INNER CODE UNIT · Python
log_sum_exp
taishi-i/nagisa · nagisa/model.py:118
def log_sum_exp(scores):
npval = scores.npvalue()
argmax_score = np.argmax(npval)
max_score_expr = dy.pick(scores, argmax_score)
max_score_expr_broadcast = dy.concatenate([max_score_expr] * self.dim_output)
return max_score_expr + dy.log(dy.sum_elems(dy.transpose(dy.exp(scores - max_score_expr_broadcast))))
init_alphas = [-1e10] * self.dim_output
init_alphas[self.sp_s] = 0
for_expr = dy.inputVector(init_alphas)
for obs in observations:
alphas_t = []
for next_tag in range(self.dim_output):
obs_broadcast = dy.concatenate([dy.pick(obs, next_tag)] * self.dim_output)
next_tag_expr = for_expr + self.trans[next_tag] + obs_broadcast
alphas_t.append(log_sum_exp(next_tag_expr))
for_expr = dy.concatenate(alphas_t)
terminal_expr = for_expr + self.trans[self.sp_e]