我在pytorch中一直遇到错误，说我输入的大小不对：

RuntimeError: size mismatch, m1: [64 x 25088], m2: [1024 x 256] at /opt/conda/conda-bld/pytorch_1524584710464/work/aten/src/THC/generic/THCTensorMathBlas.cu:249

如果我将nn.Linear设置为64 x 25088，我会得到类似的错误：

RuntimeError: size mismatch, m1: [64 x 25088], m2: [64 x 25088] at /opt/conda/conda-bld/pytorch_1524584710464/work/aten/src/THC/generic/THCTensorMathBlas.cu:249

这是怎么回事？

这是产生错误的代码：

model = models.vgg16(pretrained=True)# Freeze parameters so we don't backprop through themfor param in model.parameters():    param.requires_grad = Falsemodel.classifier = nn.Sequential(nn.Linear(64, 25088),                                 nn.ReLU(),                                 nn.Dropout(0.2),                                 nn.Linear(256, 2),                                 nn.LogSoftmax(dim=1))criterion = nn.NLLLoss()# Only train the classifier parameters, feature parameters are frozenoptimizer = optim.Adam(model.classifier.parameters(), lr=0.003)# Use GPU if it's availabledevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")model.to(device);epochs = 1steps = 0running_loss = 0print_every = 5with active_session():    for epoch in range(epochs):        for inputs, labels in trainloader:            steps += 1            # Move input and label tensors to the default device            inputs, labels = inputs.to(device), labels.to(device)            optimizer.zero_grad()            logps = model.forward(inputs)            loss = criterion(logps, labels)            loss.backward()            optimizer.step()            running_loss += loss.item()            if steps % print_every == 0:                test_loss = 0                accuracy = 0                model.eval()                with torch.no_grad():                    for inputs, labels in testloader:                        inputs, labels = inputs.to(device), labels.to(device)                        logps = model.forward(inputs)                        batch_loss = criterion(logps, labels)                        test_loss += batch_loss.item()                        # Calculate accuracy                        ps = torch.exp(logps)                        top_p, top_class = ps.topk(1, dim=1)                        equals = top_class == labels.view(*top_class.shape)                        accuracy += torch.mean(equals.type(torch.FloatTensor)).item()                print(f"Epoch {epoch+1}/{epochs}.. "                      f"Train loss: {running_loss/print_every:.3f}.. "                      f"Test loss: {test_loss/len(testloader):.3f}.. "                      f"Test accuracy: {accuracy/len(testloader):.3f}")                running_loss = 0                model.train()

回答：

在nn.Linear()中，第一个参数是输入的大小，第二个参数是输出的尺寸。如果你的数据尺寸是64 x 25088，那么你需要设置nn.Linear(64*25088, 输出尺寸)。

然而，这一层将会非常大，所以你应该尝试减小你的数据尺寸。