Source code for pixyz.models.gan

from torch import optim

from ..models.model import Model
from ..losses import AdversarialJensenShannon
from ..distributions import DataDistribution


[docs]class GAN(Model): """ Generative Adversarial Network (Adversarial) Jensen-Shannon divergence between given distributions (p_data, p) is set as the loss class of this model. """
[docs] def __init__(self, p, discriminator, optimizer=optim.Adam, optimizer_params={}, d_optimizer=optim.Adam, d_optimizer_params={}, clip_grad_norm=None, clip_grad_value=None): """ Parameters ---------- p : torch.distributions.Distribution Generative model (generator). discriminator : torch.distributions.Distribution Critic (discriminator). optimizer : torch.optim Optimization algorithm. optimizer_params : dict Parameters of optimizer clip_grad_norm : float or int Maximum allowed norm of the gradients. clip_grad_value : float or int Maximum allowed value of the gradients. """ # set distributions (for training) distributions = [p] p_data = DataDistribution(p.var) # set losses loss = AdversarialJensenShannon(p_data, p, discriminator, optimizer=d_optimizer, optimizer_params=d_optimizer_params) super().__init__(loss, test_loss=loss, distributions=distributions, optimizer=optimizer, optimizer_params=optimizer_params, clip_grad_norm=clip_grad_norm, clip_grad_value=clip_grad_value)
[docs] def train(self, train_x_dict={}, adversarial_loss=True, **kwargs): """Train the model. Parameters ---------- train_x_dict : dict, defaults to {} Input data. adversarial_loss : bool, defaults to True Whether to train the discriminator. **kwargs Returns ------- loss : torch.Tensor Train loss value. d_loss : torch.Tensor Train loss value of the discriminator (if :attr:`adversarial_loss` is True). """ if adversarial_loss: d_loss = self.loss_cls.train(train_x_dict, **kwargs) loss = super().train(train_x_dict, **kwargs) if adversarial_loss: return loss, d_loss return loss
[docs] def test(self, test_x_dict={}, adversarial_loss=True, **kwargs): """Train the model. Parameters ---------- test_x_dict : dict, defaults to {} Input data. adversarial_loss : bool, defaults to True Whether to return the discriminator loss. **kwargs Returns ------- loss : torch.Tensor Test loss value. d_loss : torch.Tensor Test loss value of the discriminator (if :attr:`adversarial_loss` is True). """ loss = super().test(test_x_dict, **kwargs) if adversarial_loss: d_loss = self.loss_cls.test(test_x_dict, **kwargs) return loss, d_loss return loss