PyTorch torch.utils.data.Dataset怎么使用

2023-07-05 04:07:19 649人浏览安东尼

摘要

本篇内容主要讲解“PyTorch torch.utils.data.Dataset怎么使用”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“PyTorch torch.utils

本篇内容主要讲解“PyTorch torch.utils.data.Dataset怎么使用”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“PyTorch torch.utils.data.Dataset怎么使用”吧!

PyTorch torch.utils.data.Dataset 介绍与实战案例

一、前言

在 PyTorch 中，提供了一个处理数据集的工具包 torch.utils.data。这里来简单介绍这个包是什么。

训练模型一般都是先处理 数据的输入问题 和 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.Dataset 与 Dataloader 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.Dataset 与 Dataloader 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？

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。
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。
torch.utils.data.Dataset 是一个类 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__ 和 __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__ 和 __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__

__len__返回的是数据集的大小
__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 Datasetimport torchclass 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lenprint('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 Datasetimport 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 mydatasetclass 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 datadata = 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：

自己定义的 dataset 类需要继承 Dataset。
需要实现必要的魔法方法:

在 __init__ 方法里面进行 读取数据文件 。
在 __getitem__ 方法里支持通过下标访问数据。
在 __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 Dataimport 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 mydatasetclass 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 datadata = 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 datasetloader = 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 Dataimport 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 mydatasetclass 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, labeldata = 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,
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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,
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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,
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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,
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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,
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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,
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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,
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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])