优化器的性能比较
代码:
import torch import torch.utils.data as Data import torch.nn.functional as F import matplotlib.pyplot as plt from torch.autograd import Variable import numpy as np torch.manual_seed(1) # reproducible #hyper param LR = 0.01 BATCH_SIZE = 32 EPOCH = 12 # 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() # 使用上节内容提到的 data loader torch_dataset = Data.TensorDataset(x, y) loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,) #神经网络 class Net(torch.nn.Module): # 继承 torch 的 Module def __init__(self, n_feature, n_hidden, n_output): super(Net, self).__init__() # 继承 __init__ 功能 self.hidden = torch.nn.Linear(n_feature, n_hidden) # 隐藏层线性输出 self.out = torch.nn.Linear(n_hidden, n_output) # 输出层线性输出 def forward(self, x): # 正向传播输入值, 神经网络分析出输出值 x = F.relu(self.hidden(x)) # 激励函数(隐藏层的线性值) x = self.out(x) # 输出值, 但是这个不是预测值, 预测值还需要再另外计算 return x # 为每个优化器创建一个 net net_SGD= Net(1,10,1) net_Momentum= Net(1,10,1) net_RMSprop= Net(1,10,1) net_Adam= Net(1,10,1) 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 = [[], [], [], []] # 记录 training 时不同神经网络的 loss if __name__ == '__main__': for epoch in range(EPOCH): print('Epoch: ', epoch) for step, (batch_x, batch_y) in enumerate(loader): # 对每个优化器, 优化属于他的神经网络 b_x=Variable(batch_x) b_y=Variable(batch_y) 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','Momentun','RMSprop','Adam'] for i in range(len(losses_his)): plt.plot(np.arange(len(losses_his[i])), losses_his[i], label=labels[i]) plt.legend(loc='best') plt.xlabel('Steps') plt.ylabel('Loss') plt.show()
性能比较图如下:
结论:RMSprop与Adam优化性能较好,SGD与Momentun较差