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| import torch
import numpy as np
from torch.utils.data import Dataset
from torch.utils.data import 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('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.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='mean')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
if __name__ == '__main__':
for epoch in range(100):
for i, data in enumerate(train_loader, 0):
inputs, labels = data
y_pred = model(inputs)
loss = criterion(y_pred, labels)
print(epoch, i, loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
|
