PyTorch入门（四）优化器比较

优化器就是在深度学习反向传播过程中，指引损失函数的各个参数往正确的方向更新合适的大小，使得更新后的各个参数让损失函数（目标函数）值不断逼近全局最小。

常见的优化器有：

梯度及梯度下降算法
随机梯度下降算法
标准动量优化算法
AdaGrad算法
RMSProp算法
Adam算法

本文将不会介绍这些算法的细节，我们通过一个例子来比较各优化器的表现。

在PyTorch，实现的优化算法有SGD（随机梯度下降算法）、Adagrad（AdaGrad算法）、RMSprop（RMSprop算法）、Adam（Adam算法），我们将会通过各优化器在优化样例目标函数的最小值来观察它们的表现。以下是Python实现代码：

# -*- coding: utf-8 -*-
# @Time : 2023/3/18 11:01
# @Author : Jclian91
# @File : optimizer_comparsion.py
# @Place : Minghang, Shanghai
# comparison in Five optimizers in PyTorch: SGD, AdaGrad, RMSProp, Adam
import matplotlib.pyplot as plt
import torch
import torch.nn
import torch.utils.data as Data
from torch.utils.data import Dataset
from matplotlib import rcParams
rcParams['font.family'] = 'SimHei'

# prepare data
x = torch.unsqueeze(torch.linspace(-1, 1, 500), dim=1)
y = x.pow(3)

# set parameters
lr = 0.01
batch_size = 15
epoch = 5
torch.manual_seed(1234)


# Dataloader
class MyDataset(Dataset):
    # load the dataset
    def __init__(self, x, y):
        self.X = x
        self.y = y

    # number of rows in the dataset
    def __len__(self):
        return len(self.X)

    # get a row at an index
    def __getitem__(self, idx):
        return [self.X[idx], self.y[idx]]


loader = Data.DataLoader(dataset=MyDataset(x, y),
                         batch_size=batch_size,
                         shuffle=True,
                         num_workers=2)


# create model
class Net(torch.nn.Module):
    def __init__(self, n_input, n_hidden, n_output):
        super(Net, self).__init__()
        self.hidden_layer = torch.nn.Linear(n_input, n_hidden)
        self.output_layer = torch.nn.Linear(n_hidden, n_output)

    def forward(self, input):
        x = torch.relu(self.hidden_layer(input))
        output = self.output_layer(x)
        return output


# train model and plot
def train():
    net_sgd = Net(1, 10, 1)
    net_adagrad = Net(1, 10, 1)
    net_rmsprop = Net(1, 10, 1)
    net_adam = Net(1, 10, 1)
    nets = [net_sgd, net_adagrad, net_rmsprop, net_adam]

    # optimizer
    opt_sgd = torch.optim.SGD(net_sgd.parameters(), lr=lr)
    opt_momentum = torch.optim.Adagrad(net_adagrad.parameters(), lr=lr, lr_decay=0)
    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 function
    loss_func = torch.nn.MSELoss()
    losses = [[], [], [], []]
    for i_epoch in range(epoch):
        for step, (batch_x, batch_y) in enumerate(loader):
            for net, optimizer, loss_list in zip(nets, optimizers, losses):
                pred_y = net(batch_x)
                loss = loss_func(pred_y, batch_y)
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
                loss_list.append(loss.data.numpy())

    plt.figure()
    labels = ['SGD', 'AdaGrad', 'RMSProp', 'Adam']
    for i, loss in enumerate(losses):
        plt.plot(loss, label=labels[i])

    plt.legend(loc='upper right', fontsize=15)
    plt.tick_params(labelsize=13)
    plt.xlabel('训练步骤', size=15)
    plt.ylabel('模型损失', size=15)
    plt.ylim((0, 0.3))
    # plt.show()
    plt.savefig('optimizer_comparison.png')


if __name__ == '__main__':
    train()