MXNet中怎么进行超参数调优

在MXNet中进行超参数调优有多种方法，其中常用的包括Grid Search（网格搜索）、Random Search（随机搜索）和Bayesian Optimization（贝叶斯优化）等。

1. Grid Search（网格搜索）：Grid Search 是一种穷举搜索的方法，通过定义一个超参数的取值范围，对每个超参数进行组合，然后训练模型并评估性能。可以通过MXNet的GridSearch类来实现。

from mxnet.gluon import nn
from mxnet.gluon.data.vision import transforms
from mxnet import autograd, gluon, init, nd
from mxnet.gluon import data as gdata, loss as gloss
from mxnet import metric as mtr
import mxnet as mx
import random
import time
import sys

grid_search = GridSearch({
    'learning_rate': [0.01, 0.1, 0.5],
    'momentum': [0.9, 0.95, 0.99],
    'batch_size': [32, 64, 128]
})

for params in grid_search:
    net = nn.Sequential()
    net.add(nn.Dense(128, activation='relu'),
            nn.Dense(64, activation='relu'),
            nn.Dense(10))
    net.initialize(init=init.Xavier())
    trainer = gluon.Trainer(net.collect_params(), 'sgd',
                            {'learning_rate': params['learning_rate'],
                            'momentum': params['momentum']})
    train(net, train_iter, test_iter, batch_size=params['batch_size'],
          trainer=trainer, num_epochs=num_epochs)

2. Random Search（随机搜索）：Random Search 是一种随机搜索的方法，通过在指定的超参数范围内随机采样，然后训练模型并评估性能。可以通过MXNet的RandomSearch类来实现。

from mxnet.gluon.contrib.model_zoo import get_model
from mxnet.gluon.data import vision
from mxnet.gluon.data.vision import transforms

random_search = RandomSearch({
    'learning_rate': (0.001, 0.1),
    'momentum': (0.5, 0.99),
    'batch_size': (32, 128)
})

for params in random_search:
    net = get_model('resnet18_v1', classes=10)
    net.initialize(init=init.Xavier())
    trainer = gluon.Trainer(net.collect_params(), 'sgd',
                            {'learning_rate': params['learning_rate'],
                            'momentum': params['momentum']})
    train(net, train_iter, test_iter, batch_size=params['batch_size'],
          trainer=trainer, num_epochs=num_epochs)

3. Bayesian Optimization（贝叶斯优化）：Bayesian Optimization 是一种基于贝叶斯优化的方法，通过在先前的结果中选择最有希望的超参数进行下一次探索。可以使用第三方库如BayesOpt进行Bayesian Optimization。

from bayes_opt import BayesianOptimization

def train_net(learning_rate, momentum, batch_size):
    net = nn.Sequential()
    net.add(nn.Dense(128, activation='relu'),
            nn.Dense(64, activation='relu'),
            nn.Dense(10))
    net.initialize(init=init.Xavier())
    trainer = gluon.Trainer(net.collect_params(), 'sgd',
                            {'learning_rate': learning_rate, 'momentum': momentum})
    train(net, train_iter, test_iter, batch_size=batch_size,
          trainer=trainer, num_epochs=num_epochs)
    return accuracy

optimizer = BayesianOptimization(
    f=train_net,
    pbounds={'learning_rate': (0.001, 0.1),
             'momentum': (0.5, 0.99),
             'batch_size': (32, 128)}
)

optimizer.maximize(init_points=5, n_iter=10)
best_params = optimizer.max['params']