train_dataset = datasets.CIFAR10_IMG('./datasets',train=True,transform=transforms.ToTensor()) test_dataset = datasets.CIFAR10_IMG('./datasets',train=False,transform=transforms.ToTensor()) train_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True) test_loader = DataLoader(datas...
dataset = load_dataset('glue', 'rte') metric = load_metric('glue', 'rte') tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased') model = BertForSequenceClassification.from_pretrained('bert-base-cased', return_dict=True) def tokenize(examples): return tokenizer(examples['hypothesis'...
importtorch fromtorch.utils.dataimportDataset fromtorch.utils.dataimportDataLoader importnumpyasnp importtime # 数据准备 filepath='./diabetes.csv' classDiabetesDataset(Dataset): def__init__(self,filepath): xy=np.loadtxt(filepath,delimiter=',',dtype=np.float32) self.len=xy.shape[0] self.x_da...
class MyDataset(Dataset): """ my dataset.""" # Initialize your data, download, etc. def __init__(self): # 读取csv文件中的数据 xy = np.loadtxt('.csv', delimiter=',', dtype=np.float32) self.len = xy.shape[0] # 除去最后一列为数据位,存在x_data中 self.x_data = torch.from_n...
classDataset_name(Dataset):def__init__(self, flag='train'):assertflagin['train','test','valid'] self.flag = flag self.__load_data__()def__getitem__(self, index):passdef__len__(self):passdef__load_data__(self, csv_paths:list):pass# print( "train_X.shape:{}\ntrain_Y.shap...
classDiabetesDataset(Dataset):def__init__(self):xy=np.loadtxt(filepath,delimiter=',',dtype=np.float32)self.len=xy.shape[0]self.x_data=torch.from_numpy(xy[:,:-1])self.y_data=torch.from_numpy(xy[:,[-1]])def__getitem__(self,index):returnself.x_data[index],self.y_data[index]...
# DiabetesDataset继承自Dataset,实现一下三个函数:# __init__()是初始化函数,加载数据集# __getitem__()魔法函数:检索索引取样本(x,y)# __len__()魔法函数:获取数据集长度classDiabetesDataset(Dataset):def__init__(self, filepath): xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)...
当我们集成了一个 Dataset类之后,我们需要重写len方法,该方法提供了dataset的大小;getitem方法, 该方法支持从 0 到 len(self)的索引 代码语言:javascript 复制 classDealDataset(Dataset):""" 下载数据、初始化数据,都可以在这里完成""" def__init__(self):xy=np.loadtxt('../dataSet/diabetes.csv.gz',delim...
class MyDataset(Dataset): def __init__(self, data_file): self.data = torch.load(data_file) def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx] # 创建DataLoader实例来分批处理数据 data_loader = DataLoader(MyDataset('path/to/your/data.pt'),...
defloaddata():iris_data=datasets.load_iris()returniris_data["data"],iris_data["target"]classIrisDataset(Dataset):def__init__(self,irisdata,target):# 传入参数# ndarray 类型的,可以是任何类型的self.irisdata=irisdataself.target=targetself.lens=len(irisdata)def__getitem__(self,index):# index...