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 np

xy = 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 x

model = 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)