Pytorch线性回归分析

import numpy as np import torch import matplotlib.pyplot as plt # output = torch.randn(3,3) # active_func = torch.nn.Sigmoid() # output = active_func(output) # target = torch.FloatTensor([[0,1,1],[1,1,1],[0,0,0]]) # print("output", output, "target", target) # loss = torch.nn.BCELoss() # loss = loss(output, target) # print(loss) x = np.linspace(-5, 5, 20, dtype=np.float32) _b = 1/(1 + np.exp(-x)) y = np.random.normal(_b, 0.001) # 20x1 x = np.float32(x.reshape(-1, 1)) y = np.float32(y.reshape(-1, 1)) class LogicRegressionModel(torch.nn.Module): def __init__(self, input_dim, output_dim): super(LogicRegressionModel, self).__init__() self.linear = torch.nn.Linear(input_dim, output_dim) def forward(self, x): out = torch.sigmoid(self.linear(x)) return out input_dim = 1 output_dim = 1 model = LogicRegressionModel(input_dim, output_dim) criterion = torch.nn.BCELoss() learning_rate = 0.01 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) for epoch in range(100): epoch += 1 # Convert numpy array to torch Variable inputs = torch.from_numpy(x).requires_grad_() labels = torch.from_numpy(y) # Clear gradients w.r.t. parameters optimizer.zero_grad() # Forward to get output outputs = model(inputs) # Calculate Loss loss = criterion(outputs, labels) # Getting gradients w.r.t. parameters loss.backward() # Updating parameters optimizer.step() print('epoch {}, loss {}'.format(epoch, loss.item())) # Purely inference predicted_y = model(torch.from_numpy(x).requires_grad_()).data.numpy() print("标签Y:", y) print("预测Y:", predicted_y) # Clear figure plt.clf() # Get predictions predicted = model(torch.from_numpy(x).requires_grad_()).data.numpy() # Plot true data plt.plot(x, y, 'go', label='True data', alpha=0.5) # Plot predictions plt.plot(x, predicted_y, '--', label='Predictions', alpha=0.5) # Legend and plot plt.legend(loc='best') plt.show()