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()
损失
100轮迭代
10000轮迭代
