成都响应式网站开发,沈阳最新公告,策划一个网站,电子商务网站设计的原则和内容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。
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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)})
