Linear_Regression.py

import torch
import matplotlib.pyplot as plt

# 创建数据集
def create_linear_data(nums_data, if_plot=False):
    """
    Create data for linear model
    Args:
        nums_data: how many data points that wanted
    Returns:
        x with shape (nums_data, 1)
    """
    x = torch.linspace(0, 1, nums_data)
    x = torch.unsqueeze(x, dim=1)
    k = 2
    y = k * x + torch.rand(x.size())

    if if_plot:
        plt.scatter(x.numpy(), y.numpy(), c=x.numpy())
        plt.show()
    data = {"x": x, "y": y}
    return data


data = create_linear_data(300, if_plot=True)
print(data["x"].size())


class LinearRegression(torch.nn.Module):
    """
    Linear Regressoin Module, the input features and output
    features are defaults both 1
    """

    def __init__(self):
        super().__init__()
        self.linear = torch.nn.Linear(1, 1)

    def forward(self, x):
        out = self.linear(x)
        return out
if torch.cuda.is_available():
    model = LinearRegression().cuda()
else:
    model = LinearRegression()

linear = LinearRegression()
print(linear)


class Linear_Model():
    def __init__(self):
        """
        Initialize the Linear Model
        """
        self.learning_rate = 0.001
        self.epoches = 100   # 默认是10000
        self.loss_function = torch.nn.MSELoss()
        self.create_model()

    def create_model(self):
        self.model = LinearRegression()
        self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)

    def train(self, data, model_save_path="model.pth"):
        """
        Train the model and save the parameters
        Args:
            model_save_path: saved name of model
            data: (x, y) = data, and y = kx + b
        Returns:
            None
        """
        x = data["x"]
        y = data["y"]
        for epoch in range(self.epoches):
            prediction = self.model(x)
            loss = self.loss_function(prediction, y)

            self.optimizer.zero_grad()
            loss.backward()
            self.optimizer.step()

            if epoch % 500 == 0:
                print("epoch: {}, loss is: {}".format(epoch, loss.item()))
        torch.save(self.model.state_dict(), "linear.pth")

    def test(self, x, model_path="linear.pth"):
        """
        Reload and test the model, plot the prediction
        Args:
            model_path: the model's path and name
            data: (x, y) = data, and y = kx + b
        Returns:
            None
        """
        x = data["x"]
        y = data["y"]
        self.model.load_state_dict(torch.load(model_path))
        prediction = self.model(x)

        plt.scatter(x.numpy(), y.numpy(), c=x.numpy())
        plt.plot(x.numpy(), prediction.detach().numpy(), color="r")
        plt.show()

    def compare_epoches(self, data):
        x = data["x"]
        y = data["y"]

        num_pictures = 16
        fig = plt.figure(figsize=(10, 10))
        current_fig = 0
        for epoch in range(self.epoches):
            prediction = self.model(x)
            loss = self.loss_function(prediction, y)

            self.optimizer.zero_grad()
            loss.backward()
            self.optimizer.step()

            if epoch % (self.epoches / num_pictures) == 0:
                current_fig += 1
                plt.subplot(4, 4, current_fig)
                plt.scatter(x.numpy(), y.numpy(), c=x.numpy())
                plt.plot(x.numpy(), prediction.detach().numpy(), color="r")
        plt.show()


linear = Linear_Model()
data = create_linear_data(100)
linear.train(data)
# linear.test(data)
linear.compare_epoches(data)