网站建设项目详情,企业免费网站制作比较好的,品牌建设网站公司排名,上海人才招聘官网2022优化超参数始终是确保模型性能最佳的关键任务。通常#xff0c;网格搜索、随机搜索和贝叶斯优化等技术是主要使用的方法。
今天分享几个常用于模型超参数优化的 Python 工具包#xff0c;如下所示#xff1a;
scikit-learn#xff1a;使用在指定参数值上进行的网格搜索或…优化超参数始终是确保模型性能最佳的关键任务。通常网格搜索、随机搜索和贝叶斯优化等技术是主要使用的方法。
今天分享几个常用于模型超参数优化的 Python 工具包如下所示
scikit-learn使用在指定参数值上进行的网格搜索或随机搜索。HyperparameterHunter构建在scikit-learn之上以使其更易于使用。Optuna使用随机搜索、Parzen估计器TPE和基于群体的训练。Hyperopt使用随机搜索和TPE。Talos构建在Keras之上以使其更易于使用。
技术交流
技术要学会分享、交流不建议闭门造车。一个人可以走的很快、一堆人可以走的更远。
本文由粉丝群小伙伴总结与分享如果你也想学习交流资料获取均可加交流群获取群友已超过2000人添加时最好的备注方式为来源兴趣方向方便找到志同道合的朋友。 方式①、添加微信号dkl88194备注来自CSDN 加群 方式②、微信搜索公众号Python学习与数据挖掘后台回复加群 现在让我们看一些使用这些库进行自动编码器模型超参数优化的Python代码示例
from keras.layers import Input, Dense
from keras.models import Model# define the Autoencoder
input_layer Input(shape(784,))
encoded Dense(32, activationrelu)(input_layer)
decoded Dense(784, activationsigmoid)(encoded)
autoencoder Model(input_layer, decoded)
autoencoder.compile(optimizeradam, lossbinary_crossentropy)
autoencoder.fit(X_train, X_train, epochs100, batch_size256, validation_data(X_test, X_test))scikit-learn
from sklearn.model_selection import GridSearchCV# define the parameter values that should be searched
param_grid {batch_size: [64, 128, 256], epochs: [50, 100, 150]}# create a KFold cross-validator
kfold KFold(n_splits10, random_state7)# create the grid search object
grid GridSearchCV(estimatorautoencoder, param_gridparam_grid, cvkfold)# fit the grid search object to the training data
grid_result grid.fit(X_train, X_train)# print the best parameters and the corresponding score
print(fBest parameters: {grid_result.best_params_})
print(fBest score: {grid_result.best_score_})HyperparameterHunter
import HyperparameterHunter as hh# create a HyperparameterHunter object
hunter hh.HyperparameterHunter(input_dataX_train, output_dataX_train, model_wrapperhh.ModelWrapper(autoencoder))# define the hyperparameter search space
hunter.setup(objectiveval_loss, metricval_loss, optimization_modeminimize, max_trials100)
hunter.add_experiment(parametershh.Real(0.1, 1, namelearning_rate, digits3, rounding4))
hunter.add_experiment(parametershh.Real(0.1, 1, namedecay, digits3, rounding4))# perform the hyperparameter search
hunter.hunt(n_jobs1, gpu_id0)# print the best hyperparameters and the corresponding score
print(fBest hyperparameters: {hunter.best_params})
print(fBest score: {hunter.best_score})Hyperopt
from hyperopt import fmin, tpe, hp# define the parameter space
param_space {batch_size: hp.quniform(batch_size, 64, 256, 1), epochs: hp.quniform(epochs, 50, 150, 1)}# define the objective function
def objective(params):autoencoder.compile(optimizeradam, lossbinary_crossentropy)autoencoder.fit(X_train, X_train, batch_sizeparams[batch_size], epochsparams[epochs], verbose0)scores autoencoder.evaluate(X_test, X_test, verbose0)return {loss: scores, status: STATUS_OK}# perform the optimization
best fmin(objective, param_space, algotpe.suggest, max_evals100)# print the best parameters and the corresponding score
print(fBest parameters: {best})
print(fBest score: {objective(best)})Optuna
import optuna# define the objective function
def objective(trial):batch_size trial.suggest_int(batch_size, 64, 256)epochs trial.suggest_int(epochs, 50, 150)autoencoder.compile(optimizeradam, lossbinary_crossentropy)autoencoder.fit(X_train, X_train, batch_sizebatch_size, epochsepochs, verbose0)score autoencoder.evaluate(X_test, X_test, verbose0)return score# create the Optuna study
study optuna.create_study()# optimize the hyperparameters
study.optimize(objective, n_trials100)# print the best parameters and the corresponding score
print(fBest parameters: {study.best_params})
print(fBest score: {study.best_value})Talos
import talos# define the parameter space
param_space {learning_rate: [0.1, 0.01, 0.001], decay: [0.1, 0.01, 0.001]}# define the objective function
def objective(params):autoencoder.compile(optimizeradam, lossbinary_crossentropy, lrparams[learning_rate], decayparams[decay])history autoencoder.fit(X_train, X_train, epochs100, batch_size256, validation_data(X_test, X_test), verbose0)score history.history[val_loss][-1]return score# perform the optimization
best talos.Scan(X_train, X_train, paramsparam_space, modelautoencoder, experiment_nameautoencoder).best_params(objective, n_trials100)# print the best parameters and the corresponding score
print(fBest parameters: {best})
print(fBest score: {objective(best)})
