( dataset, batch_size=1, shuffle=False, collate_fn=LazyDataset.ignore_none_collate, ) prediction=[] for page_num,page_as_tensor in tqdm(enumerate(dataloader)): model_output = model.inference(image_tensors=page_as_tensor[0]) output = markdown_compatible(model_output["predictions"][0]) ...
The coefficientageThe coefficientsex_femaleThe coefficientforsex_maleis-8.762584065506853The coefficientforbmiis0.3807106266997645The coefficientforchildren_0is-0.06605803000190659The coefficientforchildren_1is-0.946643170369065The coefficientforchildren_2is0.2108032984623088The coefficientforchildren_3is0.8800441822437507The ...
.split(np.arange(train_df.shape[0]), train_df.label.values) # 五折交叉验证 only_folds = global_config['only_folds'] best_values = [] for fold, (trn_idx, val_idx) in enumerate(folds): if only_folds and fold not in only_folds: # not empty continue # skip if not empty and not...
max_val, idx = max((val, idx) for (idx, val) in enumerate(membership_mat[i])) 这行代码使用了 Python 中的生成器表达式和`max` 函数来找到具有最大隶属度值的聚类中心的索引。下面是代码的详细解释: 1. `membership_mat[i]` 表示第 `i` 个数据点对所有聚类中心的隶属度值,这是一个包含隶属度...
trainlist = data_options['train'] testlist = data_options['valid'] backupdir = data_options['backup'] gpus = data_options['gpus'] # e.g. 0,1,2,3 ngpus = len(gpus.split(',')) num_workers = int(data_options['num_workers']) batch_size = int(net_options['batch']) print("...
I implemented this for the evaluation loss, where essentially to obtain losses, model.train() needs to be on: @torch.no_grad() def evaluate_loss(model, data_loader, device): val_loss = 0 model.train() for images, targets in data_loader: images = list(image.to(device) for imag...
{idx}Input name :{ input_name }, Input shape :{input_shape}, \ Input type :{input_type}")foridx, outputinenumerate(range(len(sess_output))): output_name = sess_output[output].name output_shape = sess_output[output].shape output_type = sess_output[output].type print(f"{idx}Output...
label = self.dataset.loc[idx, "label"] sample = {"text": text, "label": label} # 返回一个 dict return sample # 加载训练集 sentiment_train_set = SentimentDataset(data_path + "sentiment.train.data") sentiment_train_loader = DataLoader(sentiment_train_set, batch_size=batch_size, shuffle...
model.train() model, optimizer = ipex.optimize(model, optimizer=optimizer) for batch_idx, (data, target) in enumerate(train_loader): optimizer.zero_grad() 把它替换成如下代码,用Arc DGPU训练。并且修改参数 batch_size=32 model.train() model = model.to("xpu") criterion = criterion.to("xp...
def train(model, device, train_loader, optimizer, epoch): global batch_size # model.train() state = model.zero_state(batch_size) for batch_idx, (data, target) in enumerate(train_loader): print(f"The batch_idx value is {batch_idx}") ...