Hi! i’m new in Pytorch and to the Pytorch community.

I just started working with RNN and already ran some examples. Now i’m trying to train a RNN based on This RNN tutorial but with my own data.

The inputs are time series with 12 features in each time step, with continuous values in the [0 ,1] range. The goal is to detect when a certain event occurs, so each time step has a target associated that can be 0 (no event) and 1 (event).

My problem is that loss.backard() is not working, the grads of every parameter of the model remains **None** and thus the model is not learning…

I’ve read a lot of discussions in the post, looked at a lot of examples but i can’t make it work and i can’t figure out what’s wrong…

My code:

```
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
self.i2o = nn.Linear(input_size + hidden_size, output_size)
self.softmax = nn.LogSoftmax(dim=1)
def forward(self, input, hidden):
combined = torch.cat((input, hidden), 1)
#print('input.size: {}, hidden.size {}, combined: {}'.format(input.size(), hidden.size(), combined.size()))
hidden = self.i2h(combined)
output = self.i2o(combined)
output = self.softmax(output)
return output, hidden
def initHidden(self):
return torch.zeros(1, self.hidden_size)
n_hidden = 128
rnn = RNN(12, n_hidden, 2)
learning_rate = 0.005
criterion = nn.NLLLoss()
def train(label, sample):
hidden = rnn.initHidden()
for i in range(sample.size()[1]):
rnn.zero_grad()
curr_sample = sample[:,i].view(1,sample.size()[0])
output, hidden = rnn(curr_sample, hidden)
loss = criterion(output, label[i])
loss.backward()
# Add parameters' gradients to their values, multiplied by learning rate
for p in rnn.parameters():
p.data.add_(-learning_rate, p.grad.data)
return output, loss.item()
n_iters = 1
print_every = 1
plot_every = 10
current_loss = 0
all_losses = []
for iter in range(1, n_iters + 1):
output, loss = train(label, sample)
print(' sale output {} y loss {}'.format(output.size(), loss))
current_loss += loss
# Print iter number, loss, name and guess
if iter % print_every == 0:
print('en iteracion {}, error acumulado es: {} y error actual {}'.format(iter, current_loss, loss))
# Add current loss avg to list of losses
if iter % plot_every == 0:
all_losses.append(current_loss / plot_every)
current_loss = 0
```

**sample** has size [12, 16588] and **label** has size [1, 16588]

The error i get is:

AttributeError: ‘NoneType’ object has no attribute ‘data’

because p.grad is none.

I’ve already tried out working with optimizers and performing optimizer.step() but i get the same result.

Thanks!!