trw.hparams.params

Module Contents

Classes

HyperParam

DiscreteMapping

Map discrete value to another discrete value

DiscreteValue

Discrete value. This can be useful to select one choice among many

DiscreteIntegrer

Represent an integer hyper-parameter

DiscreteBoolean

Represent a boolean hyper-parameter

ContinuousUniform

Represent a continuous hyper-parameter

ContinuousPower

Represent a continuous power hyper-parameter

HyperParameters

Holds a repository of hyper-parameters
class trw.hparams.params.HyperParam
class trw.hparams.params.DiscreteMapping(list_name_value, current_value)

Bases: HyperParam

Map discrete value to another discrete value

e.g., this can be useful to test activation function as hyper-parameter

set_value(self, value)
get_value(self)
random_value(self)
Returns

a random value

__repr__(self)

Return repr(self).

class trw.hparams.params.DiscreteValue(values, current_value)

Bases: HyperParam

Discrete value. This can be useful to select one choice among many

set_value(self, value)
get_value(self)
random_value(self)
Returns

a random value

__repr__(self)

Return repr(self).

class trw.hparams.params.DiscreteIntegrer(current_value, min_range, max_range)

Bases: HyperParam

Represent an integer hyper-parameter

set_value(self, value)
get_value(self)
random_value(self)
Returns

a random value

__repr__(self)

Return repr(self).

class trw.hparams.params.DiscreteBoolean(current_value)

Bases: HyperParam

Represent a boolean hyper-parameter

set_value(self, value)
get_value(self)
random_value(self)
Returns

a random value

__repr__(self)

Return repr(self).

class trw.hparams.params.ContinuousUniform(current_value, min_range, max_range)

Bases: HyperParam

Represent a continuous hyper-parameter

set_value(self, value)
get_value(self)
random_value(self)
Returns

a random value

__repr__(self)

Return repr(self).

class trw.hparams.params.ContinuousPower(current_value, exponent_min, exponent_max)

Bases: HyperParam

Represent a continuous power hyper-parameter

This type of distribution can be useful to test e.g., learning rate hyper-parameter. Given a random number x generated from uniform interval (min_range, max_range), return 10 ** x

set_value(self, value)
get_value(self)
random_value(self)
__repr__(self)

Return repr(self).

class trw.hparams.params.HyperParameters(hparams=None)

Holds a repository of hyper-parameters

create(self, hparam_name, hparam)

Create an hyper parameter if it is not already present

Parameters

  • hparam_name – the name of the hyper-parameter to create

  • hparam – the hyper-parameter description and value

Returns

the hyper parameter value

generate_random_hparams(self)

Set hyper-parameter to a random value

get_value(self, name)

Return the current value of an hyper-parameter

__str__(self)

Return str(self).

__len__(self)