if args.params in ['beta', 'betas', 'b']:
params_used = beta_params # mi_params
elif args.params in ['mi', 'mis', 'm', 'constraint', 'constraints']:
params_used = mi_params
elif args.params in ['few_betas', 'small_beta']:
params_used = beta_params
if args.dmax is not None:
params_used["layers.5.layer_kwargs.d_max"] = list(args.dmax)
vary_together = False
#params = {"activation.encoder": ["softplus", "sigmoid"]}
if args.dataset == 'fmnist':
d = dataset.fMNIST()
elif args.dataset == 'binary_mnist':
d = dataset.MNIST(binary= True)
elif args.dataset == 'mnist':
d = dataset.MNIST()
elif args.dataset == 'omniglot':
d = dataset.Omniglot()
elif args.dataset == 'dsprites':
d = dataset.DSprites()
if args.per_label is not None:
d.shrink_supervised(int(args.per_label))
# name is important! = filesave location
if args.time is not None:
parser.add_argument('--beta', type=float)
parser.add_argument('--validate', type=bool, default=1)
parser.add_argument('--verbose', type=bool, default=0)
parser.add_argument('--fit_gen', type=bool, default=1)
parser.add_argument('--fit_tf', type=bool, default=0)
parser.add_argument('--per_label')
parser.add_argument('--dataset', type=str, default='binary_mnist')
args, _ = parser.parse_known_args()
if ".json" in args.config:
config = args.config
else:
config = json.loads(args.config.replace("'", '"'))
if args.dataset == 'fmnist':
d = dataset.fMNIST()
elif args.dataset == 'binary_fmnist':
d = dataset.fMNIST(binary=True)
elif args.dataset == 'binary_mnist':
d = dataset.MNIST(binary=True)
elif args.dataset == 'mnist':
d = dataset.MNIST()
elif args.dataset in ['omniglot', 'omni']:
d = dataset.Omniglot()
elif args.dataset == 'dsprites':
d = dataset.DSprites()
elif args.dataset == "cifar10" or args.dataset == 'cifar':
d = dataset.Cifar10()
if args.per_label is not None:
d.shrink_supervised(args.per_label)