5_diabetes_logistics

import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable


class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.l1 = nn.Linear(8, 6)
        self.l2 = nn.Linear(6, 4)
        self.l3 = nn.Linear(4, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        ''':arg x 數據輸入變量
        在前向函數中，我們接受輸入數據的變量，我們必須返回
        輸出數據的變量。 我們可以使用構造函數中定義的模塊作爲
        以及變量上的任意運算符。
        '''
        out1 = self.sigmoid(self.l1(x))
        out2 = self.sigmoid(self.l2(out1))
        y_pred = self.sigmoid(self.l3(out2))
        return y_pred

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

        for epoch in range(100):
            y_pred = model(x_data)
            loss = criterion(y_pred, y_data)
            print(epoch, loss.item())

            criterion.zero_grad()
            loss.backward()
            optimizer.step()


if __name__ == "__main__":
    print("Life is short, You need Python!")
    xy = np.loadtxt('.//data/diabetes.csv.gz', delimiter=',', dtype=np.float32)
    x_data = Variable(torch.from_numpy(xy[:, 0:-1]))
    y_data = Variable(torch.from_numpy(xy[:, [-1]]))
    print(x_data.data.shape)
    print(y_data.data.shape)