n_pixels_shuffles = 200 prm.run_name = 'TwoTaskTransfer_shuffled_pixels' + str(n_pixels_shuffles) + '_v2' prm.data_transform = 'Shuffled_Pixels' prm.n_pixels_shuffles = n_pixels_shuffles prm.model_name = 'FcNet3' # freeze_description = 'freeze output layer' # freeze_list = ['fc_out'] # not_freeze_list = None freeze_description = 'freeze all layers except first' not_freeze_list = ['fc1'] freeze_list = None else: raise ValueError('Unrecognized Experiment_Name') create_result_dir(prm) n_experiments = 20 limit_train_samples = 2000 # Define optimizer: prm.optim_func, prm.optim_args = optim.Adam, {'lr': prm.lr} # optim_func, optim_args = optim.SGD, {'lr': prm.lr, 'momentum': 0.9} # Learning rate decay schedule: # lr_schedule = {'decay_factor': 0.1, 'decay_epochs': [10]} prm.lr_schedule = {} # No decay
'loss_type_eval': 'Zero_One', 'val_types': [['train_loss'], ['test_loss'], ['Bound', 'McAllester', 'KL'], ['Bound', 'McAllester', 'W_Sqr'], ['Bound', 'McAllester', 'W_NoSqr']] } prm.run_name = 'temp' run_experiments = True # True/False If false, just analyze the previously saved experiments # ------------------------------------------------------------------------------------------- # Init run # ------------------------------------------------------------------------------------------- prm.data_path = get_data_path() create_result_dir(prm, run_experiments) # ------------------------------------------------------------------------------------------- # Run learning or load results # ------------------------------------------------------------------------------------------- if run_experiments: set_random_seed(prm.seed) # Generate task data set: task_generator = data_gen.Task_Generator(prm) data_loader = task_generator.get_data_loader( prm, limit_train_samples=prm.limit_train_samples) # create prior