同时打乱数据集和标签的几种方式

state = np.random.get_state()
np.random.shuffle(train)
np.random.set_state(state)
np.random.shuffle(label)

 train_row = list(range(len(train_label)))
 random.shuffle(train_row)
 train_image = train_image[train_row,:]
 train_label = train_label[train_row,:]

shuffle_ix = np.random.permutation(np.arange(len(train_data)))
train_data = train_data[shuffle_ix,:]
train_label = train_label[shuffle_ix,:]

dataset = torch.utils.data.TensorDataset(data, target)      # 设置数据集
train_iter = torch.utils.data.DataLoader(dataset, batch_size, shuffle=True) # 设置获取数据方式

import numpy as np
tes = np.array([['a'],['b'],['c'],['d']])
shuffle_ix = np.random.permutation(len(tes))
shuffle_ix = list(shuffle_ix)
print(shuffle_ix)
tes = tes[shuffle_ix,:]

[1, 3, 0, 2]

array([['b'],
       ['d'],
       ['a'],
       ['c']], dtype='<U1')