一、实现过程
1、准备数据
与PyTorch实现多维度特征输入的逻辑回归的方法不同的是：本文使用DataLoader方法，并继承DataSet抽象类，可实现对数据集进行mini_batch梯度下降优化。
代码如下：
import torch
import numpy as np
from torch.utils.data import Dataset,DataLoader
class DiabetesDataSet(Dataset):
def __init__(self, filepath):
xy = np.loadtxt(filepath,delimiter=',',dtype=np.float32)
self.len = xy.shape[0]
self.x_data = torch.from_numpy(xy[:,:-1])
self.y_data = torch.from_numpy(xy[:,[-1]])
def __getitem__(self, index):
return self.x_data[index],self.y_data[index]
def __len__(self):
return self.len
dataset = DiabetesDataSet('G:/datasets/diabetes/diabetes.csv')
train_loader = DataLoader(dataset=dataset,batch_size=32,shuffle=True,num_workers=0)2、设计模型
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(4,1)
self.activate = torch.nn.Sigmoid()
def forward(self, x):
x = self.activate(self.linear1(x))
x = self.activate(self.linear2(x))
x = self.activate(self.linear3(x))
return x
model = Model()3、构造损失函数和优化器
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(),lr=0.1)4、训练过程
每次拿出mini_batch个样本进行训练，代码如下：
epoch_list = []
loss_list = []
for epoch in range(100):
count = 0
loss1 = 0
for i, data in enumerate(train_loader,0):
# 1.Prepare data
inputs, labels = data
# 2.Forward
y_pred = model(inputs)
loss = criterion(y_pred,labels)
print(epoch,i,loss.item())
count += 1
loss1 += loss.item()
# 3.Backward
optimizer.zero_grad()
loss.backward()
# 4.Update
optimizer.step()
epoch_list.append(epoch)
loss_list.append(loss1/count)5、结果展示
plt.plot(epoch_list,loss_list,'b')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.grid()
plt.show()
二、参考文献
- [1] https://www.bilibili.com/video/BV1Y7411d7Ys?p=8
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