pytorch进行Optimizer 优化器对比

╰+攻爆jí腚メ 2022-12-30 14:50 237阅读 0赞

pytorch1.0进行Optimizer 优化器对比

import torch
    import torch.utils.data as Data  # Torch 中提供了一种帮助整理数据结构的工具, 叫做 DataLoader, 能用它来包装自己的数据, 进行批训练.
    import torch.nn.functional as F  # 包含激励函数
    import matplotlib.pyplot as plt
    
    LR = 0.01   # 学习率
    BATCH_SIZE = 32
    EPOCH = 12
    
    # 伪数据
    # fake dataset
    x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)
    y = x.pow(2) + 0.1*torch.normal(torch.zeros(*x.size()))
    
    # plot dataset
    plt.scatter(x.numpy(), y.numpy())
    plt.show()
    
    # DataLoader 是 torch 用来包装开发者自己的数据的工具.
    # 将自己的 (numpy array 或其他) 数据形式装换成 Tensor, 然后再放进这个包装器中.
    # 使用 DataLoader 的好处就是他们帮你有效地迭代数据
    
    # 先转换成 torch 能识别的 Dataset
    # put dateset into torch dataset
    torch_dataset = Data.TensorDataset(x, y)
    # 把 dataset 放入 DataLoader
    loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,) # 随机打乱数据 (打乱比较好)
    
    # 每个优化器优化一个神经网络
    
    # 默认的 network 形式
    # default network
    class Net(torch.nn.Module):
        def __init__(self):
            super(Net, self).__init__()
            self.hidden = torch.nn.Linear(1, 20)   # hidden layer
            self.predict = torch.nn.Linear(20, 1)   # 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
    
    # 创建不同的优化器, 用来训练不同的网络. 并创建一个 loss_func 用来计算误差.
    if __name__ == '__main__':
        # different nets
        net_SGD         = Net()
        net_Momentum    = Net()
        net_RMSprop     = Net()
        net_Adam        = Net()
        nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]
    
        # different optimizers
        opt_SGD         = torch.optim.SGD(net_SGD.parameters(), lr=LR)
        opt_Momentum    = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8)
        opt_RMSprop     = torch.optim.RMSprop(net_RMSprop.parameters(), lr=LR, alpha=0.9)
        opt_Adam        = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))
        optimizers = [opt_SGD, opt_Momentum, opt_RMSprop, opt_Adam]
    
        loss_func = torch.nn.MSELoss()
        losses_his = [[], [], [], []]   # record loss
    
        # 训练/出图
        # training
        for epoch in range(EPOCH):
            print('Epoch: ', epoch)
            for step, (b_x, b_y) in enumerate(loader):          # for each training step
                # 对每个优化器, 优化属于他的神经网络
                for net, opt, l_his in zip(nets, optimizers, losses_his):
                    output = net(b_x)                           # get output for every net
                    loss = loss_func(output, b_y)               # compute loss for every net
                    opt.zero_grad()                             # clear gradients for next train
                    loss.backward()                             # backpropagation, compute gradients
                    opt.step()                                  # apply gradients
                    l_his.append(loss.data.numpy())             # loss recoder
    
        labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']
        for i, l_his in enumerate(losses_his):
            plt.plot(l_his, label=labels[i])
        plt.legend(loc='best')
        plt.xlabel('Steps')
        plt.ylabel('Loss')
        plt.ylim((0, 0.2))
        plt.show()