用python写一个pytorh深度学习训练一个深度一维卷积神经网络用于分类的代码

你可以使用以下Python代码来构建一个深度一维卷积神经网络，用于分类：import torch
import torch.nn as nn#定义深度一维卷积网络
class Deep1DConvNet(nn.Module):
def __init__(self):
super(Deep1DConvNet,self).__init__()
self.conv1 = nn.Conv1d(in_channels=1, out_channels=4, kernel_size=3)
self.conv2 = nn.Conv1d(in_channels=4, out_channels=8, kernel_size=3)
self.conv3 = nn.Conv1d(in_channels=8, out_channels=16, kernel_size=3)
self.fc1 = nn.Linear(in_features=16*3, out_features=32)
self.fc2 = nn.Linear(in_features=32, out_features=2)

def forward(self,x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = x.view(-1, 16*3)
x = self.fc1(x)
x = self.fc2(x)
return x

#实例化网络
model = Deep1DConvNet()#设置优化器和损失函数
optimizer = torch.optim.Adam(model.parameters(),lr=0.001)
criterion = nn.CrossEntropyLoss()#训练模型
for epoch in range(epochs):
#正向传播
outputs = model(inputs)
#计算损失
loss = criterion(outputs, labels)
#反向传播
optimizer.zero_grad()
loss.backward()
optimizer.step()