def main(_):
# If available, resume from the latest checkpoint.
checkpoint_dir = tf.train.latest_checkpoint(FLAGS.logdir)
num_alphabets = 20
task_names = ['Omniglot-%d' % task_id for task_id in range(num_alphabets)]
task_data, num_classes_for_tasks = data.get_data_for_multitask_omniglot_setup(
num_alphabets)
router_kwargs = {}
if FLAGS.method == 'gumbel_matrix':
router_kwargs = {
'probs_init': FLAGS.probs_init,
'temperature_init': FLAGS.temperature_init,
'temperature_min': FLAGS.temperature_min
}
router_fn = utils.get_router_fn_by_name(
num_alphabets, FLAGS.method, **router_kwargs)
training_hparams = tf.contrib.training.HParams(
num_steps=8_000,
batch_size=16,
learning_rate=FLAGS.learning_rate,
router_learning_rate=FLAGS.router_learning_rate
)
pathnet_layers = construct_pathnet(
training_hparams.num_steps, task_names, num_classes_for_tasks, router_fn)
run_omniglot_experiment(
pathnet_layers, training_hparams, task_names, task_data, checkpoint_dir)
def main(_):
mnist, mnist_classes = data.get_mnist()
mnist_2, mnist_classes_2 = data.get_mnist()
mnist_rot, mnist_rot_classes = data.get_rotated_mnist()
mnist_rot_2, mnist_rot_classes_2 = data.get_rotated_mnist()
task_names = ['MNIST', 'MNIST-2', 'MNIST-Rot', 'MNIST-Rot-2']
task_data = [mnist, mnist_2, mnist_rot, mnist_rot_2]
num_classes_for_tasks = [
mnist_classes, mnist_classes_2, mnist_rot_classes, mnist_rot_classes_2
]
num_tasks = len(task_names)
router_fn = utils.get_router_fn_by_name(num_tasks, FLAGS.method)
construct_pathnet_and_run_mnist_experiment(task_names, task_data,
num_classes_for_tasks,
router_fn)
def main(_):
task_names, task_data, num_classes_for_tasks = get_all_tasks(
num_cifar=20, num_mnist=10, num_fashion_mnist=10)
router_kwargs = {}
if FLAGS.method == 'gumbel_matrix':
router_kwargs = {
'probs_init': FLAGS.probs_init,
'temperature_init': FLAGS.temperature_init,
'temperature_min': FLAGS.temperature_min
}
router_fn = utils.get_router_fn_by_name(
len(task_names), FLAGS.method, **router_kwargs)
num_steps = 20000
eval_frequency = 1000
batch_size = 32
intermediate_eval_steps = list(range(0, num_steps + 1, eval_frequency))
training_hparams = tf.contrib.training.HParams(
num_steps=num_steps,
batch_size=batch_size,
learning_rate=FLAGS.learning_rate,
router_learning_rate=FLAGS.router_learning_rate)
pathnet_layers = construct_pathnet(
training_hparams.num_steps, task_names, num_classes_for_tasks, router_fn)
run_routing_experiment(
pathnet_layers,
training_hparams,
task_names,
task_data,
None,
intermediate_eval_steps)
def main(_):
num_alphabets = 20
task_names = ['Omniglot-%d' % task_id for task_id in range(num_alphabets)]
task_data, num_classes = data.get_data_for_multitask_omniglot_setup(
num_alphabets)
batch_size = 16
for task_id in range(num_alphabets):
task_data[task_id] = data.batch_all(task_data[task_id], batch_size)
router_fn = utils.get_router_fn_by_name(num_alphabets, FLAGS.method)
session = tf.Session(graph=tf.get_default_graph())
tf.train.get_or_create_global_step()
summary_writer = tf.contrib.summary.create_file_writer(FLAGS.logdir)
summary_writer.set_as_default()
tf.contrib.summary.initialize(session=session)
p_inputs, p_task_id, out_logits = build_pathnet_eval_graph(
task_names, batch_size, num_classes, router_fn)
evaluate_on = ['train', 'validation', 'test']
p_task_accuracies = {}
accuracy_summary_op = {}
for data_split in evaluate_on:
(p_task_accuracies[data_split], accuracy_summary_op[data_split]) =\
utils.create_accuracy_summary_ops(
task_names, summary_name_prefix='eval_%s' % data_split)
# This `Saver` is not used to save variables, only to restore them from
# the checkpoints.
saver = tf.train.Saver(tf.global_variables())
previous_checkpoint_path = ''
time_waited_for_checkpoints = 0
while time_waited_for_checkpoints < _MAX_WAIT_FOR_NEW_CHECKPOINTS:
latest_checkpoint_path = tf.train.latest_checkpoint(FLAGS.logdir)
if latest_checkpoint_path in [None, previous_checkpoint_path]:
print('Found no new checkpoints')
time_waited_for_checkpoints += _CHECK_FOR_CHECKPOINTS_FREQUENCY
time.sleep(_CHECK_FOR_CHECKPOINTS_FREQUENCY)
continue
else:
time_waited_for_checkpoints = 0
print('Reloading checkpoint: %s' % latest_checkpoint_path)
previous_checkpoint_path = latest_checkpoint_path
saver.restore(session, latest_checkpoint_path)
for data_split in evaluate_on:
eval_data = [
dataset[data_split].make_one_shot_iterator().get_next()
for dataset in task_data
]
print('Evaluating on: %s' % data_split)
task_accuracies = utils.run_pathnet_evaluation(
session, p_inputs, p_task_id, out_logits, task_names, eval_data)
utils.run_accuracy_summary_ops(
session,
p_task_accuracies[data_split],
task_accuracies,
accuracy_summary_op[data_split])