Hello !
I recently implemented a name generating RNN frms scratch which was doing ok but far from perfect. So I thought about trying my luck with LSTM cells to see if it makes a difference. Indeed it does and the outpus looks way better for the first 7 ~ 8 characters. But then the networks gets caught in a loop and outputs things like “laulaulaulau” or “rourourourou” (it is supposed the generate french names).
Is it a often occuring problem ? If so do you know a way to fix it ? I’m concern about the fact the network doesn’t produce EOS tokens…
here is the model :
class pytorchLSTM(nn.Module):
def __init__(self,input_size,hidden_size):
super(pytorchLSTM,self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.lstm = nn.LSTM(input_size, hidden_size)
self.output_layer = nn.Linear(hidden_size,input_size)
self.tanh = nn.Tanh()
self.softmax = nn.LogSoftmax(dim = 2)
def forward(self, input, hidden)
out, hidden = self.lstm(input,hidden)
out = self.tanh(out)
out = self.output_layer(out)
out = self.softmax(out)
return out, hidden
training loop :
def train_lstm(model):
start = time.time()
criterion = nn.NLLLoss()
optimizer = torch.optim.Adam(model.parameters())
n_iters = 20000
print_every = 1000
plot_every = 500
all_losses = []
total_loss = 0
for iter in range(1,n_iters+1):
line = randomChoice(category_line)
input_line_tensor = inputTensor(line)
target_line_tensor = targetTensor(line).unsqueeze(-1)
optimizer.zero_grad()
loss = 0
output, hidden = model(input_line_tensor)
for i in range(input_line_tensor.size(0)):
l = criterion(output[i], target_line_tensor[i])
loss += l
loss.backward()
optimizer.step()
the sampling function :
def sample():
max_length = 20
input = torch.zeros(1,1,n_letters)
input[0][0][n_letters - 2] = 1
output_name = ""
hidden = (torch.zeros(2,1,lstm.hidden_size),torch.zeros(2,1,lstm.hidden_size))
for i in range(max_length):
output, hidden = lstm(input)
output = output[-1][:][:]
l = torch.multinomial(torch.exp(output[0]),num_samples = 1).item()
if l == n_letters - 1:
break
else:
letter = all_letters[l]
output_name += letter
input = inputTensor(letter)
return output_name
a typical sample :
Laurayeerauerararauo
Leayealouododauodouo
Meeaueeulauodalauouo
Do you know how I can improve that ?