)
self.last_t = t
-def save_examplar_vignettes(data_set, nb, name):
+def save_exemplar_vignettes(data_set, nb, name):
n = torch.randperm(data_set.nb_samples).narrow(0, 0, nb)
for k in range(0, nb):
model_filename = model.name + '_pb:' + \
str(problem_number) + '_ns:' + \
- int_to_suffix(args.nb_train_samples) + '.state'
+ int_to_suffix(args.nb_train_samples) + '.pth'
nb_parameters = 0
for p in model.parameters(): nb_parameters += p.numel()
)
if args.nb_exemplar_vignettes > 0:
- save_examplar_vignettes(train_set, args.nb_exemplar_vignettes,
- 'examplar_{:d}.png'.format(problem_number))
+ save_exemplar_vignettes(train_set, args.nb_exemplar_vignettes,
+ 'exemplar_{:d}.png'.format(problem_number))
if args.validation_error_threshold > 0.0:
validation_set = VignetteSet(problem_number,