糖尿病例子

import numpy as np
from matplotlib import pyplot as plt
import torch
data_xy = np.loadtxt('/home/chasing/Documents/pytorchbooklit/diabetes.csv.gz', delimiter=',', dtype=np.float32)

x_data = torch.from_numpy(data_xy[:,:-1])
y_data = torch.from_numpy(data_xy[:,-1]).reshape(-1,1)

class LinearExample(torch.nn.Module):
    def __init__(self):
        super(LinearExample, 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()
        self.relu = torch.nn.ReLU()

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

model = LinearExample()
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(),lr=1e-2)

loss_list = list()

if __name__ == '__main__':
    for epoch in range(300):
        y_pred = model(x_data)
        loss = criterion(y_pred, y_data)
        loss_list.append(loss.item())

        optimizer.zero_grad()
        loss.backward()

        optimizer.step()

    plt.plot(loss_list)
    plt.show()