,用于保存训练好的模型。
以下是一个示例代码,供你参考:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
# 定义数据集类
class MyDataset(Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
return self.data[idx], self.labels[idx]
# 定义模型类
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = nn.Conv1d(2801, 64, kernel_size=3, stride=1, padding=1)
self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2)
self.conv2 = nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1)
self.pool2 = nn.MaxPool1d(kernel_size=2, stride=2)
self.conv3 = nn.Conv1d(128, 256, kernel_size=3, stride=1, padding=1)
self.pool3 = nn.MaxPool1d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(256 * 175, 512)
self.fc2 = nn.Linear(512, 2)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = self.pool1(x)
x = self.conv2(x)
x = nn.functional.relu(x)
x = self.pool2(x)
x = self.conv3(x)
x = nn.functional.relu(x)
x = self.pool3(x)
x = x.view(-1, 256 * 175)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
return x
# 定义训练函数
def train(model, device, train_loader, optimizer, criterion):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
# 定义测试函数
def test(model, device, test_loader, criterion):
model.eval()
test_loss =
correct =
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += criterion(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset)
print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.f}%)'.format(
test_loss, correct, len(test_loader.dataset), accuracy))
# 加载数据
data = torch.randn(427, 2801)
labels = torch.randint(, 2, (427,))
dataset = MyDataset(data, labels)
train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
# 定义设备和模型
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MyModel().to(device)
# 定义优化器和损失函数
optimizer = optim.Adam(model.parameters(), lr=.001)
criterion = nn.CrossEntropyLoss()
# 训练模型
for epoch in range(10):
train(model, device, train_loader, optimizer, criterion)
test(model, device, train_loader, criterion)
# 保存模型
torch.save(model.state_dict(), "model.pt")
```
希望这个代码对你有所帮助。如果你有任何问题,请随时问我。
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