import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt

class DiabetesDataset(Dataset):
    def __init__(self,filepath):
        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
        self.len = xy.shape[0]
        self.x_data = torch.from_numpy(xy[:,:-1])
        self.y_data = torch.from_numpy(xy[:,[-1]])

    def __len__(self):
        return self.len

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

dataset = DiabetesDataset('PyTorch深度学习实践/diabetes.csv.gz')
train_loader = DataLoader(dataset=dataset,batch_size=32,shuffle=True)

class Model(torch.nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)  #输入是8维度，输出是6维度，将8维降为6维
        self.linear2 = torch.nn.Linear(6, 4)  #输入是6维度，输出是4维度，将6维降为4维
        self.linear3 = torch.nn.Linear(4, 1)
        self.sigmoid = torch.nn.Sigmoid()  #最后一层激活函数使用sigmoid，将输出值限制在0~1之间


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

model = Model()

#3.损失函数和优化器
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# 存储loss值用于绘图
loss_list = []
epoch_list = []


for epoch in range(100):
    running_loss = 0.0
    for i,data in enumerate(train_loader,0):
        #1.prepare data
        inputs, labels = data
        #2.forward
        y_pred = model(inputs)
        loss = criterion(y_pred, labels)


        print(epoch,i,loss.item())
        #3.backward
        optimizer.zero_grad()
        loss.backward()
        #4。update
        optimizer.step()

        running_loss += loss.item()  # 累加损失

        # 计算每个epoch的平均损失
    avg_loss = running_loss / (i + 1)
    epoch_list.append(epoch)
    loss_list.append(avg_loss)


# 5. 绘制loss变化图
plt.plot(epoch_list, loss_list)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.show()

作业：根据其他特征，判断泰坦尼克号上乘客是否活下来了。数据在kaggle网站。

具体移步：https://blog.csdn.net/qq_39804263/article/details/139685123?fromshare=blogdetail&sharetype=blogdetail&sharerId=139685123&sharerefer=PC&sharesource=m0_63829662&sharefrom=from_link