PyTorch 深度学习实践 第7讲 处理多维特征的输入

数据来源：diabetes.csv

import torch
import numpy as np
import matplotlib.pyplot as plt

xy = np.loadtxt('diabetes.csv', delimiter = ',', dtype = np.float32)
x_data = torch.from_numpy(xy[:, :-1])
y_data = torch.from_numpy(xy[:, [-1]])

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, 2)
        self.linear4 = torch.nn.Linear(2, 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))
        x = self.sigmoid(self.linear4(x))
        return x
    
model = Model()

criterion = torch.nn.BCELoss(reduction = 'mean')
optimizer = torch.optim.SGD(model.parameters(), lr = 0.1)

epoch_list = []
loss_list = []

for epoch in range(10000):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print("epoch:", epoch, "loss:", loss.item())
    epoch_list.append(epoch)
    loss_list.append(loss.data)
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    

plt.plot(epoch_list, loss_list)
plt.xlabel('epoch')
plt.ylabel('Loss')
plt.show()