如何修复RuntimeError“需要标量类型Float的对象，但参数为标量类型Double”？

Question

我正试图通过PyTorch训练分类器。但是，当我向模型提供训练数据时，我遇到了训练问题。 我在y_pred = model(X_trainTensor)上收到此错误：

RuntimeError: Expected object of scalar type Float but got scalar type Double for argument #4 'mat1'

以下是我的代码的关键部分：

# Hyper-parameters 
D_in = 47  # there are 47 parameters I investigate
H = 33
D_out = 2  # output should be either 1 or 0

# Format and load the data
y = np.array( df['target'] )
X = np.array( df.drop(columns = ['target'], axis = 1) )
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8)  # split training/test data

X_trainTensor = torch.from_numpy(X_train) # convert to tensors
y_trainTensor = torch.from_numpy(y_train)
X_testTensor = torch.from_numpy(X_test)
y_testTensor = torch.from_numpy(y_test)

# Define the model
model = torch.nn.Sequential(
    torch.nn.Linear(D_in, H),
    torch.nn.ReLU(),
    torch.nn.Linear(H, D_out),
    nn.LogSoftmax(dim = 1)
)

# Define the loss function
loss_fn = torch.nn.NLLLoss() 

for i in range(50):
    y_pred = model(X_trainTensor)
    loss = loss_fn(y_pred, y_trainTensor)
    model.zero_grad()
    loss.backward()
    with torch.no_grad():       
        for param in model.parameters():
            param -= learning_rate * param.grad

Answer 1

引用来自this github issue。

当错误是RuntimeError: Expected object of scalar type Float but got scalar type Double for argument #4 'mat1'时，您需要使用.float()函数，因为它显示Expected object of scalar type Float。

因此，解决方案是将y_pred = model(X_trainTensor)改为y_pred = model(X_trainTensor.float())。

同样，当您得到loss = loss_fn(y_pred, y_trainTensor)的另一个错误时，您需要y_trainTensor.long()，因为错误消息是Expected object of scalar type Long。

你也可以做model.double()，就像@Paddy建议的那样 .