莫烦pytorch学习笔记（七）——Optimizer优化器

各种优化器的比较

莫烦的对各种优化通俗理解的视频

 import torch

 import torch.utils.data as Data

 import torch.nn.functional as F

 from torch.autograd import Variable

 import matplotlib.pyplot as plt

 # 超参数

 LR = 0.01

 BATCH_SIZE = 

 EPOCH = 

 # 生成假数据

 # torch.unsqueeze() 的作用是将一维变二维，torch只能处理二维的数据

 x = torch.unsqueeze(torch.linspace(-, , ), dim=)  # x data (tensor), shape(, )

 # 0.2 * torch.rand(x.size())增加噪点

 y = x.pow() + 0.1 * torch.normal(torch.zeros(*x.size()))

 # 输出数据图

 # plt.scatter(x.numpy(), y.numpy())

 # plt.show()

 torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)

 loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=)

 class Net(torch.nn.Module):

     # 初始化

     def __init__(self):

         super(Net, self).__init__()

         self.hidden = torch.nn.Linear(, )

         self.predict = torch.nn.Linear(, )

     # 前向传递

     def forward(self, x):

         x = F.relu(self.hidden(x))

         x = self.predict(x)

         return x

 net_SGD = Net()

 net_Momentum = Net()

 net_RMSProp = Net()

 net_Adam = Net()

 nets = [net_SGD, net_Momentum, net_RMSProp, net_Adam]

 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()

 loss_his = [[], [], [], []]  # 记录损失

 for epoch in range(EPOCH):

     print(epoch)

     for step, (batch_x, batch_y) in enumerate(loader):

         b_x = Variable(batch_x)

         b_y = Variable(batch_y)

         for net, opt,l_his in zip(nets, optimizers, loss_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(loss_his):

     plt.plot(l_his, label=labels[i])

 plt.legend(loc='best')

 plt.xlabel('Steps')

 plt.ylabel('Loss')

 plt.ylim((, 0.2))

 plt.show()