sentiment_trainer.py 文件源码

def train(self, dataset):
        self.model.train()
        self.embedding_model.train()
        self.embedding_model.zero_grad()
        self.optimizer.zero_grad()
        loss, k = 0.0, 0
        # torch.manual_seed(789)
        indices = torch.randperm(len(dataset))
        for idx in tqdm(range(len(dataset)),desc='Training epoch '+str(self.epoch+1)+''):
            tree, sent, label = dataset[indices[idx]]
            input = Var(sent)
            target = Var(torch.LongTensor([int(label)]))
            if self.args.cuda:
                input = input.cuda()
                target = target.cuda()
            emb = F.torch.unsqueeze(self.embedding_model(input), 1)
            output, err, _, _ = self.model.forward(tree, emb, training=True)
            #params = self.model.childsumtreelstm.getParameters()
            # params_norm = params.norm()
            err = err/self.args.batchsize # + 0.5*self.args.reg*params_norm*params_norm # custom bias
            loss += err.data[0] #
            err.backward()
            k += 1
            if k==self.args.batchsize:
                for f in self.embedding_model.parameters():
                    f.data.sub_(f.grad.data * self.args.emblr)
                self.optimizer.step()
                self.embedding_model.zero_grad()
                self.optimizer.zero_grad()
                k = 0
        self.epoch += 1
        return loss/len(dataset)

    # helper function for testing