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())
def test_encode_features_sequence_column(self):
with tf.Graph().as_default():
# Inputs.
vocabulary_size = 4
# Sequence of ids. -1 values are ignored.
input_seq_ids = np.array([
[3, -1, -1], # example 0
[0, 1, -1], # example 1
])
# Sequence of numeric values.
# input_seq_nums = [
# [1.], # example 0.
# [2., 3.], # example 1
# ]
input_seq_nums = tf.sparse.SparseTensor(indices=[[0, 0], [1, 0],
[1, 1]],
values=[1., 2., 3.],
dense_shape=(2, 3))
input_features = {
"seq_ids": input_seq_ids,
"seq_nums": input_seq_nums
}
# Embedding variable.
embedding_dimension = 2
embedding_values = (
(1., 2.), # id 0
(3., 5.), # id 1
(7., 11.), # id 2
(9., 13.) # id 3
)
# Expected sequence embeddings for input_seq_ids.
expected_seq_embed = [
# example 0:
[[9., 13.], [0., 0.], [0., 0.]],
# example 1:
[[1., 2.], [3., 5.], [0., 0.]],
]
expected_seq_nums = [
# example 0:
[[1.], [0.], [0.]],
# example 1:
[[2.], [3.], [0.]],
]
# Build columns.
seq_categorical_column = (
feature_column.sequence_categorical_column_with_identity(
key="seq_ids", num_buckets=vocabulary_size))
seq_embed_column = feature_column.embedding_column(
seq_categorical_column,
dimension=embedding_dimension,
initializer=lambda shape, dtype, partition_info:
embedding_values)
seq_numeric_column = feature_column.sequence_numeric_column(
"seq_nums")
cols_to_tensors = feature_lib.encode_features(
input_features, [seq_embed_column, seq_numeric_column],
mode=tf.estimator.ModeKeys.EVAL)
actual_seq_embed = cols_to_tensors[seq_embed_column]
actual_seq_nums = cols_to_tensors[seq_numeric_column]
# Assert embedding variable and encoded sequence features.
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_seq_embed, actual_seq_embed)
self.assertAllEqual(expected_seq_nums, actual_seq_nums)
