lecture07多维输入
课程网址

Pytorch深度学习实践

部分课件内容：

import torch
import numpy as npxy = np.loadtxt('diabetes.csv.gz', delimiter=',', dtype=np.float32)
x_data = torch.from_numpy(xy[:,:-1])  #第一列开始最后一列不要
y_data = torch.from_numpy(xy[:,[-1]])  # 取最后一列class LogisticRegressionModel(torch.nn.Module):def __init__(self):super(LogisticRegressionModel, 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 xmodel = LogisticRegressionModel()criterion = torch.nn.BCELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)for epoch in range(100):y_pred = model(x_data)loss = criterion(y_pred, y_data)optimizer.zero_grad()loss.backward()optimizer.step()print(epoch,loss.data)