def shared_embedding_columns(categorical_columns,
dimension,
combiner='mean',
initializer=None,
shared_embedding_collection_name=None):
"""List of dense columns that convert from sparse, categorical input."""
for categorical_column in categorical_columns:
if not isinstance(categorical_column, _SUPPORTED_CATEGORICAL_COLUMNS):
raise TypeError(
'categorical_column for tpu '
' shared_embedding_columns must be type %s, got %s.' %
(' or '.join([
cc.__name__ for cc in _SUPPORTED_CATEGORICAL_COLUMNS
]), type(categorical_column)))
columns = fc_lib.shared_embedding_columns(
categorical_columns,
dimension,
combiner=combiner,
initializer=initializer,
shared_embedding_collection_name=shared_embedding_collection_name,
ckpt_to_load_from=None,
tensor_name_in_ckpt=None,
max_norm=None,
trainable=True)
# Use the initializer and shared_embedding_collection_name to create TPU
# version
initializer = columns[0].initializer
shared_embedding_collection_name = columns[
0].shared_embedding_collection_name
tpu_columns = []
# Create the state (_SharedEmbeddingColumnLayer) here.
for categorical_column in categorical_columns:
column = _TPUSharedEmbeddingColumn(
categorical_column=categorical_column,
dimension=dimension,
combiner=combiner,
initializer=initializer,
shared_embedding_collection_name=shared_embedding_collection_name,
ckpt_to_load_from=None,
tensor_name_in_ckpt=None,
max_norm=None,
trainable=True)
tpu_columns.append(column)
return tpu_columns
def shared_embedding_columns(categorical_columns,
dimension,
combiner='mean',
initializer=None,
shared_embedding_collection_name=None):
"""List of dense columns that convert from sparse, categorical input."""
for categorical_column in categorical_columns:
if not isinstance(categorical_column, _SUPPORTED_CATEGORICAL_COLUMNS):
raise TypeError(
'categorical_column for tpu '
' shared_embedding_columns must be type %s, got %s.' % (' or '.join([
cc.__name__ for cc in _SUPPORTED_CATEGORICAL_COLUMNS
]), type(categorical_column)))
columns = fc_lib.shared_embedding_columns(
categorical_columns,
dimension,
combiner=combiner,
initializer=initializer,
shared_embedding_collection_name=shared_embedding_collection_name,
ckpt_to_load_from=None,
tensor_name_in_ckpt=None,
max_norm=None,
trainable=True)
# Use the initializer and shared_embedding_collection_name to create TPU
# version
initializer = columns[0].initializer
shared_embedding_collection_name = columns[0].shared_embedding_collection_name
tpu_columns = []
# Create the state (_SharedEmbeddingColumnLayer) here.
for categorical_column in categorical_columns:
column = _TPUSharedEmbeddingColumn(
categorical_column=categorical_column,
dimension=dimension,
combiner=combiner,
initializer=initializer,
shared_embedding_collection_name=shared_embedding_collection_name,
ckpt_to_load_from=None,
tensor_name_in_ckpt=None,
max_norm=None,
trainable=True)
tpu_columns.append(column)
return tpu_columns
def test_encode_features(self):
with tf.Graph().as_default():
# Inputs.
vocabulary_size = 4
# -1 values are ignored.
input_a = np.array([
[3, -1, -1], # example 0, ids [3]
[0, 1, -1], # example 1, ids [0, 1]
])
input_b = np.array([
[0, -1, -1], # example 0, ids [0]
[-1, -1, -1], # example 1, ids []
])
input_features = {"aaa": input_a, "bbb": input_b}
# Embedding variable.
embedding_dimension = 2
embedding_values = (
(1., 2.), # id 0
(3., 5.), # id 1
(7., 11.), # id 2
(9., 13.) # id 3
)
# Expected lookup result, using combiner='mean'.
expected_lookups_a = (
# example 0:
(9., 13.), # ids [3], embedding = [9, 13]
# example 1:
(2., 3.5), # ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
)
expected_lookups_b = (
# example 0:
(1., 2.), # ids [0], embedding = [1, 2]
# example 1:
(0., 0.), # ids [], embedding = [0, 0]
)
# Build columns.
categorical_column_a = feature_column.categorical_column_with_identity(
key="aaa", num_buckets=vocabulary_size)
categorical_column_b = feature_column.categorical_column_with_identity(
key="bbb", num_buckets=vocabulary_size)
embed_column_a, embed_column_b = feature_column.shared_embedding_columns(
[categorical_column_a, categorical_column_b],
dimension=embedding_dimension,
initializer=lambda shape, dtype, partition_info: embedding_values,
shared_embedding_collection_name="custom_collection_name")
feature_columns = {"aaa": embed_column_a, "bbb": embed_column_b}
cols_to_tensors = feature_lib.encode_features(
input_features,
feature_columns.values(),
mode=tf.estimator.ModeKeys.EVAL)
embedding_lookup_a = cols_to_tensors[feature_columns["aaa"]]
embedding_lookup_b = cols_to_tensors[feature_columns["bbb"]]
# Assert expected embedding variable and lookups.
global_vars = tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.GLOBAL_VARIABLES)
embedding_var = global_vars[0]
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.compat.v1.tables_initializer())
self.assertAllEqual(embedding_values, embedding_var.eval())
self.assertAllEqual(expected_lookups_a, embedding_lookup_a.eval())
self.assertAllEqual(expected_lookups_b, embedding_lookup_b.eval())