在使用pytorch在对MNIST数据集进行预览时,出现了TypeError: 'module' object is not callable的错误: 上报错信息图如下: [在这里插入图片描述...] 从图中可以看出,报错位置为第35行,也就是如下位置的错误: images, labels = next(iter(data_loader_train)) 在经过多次的检查发现,引起MNIST数据集无法显现的...
Training Loop ...")forepochinrange(num_epochs): curr_loss = train(data_loader_train, ...
在我的代码里根本就没有找到任何inplace操作,因此上面这种方法行不通。自己盯着代码,debug,啥也看不出来,好久... 忽然有了新idea。我的训练阶段的代码如下: forepochinrange(1, epochs +1):foridx, (lr, hr)inenumerate(traindata_loader): lrs = lr.to(device) hrs = hr.to(device)# update the disc...
eval_losses.append(eval_loss/len(test_loader)) eval_acces.append(eval_acc/len(test_loader)) print('epoch:{},Train Loss:{:.4f},Train Acc:{:.4f},Test Loss:{:.4f},Test Acc:{:.4f}' .format(epoch,train_loss/len(train_loader),train_acc/len(train_loader), eval_loss/len(test_loader...
in load return loader.get_single_data() File "/root/miniconda3/lib/python3.8/site-packages/yaml/constructor.py", line 51, in get_single_data return self.construct_document(node) File "/root/miniconda3/lib/python3.8/site-packages/yaml/constructor.py", line 60, in construct_document for dumm...
self.trainer.train( File "/data/mindformers/mindformers/trainer/causal_language_modeling/causal_language_modeling.py", line 113, in train self.training_process( File "/data/mindformers/mindformers/trainer/base_trainer.py", line 668, in training_process ...
for fold in range(5): print('===Fold-{}==='.format(fold)) print('---Generate data loader---') train_data, train_pep_inputs, train_hla_inputs, train_labels, train_loader = data_with_loader(type_ = 'train', fold = fold, batch_size = batch_size) val_data, val...
可以从官网下载最新版本iNode按照以下方式安装试一下能否正常使用
model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train) File "/workspace/LLaMA-Factory-main/src/llmtuner/model/loader.py", line 91, in load_model_and_tokenizer model = init_adapter(model, model_args, finetuning_args, is_trainable) ...
for t in range(nb_epoch): cumulative_loss = 0 for X, Y1, Y2 in train_data_loader: loss, log_vars = mtl(X, [Y1, Y2]) optimizer.zero_grad() loss.backward() optimizer.step() cumulative_loss += loss.item() loss_list.append(cumulative_loss/batch_size) ...