for problem_number in range(1, 24):
- model_filename = model.name + '_' + \
- str(problem_number) + '_' + \
- str(args.nb_train_batches) + '.param'
-
model = AfrozeShallowNet()
if torch.cuda.is_available():
model.cuda()
+ model_filename = model.name + '_' + \
+ str(problem_number) + '_' + \
+ str(args.nb_train_batches) + '.param'
+
nb_parameters = 0
for p in model.parameters(): nb_parameters += p.numel()
log_string('nb_parameters {:d}'.format(nb_parameters))