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.
'''