def testEmptyFeatures(self):
"""Tests strip_neighbor_features with empty input."""
features = dict()
neighbor_config = configs.GraphNeighborConfig()
sample_features = utils.strip_neighbor_features(features, neighbor_config)
# We create a dummy tensor so that the computation graph is not empty.
dummy_tensor = tf.constant(1.0)
sample_features, dummy_tensor = self.evaluate(
[sample_features, dummy_tensor])
self.assertEmpty(sample_features)
def testNoNeighborFeatures(self):
"""Tests strip_neighbor_features when there are no neighbor features."""
features = {'F0': tf.constant(11.0, shape=[2, 2])}
neighbor_config = configs.GraphNeighborConfig()
sample_features = utils.strip_neighbor_features(features, neighbor_config)
expected_sample_features = {'F0': tf.constant(11.0, shape=[2, 2])}
sample_features = self.evaluate(sample_features)
# Check that only the sample features are retained.
feature_keys = sorted(sample_features.keys())
self.assertListEqual(feature_keys, ['F0'])
# Check that the values of the sample feature remains unchanged.
self.assertAllEqual(sample_features['F0'], expected_sample_features['F0'])
def testBatchedFeatures(self):
"""Tests strip_neighbor_features with batched input features."""
features = {
'F0':
tf.constant(11.0, shape=[2, 2]),
'F1':
tf.SparseTensor(indices=[[0, 0], [0, 1]],
values=[1.0, 2.0],
dense_shape=[2, 4]),
'NL_nbr_0_F0':
tf.constant(22.0, shape=[2, 2]),
'NL_nbr_0_F1':
tf.SparseTensor(indices=[[1, 0], [1, 1]],
values=[3.0, 4.0],
dense_shape=[2, 4]),
'NL_nbr_0_weight':
tf.constant(0.25, shape=[2, 1]),
}
neighbor_config = configs.GraphNeighborConfig()
sample_features = utils.strip_neighbor_features(
features, neighbor_config)
expected_sample_features = {
'F0':
tf.constant(11.0, shape=[2, 2]),
'F1':
tf.SparseTensor(indices=[[0, 0], [0, 1]],
values=[1.0, 2.0],
dense_shape=[2, 4]),
}
sample_features = self.evaluate(sample_features)
# Check that only the sample features are retained.
feature_keys = sorted(sample_features.keys())
self.assertListEqual(feature_keys, ['F0', 'F1'])
# Check that the values of the sample features remain unchanged.
self.assertAllEqual(sample_features['F0'],
expected_sample_features['F0'])
self.assertAllEqual(sample_features['F1'].values,
expected_sample_features['F1'].values)
self.assertAllEqual(sample_features['F1'].indices,
expected_sample_features['F1'].indices)
self.assertAllEqual(sample_features['F1'].dense_shape,
expected_sample_features['F1'].dense_shape)
def _strip_neighbor_features(features):
return utils.strip_neighbor_features(features, neighbor_config)
def graph_reg_model_fn(features, labels, mode, params=None, config=None):
"""The graph-regularized model function.
Args:
features: This is the first item returned from the `input_fn` passed to
`train`, `evaluate`, and `predict`. This should be a dictionary
containing sample features as well as corresponding neighbor features
and neighbor weights.
labels: This is the second item returned from the `input_fn` passed to
`train`, `evaluate`, and `predict`. This should be a single `Tensor` or
`dict` of same (for multi-head models). If mode is
`tf.estimator.ModeKeys.PREDICT`, `labels=None` will be passed. If the
`model_fn`'s signature does not accept `mode`, the `model_fn` must still
be able to handle `labels=None`.
mode: Optional. Specifies if this is training, evaluation, or prediction.
See `tf.estimator.ModeKeys`.
params: Optional `dict` of hyperparameters. Will receive what is passed to
Estimator in the `params` parameter. This allows users to configure
Estimators from hyper parameter tuning.
config: Optional `tf.estimator.RunConfig` object. Will receive what is
passed to Estimator as its `config` parameter, or a default value.
Allows setting up things in the `model_fn` based on configuration such
as `num_ps_replicas`, or `model_dir`. Unused currently.
Returns:
A `tf.estimator.EstimatorSpec` with graph regularization.
"""
# Parameters 'params' and 'config' are optional. If they are not passed,
# then it is possible for base_model_fn not to accept these arguments.
# See documentation for tf.estimator.Estimator for additional context.
kwargs = {'mode': mode}
embedding_fn_kwargs = dict()
if 'params' in base_model_fn_args:
kwargs['params'] = params
embedding_fn_kwargs['params'] = params
if 'config' in base_model_fn_args:
kwargs['config'] = config
# Uses the same variable scope for calculating the original objective and
# the graph regularization loss term.
with tf.compat.v1.variable_scope(tf.compat.v1.get_variable_scope(),
reuse=tf.compat.v1.AUTO_REUSE,
auxiliary_name_scope=False):
nbr_features = dict()
nbr_weights = None
if mode == tf.estimator.ModeKeys.TRAIN:
# Extract sample features, neighbor features, and neighbor weights if we
# are in training mode.
sample_features, nbr_features, nbr_weights = (
utils.unpack_neighbor_features(
features, graph_reg_config.neighbor_config))
else:
# Otherwise, we strip out all neighbor features and use just the
# sample's features.
sample_features = utils.strip_neighbor_features(
features, graph_reg_config.neighbor_config)
base_spec = base_model_fn(sample_features, labels, **kwargs)
has_nbr_inputs = nbr_weights is not None and nbr_features
# Graph regularization happens only if all the following conditions are
# satisfied:
# - the mode is training
# - neighbor inputs exist
# - the graph regularization multiplier is greater than zero.
# So, return early if any of these conditions is false.
if (not has_nbr_inputs or mode != tf.estimator.ModeKeys.TRAIN
or graph_reg_config.multiplier <= 0):
return base_spec
# Compute sample embeddings.
sample_embeddings = embedding_fn(sample_features, mode,
**embedding_fn_kwargs)
# Compute the embeddings of the neighbors.
nbr_embeddings = embedding_fn(nbr_features, mode,
**embedding_fn_kwargs)
replicated_sample_embeddings = utils.replicate_embeddings(
sample_embeddings,
graph_reg_config.neighbor_config.max_neighbors)
# Compute the distance between the sample embeddings and each of their
# corresponding neighbor embeddings.
graph_loss = distances.pairwise_distance_wrapper(
replicated_sample_embeddings,
nbr_embeddings,
weights=nbr_weights,
distance_config=graph_reg_config.distance_config)
scaled_graph_loss = graph_reg_config.multiplier * graph_loss
tf.compat.v1.summary.scalar('loss/scaled_graph_loss',
scaled_graph_loss)
supervised_loss = base_spec.loss
tf.compat.v1.summary.scalar('loss/supervised_loss',
supervised_loss)
total_loss = supervised_loss + scaled_graph_loss
if not optimizer_fn:
# Default to Adagrad optimizer, the same as the canned DNNEstimator.
optimizer = tf.compat.v1.train.AdagradOptimizer(
learning_rate=0.05)
else:
optimizer = optimizer_fn()
train_op = optimizer.minimize(
loss=total_loss,
global_step=tf.compat.v1.train.get_global_step())
update_ops = tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.UPDATE_OPS)
if update_ops:
train_op = tf.group(train_op, *update_ops)
return base_spec._replace(loss=total_loss, train_op=train_op)
