如何修复“ValueError：预期输入 batch_size (6) 以匹配目标 batch_size (1)。”在 PyTorch 中

Question

我正在尝试训练连续词袋架构，但每当我尝试使用 DataLoader 训练模型时，都会不断出现此错误。如果我不使用任何批次，损失就会减少。

class CBOW(nn.Module):
    def __init__(self, vocab_size, emb_dim, hidden_dim):
        super(CBOW, self).__init__()
        self.embedding = nn.Embedding(vocab_size, emb_dim)
        self.linear1 = nn.Linear(emb_dim, hidden_dim)
        self.activation = nn.ReLU()
        self.linear2 = nn.Linear(hidden_dim, vocab_size)
        self.activation_function2 = nn.LogSoftmax(dim = -1)
        
    def forward(self, inputs):
        embeds = self.embedding(inputs).mean(dim=0)
        out = self.activation(self.linear1(embeds))
        out = self.linear2(out)
        return out


# Define hyperparameters
vocab_size = len(vocab)  
emb_dim = 12
hidden_dim = 128
lr = 0.01
num_epochs = 2

# Initialize model and optimizer
model = CBOW(vocab_size, emb_dim, hidden_dim)
#model = model.to(device)
optimizer = optim.Adam(model.parameters(), lr=lr)
loss_fn = nn.CrossEntropyLoss()

def train_one_epoch(model, loader, num_epochs):
    running_loss = 0
    last_loss = 0
    for epoch in range(num_epochs):
    
        loss = 0.0
    
        for inputs, labels in loader:
        
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = loss_fn(outputs, labels.unsqueeze())
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
            
    return last_loss

加载器中的样本由元组组成。

val_loader.dataset[0][0].shape 输出 torch.Size([6]) 这是context.

val_loader.dataset[0][1].shape 输出 torch.Size([]) 这是target.

我真的不明白到底发生了什么。