视频截图

一些知识点的说明：
1、DataSet 是抽象类，不能实例化对象，我们需要自己写一个数据集的类去继承。继承DataSet 要重写init，getitem,len魔法函数。分别是为了加载数据集，获取数据索引，获取数据总量。
2、DataLoader 需要获取DataSet提供的索引[i]和len;用来帮助我们加载数据，比如说做shuffle(打乱数据，提高数据集的随机性)，batch_size,能拿出Mini-Batch进行训练。它帮我们自动完成这些工作。DataLoader可实例化对象。
3、__ getitem __函数目的是为支持下标(索引)操作
    我在其代码的基础上做了一些修改，首先是将训练数据拆分，分为0.8的训练集和0.2的测试集，将训练过程封装成train()函数，将测试过程封装成test()函数。

import numpy as np 
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader 
from sklearn.model_selection import train_test_split
import pdb

#定义一个cpu
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# 读取原始数据集，并且划分为训练集和测试集
xy = np.loadtxt('diabetes.csv', delimiter = ',', dtype = np.float32)
x = xy[:, :-1]
y = xy[:, [-1]]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3)
x_test = torch.from_numpy(x_test).to(device)
y_test = torch.from_numpy(y_test).to(device)

# prepare  dataset
class DiabetesDataset(Dataset):
    def __init__(self, data, labels):
        #xy = np.loadtxt(filepath, delimiter = ',', dtype = np.float32)
        self.len = data.shape[0]  # shape(行，列)
        self.x_data = torch.from_numpy(data)
        self.y_data = torch.from_numpy(labels)

    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    def __len__(self):
        return self.len

train_dataset = DiabetesDataset(x_train, y_train)
train_loader = DataLoader(dataset = train_dataset, batch_size = 32, shuffle = True, num_workers = 0) #num_workers 多线程

# design model using class
class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)
        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()
model.to(device)

# construct loss and optimizer
criterion = torch.nn.BCELoss(reduction = 'mean')
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
# optimizer = torch.optim.Adam(model.parameters(),
#                 lr=0.001,
#                 betas=(0.9, 0.999),
#                 eps=1e-08,
#                 weight_decay=0,
#                 amsgrad=False)

# training cycle forward, backward, update
def train(epoch):
    train_loss = 0.0
    count = 0.0
    for i, data in enumerate(train_loader, 0):  # start = 0，train_loader 是先shuffle后mini_batch
        #inputs, labels = data
        inputs, labels = data[0].to(device), data[1].to(device) # 使用gpu训练
        y_pred = model(inputs)
        loss = criterion(y_pred, labels)
        #print(epoch, i, loss.item())

        optimizer.zero_grad()
        loss.backward()

        optimizer.step()
        train_loss += loss.item()
        count = i

    if epoch % 1000 == 999:
        #pdb.set_trace()
        print('epoch:', epoch+1, 'train loss:', train_loss/count, end = ',')

def test():
    # 在with torch.no_grad()下对变量的操作，均不会让求梯度为真
    with torch.no_grad():
        y_pred = model(x_test)
        y_pred_label = torch.where(y_pred >= 0.5, torch.tensor([1.0]).to(device), torch.tensor([0.0]).to(device))
        acc = torch.eq(y_pred_label, y_test).sum().item() / y_test.size(0)
        print('test acc:', acc)


if __name__ == '__main__':
    for epoch in range(50000):
        train(epoch)
        if epoch % 1000 == 999:
            test()