model.py 文件源码

def _forward_alg(self, feats):
        # calculate in log domain
        # feats is len(sentence) * tagset_size
        # initialize alpha with a Tensor with values all equal to -10000.
        init_alphas = torch.Tensor(1, self.tagset_size).fill_(-10000.)
        init_alphas[0][self.tag_to_ix[START_TAG]] = 0.
        forward_var = autograd.Variable(init_alphas)
        if self.use_gpu:
            forward_var = forward_var.cuda()
        for feat in feats:
            emit_score = feat.view(-1, 1)
            tag_var = forward_var + self.transitions + emit_score
            max_tag_var, _ = torch.max(tag_var, dim=1)
            tag_var = tag_var - max_tag_var.view(-1, 1)
            forward_var = max_tag_var + torch.log(torch.sum(torch.exp(tag_var), dim=1)).view(1, -1) # ).view(1, -1)
        terminal_var = (forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]).view(1, -1)
        alpha = log_sum_exp(terminal_var)
        # Z(x)
        return alpha