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)

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()

criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(),lr=0.1)

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)

plt.plot(epoch_list,loss_list,'b')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.grid()
plt.show()