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
