CRFv2.py 文件源码

def _forward_alg(self, feats):      
        init_alphas = torch.Tensor(self.tagset_size, 1).fill_(0.).type(self.dtype)      
        forward_var = autograd.Variable(init_alphas).type(self.dtype)       
        for ix,feat in enumerate(feats):
            if ix == 0:
                forward_var += feat.view(self.tagset_size,1) + self.initial_weights
            else:           
                forward_var = feat.view(self.tagset_size,1) + log_sum_exp_mat( self.transitions + torch.transpose(forward_var.repeat(1, self.tagset_size), 0, 1), 1)
        terminal_var = forward_var + self.final_weights
        alpha = log_sum_exp_mat(terminal_var, 0 )
        return alpha