【PyTorch】torch.utils.data.Dataset 介绍与实战

文章目录

  • 一、前言
  • 二、torch.utils.data.Dataset 是什么
  • 1. 干什么用的?
  • 2. 长什么样子?
  • 三、通过继承 torch.utils.data.Dataset 定义自己的数据集类
  • 四、为什么要定义自己的数据集类?
  • 五、实战:torch.utils.data.Dataset + Dataloader 实现数据集读取和迭代
  • 实例 1
  • 实例 2:进阶
  • 参考链接

  • 一、前言

    训练模型一般都是先处理 数据的输入问题预处理问题 。Pytorch提供了几个有用的工具:torch.utils.data.Dataset 类和 torch.utils.data.DataLoader 类 。

    流程是先把原始数据转变成 torch.utils.data.Dataset 类,随后再把得到的 torch.utils.data.Dataset 类当作一个参数传递给 torch.utils.data.DataLoader 类,得到一个数据加载器,这个数据加载器每次可以返回一个 Batch 的数据供模型训练使用。

    在 pytorch 中,提供了一种十分方便的数据读取机制,即使用 torch.utils.data.DatasetDataloader 组合得到数据迭代器。在每次训练时,利用这个迭代器输出每一个 batch 数据,并能在输出时对数据进行相应的预处理或数据增广操作。

    本文我们主要介绍对 torch.utils.data.Dataset 的理解,对 Dataloader 的介绍请参考我的另一篇文章:【PyTorch】torch.utils.data.DataLoader 简单介绍与使用

    在本文的最后将给出 torch.utils.data.DatasetDataloader 结合使用处理数据的实战代码。


    二、torch.utils.data.Dataset 是什么

    1. 干什么用的?

    1. pytorch 提供了一个数据读取的方法,其由两个类构成:torch.utils.data.Dataset 和 DataLoader。
    2. 如果我们要自定义自己读取数据的方法,就需要继承类 torch.utils.data.Dataset ,并将其封装到DataLoader 中。
    3. torch.utils.data.Dataset 是一个 Dataset 。通过重写定义在该类上的方法,我们可以实现多种数据读取及数据预处理方式。

    2. 长什么样子?

    torch.utils.data.Dataset 的源码:

    class Dataset(object):
        """An abstract class representing a Dataset.
    
        All other datasets should subclass it. All subclasses should override
        ``__len__``, that provides the size of the dataset, and ``__getitem__``,
        supporting integer indexing in range from 0 to len(self) exclusive.
        """
    
        def __getitem__(self, index):
            raise NotImplementedError
    
        def __len__(self):
            raise NotImplementedError
    
        def __add__(self, other):
            return ConcatDataset([self, other])
    

    注释翻译:

    表示一个数据集的抽象类。

    所有其他数据集都应该对其进行子类化。 所有子类都应该重写提供数据集大小的 __len____getitem__ ,支持从 0 到 len(self) 独占的整数索引。

    理解:

    就是说,Dataset 是一个 数据集 抽象类,它是其他所有数据集类的父类(所有其他数据集类都应该继承它),继承时需要重写方法 __len____getitem____len__ 是提供数据集大小的方法, __getitem__ 是可以通过索引号找到数据的方法。


    三、通过继承 torch.utils.data.Dataset 定义自己的数据集类

    torch.utils.data.Dataset 是代表自定义数据集的抽象类,我们可以定义自己的数据类抽象这个类,只需要重写__len__和__getitem__这两个方法就可以。

    要自定义自己的 Dataset 类,至少要重载两个方法:__len__, __getitem__

    1. __len__返回的是数据集的大小
    2. __getitem__实现索引数据集中的某一个数据

    下面将简单实现一个返回 torch.Tensor 类型的数据集:

    from torch.utils.data import Dataset
    import torch
    
    class TensorDataset(Dataset):
        # TensorDataset继承Dataset, 重载了__init__, __getitem__, __len__
        # 实现将一组Tensor数据对封装成Tensor数据集
        # 能够通过index得到数据集的数据,能够通过len,得到数据集大小
    
        def __init__(self, data_tensor, target_tensor):
            self.data_tensor = data_tensor
            self.target_tensor = target_tensor
    
        def __getitem__(self, index):
            return self.data_tensor[index], self.target_tensor[index]
    
        def __len__(self):
            return self.data_tensor.size(0)    # size(0) 返回当前张量维数的第一维
    
    # 生成数据
    data_tensor = torch.randn(4, 3)   # 4 行 3 列,服从正态分布的张量
    print(data_tensor)
    target_tensor = torch.rand(4)     # 4 个元素,服从均匀分布的张量
    print(target_tensor)
    
    # 将数据封装成 Dataset (用 TensorDataset 类)
    tensor_dataset = TensorDataset(data_tensor, target_tensor)
    
    # 可使用索引调用数据
    print('tensor_data[0]: ', tensor_dataset[0])
    
    # 可返回数据len
    print('len os tensor_dataset: ', len(tensor_dataset))
    

    输出结果:

    tensor([[ 0.8618,  0.4644, -0.5929],
            [ 0.9566, -0.9067,  1.5781],
            [ 0.3943, -0.7775,  2.0366],
            [-1.2570, -0.3859, -0.3542]])
    tensor([0.1363, 0.6545, 0.4345, 0.9928])
    tensor_data[0]:  (tensor([ 0.8618,  0.4644, -0.5929]), tensor(0.1363))
    len os tensor_dataset:  4
    

    四、为什么要定义自己的数据集类?

    因为我们可以通过定义自己的数据集类并重写该类上的方法 实现多种多样的(自定义的)数据读取方式

    比如,我们重写 __init__ 实现用 pd.read_csv 读取 csv 文件:

    from torch.utils.data import Dataset
    import pandas as pd  # 这个包用来读取CSV数据
    
    # 继承Dataset,定义自己的数据集类 mydataset
    class mydataset(Dataset):
        def __init__(self, csv_file):   # self 参数必须,其他参数及其形式随程序需要而不同,比如(self,*inputs)
            self.csv_data = pd.read_csv(csv_file)
        def __len__(self):
            return len(self.csv_data)
        def __getitem__(self, idx):
            data = self.csv_data.values[idx]
            return data
    
    data = mydataset('spambase.csv')
    print(data[3])
    print(len(data))
    

    输出结果:

    [0.000e+00 0.000e+00 0.000e+00 0.000e+00 6.300e-01 0.000e+00 3.100e-01
     6.300e-01 3.100e-01 6.300e-01 3.100e-01 3.100e-01 3.100e-01 0.000e+00
     0.000e+00 3.100e-01 0.000e+00 0.000e+00 3.180e+00 0.000e+00 3.100e-01
     0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
     0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
     0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
     0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
     1.370e-01 0.000e+00 1.370e-01 0.000e+00 0.000e+00 3.537e+00 4.000e+01
     1.910e+02 1.000e+00]
    4601
    

    要点:

    1. 自己定义的 dataset 类需要继承 Dataset。
    2. 需要实现必要的魔法方法:
      __init__ 方法里面进行 读取数据文件
      __getitem__ 方法里支持通过下标访问数据。
      __len__ 方法里返回自定义数据集的大小,方便后期遍历。

    五、实战:torch.utils.data.Dataset + Dataloader 实现数据集读取和迭代

    实例 1

    数据集 spambase.csv 用的是 UCI 机器学习存储库里的垃圾邮件数据集,它一条数据有57个特征和1个标签。

    import torch.utils.data as Data
    import pandas as pd  # 这个包用来读取CSV数据
    import torch
    
    
    # 继承Dataset,定义自己的数据集类 mydataset
    class mydataset(Data.Dataset):
        def __init__(self, csv_file):   # self 参数必须,其他参数及其形式随程序需要而不同,比如(self,*inputs)
            data_csv = pd.DataFrame(pd.read_csv(csv_file))   # 读数据
            self.csv_data = data_csv.drop(axis=1, columns='58', inplace=False)  # 删除最后一列标签
        def __len__(self):
            return len(self.csv_data)
        def __getitem__(self, idx):
            data = self.csv_data.values[idx]
            return data
    
    
    data = mydataset('spambase.csv')
    x = torch.tensor(data[:5])         # 前五个数据
    y = torch.tensor([1, 1, 1, 1, 1])  # 标签
    
    
    torch_dataset = Data.TensorDataset(x, y)  # 对给定的 tensor 数据,将他们包装成 dataset
    
    loader = Data.DataLoader(
        # 从数据库中每次抽出batch size个样本
        dataset = torch_dataset,       # torch TensorDataset format
        batch_size = 2,                # mini batch size
        shuffle=True,                  # 要不要打乱数据 (打乱比较好)
        num_workers=2,                 # 多线程来读数据
    )
    
    def show_batch():
        for step, (batch_x, batch_y) in enumerate(loader):
            print("steop:{}, batch_x:{}, batch_y:{}".format(step, batch_x, batch_y))
    
    show_batch()
    

    输出结果:

    steop:0, batch_x:tensor([[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 6.3000e-01, 0.0000e+00,
             3.1000e-01, 6.3000e-01, 3.1000e-01, 6.3000e-01, 3.1000e-01, 3.1000e-01,
             3.1000e-01, 0.0000e+00, 0.0000e+00, 3.1000e-01, 0.0000e+00, 0.0000e+00,
             3.1800e+00, 0.0000e+00, 3.1000e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 1.3500e-01, 0.0000e+00, 1.3500e-01, 0.0000e+00, 0.0000e+00,
             3.5370e+00, 4.0000e+01, 1.9100e+02],
            [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 6.3000e-01, 0.0000e+00,
             3.1000e-01, 6.3000e-01, 3.1000e-01, 6.3000e-01, 3.1000e-01, 3.1000e-01,
             3.1000e-01, 0.0000e+00, 0.0000e+00, 3.1000e-01, 0.0000e+00, 0.0000e+00,
             3.1800e+00, 0.0000e+00, 3.1000e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 1.3700e-01, 0.0000e+00, 1.3700e-01, 0.0000e+00, 0.0000e+00,
             3.5370e+00, 4.0000e+01, 1.9100e+02]], dtype=torch.float64), batch_y:tensor([1, 1])
    steop:1, batch_x:tensor([[2.1000e-01, 2.8000e-01, 5.0000e-01, 0.0000e+00, 1.4000e-01, 2.8000e-01,
             2.1000e-01, 7.0000e-02, 0.0000e+00, 9.4000e-01, 2.1000e-01, 7.9000e-01,
             6.5000e-01, 2.1000e-01, 1.4000e-01, 1.4000e-01, 7.0000e-02, 2.8000e-01,
             3.4700e+00, 0.0000e+00, 1.5900e+00, 0.0000e+00, 4.3000e-01, 4.3000e-01,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             7.0000e-02, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 1.3200e-01, 0.0000e+00, 3.7200e-01, 1.8000e-01, 4.8000e-02,
             5.1140e+00, 1.0100e+02, 1.0280e+03],
            [6.0000e-02, 0.0000e+00, 7.1000e-01, 0.0000e+00, 1.2300e+00, 1.9000e-01,
             1.9000e-01, 1.2000e-01, 6.4000e-01, 2.5000e-01, 3.8000e-01, 4.5000e-01,
             1.2000e-01, 0.0000e+00, 1.7500e+00, 6.0000e-02, 6.0000e-02, 1.0300e+00,
             1.3600e+00, 3.2000e-01, 5.1000e-01, 0.0000e+00, 1.1600e+00, 6.0000e-02,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 6.0000e-02, 0.0000e+00, 0.0000e+00,
             1.2000e-01, 0.0000e+00, 6.0000e-02, 6.0000e-02, 0.0000e+00, 0.0000e+00,
             1.0000e-02, 1.4300e-01, 0.0000e+00, 2.7600e-01, 1.8400e-01, 1.0000e-02,
             9.8210e+00, 4.8500e+02, 2.2590e+03]], dtype=torch.float64), batch_y:tensor([1, 1])
    steop:2, batch_x:tensor([[  0.0000,   0.6400,   0.6400,   0.0000,   0.3200,   0.0000,   0.0000,
               0.0000,   0.0000,   0.0000,   0.0000,   0.6400,   0.0000,   0.0000,
               0.0000,   0.3200,   0.0000,   1.2900,   1.9300,   0.0000,   0.9600,
               0.0000,   0.0000,   0.0000,   0.0000,   0.0000,   0.0000,   0.0000,
               0.0000,   0.0000,   0.0000,   0.0000,   0.0000,   0.0000,   0.0000,
               0.0000,   0.0000,   0.0000,   0.0000,   0.0000,   0.0000,   0.0000,
               0.0000,   0.0000,   0.0000,   0.0000,   0.0000,   0.0000,   0.0000,
               0.0000,   0.0000,   0.7780,   0.0000,   0.0000,   3.7560,  61.0000,
             278.0000]], dtype=torch.float64), batch_y:tensor([1])
    

    一共 5 条数据,batch_size 设为 2 ,则数据被分为三组,每组的数据量为:2,2,1。

    实例 2:进阶

    import torch.utils.data as Data
    import pandas as pd  # 这个包用来读取CSV数据
    import numpy as np
    
    # 继承Dataset,定义自己的数据集类 mydataset
    class mydataset(Data.Dataset):
        def __init__(self, csv_file):   # self 参数必须,其他参数及其形式随程序需要而不同,比如(self,*inputs)
            # 读取数据
            frame = pd.DataFrame(pd.read_csv('spambase.csv'))
            spam = frame[frame['58'] == 1]
            ham = frame[frame['58'] == 0]
            SpamNew = spam.drop(axis=1, columns='58', inplace=False)  # 删除第58列,inplace=False不改变原数据,返回一个新dataframe
            HamNew = ham.drop(axis=1, columns='58', inplace=False)
            # 数据
            self.csv_data = np.vstack([np.array(SpamNew), np.array(HamNew)])  # 将两个N维数组进行连接,形成X
            # 标签
            self.Label = np.array([1] * len(spam) + [0] * len(ham))  # 形成标签值列表y
        def __len__(self):
            return len(self.csv_data)
        def __getitem__(self, idx):
            data = self.csv_data[idx]
            label = self.Label[idx]
            return data, label
    
    
    data = mydataset('spambase.csv')
    print(len(data))
    
    loader = Data.DataLoader(
        # 从数据库中每次抽出batch size个样本
        dataset = data,       # torch TensorDataset format
        batch_size = 460,                # mini batch size
        shuffle=True,                  # 要不要打乱数据 (打乱比较好)
        num_workers=2,                 # 多线程来读数据
    )
    
    def show_batch():
        for step, (batch_x, batch_y) in enumerate(loader):
            print("steop:{}, batch_x:{}, batch_y:{}".format(step, batch_x, batch_y))
    
    show_batch()
    

    输出结果:

    4601
    steop:0, batch_x:tensor([[0.0000e+00, 2.4600e+00, 0.0000e+00,  ..., 2.1420e+00, 1.0000e+01,
             7.5000e+01],
            [0.0000e+00, 0.0000e+00, 1.6000e+00,  ..., 2.0650e+00, 1.2000e+01,
             9.5000e+01],
            [0.0000e+00, 0.0000e+00, 3.6000e-01,  ..., 3.7220e+00, 2.0000e+01,
             2.6800e+02],
            ...,
            [7.7000e-01, 3.8000e-01, 7.7000e-01,  ..., 1.4619e+01, 5.2500e+02,
             9.2100e+02],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e+00, 1.0000e+00,
             5.0000e+00],
            [4.0000e-01, 1.8000e-01, 3.2000e-01,  ..., 3.3050e+00, 1.8100e+02,
             1.6130e+03]], dtype=torch.float64), batch_y:tensor([0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,
            0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0,
            0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0,
            1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
            0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0,
            0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
            1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,
            0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0,
            0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,
            1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,
            0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,
            1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,
            0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0,
            0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,
            0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,
            1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,
            0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,
            1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1,
            0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1,
            0, 1, 0, 1])
    steop:1, batch_x:tensor([[0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e+00, 1.0000e+00,
             2.0000e+00],
            [4.9000e-01, 0.0000e+00, 7.4000e-01,  ..., 3.9750e+00, 4.7000e+01,
             4.8500e+02],
            [0.0000e+00, 0.0000e+00, 7.1000e-01,  ..., 4.0220e+00, 9.7000e+01,
             5.4300e+02],
            ...,
            [0.0000e+00, 1.4000e-01, 1.4000e-01,  ..., 5.3310e+00, 8.0000e+01,
             1.0290e+03],
            [0.0000e+00, 0.0000e+00, 3.6000e-01,  ..., 3.1760e+00, 5.1000e+01,
             2.7000e+02],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.1660e+00, 2.0000e+00,
             7.0000e+00]], dtype=torch.float64), batch_y:tensor([0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
            1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0,
            0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0,
            1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0,
            1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0,
            0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0,
            1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0,
            0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0,
            1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1,
            1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1,
            0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0,
            0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,
            0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0,
            0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
            0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1,
            1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1,
            1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,
            0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,
            0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1,
            1, 0, 0, 0])
    steop:2, batch_x:tensor([[0.0000e+00, 0.0000e+00, 1.4700e+00,  ..., 3.0000e+00, 3.3000e+01,
             1.7700e+02],
            [2.6000e-01, 4.6000e-01, 9.9000e-01,  ..., 1.3235e+01, 2.7200e+02,
             1.5750e+03],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 2.0450e+00, 6.0000e+00,
             4.5000e+01],
            ...,
            [4.0000e-01, 0.0000e+00, 0.0000e+00,  ..., 1.1940e+00, 5.0000e+00,
             1.2900e+02],
            [2.6000e-01, 0.0000e+00, 0.0000e+00,  ..., 1.8370e+00, 1.1000e+01,
             1.5800e+02],
            [5.0000e-02, 0.0000e+00, 1.0000e-01,  ..., 3.7150e+00, 1.0700e+02,
             1.3860e+03]], dtype=torch.float64), batch_y:tensor([1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
            0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0,
            1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
            0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,
            0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
            0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
            0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0,
            0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0,
            0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1,
            0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,
            1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0,
            0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0,
            0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0,
            1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
            1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1,
            0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,
            0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0,
            0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1,
            1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0,
            1, 1, 0, 0])
    steop:3, batch_x:tensor([[2.6000e-01, 0.0000e+00, 5.3000e-01,  ..., 2.6460e+00, 7.7000e+01,
             1.7200e+02],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 2.4280e+00, 5.0000e+00,
             1.7000e+01],
            [3.4000e-01, 0.0000e+00, 1.7000e+00,  ..., 6.6700e+02, 1.3330e+03,
             1.3340e+03],
            ...,
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e+00, 1.0000e+00,
             7.0000e+00],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 2.7010e+00, 2.0000e+01,
             1.8100e+02],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 4.0000e+00, 1.1000e+01,
             3.6000e+01]], dtype=torch.float64), batch_y:tensor([0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
            1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1,
            0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
            1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0,
            0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
            0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0,
            1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
            1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,
            0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0,
            0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1,
            0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0,
            0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0,
            0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1,
            0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,
            0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0,
            0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1,
            1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,
            1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0,
            1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0,
            1, 0, 0, 1])
    steop:4, batch_x:tensor([[  0.0000,   0.0000,   0.3100,  ...,   5.7080, 138.0000, 274.0000],
            [  0.0000,   0.0000,   0.3400,  ...,   2.2570,  17.0000, 158.0000],
            [  1.0400,   0.0000,   0.0000,  ...,   1.0000,   1.0000,  17.0000],
            ...,
            [  0.0000,   0.0000,   0.0000,  ...,   4.0000,  12.0000,  28.0000],
            [  0.3300,   0.0000,   0.0000,  ...,   1.7880,   6.0000,  93.0000],
            [  0.0000,  14.2800,   0.0000,  ...,   1.8000,   5.0000,   9.0000]],
           dtype=torch.float64), batch_y:tensor([1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1,
            0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,
            0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0,
            1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,
            0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
            1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0,
            0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0,
            0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,
            0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0,
            1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1,
            1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0,
            0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,
            1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1,
            0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
            0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1,
            1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,
            0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
            0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1,
            1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,
            1, 1, 0, 0])
    steop:5, batch_x:tensor([[7.0000e-01, 0.0000e+00, 1.0500e+00,  ..., 1.1660e+00, 1.3000e+01,
             1.8900e+02],
            [0.0000e+00, 3.3600e+00, 1.9200e+00,  ..., 6.1370e+00, 1.0700e+02,
             1.7800e+02],
            [5.4000e-01, 0.0000e+00, 1.0800e+00,  ..., 5.4540e+00, 6.8000e+01,
             1.8000e+02],
            ...,
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 3.8330e+00, 9.0000e+00,
             2.3000e+01],
            [6.0000e-02, 6.5000e-01, 7.1000e-01,  ..., 4.7420e+00, 1.1700e+02,
             1.3420e+03],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 2.6110e+00, 1.2000e+01,
             4.7000e+01]], dtype=torch.float64), batch_y:tensor([1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1,
            1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
            0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
            0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0,
            0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1,
            0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1,
            0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
            0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0,
            0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1,
            1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1,
            0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1,
            1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1,
            0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1,
            0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0,
            0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1,
            0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1,
            0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
            1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,
            0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
            0, 1, 1, 1])
    steop:6, batch_x:tensor([[0.0000e+00, 1.4280e+01, 0.0000e+00,  ..., 1.8000e+00, 5.0000e+00,
             9.0000e+00],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.9280e+00, 1.5000e+01,
             5.4000e+01],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0692e+01, 6.5000e+01,
             1.3900e+02],
            ...,
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.5000e+00, 5.0000e+00,
             2.4000e+01],
            [7.6000e-01, 1.9000e-01, 3.8000e-01,  ..., 3.7020e+00, 4.5000e+01,
             1.0700e+03],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 2.0000e+00, 1.2000e+01,
             8.8000e+01]], dtype=torch.float64), batch_y:tensor([0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1,
            0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1,
            0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
            1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1,
            1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0,
            0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1,
            0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0,
            0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
            1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
            0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
            0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
            1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0,
            0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
            1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1,
            0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1,
            0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0,
            0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1,
            1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
            1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
            1, 0, 1, 0])
    steop:7, batch_x:tensor([[0.0000e+00, 2.7000e-01, 0.0000e+00,  ..., 5.8020e+00, 4.3000e+01,
             4.1200e+02],
            [0.0000e+00, 3.5000e-01, 7.0000e-01,  ..., 3.6390e+00, 6.1000e+01,
             3.1300e+02],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.5920e+00, 7.0000e+00,
             1.2900e+02],
            ...,
            [8.0000e-02, 1.6000e-01, 8.0000e-02,  ..., 2.7470e+00, 8.6000e+01,
             1.9950e+03],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.6130e+00, 1.1000e+01,
             7.1000e+01],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.9110e+00, 1.5000e+01,
             6.5000e+01]], dtype=torch.float64), batch_y:tensor([0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0,
            0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,
            1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1,
            0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1,
            0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,
            0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
            0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
            0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
            0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0,
            1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
            1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0,
            0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
            0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,
            0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
            0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,
            0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0,
            1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1,
            0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1,
            0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1,
            1, 0, 0, 0])
    steop:8, batch_x:tensor([[1.7000e-01, 0.0000e+00, 1.7000e-01,  ..., 1.7960e+00, 1.2000e+01,
             4.5800e+02],
            [3.7000e-01, 0.0000e+00, 6.3000e-01,  ..., 1.1810e+00, 4.0000e+00,
             1.0400e+02],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e+00, 1.0000e+00,
             7.0000e+00],
            ...,
            [2.3000e-01, 0.0000e+00, 4.7000e-01,  ..., 2.4200e+00, 1.2000e+01,
             3.3400e+02],
            [0.0000e+00, 0.0000e+00, 1.2900e+00,  ..., 1.3500e+00, 4.0000e+00,
             2.7000e+01],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.3730e+00, 1.1000e+01,
             1.6900e+02]], dtype=torch.float64), batch_y:tensor([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1,
            0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0,
            1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0,
            0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1,
            1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,
            0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0,
            0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,
            0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1,
            0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
            1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1,
            0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0,
            1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
            0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
            1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
            0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,
            1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0,
            1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0,
            0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
            1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1,
            0, 0, 0, 0])
    steop:9, batch_x:tensor([[0.0000e+00, 6.3000e-01, 0.0000e+00,  ..., 2.2150e+00, 2.2000e+01,
             1.1300e+02],
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e+00, 1.0000e+00,
             5.0000e+00],
            [0.0000e+00, 0.0000e+00, 2.0000e-01,  ..., 1.1870e+00, 1.1000e+01,
             1.1400e+02],
            ...,
            [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 2.3070e+00, 1.6000e+01,
             3.0000e+01],
            [5.1000e-01, 4.3000e-01, 2.9000e-01,  ..., 6.5900e+00, 7.3900e+02,
             2.3330e+03],
            [6.8000e-01, 6.8000e-01, 6.8000e-01,  ..., 2.4720e+00, 9.0000e+00,
             8.9000e+01]], dtype=torch.float64), batch_y:tensor([0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
            0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
            0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0,
            0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1,
            1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0,
            0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0,
            0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
            1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1,
            0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1,
            0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1,
            1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0,
            1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
            0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,
            1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
            0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
            1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
            1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
            1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0,
            1, 1, 1, 1])
    steop:10, batch_x:tensor([[0.0000e+00, 2.5000e-01, 7.5000e-01, 0.0000e+00, 1.0000e+00, 2.5000e-01,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 2.5000e-01, 2.5000e-01,
             1.2500e+00, 0.0000e+00, 0.0000e+00, 2.5000e-01, 0.0000e+00, 1.2500e+00,
             2.5100e+00, 0.0000e+00, 1.7500e+00, 0.0000e+00, 2.5000e-01, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 2.5000e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
             0.0000e+00, 0.0000e+00, 0.0000e+00, 4.2000e-02, 0.0000e+00, 0.0000e+00,
             1.2040e+00, 7.0000e+00, 1.1800e+02]], dtype=torch.float64), batch_y:tensor([0])
    

    一共 4601 条数据,按 batch_size = 460 来分:能划分为 11 组,前 10 组的数据量为 460,最后一组的数据量为 1 。


    参考链接

    1. torch.Tensor.size()方法的使用举例
    2. Pytorch笔记05-自定义数据读取方式orch.utils.data.Dataset与Dataloader
    3. pytorch 可训练数据集创建(torch.utils.data)
    4. Pytorch的第一步:(1) Dataset类的使用
    5. pytorch中的torch.utils.data.Dataset和torch.utils.data.DataLoader

    来源:想变厉害的大白菜

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    物联沃-IOTWORD物联网 » 【PyTorch】torch.utils.data.Dataset 介绍与实战

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