301_PyTorch中文教程：迴歸分析-Regression

301_PyTorch中文教程：迴歸分析-Regression

• 更多參考: https://morvanzhou.github.io/tutorials/
• 油管頻道: https://www.youtube.com/user/MorvanZhou

依賴軟件包

• torch
• matplotlib

import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
%matplotlib inline

torch.manual_seed(1)    # reproducible

<torch._C.Generator at 0x7f2c68165e90>

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)

plt.scatter(x.data.numpy(), y.data.numpy())
plt.show()

x[:10]

tensor([[-1.0000],
        [-0.9798],
        [-0.9596],
        [-0.9394],
        [-0.9192],
        [-0.8990],
        [-0.8788],
        [-0.8586],
        [-0.8384],
        [-0.8182]])

y[:10]

tensor([[1.1515],
        [1.0159],
        [1.0014],
        [1.0294],
        [0.8508],
        [0.9682],
        [0.8517],
        [0.8880],
        [0.8168],
        [0.7572]])

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

Net(
  (hidden): Linear(in_features=1, out_features=10, bias=True)
  (predict): Linear(in_features=10, out_features=1, bias=True)
)

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(100):
    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 % 10 == 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.show()
        plt.pause(0.1)

plt.ioff()