PyTorch实现一维线性回归模型

迷南。 2023-06-08 12:00 1阅读 0赞

import numpy as np
    import torch
    import matplotlib.pyplot as plt
    from torch import nn
    from torch.nn import Linear
    from torch.autograd import Variable
    
    x_train = np.array([[3.3],[4.4],[5.5],[6.71],[6.93],[4.168],
                       [9.779],[6.182],[7.59],[2.167],[7.042],
                       [10.791],[5.313],[7.997],[3.11]],dtype=np.float32)
    
    y_train = np.array([[1.7],[2.76],[2.09],[3.19],[1.694],[1.573],
                       [3.336],[2.596],[2.53],[1.221],[2.827],
                       [3.465],[1.65],[2.904],[1.31]],dtype=np.float32)
    
    #将numpy.array转换成Tensor
    x_train = torch.from_numpy(x_train)
    y_train = torch.from_numpy(y_train)
     
    #初始化模型
    #model = nn.Linear(1,1)也可以
    class LinearRegression(nn.Module):
        def __init__(self):
            super(LinearRegression, self).__init__()
            self.linear = Linear(1, 1)
        
        def forward(self, x) :
            out = self.linear(x)
            return out
    
    model = LinearRegression()
        
    print(list(model.parameters())) 
    
    #定义损失和优化函数
    criterion = nn.MSELoss()
    optimizer =torch.optim.SGD(model.parameters(),lr = 1e-3)
    
    #训练模型
    num_epochs = 1000
    for epoch in range(num_epochs):
        inputs = Variable(x_train)
        target = Variable(y_train)
        #forward
        out = model(inputs)
        loss = criterion(out,target)
        #backward
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        if(epoch + 1) % 20 == 0:
            print('Epoch[{}/{}],loss:{:.6f}'.format(epoch+1,num_epochs,loss.item()))
            
    #预测结果
    model.eval() #将模型变成测试模式
    predict = model(Variable(x_train))
    predict = predict.data.numpy()
    plt.plot(x_train.numpy(),y_train.numpy(),'ro',label='Orignal Data')
    plt.plot(x_train.numpy(),predict,label='Fitting Line')
    plt.show()