Hello all, I am quite new to PyTorch so this might be quite a trivial question. I have some time-series data from 4 classes and using the CrossEntropyLoss(). I keep getting the error as mentioned in the topic. I did read a few other threads regarding this error but was not able to figure it out.

The model class is below including the train function

```
class pytorch_lstm(net.Module):
def __init__(self, features, hidden_size, sequence_length):
super(pytorch_lstm, self).__init__()
self.loss = net.CrossEntropyLoss()
self.criterion = self.loss
self.features = features
self.hidden_size = hidden_size
self.seq_length = sequence_length
self.lstm = net.LSTM(
input_size=self.features,
hidden_size=hidden_size,
num_layers=313,
batch_first=True
)
self.linear = net.Linear(self.hidden_size*self.seq_length, 4)
def init_Hidden(self):
hidden_state = torch.zeros(313, 1, self.hidden_size)
cell_state = torch.zeros(313, 1, self.hidden_size)
self.hidden = (hidden_state, cell_state)
def forward(self, X):
lstm_out, self.hidden = self.lstm(X, self.hidden)
out = self.linear(lstm_out.view(-1))
return out
def train_model(self, model, dataloader, num_epochs):
least_loss = 1
optimizer = torch.optim.Adam(model.parameters())
training_loss = []
for i in range(num_epochs):
optimizer.zero_grad()
st = time.time()
epoch_loss = 0
for _, (x, y) in enumerate(dataloader):
model.init_Hidden()
x = x.float()
y = y.float()
#x = x.cuda()
#y = y.cuda()
print(y)
output = model(x)
print(output)
loss = self.criterion(output, y)
loss.backward()
optimizer.step()
```

The data is being loaded in the following manner, where labels are one hot encoded

```
train_data = []
Data = []
Labels = []
folders = ['769', '770', '771', '772']
for folder in folders:
files = os.listdir(path + '/' + folder)
os.chdir(path + '/' + folder)
for file in files:
data = numpy.load(file)
data = numpy.transpose(data)
#Data.append(data)
#Labels.append((int(folder)))
if folder =='769':
label = numpy.array([1, 0 ,0, 0])
elif folder=='770':
label = numpy.array([0, 1, 0, 0])
elif folder == '771':
label = numpy.array([0, 0, 1, 0])
elif folder == '772':
label = numpy.array([0 ,0, 0, 1])
train_data.append([data, label])
train_loader = torch.utils.data.DataLoader(train_data, batch_size=1, shuffle=True)
return train_loader
```

Can someone please tell where I am going wrong.

TIA