Modules
Core
- class hyperpy.core.models(initnorm=<Mock name='mock.initializers.RandomNormal()' id='140024184255760'>, min_layers: int = 1, max_layers: int = 13, min_units: int = 4, max_units: int = 128)
Class to build a model with a given topology
- BuildModelSimply(self) <Mock name='mock.models.Model' id='140024185113744'>
BuildModelSimply Standar model
- Parameters
trial (optuna.Trial) – trial to build the model
- Returns
sequential model
- Return type
keras.models.Model.Sequential
- BuildModel(self) <Mock name='mock.models.Model' id='140024185113744'>
BuildModel Standar model
- Parameters
trial (optuna.Trial) – trial to build the model
- Returns
sequential model
- Return type
keras.models.Model
- class hyperpy.core.optimizers
class to build a model optimizer
- optimizerAdam() <Mock name='mock.optimizers.Adam' id='140024185367504'>
optimizerAdam method to build a model optimizer with Adam
- Parameters
trial (optuna.Trial) – trial to build the model
- Returns
optimizer
- Return type
keras.optimizers.Adam
- optimizerRMSprop() <Mock name='mock.optimizers.RMSprop' id='140024185110672'>
optimizerRMSprop method to build a model optimizer with RMSprop
- Parameters
trial (optuna.Trial) – trial to build the model
- Returns
optimizer
- Return type
keras.optimizers.RMSprop
- optimizerSGD() <Mock name='mock.optimizers.SGD' id='140024185112912'>
optimizerSGD method to build a model optimizer with SGD
- Parameters
trial (optuna.Trial) – trial to build the model
- Returns
optimizer
- Return type
keras.optimizers.SGD
- buildOptimizer() None
buildOptimizer method to build a model optimizer
- Parameters
trial (optuna.Trial) – trial to build the model
- Returns
optimizer
- Return type
keras.optimizers
- class hyperpy.core.trainers(trial, feat_X, Y, verbose: int = 0, model: hyperpy.core.models = <class 'hyperpy.core.models'>, optimizer: hyperpy.core.optimizers = <class 'hyperpy.core.optimizers'>, type: str = 'Build', initnorm=<Mock name='mock.initializers.RandomNormal()' id='140024184255760'>)
trainers class to build a model trainer
- trainer(save: bool = False) None
trainer trainer Method define how to train Neural Network. This works by maximizing the test data set (Exactitud de Validación).
- Parameters
save (bool, optional) – save model, defaults to False
- Returns
model, cv_x, cv_y
- Return type
keras.models, pandas.DataFrame, pandas.Series
- class hyperpy.core.run(feat_X, Y, study_name: str = 'First try', direction: str = 'maximize', n_trials: int = 10)
run class is used to run the experiment.
- objective(trial)
objective function is used to define the objective function.
- Parameters
trial (optuna.trial.Trial) – trial object
- Returns
objective function
- Return type
float
- buildStudy()
buildStudy function is used to build the study.
- Returns
study
- Return type
optuna.study.Study