逻辑回归，做分类问题 minist dataset
线性回归得到的是具体的值，而逻辑回归得到的是概率。 如果只有两个类别，得到的就是二分类。 sigmoid 代码运行有问题，不知道问题出在哪里，感兴趣的小伙伴可以试试看
import torch import numpy as np import matplotlib.pyplot as plt import torch.nn.functional as F # 1 数据准备，使用torch。tensor进行 x_data = torch.tensor([[1.0],[2.0],[3.0]]) # y_data = torch.tensor([[2.0],[4.0],[6.0]]) y_data = torch.tensor([[0],[0],[1]]) # 分类 # 2 解释：定义一个类，继承Module，然后进行初始化操作 class LogisticRegressionModel(torch.nn.Module): def __init__(self): super(LogisticRegressionModel,self).__init__() self.linear = torch.nn.Linear(1,1) # self.linear = torch.nn.Linear(1,1) 定义一个可变长的，只用输入输入和输出即可 def forward(self,x): y_pred = torch.sigmoid (self.linear(x)) return y_pred model = LogisticRegressionModel() # 创建一个类LinearModel criterion = torch.nn.BCELoss(reduction='sum') # 使用size_average出现一个userwarning，please use reduction='sum' instead. 按照提示修改如下 # criterion = torch.nn.MSELoss(reduction='sum') # 应该是函数已经更新了 optimizer = torch.optim.SGD(model.parameters(),lr = 0.01) for epoch in range(1000): #使用100轮训练效果不好，说明还没有达到最优，可以修改更多轮数进行查看，效果有所改善 y_pred = model(x_data) loss = criterion(y_pred,y_data) print(epoch,loss.item()) # 可以看到这里出现直接可以打印的loss.那是因为在内部调用了__str__，实现的打印 optimizer.zero_grad() # 涉及到权重的计算需要清零 loss.backward() # 进行反向传播 optimizer.step() # 进行更新 # output weight and bias print('w = ',model.linear.weight.item()) print('b = ',model.linear.bias.item()) # test model x_test = torch.tensor([[4.0]]) y_test = model(x_test) print('y_pred',y_test.data) x = np.linspace(0,10,2000) x_t = torch.tensor(x,dtype=torch.long).view((2000,1)) y_t = model(x_t) y = y_t.data.numpy() plt.plot(x,y) plt.plot([0,10],[0.5,0.5],c='r') plt.xlabel('Hours') plt.ylabel('Probaility of Pass') plt.grid() plt.show()File “E:/PycharmSoftware/project1/20200904_卷积神经网络06（逻辑回归）.py”, line 31, in loss = criterion(y_pred,y_data) File “E:\Anaconda\envs\Pytorch\lib\site-packages\torch\nn\modules\module.py”, line 722, in _call_impl result = self.forward(*input, **kwargs) File “E:\Anaconda\envs\Pytorch\lib\site-packages\torch\nn\modules\loss.py”, line 529, in forward return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction) File “E:\Anaconda\envs\Pytorch\lib\site-packages\torch\nn\functional.py”, line 2484, in binary_cross_entropy input, target, weight, reduction_enum) RuntimeError: Found dtype Long but expected Float
