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:
a = session.Session(name=args.name, config=args.config, dataset = d, parameters= params_used, time = args.time, verbose = args.verbose, per_label = args.per_label, vary_together= vary_together)
else:
a = session.Session(name=args.name, config=args.config, dataset = d, parameters= params_used, verbose = args.verbose, per_label = args.per_label, vary_together = vary_together)
#for i in a.configs:
# print(i)
# print()
def _parse_args(self):
# Note : can also pass dataset directly
if isinstance(self.dataset, str):
if self.dataset == 'mnist':
self.dataset = dataset.MNIST(binary=False)
elif self.dataset == 'binary_mnist':
self.dataset = dataset.MNIST(binary=True)
elif self.dataset == 'omniglot':
self.dataset = dataset.Omniglot()
elif self.dataset == 'celeb_a':
pass
elif self.dataset == 'dsprites':
self.dataset = dataset.DSprites()
# selects per_label examples from each class for reduced training data
if self.per_label is not None:
self.dataset.shrink_supervised(self.per_label)
self.dataset_clean = copy(self.dataset)
if self.optimizer_params.get("norm", False):
opt_norm = self.optimizer_params["norm"]
del self.optimizer_params["norm"]
else:
opt_norm = None
self.optimizer = getattr(keras.optimizers,
self.optimizer)(**self.optimizer_params)
#if opt_norm is not None:
# self.optimizer = NormalizedOptimizer(self.optimizer, normalization = opt_norm)
self.lr_callback = False
if isinstance(self.lr, str):
try:
mod = importlib.import_module(str('lr_sched'))
# LR Callback will be True /// self.lr = function of epochs -> lr
self.lr_callback = isinstance(self.lr, str)
self.lr = getattr(mod, self.lr)
except:
try:
mod = importlib.import_module(
str('custom_functions.lr_sched'))
# LR Callback will be True /// self.lr = function of epochs -> lr
self.lr_callback = isinstance(self.lr, str)
self.lr = getattr(mod, self.lr)
except:
#self.lr = dflt.get('lr', .001)
#print()
warnings.warn(
"Cannot find LR Schedule function. Proceeding with default, constant learning rate of 0.001."
)
#print()
# Architecture Args
if self.encoder_dims is None:
try:
self.encoder_dims = self.latent_dims
except Exception as e:
print(e)
raise ValueError
if self.decoder_dims is None:
self.decoder_dims = list(reversed(self.encoder_dims[:-1]))
self.decoder_dims.append(self.dataset.dim)
else:
pass