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Kenil Vasani
Kenil Vasani

Kenil Vasani

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Kenil Vasani
Asked: December 10, 20202020-12-10T22:50:20+00:00 2020-12-10T22:50:20+00:00In: Python

How to fix RuntimeError “Expected object of scalar type Float but got scalar type Double for argument”?

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I’m trying to train a classifier via PyTorch. However, I am experiencing problems with training when I feed the model with training data.
I get this error on y_pred = model(X_trainTensor):

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

Here are key parts of my code:

# 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
classificationdeep-learningneural-networkpythonpytorch
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    1. Kenil Vasani

      Kenil Vasani

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      Kenil Vasani
      2020-12-10T22:47:00+00:00Added an answer on December 10, 2020 at 10:47 pm

      Reference is from this github issue.

      When the error is RuntimeError: Expected object of scalar type Float but got scalar type Double for argument #4 'mat1', you would need to use the .float() function since it says Expected object of scalar type Float.

      Therefore, the solution is changing y_pred = model(X_trainTensor) to y_pred = model(X_trainTensor.float()).

      Likewise, when you get another error for loss = loss_fn(y_pred, y_trainTensor), you need y_trainTensor.long() since the error message says Expected object of scalar type Long.

      You could also do model.double(), as suggested by @Paddy
      .

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