When executing the following in a Jupyter notebook (below)  it always give the error

RuntimeError                              Traceback (most recent call last)
 in ()
1 epochs=10
2 for epoch in range(1, epochs + 1):
----> 3     train(model, device, train_loader, optimizer, epoch)
4     test(model, device, test_loader)
 in train(model, device, train_loader, optimizer, epoch)
59         optimizer.zero_grad()
60         output = model(data)
—> 61         loss = F.nll_loss(output, target)
62         loss.backward()
63         optimizer.step()
~/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1788                          .format(input.size(0), target.size(0)))
1789     if dim == 2:
-> 1790         ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
1791     elif dim == 4:
1792         ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: Expected object of scalar type Long but got scalar type Float for argument #2 ‘target’
Jupyter Notebook
import os
import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
from torch.utils.data import DataLoader, Dataset
import torch.utils.data as data_utils

dtype = torch.float
#device = torch.device("cpu")
device = torch.device("cuda" )

# Prepare data
wdata = np.load("data.npy")
data1=torch.from_numpy(wdata) 
X1 = data1[:,:4]
Y1 = data1[:,4:]
U1 = data1[:,:4]
V1 = data1[:,4:]

X=X1.type(torch.FloatTensor)
Y=Y1.type(torch.FloatTensor)
U=U1.type(torch.FloatTensor)
V=V1.type(torch.FloatTensor)

train = data_utils.TensorDataset(X, Y)
train_loader = data_utils.DataLoader(train, batch_size=128, shuffle=True)

test = data_utils.TensorDataset(U, V)
test_loader= data_utils.DataLoader(test, batch_size=4, shuffle=True)

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(4,512)
        self.drp1 = nn.Dropout(0.2)
        self.fc2 = nn.Linear(512, 512)
        self.drp2 = nn.Dropout(0.2)
        self.fc3 =  nn.Linear(512, 4)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
    
model = Net().to(device)

optimizer = optim.Adam(model.parameters(), lr=1e-4)   
    

def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        
            
def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

epochs=10
for epoch in range(1, epochs + 1):
    train(model, device, train_loader, optimizer, epoch)
    test(model, device, test_loader)




However, when executing
input=s.to(device)
out = model(input)
print(out)

I get
tensor([[-0.0278, 0.0300, 0.0424, -0.1008]], device=‘cuda:0’, grad_fn=<AddmmBackward>)
Data.npy is 8 columns x 2203 Rows where
X = first 4 and Y= last four