MXNet中怎么進(jìn)行超參數(shù)調(diào)優(yōu)

小億
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2024-03-25 13:13:06

在MXNet中進(jìn)行超參數(shù)調(diào)優(yōu)有多種方法，其中常用的包括Grid Search（網(wǎng)格搜索）、Random Search（隨機(jī)搜索）和Bayesian Optimization（貝葉斯優(yōu)化）等。

  1. Grid Search（網(wǎng)格搜索）：Grid Search 是一種窮舉搜索的方法，通過(guò)定義一個(gè)超參數(shù)的取值范圍，對(duì)每個(gè)超參數(shù)進(jìn)行組合，然后訓(xùn)練模型并評(píng)估性能?？梢酝ㄟ^(guò)MXNet的GridSearch類(lèi)來(lái)實(shí)現(xiàn)。
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)
  1. Random Search（隨機(jī)搜索）：Random Search 是一種隨機(jī)搜索的方法，通過(guò)在指定的超參數(shù)范圍內(nèi)隨機(jī)采樣，然后訓(xùn)練模型并評(píng)估性能?？梢酝ㄟ^(guò)MXNet的RandomSearch類(lèi)來(lái)實(shí)現(xiàn)。
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)
  1. Bayesian Optimization（貝葉斯優(yōu)化）：Bayesian Optimization 是一種基于貝葉斯優(yōu)化的方法，通過(guò)在先前的結(jié)果中選擇最有希望的超參數(shù)進(jìn)行下一次探索?？梢允褂玫谌綆?kù)如BayesOpt進(jìn)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']

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