Source code for pygan.generative_model

# -*- coding: utf-8 -*-
from abc import ABCMeta, abstractmethod
from pygan.noise_sampler import NoiseSampler

[docs]class GenerativeModel(metaclass=ABCMeta): ''' Sampler which draws samples from the `fake` distribution. ''' # is-a `NoiseSampler`. __noise_sampler = None
[docs] def get_noise_sampler(self): ''' getter ''' return self.__noise_sampler
[docs] def set_noise_sampler(self, value): ''' setter ''' if isinstance(value, NoiseSampler) is False: raise TypeError("The type of `__noise_sampler` must be `NoiseSampler`.") self.__noise_sampler = value
noise_sampler = property(get_noise_sampler, set_noise_sampler)
[docs] @abstractmethod def draw(self): ''' Draws samples from the `fake` distribution. Returns: `np.ndarray` of samples. ''' raise NotImplementedError()
[docs] @abstractmethod def learn(self, grad_arr): ''' Update this Generator by ascending its stochastic gradient. Args: grad_arr: `np.ndarray` of gradients. Returns: `np.ndarray` of delta or gradients. ''' raise NotImplementedError()
[docs] @abstractmethod def switch_inferencing_mode(self, inferencing_mode=True): ''' Set inferencing mode in relation to concrete regularizations. Args: inferencing_mode: Inferencing mode or not. ''' raise NotImplementedError()