07 Mutilple Dimension Input

Multiple Dimension Logistic Regression Model

Logistic Regression Model

\[\hat{y}=\sigma(x*\omega+b) \ \ \ \ \ \ \ \ (一维) \]

\[\hat{y}^{(i)}=\sigma(\sum_{n=1}^N x_{n}^{(i)}*\omega_{n}+b)\ \ \ \ \ \ \ \ (N维) \]

from abc import ABC

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

xy = np.loadtxt('diabetes.csv.gz', delimiter=',', dtype=np.float32)  # 指定文件名, 分隔符, 数据类型 加载文件
x_data = torch.from_numpy(xy[::, :-1])  # 选取xy的所有行, 除去最后一列后 所有列的数据
y_data = torch.from_numpy(xy[:, [-1]])  # 选取xy所有行的最后一列数据, 这里-1需要加中括号使数据为矩阵形式, 否则将是一个向量


class Model(torch.nn.Module, ABC):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)  # 维度 8 -> 6 -> 4 -> 1
        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()
criterion = torch.nn.BCELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

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

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

epoch_list = list(range(100))
plt.plot(epoch_list, loss_list, c='b')
plt.xlabel("epoch")
plt.ylabel("loss")
plt.show()