卡在 Collab RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:答案

【问题标题】：Stuck at Collab RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:卡在 Collab RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:
【发布时间】：2021-12-24 09:31:23
【问题描述】：

我检查并看到我已经在 GPU (context_vector) 中定义了我的张量，但是当我在 Colab 上运行时，它总是显示错误“预期所有张量都在同一设备上，但发现至少两个设备、cpu 和 cuda" 错误轨迹是这样的：

RuntimeError                              Traceback (most recent call last)
<ipython-input-16-bf7edd8e474b> in <module>()
     85     for context, target in data:
     86         context_vector = make_context_vector(context)
---> 87         log_probs = model(context_vector)
     88         total_loss += loss_function(log_probs, torch.tensor([target]).to(device))
     89     #optimize at the end of each epoch

这是我的 make_context_vector 函数：

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')    
def make_context_vector(context):
    return torch.tensor(context, dtype=torch.long).to(device)

这是我的转发功能：

def forward(self, inputs):
        embeds = sum(self.embeddings(inputs)).view(1,-1)
        # print('embeds size: {}'.format(embeds.shape))
        out = self.linear1(embeds)
        out = self.activation_function1(out)
        # print('out1 size: {}'.format(out.shape))
        out = self.linear2(out)
        out = self.activation_function2(out)
        return out

#TRAINING
for epoch in range(50):
    total_loss = 0
    for context, target in data:
        context_vector = make_context_vector(context)  
        log_probs = model(context_vector)
        total_loss += loss_function(log_probs, torch.tensor([target], device = device))
    #optimize at the end of each epoch
    optimizer.zero_grad()
    total_loss.backward()
    optimizer.step()

请大家帮忙！谢谢

【问题讨论】：

标签： python gpu google-colaboratory

【解决方案1】：

哦，我的朋友支持我我发现不仅要为输入设置GPU，还需要为模型设置GPU。因此，在训练代码中，我将其更改为：

model = CBOW(vocab_size, embedding_dim)**.to(device)**

然后，它运行良好

【讨论】：