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Question

loss_fn = nn.CrossEntropyLoss() optimizer= optim.Adam（model.parameters（），lr = 0.01）#lr可选，但通常更好地指定指定

def MultiClassNN_train():
    n_epochs = 100
    batch_size = 10

    for epoch in range(n_epochs):
        model.train()
        total_loss = 0
        for i in range(0, len(X_train), batch_size):
            Xbatch = X_train[i:i+batch_size]
            ybatch = y_train[i:i+batch_size].type(torch.long)
        
            optimizer.zero_grad()
            outputs = model(Xbatch)
            loss = loss_fn(outputs, ybatch)
            loss.backward()
            optimizer.step()
        
            total_loss += loss.item()
        
        avg_loss = total_loss / (len(X_train) // batch_size)
        print(f'Epoch {epoch+1}, Average Loss: {avg_loss:.4f}')
    
        # Evaluating on test set after each epoch
        model.eval()
        with torch.no_grad():
            test_outputs = model(X_test)
            test_loss = loss_fn(test_outputs,
             y_test.type(torch.long))
            predicted = torch.argmax(test_outputs, dim=1)
            accuracy = (predicted == y_test).float().mean()
            print(f'Test Loss: {test_loss:.4f}, Accuracy:                 
            {accuracy:.4f}')

       

help将不胜感激。

您确定您没有更改原始数据集的任何列吗？ 我只是尝试重现问题，我在代码中几乎没有更改，并且我的输出不同：

https：//colab.research.google.com/drive/1MYV1BPK9WBLAZEQ8TYKIOCQ-PFSLZVSSS？usp =shareing