# @Time : 2020/11/17
# @Author : Xiaoxuan Hu
# @Email : huxiaoxuan@ruc.edu.cn
r"""
MaliGAN
################################################
Reference:
Che et al. "Maximum-Likelihood Augmented Discrete Generative Adversarial Networks".
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from textbox.model.abstract_generator import GenerativeAdversarialNet
from textbox.module.Generator.MaliGANGenerator import MaliGANGenerator
from textbox.module.Discriminator.MaliGANDiscriminator import MaliGANDiscriminator
[docs]class MaliGAN(GenerativeAdversarialNet):
r"""MaliGAN is a generative adversarial network using a normalized maximum likelihood optimization.
"""
def __init__(self, config, dataset):
super(MaliGAN, self).__init__(config, dataset)
self.generator = MaliGANGenerator(config, dataset)
self.discriminator = MaliGANDiscriminator(config, dataset)
[docs] def calculate_g_train_loss(self, corpus, epoch_idx):
return self.generator.calculate_loss(corpus)
[docs] def calculate_d_train_loss(self, real_data, fake_data, epoch_idx):
return self.discriminator.calculate_loss(real_data, fake_data)
[docs] def calculate_g_adversarial_loss(self, epoch_idx):
self.discriminator.eval()
loss = self.generator.adversarial_loss(self.discriminator.forward)
self.discriminator.train()
return loss
[docs] def calculate_nll_test(self, corpus, epoch_idx):
return self.generator.calculate_loss(corpus, nll_test=True)
[docs] def generate(self, batch_data, eval_data):
return self.generator.generate(batch_data, eval_data)
[docs] def sample(self, sample_num):
samples = self.generator.sample(sample_num)
return samples