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 = 32

EPOCH = 12

 

# 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(-1, 1, 1000), dim=1)  # x data (tensor), shape(100, 1)

# 0.2 * torch.rand(x.size())增加噪点

y = x.pow(2) + 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=0)

 

 

class Net(torch.nn.Module):

    # 初始化

    def __init__(self):

        super(Net, self).__init__()

        self.hidden = torch.nn.Linear(1, 20)

        self.predict = torch.nn.Linear(20, 1)

 

    # 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, 0.2))

plt.show()