Source code for domainlab.models.args_vae

[docs] def add_args2parser_vae(parser): parser.add_argument( "--zd_dim", type=int, default=64, help="diva: size of latent space for domain" ) parser.add_argument( "--zx_dim", type=int, default=0, help="diva: size of latent space for unobserved", ) parser.add_argument( "--zy_dim", type=int, default=64, help="diva, hduva: size of latent space for class", ) # HDUVA parser.add_argument( "--topic_dim", type=int, default=3, help="hduva: number of topics" ) parser.add_argument( "--nname_encoder_x2topic_h", type=str, default=None, help="hduva: network from image to topic distribution", ) parser.add_argument( "--npath_encoder_x2topic_h", type=str, default=None, help="hduva: network from image to topic distribution", ) parser.add_argument( "--nname_encoder_sandwich_x2h4zd", type=str, default=None, help="hduva: network from image and topic to zd", ) parser.add_argument( "--npath_encoder_sandwich_x2h4zd", type=str, default=None, help="hduva: network from image and topic to zd", ) # ERM, ELBO parser.add_argument( "--gamma_y", type=float, default=None, help="diva, hduva: multiplier for y classifier", ) parser.add_argument( "--gamma_d", type=float, default=None, help="diva: multiplier for d classifier from zd", ) # Beta VAE part parser.add_argument( "--beta_t", type=float, default=1.0, help="hduva: multiplier for KL topic" ) parser.add_argument( "--beta_d", type=float, default=1.0, help="diva: multiplier for KL d" ) parser.add_argument( "--beta_x", type=float, default=1.0, help="diva: multiplier for KL x" ) parser.add_argument( "--beta_y", type=float, default=1.0, help="diva, hduva: multiplier for KL y" ) return parser