def beam_search_forward(self, col, score, embedding, pre_hid_info,
s_embedding):
n = col.shape[0]
input_info = T.concatenate([embedding, col, pre_hid_info], axis=-1)
u1 = get_output(self.gru_update_3, input_info)
r1 = get_output(self.gru_reset_3, input_info)
reset_h1 = pre_hid_info * r1
c_in = T.concatenate([embedding, col, reset_h1], axis=1)
c1 = get_output(self.gru_candidate_3, c_in)
h1 = (1.0 - u1) * pre_hid_info + u1 * c1
s = get_output(self.score, h1)
sample_score = T.dot(s, s_embedding.T)
k = sample_score.shape[-1]
sample_score = sample_score.reshape((n, 1, k))
sample_score += score
sample_score = sample_score.reshape((n, 10 * k))
sort_index = T.argsort(-sample_score, axis=-1)
sample_score = T.sort(-sample_score, axis=-1)
tops = sort_index[:, :10]
sample_score = -sample_score[:, :10]
tops = T.cast(T.divmod(tops, self.target_vocab_size), "int8")
embedding = get_output(self.target_input_embedding, tops)
d = embedding.shape[-1]
embedding = embedding.reshape((n * 10, d))
return sample_score, embedding, h1, tops
def beam_search_backward(self, top, idx):
max_idx = top[idx]
idx = T.true_div(max_idx, self.target_vocab_size)
max_idx = T.divmod(max_idx, self.target_vocab_size)
return idx, max_idx
def divmod(self, l, r):
return T.divmod(l, r)