多维输入逻辑斯蒂回归
多层网络
完整代码：
import torch import torch.nn.functional as F import numpy as np import matplotlib.pyplot as plt # 1.准备数据,从csv文件读取 xy = np.loadtxt('diabetes.csv.gz', delimiter=',', dtype=np.float32) x_data = torch.from_numpy(xy[:,:-1]) y_data = torch.from_numpy(xy[:,[-1]]) # 2.设计模型（类） 继承nn.Module 以便使用其方法 class Model(torch.nn.Module): # 初始化 def __init__(self): super(Model, self).__init__() #3 层神经网络 self.linear1 = torch.nn.Linear(8, 6) # Linear是一个线性单元 self.linear2 = torch.nn.Linear(6, 4) self.linear3 = torch.nn.Linear(4, 1) self.sigmoid = torch.nn.Sigmoid() # 前馈方法 def forward(self, x): #每层输出是下层输入 x = self.sigmoid(self.linear1(x)) x = self.sigmoid(self.linear2(x)) x = self.sigmoid(self.linear3(x)) return x model = Model() # 3 loss 和 optimizer(优化器) criterion = torch.nn.BCELoss(size_average=True) # 优化器。 model.parameters()获取模型中需要优化的参数，lr（learning rate，学习率） optimizer = torch.optim.SGD(model.parameters(), lr=0.01) # 4 训练过程 for epoch in range(100): # 前馈 y_pred = model(x_data) # 计算损失 loss = criterion(y_pred, y_data) print("epoch={},loss={}".format(epoch, loss)) optimizer.zero_grad() # 归零 # 反向传播 loss.backward() # 更新、优化参数 optimizer.step()
