08 Dataset and Dataloader

Mini-Batch

为什么需要有 Batch_Size 这个参数

代码实现

模型仍为上一讲中多维输入的模型，损失和优化器亦不变

from abc import ABC

import numpy as np
import torch
from torch.utils.data import Dataset  # Dataset是抽象类, 不能实例化
from torch.utils.data import DataLoader


class DiabetesDataset(Dataset):
    """
    继承自抽象类Dataset
    """
    def __init__(self, filepath):
        """
        :param filepath: 文件路径
        """
        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
        self.len = xy.shape[0]  # 比如我们的数据集为N行M列, shape[0]取第0列的维度, 也即N. 方便__len__的构造
        self.x_data = torch.from_numpy(xy[:, :-1])
        self.y_data = torch.from_numpy(xy[:, [-1]])

    def __getitem__(self, index):
        """
        :param index: 数据下标
        :return: 获取下标对应的数据
        """
        return self.x_data[index], self.y_data[index]  # 直接返回xy对应下标的元素即可, 返回值是一个元组

    def __len__(self):
        """
        :return: 反馈数据集中的数据条数
        """
        return self.len


dataset = DiabetesDataset('diabetes.csv.gz')
train_loader = DataLoader(dataset=dataset,  # dataset即数据集
                          batch_size=2,     # batch_size为小批量的容量大小
                          shuffle=True,     # shuffle决定是否打乱数据, True打乱
                          num_workers=2)    # 加载数据的线程数码你


class Model(torch.nn.Module, ABC):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)  # 维度 8 -> 6 -> 4 -> 1
        self.linear2 = torch.nn.Linear(6, 4)
        self.linear3 = torch.nn.Linear(4, 1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x


model = Model()
criterion = torch.nn.BCELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)


if __name__ == '__main__':  # 使用多线程的时候需要用if语句包裹起来, 否则会报RuntimeError
    for epoch in range(100):
        
        for i, data in enumerate(train_loader, 0):
            """
            使用enumerate是为了获取当前是第几[i]次迭代, train_loader中取出的(x, y)给data
            因为DataLoader我们设定了batch_size为2, 而我们的训练集中共有759条数据, 所以每个epoch都需要做i: 0~379的迭代
            """
            inputs, labels = data
            y_pred = model(inputs)
            loss = criterion(y_pred, labels)
            print(epoch, i, loss.item())
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

epoch0, epoch99

Reference

https://www.bilibili.com/video/BV1Y7411d7Ys?p=8