这个采样器常见的使用场景是将训练集划分成训练集和验证集,示例如下: n_train=len(train_dataset)split=n_train//3indices=random.shuffle(list(range(n_train)))train_sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:])valid_sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:spl...
# 设置当前的 epoch,为了让不同的结点之间保持同步。 train_sampler.set_epoch(epoch)
按照网上可以搜集到的资料,Subset Random Sampler应该用于训练集、测试集和验证集的划分,下面将data划分为train和val两个部分,再次指出__iter__()返回的的不是索引,而是索引对应的数据: sub_sampler_train = sampler.SubsetRandomSampler(indices=data[0:2]) sub_sampler_val = sampler.SubsetRandomSampler(indices=...
n_train = len(train_dataset) split = n_train // 3 indices = random.shuffle(list(range(n_train))) train_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[split:]) valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[:split]) train_loader = DataLoader(..., ...
sub_sampler_val= sampler.SubsetRandomSampler(indices=data[2:])#下面是train输出index: 17index:22 ***#下面是val输出index: 8index:41index:3 5、加权随机采样WeightedRandomSampler classWeightedRandomSampler(Sampler): r"""Samples elements from ``[0,..,len(weights)-1]`` with given probabilities...
train_loader = DataLoader(dataset, batch_size=2, sampler=train_sampler) val_loader = DataLoader(dataset, batch_size=2, sampler=val_sampler) # 创建一个使用WeightedRandomSampler的DataLoader weights = [0.1, 0.9] weighted_sampler = WeightedRandomSampler(weights, num_samples=10, replacement=True) ...
需要注意的仍然是采样是不重复的,也是通过randperm()函数实现的。按照网上可以搜集到的资料,Subset Random Sampler应该用于训练集、测试集和验证集的划分,下面将data划分为train和val两个部分,再次指出__iter__()返回的的不是索引,而是索引对应的数据: sub_sampler_train=sampler.SubsetRandomSampler(indices=data[0:...
train_loader=DataLoader(train_data,batch_size=batch_size,sampler=triain_sampler)具体实现 为解决本文...
sampler=train_sampler) validation_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, sampler=valid_sampler)# Usage Example:print("train data:")forbatch_index, (data, labels)inenumerate(train_loader):print(data, labels)print("\nvalidation data:")forbatch_index, (data, labels...
The relations between the marshalling modes of train units and the market factors,as well as the effect of selection of such comprehensive technologies as driving,and changing of design concept on diversifying of products are discussed. 从客运市场的发展与变化出发 ,讨论了动车组的编组形式与市场因素...