def setUp(self):
super(LEOTest, self).setUp()
self._problem = _random_problem_instance(5, 7, 4)
# This doesn"t call any function, so doesn't need the mocks to be started.
self._config = get_test_config()
self._leo = model.LEO(config=self._config)
self.addCleanup(mock.patch.stopall)
def construct_graph(outer_model_config):
"""Constructs the optimization graph."""
inner_model_config = config.get_inner_model_config()
tf.logging.info("inner_model_config: {}".format(inner_model_config))
leo = model.LEO(inner_model_config, use_64bits_dtype=False)
num_classes = outer_model_config["num_classes"]
num_tr_examples_per_class = outer_model_config["num_tr_examples_per_class"]
metatrain_batch = _construct_examples_batch(
outer_model_config["metatrain_batch_size"], "train", num_classes,
num_tr_examples_per_class,
outer_model_config["num_val_examples_per_class"])
metatrain_loss, metatrain_accuracy = _construct_loss_and_accuracy(
leo, metatrain_batch, True)
metatrain_gradients, metatrain_variables = leo.grads_and_vars(
metatrain_loss)
# Avoids NaNs in summaries.
metatrain_loss = tf.cond(tf.is_nan(metatrain_loss),
lambda: tf.zeros_like(metatrain_loss),
lambda: metatrain_loss)
metatrain_gradients = _clip_gradients(
metatrain_gradients, outer_model_config["gradient_threshold"],
outer_model_config["gradient_norm_threshold"])
_construct_training_summaries(metatrain_loss, metatrain_accuracy,
metatrain_gradients, metatrain_variables)
optimizer = tf.train.AdamOptimizer(
learning_rate=outer_model_config["outer_lr"])
global_step = tf.train.get_or_create_global_step()
train_op = optimizer.apply_gradients(
list(zip(metatrain_gradients, metatrain_variables)), global_step)
data_config = config.get_data_config()
tf.logging.info("data_config: {}".format(data_config))
total_examples_per_class = data_config["total_examples_per_class"]
metavalid_batch = _construct_examples_batch(
outer_model_config["metavalid_batch_size"], "val", num_classes,
num_tr_examples_per_class,
total_examples_per_class - num_tr_examples_per_class)
metavalid_loss, metavalid_accuracy = _construct_loss_and_accuracy(
leo, metavalid_batch, False)
metatest_batch = _construct_examples_batch(
outer_model_config["metatest_batch_size"], "test", num_classes,
num_tr_examples_per_class,
total_examples_per_class - num_tr_examples_per_class)
_, metatest_accuracy = _construct_loss_and_accuracy(
leo, metatest_batch, False)
_construct_validation_summaries(metavalid_loss, metavalid_accuracy)
return (train_op, global_step, metatrain_accuracy, metavalid_accuracy,
metatest_accuracy)
def construct_graph(outer_model_config):
"""Constructs the optimization graph."""
inner_model_config = config.get_inner_model_config()
tf.logging.info("inner_model_config: {}".format(inner_model_config))
leo = model.LEO(inner_model_config, use_64bits_dtype=False)
num_classes = outer_model_config["num_classes"]
num_tr_examples_per_class = outer_model_config["num_tr_examples_per_class"]
metatrain_batch = _construct_examples_batch(
outer_model_config["metatrain_batch_size"], "train", num_classes,
num_tr_examples_per_class,
outer_model_config["num_val_examples_per_class"])
metatrain_loss, metatrain_accuracy, metatrain_generalization_loss, \
kl_components, adapted_kl_components, kl_zn, adapted_kl_zn, kl, adapted_kl, \
latents, adapted_latents, spurious = _construct_loss_and_accuracy(
leo, metatrain_batch, True) #returned by the inner_leo_loop
metatrain_gradients, metatrain_variables = leo.grads_and_vars(metatrain_loss)
# Avoids NaNs in summaries.
metatrain_loss = tf.cond(tf.is_nan(metatrain_loss),
lambda: tf.zeros_like(metatrain_loss),
lambda: metatrain_loss)
# adapted_kl_components = tf.cond(tf.is_nan(adapted_kl_components),
# lambda: tf.zeros_like(adapted_kl_components),
# lambda: adapted_kl_components)
#
# adapted_kl_zn = tf.cond(tf.is_nan(adapted_kl_zn),
# lambda: tf.zeros_like(adapted_kl_zn),
# lambda: adapted_kl_zn)
# adapted_kl = tf.cond(tf.is_nan(adapted_kl),
# lambda: tf.zeros_like(adapted_kl),
# lambda: adapted_kl)
# kl = tf.cond(tf.is_nan(kl),
# lambda: tf.zeros_like(kl),
# lambda: kl)
metatrain_gradients = _clip_gradients(
metatrain_gradients, outer_model_config["gradient_threshold"],
outer_model_config["gradient_norm_threshold"])
_construct_training_summaries(metatrain_loss, metatrain_accuracy,
metatrain_gradients, metatrain_variables, metatrain_generalization_loss,
kl_components, adapted_kl_components, kl_zn, adapted_kl_zn, kl, adapted_kl, latents, adapted_latents, spurious)
optimizer = tf.train.AdamOptimizer(
learning_rate=outer_model_config["outer_lr"])
global_step = tf.train.get_or_create_global_step()
train_op = optimizer.apply_gradients(
list(zip(metatrain_gradients, metatrain_variables)), global_step)
#after applying the gradients, compute the meta-validation loss using the same algorithm
data_config = config.get_data_config()
tf.logging.info("data_config: {}".format(data_config))
total_examples_per_class = data_config["total_examples_per_class"]
metavalid_batch = _construct_examples_batch(
outer_model_config["metavalid_batch_size"], "val", num_classes,
num_tr_examples_per_class,
total_examples_per_class - num_tr_examples_per_class)
metavalid_loss, metavalid_accuracy, metavalid_generalization_loss, _, _, _, _, _, _, _, _, _ = _construct_loss_and_accuracy(
leo, metavalid_batch, False)
metatest_batch = _construct_examples_batch(
outer_model_config["metatest_batch_size"], "test", num_classes,
num_tr_examples_per_class,
total_examples_per_class - num_tr_examples_per_class)
_, metatest_accuracy, _, _, _, _, _, _, _, _, _, _ = _construct_loss_and_accuracy(
leo, metatest_batch, False)
_construct_validation_summaries(metavalid_loss, metavalid_accuracy, metavalid_generalization_loss)
return (train_op, global_step, metatrain_accuracy, metavalid_accuracy,
metatest_accuracy, kl_components, adapted_kl_components, kl_zn, adapted_kl_zn, kl, adapted_kl, latents, adapted_latents, spurious)
def test_construct_float32_leo_graph(self):
leo = model.LEO(use_64bits_dtype=False, config=self._config)
problem_instance_32_bits = _random_problem_instance(
use_64bits_dtype=False)
leo(problem_instance_32_bits)
