如何修复RuntimeError“标量类型为Float的预期对象，但参数为标量类型Double”？

如何解决如何修复RuntimeError“标量类型为Float的预期对象，但参数为标量类型Double”？

参考来自这个github问题。

当错误出现时RuntimeError: Expected object of scalar type Float but got scalar type Double for argument #4 'mat1'，您将需要使用该.float()函数，因为它说的是Expected object of scalar type Float。

因此，解决方案更改y_pred = model(X_trainTensor)为y_pred = model(X_trainTensor.float())。

同样，当您遇到另一个错误时loss = loss_fn(y_pred, y_trainTensor)，y_trainTensor.long()由于错误消息中显示，因此您也需要Expected object of scalar type Long。

您也可以model.double()按照@Paddy的建议进行操作。

解决方法

我正在尝试通过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。我收到此错误y_pred = model(X_trainTensor)：

RuntimeError：标量类型为Float的预期对象，但参数＃4’mat1’的标量类型为Double

这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