Source code for pygan.generativemodel.conditional_generative_model

# -*- coding: utf-8 -*-
from pygan.generative_model import GenerativeModel
from abc import abstractmethod

[docs]class ConditionalGenerativeModel(GenerativeModel): ''' Generate samples based on the conditional noise prior. `GenerativeModel` that has a `Conditioner`, where the function of `Conditioner` is a conditional mechanism to use previous knowledge to condition the generations, incorporating information from previous observed data points to itermediate layers of the `Generator`. In this method, this model can "look back" without a recurrent unit as used in RNN or LSTM. This model observes not only random noises but also any other prior information as a previous knowledge and outputs feature points. Dut to the `Conditioner`, this model has the capacity to exploit whatever prior knowledge that is available and can be represented as a matrix or tensor. References: - Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. '''
[docs] @abstractmethod def extract_conditions(self): ''' Extract samples of conditions. Returns: `np.ndarray` of samples. ''' raise NotImplementedError()