【pytorch】优化器

摘自here
优化器的参数有待进一步了解

import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
import torch.utils.data as Data

# super param
LR = 0.01
BATCH_SIZE=32
EPOCH=12

x = torch.unsqueeze(torch.linspace(-1,1,1000),dim = 1)  #压缩为2维，因为torch 中 只会处理二维的数据
y = x.pow(2) + 0.2 * torch.rand(x.size())
print(x.numpy(),y.numpy())

torch_dataset = Data.TensorDataset(x, y)

loader = Data.DataLoader(
    dataset = torch_dataset,
    batch_size = BATCH_SIZE,
    shuffle = True,# true表示数据每次epoch是是打乱顺序抽样的
    num_workers = 2, # 每次训练有两个线程进行的？？？？？ 改成 1 和 2 暂时没看出区别
)

class Net(torch.nn.Module):  # 继承 torch 的 Module
    def __init__(self):
        super(Net, self).__init__()     # 继承 __init__ 功能
        # 定义每层用什么样的形式
        self.hidden = torch.nn.Linear(1,20)   # 隐藏层线性输出
        self.predict = torch.nn.Linear(20,1)   # 输出层线性输出

    def forward(self, x):   # 这同时也是 Module 中的 forward 功能
        # 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.predict(x)             # 输出值
        return x

net_SGD = Net()
net_Momentum = Net()
net_RMSProp = Net()
net_Adam= Net()
nets = [net_SGD,net_Momentum,net_RMSProp,net_Adam] # 一个比一个高级

opt_SGD = torch.optim.SGD(net_SGD.parameters(),lr=LR)
opt_Momentum = torch.optim.SGD(net_Momentum.parameters(),lr = LR,momentum=0.8) # 是SGD的改进，加了动量效果
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]
# 比较这4个优化器会发现，并不一定越高级的效率越高，需要自己找适合自己数据的优化器

loss_func = torch.nn.MSELoss()
losses_his = [[],[],[],[]]

if __name__ == '__main__': # EPOCH + win10 需要if main函数才能正确运行，
    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, losses_his):
                output = net(b_x)  # get_out for every net
                loss = loss_func(output,b_y)  # compute loss for every net
                opt.zero_grad()
                loss.backward()
                opt.step() # apply gradient
                l_his.append(loss.data[0]) # 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()