train_word2vec_subword_chainer_input.py 文件源码

def __call__(self, x, context):

        x = F.broadcast_to(x[:, None], (context.shape[0], context.shape[1]))
        x = F.reshape(x, (context.shape[0] * context.shape[1],))

        if args.subword == 'rnn':
            context = context.reshape((context.shape[0] * context.shape[1]))
            e = self.rnn.charRNN(context)

        if args.subword == 'none':
            e = self.embed(context)
            e = F.reshape(e, (e.shape[0] * e.shape[1], e.shape[2]))

        loss = self.loss_func(e, x)
        reporter.report({'loss': loss}, self)
        return loss