def __init__(self, opt): super(LeakGANInstructor, self).__init__(opt) # generator, discriminator self.gen = LeakGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim, cfg.vocab_size, cfg.max_seq_len, cfg.padding_idx, cfg.goal_size, cfg.step_size, cfg.CUDA) self.dis = LeakGAN_D(cfg.dis_embed_dim, cfg.vocab_size, cfg.padding_idx, gpu=cfg.CUDA) self.init_model() # optimizer mana_params, work_params = self.gen.split_params() mana_opt = optim.Adam(mana_params, lr=cfg.gen_lr) work_opt = optim.Adam(work_params, lr=cfg.gen_lr) self.gen_opt = [mana_opt, work_opt] self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr) # Criterion self.mle_criterion = nn.NLLLoss() self.dis_criterion = nn.CrossEntropyLoss() # DataLoader self.gen_data = GenDataIter( self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis))
def __init__(self, opt): super(LeakGANInstructor, self).__init__(opt) # generator, discriminator self.gen = LeakGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim, cfg.vocab_size, cfg.max_seq_len, cfg.padding_idx, cfg.goal_size, cfg.step_size, cfg.CUDA) self.dis = LeakGAN_D(cfg.dis_embed_dim, cfg.vocab_size, cfg.padding_idx, gpu=cfg.CUDA) self.init_model() # optimizer mana_params, work_params = self.gen.split_params() mana_opt = optim.Adam(mana_params, lr=cfg.gen_lr) work_opt = optim.Adam(work_params, lr=cfg.gen_lr) self.gen_opt = [mana_opt, work_opt] self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr) # Criterion self.mle_criterion = nn.NLLLoss() self.dis_criterion = nn.BCEWithLogitsLoss() # DataLoader self.gen_data = GenDataIter(self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis)) self.dis_data = DisDataIter(self.gen_data.random_batch()['target'], self.train_data.random_batch()['target']) # Metrics self.bleu = BLEU(test_text=tensor_to_tokens(self.gen_data.target, self.index_word_dict), real_text=tensor_to_tokens(self.test_data.target, self.test_data.index_word_dict), gram=3) self.self_bleu = BLEU(test_text=tensor_to_tokens(self.gen_data.target, self.index_word_dict), real_text=tensor_to_tokens(self.gen_data.target, self.index_word_dict), gram=3)
def __init__(self, opt): super(LeakGANInstructor, self).__init__(opt) # generator, discriminator self.gen = LeakGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim, cfg.vocab_size, cfg.max_seq_len, cfg.padding_idx, cfg.goal_size, cfg.step_size, cfg.CUDA) self.dis = LeakGAN_D(cfg.dis_embed_dim, cfg.vocab_size, cfg.padding_idx, gpu=cfg.CUDA) self.init_model() # optimizer mana_params, work_params = self.gen.split_params() mana_opt = optim.Adam(mana_params, lr=cfg.gen_lr) work_opt = optim.Adam(work_params, lr=cfg.gen_lr) self.gen_opt = [mana_opt, work_opt] self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)