def test_with_scope_validation(self):
categorical_column = fc_lib.categorical_column_with_identity(
key='aaa', num_buckets=3)
embedding_dimension = 2
initializer = init_ops.truncated_normal_initializer(mean=0.0,
stddev=.5)
embedding_column = tpu_fc._TPUEmbeddingColumnV2(
categorical_column=categorical_column,
dimension=embedding_dimension,
combiner='mean',
initializer=initializer,
max_sequence_length=0,
learning_rate_fn=None,
use_safe_embedding_lookup=True,
bypass_scope_validation=False)
self.assertIs(categorical_column, embedding_column.categorical_column)
self.assertEqual(embedding_dimension, embedding_column.dimension)
state_manager = _TestStateManager()
with tpu_function.tpu_shard_context(1):
with variable_scope.variable_scope('tower1/scope1'):
embedding_column.create_state(state_manager)
with variable_scope.variable_scope('tower2/scope2'):
# With default scope validation, the same column cannot be used in a new
# variable scope.
with self.assertRaisesRegex(
ValueError, 'the variable scope name is different'):
embedding_column.create_state(state_manager)
def test_empty_row(self):
# Inputs.
vocabulary_size = 3
input_sparse_tensor = sparse_tensor.SparseTensorValue(
# example 0, ids []
# example 1, ids [0, 1, 3]
indices=((1, 0), (1, 1), (1, 4)),
values=(0, 1, 3),
dense_shape=(2, 5))
input_features = {'inp': input_sparse_tensor}
# Embedding variable.
embedding_dimension = 2
embedding_values = (
(1., 2.), # id 0
(3., 5.), # id 1
(7., 11.), # id 2
(13., 17.) # id 3
)
def _initializer(shape, dtype, partition_info=None):
self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
self.assertEqual(dtypes.float32, dtype)
self.assertIsNone(partition_info)
return embedding_values
# Build columns.
categorical_column_input = fc_lib.categorical_column_with_identity(
key='inp', num_buckets=vocabulary_size)
# Set tensor_core_shape to be [None, 20] to ensure some padding and
# dynamic batch size.
embedding_column = tpu_fc.embedding_column_v2(
categorical_column_input,
dimension=embedding_dimension,
initializer=_initializer,
combiner='mean',
embedding_lookup_device='tpu_tensor_core',
tensor_core_shape=[None, 3])
# Run in TPUContexts so that we hit the intended densification case.
context = tpu._TPUInferenceContext('tpu_inference')
context.Enter()
with tpu_function.tpu_shard_context(1):
dense_features = fc_lib.DenseFeatures(embedding_column)
expected_lookups = (
# example 0:
(0., 0.), # ids [], embedding = [0, 0]
# example 1:
(2., 3.5
), # ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
)
embedding_lookup = dense_features(input_features)
# Assert expected embedding variable and lookups.
global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
self.assertCountEqual(
('dense_features/inp_embedding/embedding_weights:0', ),
tuple([v.name for v in global_vars]))
embedding_var = global_vars[0]
with _initialized_session():
self.assertAllEqual(embedding_values, embedding_var)
eval_res = embedding_lookup.eval()
self.assertAllEqual(expected_lookups, eval_res)
context.Exit()