Classes

Class models

The class models buils a model from a set of parameters.

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

The fact, all parameters for build model are (default):

initnorm=keras.initializers.RandomNormal(mean=0.0, stddev=0.05, seed=1),

min_layers:int=1,

max_layers:int=13,

min_units:int=4,

max_units:int=128

and at the moment we can manipulate the model with the following methods:

hyperpy.core.models.BuildModelSimply(trial: <Mock name='mock.Trial' id='140024184256400'>, 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

hyperpy.core.models.BuildModel(trial: <Mock name='mock.Trial' id='140024184256400'>, 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

The difference between th two methods is the first use the same activation function for all layers, the second use different activations funcion for each layer.

Class optimizers

The class optimizers build optimizers for the 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

At the moment, we can select between:

hyperpy.core.optimizers.optimizerAdam(trial: <Mock name='mock.Trial' id='140024184256400'>) → <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

hyperpy.core.optimizers.optimizerRMSprop(trial: <Mock name='mock.Trial' id='140024184256400'>) → <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

hyperpy.core.optimizers.optimizerSGD(trial: <Mock name='mock.Trial' id='140024184256400'>) → <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

And if we want that the model is trained with several optimizers, we can use the method:

hyperpy.core.optimizers.buildOptimizer(trial: <Mock name='mock.Trial' id='140024184256400'>) → None

buildOptimizer method to build a model optimizer

Parameters: trial (optuna.Trial) – trial to build the model
Returns: optimizer
Return type: keras.optimizers

Class trainers

The class trainers build trainers for the model.

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

The final idea, is to select by several type of trainers. By the way, at moment have onle one trainer:

hyperpy.core.trainers.trainer(self, 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 run

To run a study, you could call hy.run(feat_X, Y) function:

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

hyperpy.core.run.buildStudy(self)

buildStudy function is used to build the study.

Returns: study
Return type: optuna.study.Study

hyperpy.core.run.objective(self, trial)

objective function is used to define the objective function.

Parameters: trial (optuna.trial.Trial) – trial object
Returns: objective function
Return type: float

Class results

To read results from a study, you could call hy.results(study) function:

class hyperpy.core.results

results class is used to get the results of the study.

results()

results function is used to get the results of the study.

Parameters: study (optuna.study.Study) – study object
Returns: results
Return type: pandas.DataFrame

hyperpy.core.results.results(study)

results function is used to get the results of the study.

Parameters: study (optuna.study.Study) – study object
Returns: results
Return type: pandas.DataFrame