- log_string('training_model')
- train_model(model, train_input, train_target)
+ need_to_train = True
+
+ if need_to_train:
+
+ log_string('training_model ' + model_filename)
+
+ t = time.time()
+
+ if args.compress_vignettes:
+ train_set = CompressedVignetteSet(problem_number,
+ args.nb_train_batches, args.batch_size,
+ cuda=torch.cuda.is_available())
+ else:
+ train_set = VignetteSet(problem_number,
+ args.nb_train_batches, args.batch_size,
+ cuda=torch.cuda.is_available())
+
+ log_string('data_generation {:0.2f} samples / s'.format(
+ (train_set.nb_samples + test_set.nb_samples) / (time.time() - t))
+ )
+
+ train_model(model, train_set)