def test_get_sparse_tensors_dynamic_zero_length(self):
"""Tests _get_sparse_tensors with a dynamic sequence length."""
with ops.Graph().as_default():
inputs = sparse_tensor.SparseTensorValue(indices=np.zeros((0, 2)),
values=[],
dense_shape=(2, 0))
expected = sparse_tensor.SparseTensorValue(indices=np.zeros(
(0, 3)),
values=np.array(
(), dtype=np.int64),
dense_shape=(2, 0, 1))
column = sfc.sequence_categorical_column_with_vocabulary_file(
key='aaa',
vocabulary_file=self._wire_vocabulary_file_name,
vocabulary_size=self._wire_vocabulary_size)
input_placeholder_shape = list(inputs.dense_shape)
# Make second dimension (sequence length) dynamic.
input_placeholder_shape[1] = None
input_placeholder = array_ops.sparse_placeholder(
dtypes.string, shape=input_placeholder_shape)
id_weight_pair = _get_sparse_tensors(column,
{'aaa': input_placeholder})
self.assertIsNone(id_weight_pair.weight_tensor)
with _initialized_session() as sess:
result = id_weight_pair.id_tensor.eval(
session=sess, feed_dict={input_placeholder: inputs})
_assert_sparse_tensor_value(self, expected, result)
def test_get_sparse_tensors(self, inputs_args, expected_args):
inputs = sparse_tensor.SparseTensorValue(**inputs_args)
expected = sparse_tensor.SparseTensorValue(**expected_args)
column = sfc.sequence_categorical_column_with_vocabulary_file(
key='aaa',
vocabulary_file=self._wire_vocabulary_file_name,
vocabulary_size=self._wire_vocabulary_size)
id_weight_pair = _get_sparse_tensors(column, {'aaa': inputs})
self.assertIsNone(id_weight_pair.weight_tensor)
self.evaluate(variables_lib.global_variables_initializer())
self.evaluate(lookup_ops.tables_initializer())
_assert_sparse_tensor_value(self, expected,
self.evaluate(id_weight_pair.id_tensor))
def _get_sequence_categorical_column(params: dict) -> fc.SequenceCategoricalColumn:
key = params['key']
if 'vocabulary' in params.keys():
feature = sfc.sequence_categorical_column_with_vocabulary_list(key,
vocabulary_list=_parse_vocabulary(
params['vocabulary']),
default_value=0)
elif 'bucket_size' in params.keys():
feature = sfc.sequence_categorical_column_with_hash_bucket(
key, hash_bucket_size=params['bucket_size'])
elif 'file' in params.keys():
feature = sfc.sequence_categorical_column_with_vocabulary_file(key,
vocabulary_file=params['file'],
default_value=0)
elif 'num_buckets' in params.keys():
feature = sfc.sequence_categorical_column_with_identity(key,
num_buckets=params['num_buckets'])
else:
raise Exception("params error")
return feature