# Run learning of task 1
write_to_log('--- Standard learning of task #1', prm)
test_err_orig[i], transferred_model = learn_single_standard.run_learning(task1_data, prm)
# Generate the task 2 data set:
write_to_log('--- Generating task #2 with at most {} samples'.format(limit_train_samples), prm)
task2_data = task_generator.get_data_loader(prm, limit_train_samples = limit_train_samples)
# Run learning of task 2 from scratch:
write_to_log('--- Standard learning of task #2 from scratch', prm)
test_err_scratch[i], _ = learn_single_standard.run_learning(task2_data, prm, verbose=0)
# Run Bayesian-learning of task 2 from scratch:
write_to_log('---- Bayesian learning of task #2 from scratch', prm)
_, test_err_scratch_bayes[i], _, _ = learn_single_Bayes.run_learning(task2_data, prm, verbose=0)
# Run learning of task 2 using transferred initial point:
write_to_log('--- Standard learning of task #2 using transferred weights as initial point', prm)
test_err_transfer[i], _ = learn_single_standard.run_learning(task2_data, prm, initial_model=transferred_model, verbose=0)
# Run learning of task 2 using transferred initial point + freeze some layers:
write_to_log('--- Standard learning of task #2 using transferred weights as initial point + ' + freeze_description, prm_freeze)
test_err_freeze[i], _ = learn_single_standard.run_learning(task2_data, prm_freeze, initial_model=transferred_model, verbose=0)
#
# # Run learning of task 2 from scratch + weight regularization:
# write_to_log('--- Standard learning of task #2 from scratch', prm_reg)
# test_err_scratch_reg[i], _ = learn_single_standard.run_learning(task2_data, prm_reg, verbose=0)
# -------------------------------------------------------------------------------------------
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
prior_model = get_model(prm)
set_model_values(prior_model, prm.prior_mean, prm.prior_log_var)
# Learn a posterior which minimizes some bound with some loss function
post_model, test_err, test_loss, log_mat = learn_single_Bayes.run_learning(
data_loader, prm, prior_model, init_from_prior=prm.init_from_prior)
save_run_data(
prm, {
'test_err': test_err,
'test_loss': test_loss,
'log_mat': log_mat,
'post_model': post_model
})
else:
loaded_prm, loaded_dict = load_saved_vars(prm.result_dir)
prm = loaded_prm
# get learned posterior and results
test_err, test_loss, log_mat, post_model = loaded_dict.values()
set_random_seed(prm.seed)
# Number of Monte-Carlo iterations (for re-parametrization trick):
prm.n_MC = 1
# prm.use_randomness_schedeule = True # False / True
# prm.randomness_init_epoch = 0
# prm.randomness_full_epoch = 500000000
# Define optimizer:
prm.optim_func, prm.optim_args = optim.Adam, {'lr': prm.lr}
# prm.optim_func, prm.optim_args = optim.SGD, {'lr': prm.lr, 'momentum': 0.9}
# Learning rate decay schedule:
# prm.lr_schedule = {'decay_factor': 0.1, 'decay_epochs': [10, 30]}
prm.lr_schedule = {} # No decay
# Test type:
prm.test_type = 'MaxPosterior' # 'MaxPosterior' / 'MajorityVote'
# 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)
# -------------------------------------------------------------------------------------------
# Run learning
# -------------------------------------------------------------------------------------------
test_err, _ = learn_single_Bayes.run_learning(data_loader, prm)
save_run_data(prm, {'test_err': test_err})
prm.n_MC = 1
# prm.use_randomness_schedeule = True # False / True
# prm.randomness_init_epoch = 0
# prm.randomness_full_epoch = 500000000
# Define optimizer:
prm.optim_func, prm.optim_args = optim.Adam, {'lr': prm.lr}
# prm.optim_func, prm.optim_args = optim.SGD, {'lr': prm.lr, 'momentum': 0.9}
# Learning rate decay schedule:
# prm.lr_schedule = {'decay_factor': 0.1, 'decay_epochs': [10, 30]}
prm.lr_schedule = {} # No decay
# Test type:
prm.test_type = 'MaxPosterior' # 'MaxPosterior' / 'MajorityVote'
# 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)
# -------------------------------------------------------------------------------------------
# Run learning
# -------------------------------------------------------------------------------------------
post_model, test_err, test_loss, log_mat = learn_single_Bayes.run_learning(
data_loader, prm)
save_run_data(prm, {'test_err': test_err, 'test_loss': test_loss})