I’m trying to create a model that will learn the function x % 500 from the numbers between 0 - 2000.

Regardless of my approach, which I am planning to experiment with, I am running into a type error that I’ve been stuck on for a while. No combination of `to(torch.Long)`

or `type(torch.LongTensor)`

seems to fix the issue. Any insight is appreciated.

```
X = np.arange(0, 2000)
y = X%500
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)
X_train = np.array([x // 10 ** np.arange(4) % 10 for x in X_train])
y_train = np.array([y // 10 ** np.arange(4) % 10 for y in y_train])
class Net(nn.Module):
def __init__(self):
super(Net,self).__init__()
self.fc1 = nn.Linear(4, 40)
self.fc2 = nn.Linear(40, 40)
self.fc3 = nn.Linear(40, 40)
self.fc4 = nn.Linear(40, 40)
self.fc5 = nn.Linear(40, 40)
self.relu = nn.ReLU()
def forward(self,x):
out = self.relu(self.fc1(x))
out = self.relu(self.fc2(x))
out = self.relu(self.fc3(x))
out = self.relu(self.fc4(x))
out = self.relu(self.fc5(x))
out = nn.Softmax(out)
return out
#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device = torch.device("cpu")
lr = 0.001 # learning_rate
epochs = 10 # How much to train a model
model = Net().to(device)
model.train()
X_train_tensor = torch.from_numpy(X_train).type(torch.LongTensor)
sample_out = model(X_train_tensor[0])
```

It seems that the last line is causing the following error:

`RuntimeError: expected scalar type Long but found Float`