转载：莫凡线性回归

怼烎@ 2022-04-08 08:15 162阅读 0赞

import torch
    import torch.nn.functional as F
    import matplotlib.pyplot as plt
    
    # torch.manual_seed(1)    # reproducible
    
    x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)
    y = x.pow(2) + 0.2*torch.rand(x.size())                 # noisy y data (tensor), shape=(100, 1)
    
    # torch can only train on Variable, so convert them to Variable
    # The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors
    # x, y = Variable(x), Variable(y)
    
    # plt.scatter(x.data.numpy(), y.data.numpy())
    # plt.show()
    
    
    class Net(torch.nn.Module):
        def __init__(self, n_feature, n_hidden, n_output):
            super(Net, self).__init__()
            self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer
            self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer
    
        def forward(self, x):
            x = F.relu(self.hidden(x))      # activation function for hidden layer
            x = self.predict(x)             # linear output
            return x
    
    net = Net(n_feature=1, n_hidden=10, n_output=1)     # define the network
    print(net)  # net architecture
    
    optimizer = torch.optim.SGD(net.parameters(), lr=0.2)
    loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss
    
    plt.ion()   # something about plotting
    
    for t in range(200):
        prediction = net(x)     # input x and predict based on x
    
        loss = loss_func(prediction, y)     # must be (1. nn output, 2. target)
    
        optimizer.zero_grad()   # clear gradients for next train
        loss.backward()         # backpropagation, compute gradients
        optimizer.step()        # apply gradients
    
        if t % 5 == 0:
            # plot and show learning process
            plt.cla()
            plt.scatter(x.data.numpy(), y.data.numpy())
            plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
            plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color':  'red'})
            plt.pause(0.1)
    
    plt.ioff()
    plt.show()

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