Hey there,
When using nn.LSTM, I can’t figure out how to manage the output of the layer. This layer outputs a tensor of size [length_seq, batch, nb_hidden]. Hence, for a sequence of length superior to one, do you have to unroll the tensor, like you’d do it when reshaping the output of a convolution layer ?
Here’s what I wrote:
class Net(nn.Module):
def __init__(self): 

	nn.Module.__init__(self)
	self.l1 = nn.Linear(1,32)
	self.rnn = nn.LSTM(32,64,1)
	self.l2 = nn.Linear(64,1)

	self.adam = optim.Adam(self.parameters(), 3e-4)


def forward(self, x, hidden = None):
	length_seq = 5
	batch = 1 
	input_size = 1 

	x = torch.rand((length_seq,batch,input_size))
	x = self.l1(x)
	out, h = self.rnn(x, hidden)
	out = self.l2(out)

	# out.shape is actually [5,1,1] 

	return out, h

Also, is it just me or is running a network with LSTM much slower than when there’s no recurrence ?
Thanks a lot !