pytorch-06. Logistic regression

#cross-entropy#BCELoss Binary Cross Entropyimport torchimport numpy as npimport matplotlib.pyplot as pltx_data = torch.Tensor([[1.0],[2.0],[3.0]])y_data = torch.Tensor([[0],[0],[1]])class LogisticRegressionModel(torch.nn.Module):def __init__(self):super(LogisticRegressionModel, self).__init__()self.linear = torch.nn.Linear(1,1)def forward(self,x):y_pred = torch.sigmoid(self.linear(x))return y_predmodel = LogisticRegressionModel()criterion = torch.nn.BCELoss(size_average=False)optimizer = torch.optim.SGD(model.parameters(),lr=0.01)for epoch in range (1000):y_pred = model(x_data)loss = criterion(y_pred,y_data)print('epoch=', epoch, " loss=", loss.item())optimizer.zero_grad()loss.backward()optimizer.step()# Output weight and biasprint('w=',model.linear.weight.item())print('b=',model.linear.bias.item())#--------------------------------------------------------# Test Modelx_test = torch.Tensor([[4.0]])y_test = model(x_test)print('y_pred=',y_test.data.item())x = np.linspace(0,10,200) #0 to 10 took 200 pointsx_t = torch.Tensor(x).view((200,1)) # becomes a matrix with 200 rows and 1 columny_t = model(x_t)y = y_t.data.numpy()plt.plot(x,y)plt.plot([0,10],[0.5,0.5],c='r') #Draw a red lineplt.xlabel('Hours')plt.ylabel('Probability of Pass')plt.grid()plt.show()