小编Lin*_*ama的帖子

我是 pytorch 和深度学习的新手

我的数据集 53502 x 58，

我的代码有问题

model = nn.Sequential(
    nn.Linear(58,64),
    nn.ReLU(),
    nn.Linear(64,32),
    nn.ReLU(),
    nn.Linear(32,16),
    nn.ReLU(),
    nn.Linear(16,2),
    nn.LogSoftmax(1)
)

criterion = nn.NLLLoss()
optimizer = optim.AdamW(model.parameters(), lr = 0.0001)

epoch = 500
train_cost, test_cost = [], []
for i in range(epoch):
    model.train()
    cost = 0
    for feature, target in trainloader:
        output = model(feature)          #feedforward
        loss = criterion(output, target) #loss
        loss.backward()                  #backprop
        
        optimizer.step()                 #update weight
        optimizer.zero_grad()            #zero grad
        
        cost += loss.item() * feature.shape[0]
    train_cost.append(cost / len(train_set))    
    
    with torch.no_grad():
        model.eval()
        cost = …