def _step(m_, x_, h_, c_):
x_and_h = tensor.concatenate([x_, h_], axis=1)
preact = tensor.dot(x_and_h, tparams["WLSTM"]) + tparams["bLSTM"]
i = tensor.nnet.sigmoid(_lstm_slice(preact, 0, hidden_size))
f = tensor.nnet.sigmoid(_lstm_slice(preact, 1, hidden_size))
o = tensor.nnet.sigmoid(_lstm_slice(preact, 2, hidden_size))
c = tensor.tanh(_lstm_slice(preact, 3, hidden_size))
c = f * c_ + i * c
c = m_[:, None] * c + (1. - m_)[:, None] * c_
h = o * tensor.tanh(c)
h = m_[:, None] * h + (1. - m_)[:, None] * h_
return h, c
def _step(x_, h_, c_, is_complete_, n_samples):
x_and_h = tensor.concatenate([x_, h_], axis=1)
preact = tensor.dot(x_and_h, tparams["WLSTM"]) + tparams["bLSTM"]
i = tensor.nnet.sigmoid(_lstm_slice(preact, 0, hidden_size))
f = tensor.nnet.sigmoid(_lstm_slice(preact, 1, hidden_size))
o = tensor.nnet.sigmoid(_lstm_slice(preact, 2, hidden_size))
c = tensor.tanh(_lstm_slice(preact, 3, hidden_size))
c = f * c_ + i * c
h = o * tensor.tanh(c)
decoder = tensor.dot(h, tparams['Wd']) + tparams['bd']
softmax = tensor.nnet.softmax(decoder)
predicted_prob, predicted_idx = tensor.max_and_argmax(softmax, axis=1)
predicted_word_vector = tparams['Ws'][predicted_idx]
is_end_reached = predicted_idx <= 0
is_complete_ = is_complete_ + is_end_reached
is_complete_sum = tensor.sum(is_complete_)
return (predicted_word_vector, h, c, is_complete_, predicted_idx, predicted_prob), scan_module.until(tensor.eq(is_complete_sum, n_samples))