I want to implement a decoder but I don’t need its input. Do I just pass a torch.zeros for its input?
e.g.
def main_test_zero_input_to_lstm():
## model params
n_layers, nb_directions = 1, 1
hidden_size = 64
input_size = 3
decoder = nn.LSTM(input_size=input_size,hidden_size=hidden_size).to(device)
print(lstm.hidden_size)
## initialize the hidden state(s)
batch_size = 1
h_n = torch.randn(n_layers*nb_directions, batch_size, hidden_size)
c_n = torch.randn(n_layers*nb_directions, batch_size, hidden_size)
hidden = (h_n, c_n)
## pass through fake data
seq_len, embedding_dim = 1, input_size
zeros_fake_data = torch.zeros(seq_len, batch_size, embedding_dim)
#zeros_fake_data = torch.zeros(seq_len, embedding_dim) # this throws an error, we need the batch dimension!!!
## do a decoder step
out, hidden = decoder(zeros_fake_data, hidden)
##
print(f'out size equals hidden state size: {out.size() == hidden[0].size()}')
print(f'out.size() = {out.size()}')
print(out)
if __name__ == '__main__':
print('start')
main_test_zero_input_to_lstm()
print('DONE \a')
or is this bad practice? Perhaps I do need its input from previous step…?