def _map_fn(i):
return sparse_tensor.SparseTensorValue(indices=[[0, 0], [1, 1]],
values=(i * [1, -1]),
dense_shape=[2, 2])
def _sparse(i):
return sparse_tensor.SparseTensorValue(
indices=array_ops.expand_dims(
math_ops.range(i, dtype=dtypes.int64), 1),
values=array_ops.fill([math_ops.cast(i, dtypes.int32)], i),
dense_shape=[i])
class StructureTest(test_base.DatasetTestBase, parameterized.TestCase,
test_util.TensorFlowTestCase):
# pylint: disable=g-long-lambda,protected-access
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant(37.0), tensor_spec.TensorSpec,
[dtypes.float32], [[]]),
("TensorArray", lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
tensor_array_ops.TensorArraySpec, [dtypes.variant], [[]]),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]),
sparse_tensor.SparseTensorSpec, [dtypes.variant], [None]),
("RaggedTensor",
lambda: ragged_factory_ops.constant([[1, 2], [], [4]]),
ragged_tensor.RaggedTensorSpec, [dtypes.variant], [None]),
("Nested_0", lambda:
(constant_op.constant(37.0), constant_op.constant([1, 2, 3])), tuple,
[dtypes.float32, dtypes.int32], [[], [3]]),
("Nested_1", lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, dict, [dtypes.float32, dtypes.int32], [[], [3]]),
("Nested_2", lambda: {
"a":
constant_op.constant(37.0),
"b":
(sparse_tensor.
SparseTensor(indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]))
}, dict, [dtypes.float32, dtypes.variant, dtypes.variant], [[], None,
None]),
)
def testFlatStructure(self, value_fn, expected_structure, expected_types,
expected_shapes):
value = value_fn()
s = structure.type_spec_from_value(value)
self.assertIsInstance(s, expected_structure)
flat_types = structure.get_flat_tensor_types(s)
self.assertEqual(expected_types, flat_types)
flat_shapes = structure.get_flat_tensor_shapes(s)
self.assertLen(flat_shapes, len(expected_shapes))
for expected, actual in zip(expected_shapes, flat_shapes):
if expected is None:
self.assertEqual(actual.ndims, None)
else:
self.assertEqual(actual.as_list(), expected)
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant(37.0), lambda: [
constant_op.constant(38.0),
array_ops.placeholder(dtypes.float32),
variables.Variable(100.0), 42.0,
np.array(42.0, dtype=np.float32)
],
lambda: [constant_op.constant([1.0, 2.0]),
constant_op.constant(37)]),
("TensorArray", lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0), lambda: [
tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=10)
], lambda: [
tensor_array_ops.TensorArray(
dtype=dtypes.int32, element_shape=(3, ), size=0),
tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(), size=0)
]),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]), lambda: [
sparse_tensor.SparseTensor(indices=[[1, 1], [3, 4]],
values=[10, -1],
dense_shape=[4, 5]),
sparse_tensor.SparseTensorValue(indices=[[1, 1], [3, 4]],
values=[10, -1],
dense_shape=[4, 5]),
array_ops.sparse_placeholder(dtype=dtypes.int32),
array_ops.sparse_placeholder(dtype=dtypes.int32,
shape=[None, None])
], lambda: [
constant_op.constant(37, shape=[4, 5]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[5, 6]),
array_ops.sparse_placeholder(dtype=dtypes.int32,
shape=[None, None, None]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1.0], dense_shape=[4, 5])
]),
("RaggedTensor",
lambda: ragged_factory_ops.constant([[1, 2], [], [3]]), lambda: [
ragged_factory_ops.constant([[1, 2], [3, 4], []]),
ragged_factory_ops.constant([[1], [2, 3, 4], [5]]),
], lambda: [
ragged_factory_ops.constant(1),
ragged_factory_ops.constant([1, 2]),
ragged_factory_ops.constant([[1], [2]]),
ragged_factory_ops.constant([["a", "b"]]),
]),
("Nested", lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, lambda: [{
"a": constant_op.constant(15.0),
"b": constant_op.constant([4, 5, 6])
}], lambda: [{
"a": constant_op.constant(15.0),
"b": constant_op.constant([4, 5, 6, 7])
}, {
"a": constant_op.constant(15),
"b": constant_op.constant([4, 5, 6])
}, {
"a":
constant_op.constant(15),
"b":
sparse_tensor.SparseTensor(
indices=[[0], [1], [2]], values=[4, 5, 6], dense_shape=[3])
}, (constant_op.constant(15.0), constant_op.constant([4, 5, 6]))]),
)
@test_util.run_deprecated_v1
def testIsCompatibleWithStructure(self, original_value_fn,
compatible_values_fn,
incompatible_values_fn):
original_value = original_value_fn()
compatible_values = compatible_values_fn()
incompatible_values = incompatible_values_fn()
s = structure.type_spec_from_value(original_value)
for compatible_value in compatible_values:
self.assertTrue(
structure.are_compatible(
s, structure.type_spec_from_value(compatible_value)))
for incompatible_value in incompatible_values:
self.assertFalse(
structure.are_compatible(
s, structure.type_spec_from_value(incompatible_value)))
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant(37.0),
lambda: constant_op.constant(42.0),
lambda: constant_op.constant([5])),
("TensorArray", lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
lambda: tensor_array_ops.TensorArray(
dtype=dtypes.int32, element_shape=(), size=0)),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]),
lambda: sparse_tensor.SparseTensor(
indices=[[1, 2]], values=[42], dense_shape=[4, 5]),
lambda: sparse_tensor.SparseTensor(
indices=[[3]], values=[-1], dense_shape=[5]),
lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[1.0], dense_shape=[4, 5])),
("RaggedTensor",
lambda: ragged_factory_ops.constant([[[1, 2]], [[3]]]),
lambda: ragged_factory_ops.constant([[[5]], [[8], [3, 2]]]), lambda:
ragged_factory_ops.constant([[[1]], [[2], [3]]], ragged_rank=1),
lambda: ragged_factory_ops.constant([[[1.0, 2.0]], [[3.0]]]),
lambda: ragged_factory_ops.constant([[[1]], [[2]], [[3]]])),
("Nested", lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, lambda: {
"a": constant_op.constant(42.0),
"b": constant_op.constant([4, 5, 6])
}, lambda: {
"a": constant_op.constant([1, 2, 3]),
"b": constant_op.constant(37.0)
}),
) # pyformat: disable
def testStructureFromValueEquality(self, value1_fn, value2_fn,
*not_equal_value_fns):
# pylint: disable=g-generic-assert
s1 = structure.type_spec_from_value(value1_fn())
s2 = structure.type_spec_from_value(value2_fn())
self.assertEqual(s1, s1) # check __eq__ operator.
self.assertEqual(s1, s2) # check __eq__ operator.
self.assertFalse(s1 != s1) # check __ne__ operator.
self.assertFalse(s1 != s2) # check __ne__ operator.
for c1, c2 in zip(nest.flatten(s1), nest.flatten(s2)):
self.assertEqual(hash(c1), hash(c1))
self.assertEqual(hash(c1), hash(c2))
for value_fn in not_equal_value_fns:
s3 = structure.type_spec_from_value(value_fn())
self.assertNotEqual(s1, s3) # check __ne__ operator.
self.assertNotEqual(s2, s3) # check __ne__ operator.
self.assertFalse(s1 == s3) # check __eq_ operator.
self.assertFalse(s2 == s3) # check __eq_ operator.
@parameterized.named_parameters(
("RaggedTensor_RaggedRank",
ragged_tensor.RaggedTensorSpec(None, dtypes.int32, 1),
ragged_tensor.RaggedTensorSpec(None, dtypes.int32, 2)),
("RaggedTensor_Shape",
ragged_tensor.RaggedTensorSpec([3, None], dtypes.int32, 1),
ragged_tensor.RaggedTensorSpec([5, None], dtypes.int32, 1)),
("RaggedTensor_DType",
ragged_tensor.RaggedTensorSpec(None, dtypes.int32, 1),
ragged_tensor.RaggedTensorSpec(None, dtypes.float32, 1)),
)
def testRaggedStructureInequality(self, s1, s2):
# pylint: disable=g-generic-assert
self.assertNotEqual(s1, s2) # check __ne__ operator.
self.assertFalse(s1 == s2) # check __eq__ operator.
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant(37.0),
lambda: constant_op.constant(42.0),
lambda: constant_op.constant([5])),
("TensorArray", lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
lambda: tensor_array_ops.TensorArray(
dtype=dtypes.int32, element_shape=(), size=0)),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]),
lambda: sparse_tensor.SparseTensor(
indices=[[1, 2]], values=[42], dense_shape=[4, 5]),
lambda: sparse_tensor.SparseTensor(
indices=[[3]], values=[-1], dense_shape=[5])),
("Nested", lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, lambda: {
"a": constant_op.constant(42.0),
"b": constant_op.constant([4, 5, 6])
}, lambda: {
"a": constant_op.constant([1, 2, 3]),
"b": constant_op.constant(37.0)
}),
)
def testHash(self, value1_fn, value2_fn, value3_fn):
s1 = structure.type_spec_from_value(value1_fn())
s2 = structure.type_spec_from_value(value2_fn())
s3 = structure.type_spec_from_value(value3_fn())
for c1, c2, c3 in zip(nest.flatten(s1), nest.flatten(s2),
nest.flatten(s3)):
self.assertEqual(hash(c1), hash(c1))
self.assertEqual(hash(c1), hash(c2))
self.assertNotEqual(hash(c1), hash(c3))
self.assertNotEqual(hash(c2), hash(c3))
@parameterized.named_parameters(
(
"Tensor",
lambda: constant_op.constant(37.0),
),
(
"SparseTensor",
lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]),
),
("TensorArray", lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(), size=1).write(0, 7)),
(
"RaggedTensor",
lambda: ragged_factory_ops.constant([[1, 2], [], [3]]),
),
(
"Nested_0",
lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
},
),
(
"Nested_1",
lambda: {
"a":
constant_op.constant(37.0),
"b": (sparse_tensor.SparseTensor(
indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]))
},
),
)
def testRoundTripConversion(self, value_fn):
value = value_fn()
s = structure.type_spec_from_value(value)
def maybe_stack_ta(v):
if isinstance(v, tensor_array_ops.TensorArray):
return v.stack()
else:
return v
before = self.evaluate(maybe_stack_ta(value))
after = self.evaluate(
maybe_stack_ta(
structure.from_tensor_list(s,
structure.to_tensor_list(s,
value))))
flat_before = nest.flatten(before)
flat_after = nest.flatten(after)
for b, a in zip(flat_before, flat_after):
if isinstance(b, sparse_tensor.SparseTensorValue):
self.assertAllEqual(b.indices, a.indices)
self.assertAllEqual(b.values, a.values)
self.assertAllEqual(b.dense_shape, a.dense_shape)
elif isinstance(b, (ragged_tensor.RaggedTensor,
ragged_tensor_value.RaggedTensorValue)):
self.assertAllEqual(b, a)
else:
self.assertAllEqual(b, a)
# pylint: enable=g-long-lambda
def preserveStaticShape(self):
rt = ragged_factory_ops.constant([[1, 2], [], [3]])
rt_s = structure.type_spec_from_value(rt)
rt_after = structure.from_tensor_list(
rt_s, structure.to_tensor_list(rt_s, rt))
self.assertEqual(rt_after.row_splits.shape.as_list(),
rt.row_splits.shape.as_list())
self.assertEqual(rt_after.values.shape.as_list(), [None])
st = sparse_tensor.SparseTensor(indices=[[3, 4]],
values=[-1],
dense_shape=[4, 5])
st_s = structure.type_spec_from_value(st)
st_after = structure.from_tensor_list(
st_s, structure.to_tensor_list(st_s, st))
self.assertEqual(st_after.indices.shape.as_list(), [None, 2])
self.assertEqual(st_after.values.shape.as_list(), [None])
self.assertEqual(st_after.dense_shape.shape.as_list(),
st.dense_shape.shape.as_list())
def testIncompatibleStructure(self):
# Define three mutually incompatible values/structures, and assert that:
# 1. Using one structure to flatten a value with an incompatible structure
# fails.
# 2. Using one structure to restructre a flattened value with an
# incompatible structure fails.
value_tensor = constant_op.constant(42.0)
s_tensor = structure.type_spec_from_value(value_tensor)
flat_tensor = structure.to_tensor_list(s_tensor, value_tensor)
value_sparse_tensor = sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1])
s_sparse_tensor = structure.type_spec_from_value(value_sparse_tensor)
flat_sparse_tensor = structure.to_tensor_list(s_sparse_tensor,
value_sparse_tensor)
value_nest = {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}
s_nest = structure.type_spec_from_value(value_nest)
flat_nest = structure.to_tensor_list(s_nest, value_nest)
with self.assertRaisesRegexp(
ValueError,
r"SparseTensor.* is not convertible to a tensor with "
r"dtype.*float32.* and shape \(\)"):
structure.to_tensor_list(s_tensor, value_sparse_tensor)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_tensor, value_nest)
with self.assertRaisesRegexp(
TypeError, "Neither a SparseTensor nor SparseTensorValue"):
structure.to_tensor_list(s_sparse_tensor, value_tensor)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_sparse_tensor, value_nest)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_nest, value_tensor)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_nest, value_sparse_tensor)
with self.assertRaisesRegexp(ValueError, r"Incompatible input:"):
structure.from_tensor_list(s_tensor, flat_sparse_tensor)
with self.assertRaisesRegexp(ValueError,
"Expected 1 tensors but got 2."):
structure.from_tensor_list(s_tensor, flat_nest)
with self.assertRaisesRegexp(ValueError, "Incompatible input: "):
structure.from_tensor_list(s_sparse_tensor, flat_tensor)
with self.assertRaisesRegexp(ValueError,
"Expected 1 tensors but got 2."):
structure.from_tensor_list(s_sparse_tensor, flat_nest)
with self.assertRaisesRegexp(ValueError,
"Expected 2 tensors but got 1."):
structure.from_tensor_list(s_nest, flat_tensor)
with self.assertRaisesRegexp(ValueError,
"Expected 2 tensors but got 1."):
structure.from_tensor_list(s_nest, flat_sparse_tensor)
def testIncompatibleNestedStructure(self):
# Define three mutually incompatible nested values/structures, and assert
# that:
# 1. Using one structure to flatten a value with an incompatible structure
# fails.
# 2. Using one structure to restructure a flattened value with an
# incompatible structure fails.
value_0 = {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}
s_0 = structure.type_spec_from_value(value_0)
flat_s_0 = structure.to_tensor_list(s_0, value_0)
# `value_1` has compatible nested structure with `value_0`, but different
# classes.
value_1 = {
"a":
constant_op.constant(37.0),
"b":
sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1])
}
s_1 = structure.type_spec_from_value(value_1)
flat_s_1 = structure.to_tensor_list(s_1, value_1)
# `value_2` has incompatible nested structure with `value_0` and `value_1`.
value_2 = {
"a":
constant_op.constant(37.0),
"b": (sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1]),
sparse_tensor.SparseTensor(indices=[[3, 4]],
values=[-1],
dense_shape=[4, 5]))
}
s_2 = structure.type_spec_from_value(value_2)
flat_s_2 = structure.to_tensor_list(s_2, value_2)
with self.assertRaisesRegexp(
ValueError,
r"SparseTensor.* is not convertible to a tensor with "
r"dtype.*int32.* and shape \(3,\)"):
structure.to_tensor_list(s_0, value_1)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_0, value_2)
with self.assertRaisesRegexp(
TypeError, "Neither a SparseTensor nor SparseTensorValue"):
structure.to_tensor_list(s_1, value_0)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_1, value_2)
# NOTE(mrry): The repr of the dictionaries is not sorted, so the regexp
# needs to account for "a" coming before or after "b". It might be worth
# adding a deterministic repr for these error messages (among other
# improvements).
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_2, value_0)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_2, value_1)
with self.assertRaisesRegexp(ValueError, r"Incompatible input:"):
structure.from_tensor_list(s_0, flat_s_1)
with self.assertRaisesRegexp(ValueError,
"Expected 2 tensors but got 3."):
structure.from_tensor_list(s_0, flat_s_2)
with self.assertRaisesRegexp(ValueError, "Incompatible input: "):
structure.from_tensor_list(s_1, flat_s_0)
with self.assertRaisesRegexp(ValueError,
"Expected 2 tensors but got 3."):
structure.from_tensor_list(s_1, flat_s_2)
with self.assertRaisesRegexp(ValueError,
"Expected 3 tensors but got 2."):
structure.from_tensor_list(s_2, flat_s_0)
with self.assertRaisesRegexp(ValueError,
"Expected 3 tensors but got 2."):
structure.from_tensor_list(s_2, flat_s_1)
@parameterized.named_parameters(
("Tensor", dtypes.float32, tensor_shape.scalar(), ops.Tensor,
tensor_spec.TensorSpec([], dtypes.float32)),
("SparseTensor", dtypes.int32, tensor_shape.matrix(
2, 2), sparse_tensor.SparseTensor,
sparse_tensor.SparseTensorSpec([2, 2], dtypes.int32)),
("TensorArray_0", dtypes.int32,
tensor_shape.as_shape([None, True, 2, 2
]), tensor_array_ops.TensorArray,
tensor_array_ops.TensorArraySpec(
[2, 2], dtypes.int32, dynamic_size=None, infer_shape=True)),
("TensorArray_1", dtypes.int32,
tensor_shape.as_shape([True, None, 2, 2
]), tensor_array_ops.TensorArray,
tensor_array_ops.TensorArraySpec(
[2, 2], dtypes.int32, dynamic_size=True, infer_shape=None)),
("TensorArray_2", dtypes.int32,
tensor_shape.as_shape([True, False, 2, 2
]), tensor_array_ops.TensorArray,
tensor_array_ops.TensorArraySpec(
[2, 2], dtypes.int32, dynamic_size=True, infer_shape=False)),
("RaggedTensor", dtypes.int32, tensor_shape.matrix(2, None),
ragged_tensor.RaggedTensorSpec([2, None], dtypes.int32, 1),
ragged_tensor.RaggedTensorSpec([2, None], dtypes.int32, 1)),
("Nested", {
"a": dtypes.float32,
"b": (dtypes.int32, dtypes.string)
}, {
"a": tensor_shape.scalar(),
"b": (tensor_shape.matrix(2, 2), tensor_shape.scalar())
}, {
"a": ops.Tensor,
"b": (sparse_tensor.SparseTensor, ops.Tensor)
}, {
"a":
tensor_spec.TensorSpec([], dtypes.float32),
"b": (sparse_tensor.SparseTensorSpec([2, 2], dtypes.int32),
tensor_spec.TensorSpec([], dtypes.string))
}),
)
def testConvertLegacyStructure(self, output_types, output_shapes,
output_classes, expected_structure):
actual_structure = structure.convert_legacy_structure(
output_types, output_shapes, output_classes)
self.assertEqual(actual_structure, expected_structure)
def testNestedNestedStructure(self):
s = (tensor_spec.TensorSpec([], dtypes.int64),
(tensor_spec.TensorSpec([], dtypes.float32),
tensor_spec.TensorSpec([], dtypes.string)))
int64_t = constant_op.constant(37, dtype=dtypes.int64)
float32_t = constant_op.constant(42.0)
string_t = constant_op.constant("Foo")
nested_tensors = (int64_t, (float32_t, string_t))
tensor_list = structure.to_tensor_list(s, nested_tensors)
for expected, actual in zip([int64_t, float32_t, string_t],
tensor_list):
self.assertIs(expected, actual)
(actual_int64_t,
(actual_float32_t,
actual_string_t)) = structure.from_tensor_list(s, tensor_list)
self.assertIs(int64_t, actual_int64_t)
self.assertIs(float32_t, actual_float32_t)
self.assertIs(string_t, actual_string_t)
(actual_int64_t,
(actual_float32_t,
actual_string_t)) = (structure.from_compatible_tensor_list(
s, tensor_list))
self.assertIs(int64_t, actual_int64_t)
self.assertIs(float32_t, actual_float32_t)
self.assertIs(string_t, actual_string_t)
@parameterized.named_parameters(
("Tensor", tensor_spec.TensorSpec([], dtypes.float32), 32,
tensor_spec.TensorSpec([32], dtypes.float32)),
("TensorUnknown", tensor_spec.TensorSpec([], dtypes.float32), None,
tensor_spec.TensorSpec([None], dtypes.float32)),
("SparseTensor", sparse_tensor.SparseTensorSpec([None],
dtypes.float32), 32,
sparse_tensor.SparseTensorSpec([32, None], dtypes.float32)),
("SparseTensorUnknown",
sparse_tensor.SparseTensorSpec([4], dtypes.float32), None,
sparse_tensor.SparseTensorSpec([None, 4], dtypes.float32)),
("RaggedTensor",
ragged_tensor.RaggedTensorSpec([2, None], dtypes.float32, 1), 32,
ragged_tensor.RaggedTensorSpec([32, 2, None], dtypes.float32, 2)),
("RaggedTensorUnknown",
ragged_tensor.RaggedTensorSpec([4, None], dtypes.float32, 1), None,
ragged_tensor.RaggedTensorSpec([None, 4, None], dtypes.float32, 2)),
("Nested", {
"a":
tensor_spec.TensorSpec([], dtypes.float32),
"b": (sparse_tensor.SparseTensorSpec([2, 2], dtypes.int32),
tensor_spec.TensorSpec([], dtypes.string))
}, 128, {
"a":
tensor_spec.TensorSpec([128], dtypes.float32),
"b": (sparse_tensor.SparseTensorSpec([128, 2, 2], dtypes.int32),
tensor_spec.TensorSpec([128], dtypes.string))
}),
)
def testBatch(self, element_structure, batch_size,
expected_batched_structure):
batched_structure = nest.map_structure(
lambda component_spec: component_spec._batch(batch_size),
element_structure)
self.assertEqual(batched_structure, expected_batched_structure)
@parameterized.named_parameters(
("Tensor", tensor_spec.TensorSpec(
[32], dtypes.float32), tensor_spec.TensorSpec([], dtypes.float32)),
("TensorUnknown", tensor_spec.TensorSpec([None], dtypes.float32),
tensor_spec.TensorSpec([], dtypes.float32)),
("SparseTensor",
sparse_tensor.SparseTensorSpec([32, None], dtypes.float32),
sparse_tensor.SparseTensorSpec([None], dtypes.float32)),
("SparseTensorUnknown",
sparse_tensor.SparseTensorSpec([None, 4], dtypes.float32),
sparse_tensor.SparseTensorSpec([4], dtypes.float32)),
("RaggedTensor",
ragged_tensor.RaggedTensorSpec([32, None, None], dtypes.float32, 2),
ragged_tensor.RaggedTensorSpec([None, None], dtypes.float32, 1)),
("RaggedTensorUnknown",
ragged_tensor.RaggedTensorSpec([None, None, None], dtypes.float32, 2),
ragged_tensor.RaggedTensorSpec([None, None], dtypes.float32, 1)),
("Nested", {
"a":
tensor_spec.TensorSpec([128], dtypes.float32),
"b": (sparse_tensor.SparseTensorSpec([128, 2, 2], dtypes.int32),
tensor_spec.TensorSpec([None], dtypes.string))
}, {
"a":
tensor_spec.TensorSpec([], dtypes.float32),
"b": (sparse_tensor.SparseTensorSpec([2, 2], dtypes.int32),
tensor_spec.TensorSpec([], dtypes.string))
}),
)
def testUnbatch(self, element_structure, expected_unbatched_structure):
unbatched_structure = nest.map_structure(
lambda component_spec: component_spec._unbatch(),
element_structure)
self.assertEqual(unbatched_structure, expected_unbatched_structure)
# pylint: disable=g-long-lambda
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant([[1.0, 2.0], [3.0, 4.0]]),
lambda: constant_op.constant([1.0, 2.0])),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 1]], values=[13, 27], dense_shape=[2, 2]),
lambda: sparse_tensor.SparseTensor(
indices=[[0]], values=[13], dense_shape=[2])),
("RaggedTensor", lambda: ragged_factory_ops.constant([[[1]], [[2]]]),
lambda: ragged_factory_ops.constant([[1]])),
("Nest", lambda:
(constant_op.constant([[1.0, 2.0], [3.0, 4.0]]),
sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 1]], values=[13, 27], dense_shape=[2, 2])),
lambda: (constant_op.constant([1.0, 2.0]),
sparse_tensor.SparseTensor(
indices=[[0]], values=[13], dense_shape=[2]))),
)
def testToBatchedTensorList(self, value_fn, element_0_fn):
batched_value = value_fn()
s = structure.type_spec_from_value(batched_value)
batched_tensor_list = structure.to_batched_tensor_list(
s, batched_value)
# The batch dimension is 2 for all of the test cases.
# NOTE(mrry): `tf.shape()` does not currently work for the DT_VARIANT
# tensors in which we store sparse tensors.
for t in batched_tensor_list:
if t.dtype != dtypes.variant:
self.assertEqual(2, self.evaluate(array_ops.shape(t)[0]))
# Test that the 0th element from the unbatched tensor is equal to the
# expected value.
expected_element_0 = self.evaluate(element_0_fn())
unbatched_s = nest.map_structure(
lambda component_spec: component_spec._unbatch(), s)
actual_element_0 = structure.from_tensor_list(
unbatched_s, [t[0] for t in batched_tensor_list])
for expected, actual in zip(nest.flatten(expected_element_0),
nest.flatten(actual_element_0)):
self.assertValuesEqual(expected, actual)
def dense_to_sparse_scalar(tensor):
indices = [[]]
values = array_ops.expand_dims(tensor, 0)
shape = []
return sparse_tensor.SparseTensorValue(indices, values, shape)
def _test_dense_features(self, trainable=True):
# Inputs.
vocabulary_size = 3
sparse_input_a = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 4)),
values=(2, 0, 1),
dense_shape=(2, 5))
sparse_input_b = sparse_tensor.SparseTensorValue(
# example 0, ids [0]
# example 1, ids []
indices=((0, 0), ),
values=(0, ),
dense_shape=(2, 5))
sparse_input_c = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 1), (1, 1), (1, 3)),
values=(2, 0, 1),
dense_shape=(2, 5))
sparse_input_d = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids []
indices=((0, 1), ),
values=(2, ),
dense_shape=(2, 5))
# Embedding variable.
embedding_dimension = 2
embedding_values = (
(1., 2.), # id 0
(3., 5.), # id 1
(7., 11.) # id 2
)
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
# Expected lookup result, using combiner='mean'.
expected_lookups = (
# example 0:
# A ids [2], embedding = [7, 11]
# B ids [0], embedding = [1, 2]
# C ids [2], embedding = [7, 11]
# D ids [2], embedding = [7, 11]
(7., 11., 1., 2., 7., 11., 7., 11.),
# example 1:
# A ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
# B ids [], embedding = [0, 0]
# C ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
# D ids [], embedding = [0, 0]
(2., 3.5, 0., 0., 2., 3.5, 0., 0.),
)
# Build columns.
categorical_column_a = fc.categorical_column_with_identity(
key='aaa', num_buckets=vocabulary_size)
categorical_column_b = fc.categorical_column_with_identity(
key='bbb', num_buckets=vocabulary_size)
categorical_column_c = fc.categorical_column_with_identity(
key='ccc', num_buckets=vocabulary_size)
categorical_column_d = fc.categorical_column_with_identity(
key='ddd', num_buckets=vocabulary_size)
embedding_column_a, embedding_column_b = fc.shared_embedding_columns_v2(
[categorical_column_a, categorical_column_b],
dimension=embedding_dimension,
initializer=_initializer,
trainable=trainable)
embedding_column_c, embedding_column_d = fc.shared_embedding_columns_v2(
[categorical_column_c, categorical_column_d],
dimension=embedding_dimension,
initializer=_initializer,
trainable=trainable)
features = {
'aaa': sparse_input_a,
'bbb': sparse_input_b,
'ccc': sparse_input_c,
'ddd': sparse_input_d
}
# Provide sparse input and get dense result.
dense_features = df.DenseFeatures(
feature_columns=(embedding_column_b, embedding_column_a,
embedding_column_c, embedding_column_d))(features)
# Assert expected embedding variable and lookups.
global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
self.assertCountEqual(
['aaa_bbb_shared_embedding:0', 'ccc_ddd_shared_embedding:0'],
tuple([v.name for v in global_vars]))
for v in global_vars:
self.assertIsInstance(v, variables_lib.Variable)
trainable_vars = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
if trainable:
self.assertCountEqual(
['aaa_bbb_shared_embedding:0', 'ccc_ddd_shared_embedding:0'],
tuple([v.name for v in trainable_vars]))
else:
self.assertCountEqual([], tuple([v.name for v in trainable_vars]))
shared_embedding_vars = global_vars
self.evaluate(variables_lib.global_variables_initializer())
self.evaluate(lookup_ops.tables_initializer())
self.assertAllEqual(embedding_values,
self.evaluate(shared_embedding_vars[0]))
self.assertAllEqual(expected_lookups, self.evaluate(dense_features))
def make_tensor(i):
return sparse_tensor.SparseTensorValue(indices=[[0, 0]],
values=(i * [1]),
dense_shape=[2, 2])
def _sparse(i):
return sparse_tensor.SparseTensorValue(
indices=np.array([[0, 0]]),
values=(i * np.array([1], dtype=np.int64)),
dense_shape=np.array([1, 1]))
class StringSplitOpTest(test.TestCase, parameterized.TestCase):
def testStringSplit(self):
strings = ["pigs on the wing", "animals"]
with self.cached_session():
tokens = string_ops.string_split(strings)
indices, values, shape = self.evaluate(tokens)
self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], [0, 3], [1, 0]])
self.assertAllEqual(values, [b"pigs", b"on", b"the", b"wing", b"animals"])
self.assertAllEqual(shape, [2, 4])
@test_util.run_deprecated_v1
def testStringSplitEmptyDelimiter(self):
strings = ["hello", "hola", b"\xF0\x9F\x98\x8E"] # Last string is U+1F60E
with self.cached_session():
tokens = string_ops.string_split(strings, delimiter="")
indices, values, shape = self.evaluate(tokens)
self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], [0, 3], [0, 4],
[1, 0], [1, 1], [1, 2], [1, 3], [2, 0],
[2, 1], [2, 2], [2, 3]])
expected = np.array(
[
"h", "e", "l", "l", "o", "h", "o", "l", "a", b"\xf0", b"\x9f",
b"\x98", b"\x8e"
],
dtype="|S1")
self.assertAllEqual(values.tolist(), expected)
self.assertAllEqual(shape, [3, 5])
def testStringSplitEmptyToken(self):
strings = ["", " a", "b ", " c", " ", " d ", " e", "f ", " g ", " "]
with self.cached_session():
tokens = string_ops.string_split(strings)
indices, values, shape = self.evaluate(tokens)
self.assertAllEqual(
indices,
[[1, 0], [2, 0], [3, 0], [5, 0], [6, 0], [7, 0], [8, 0]])
self.assertAllEqual(values, [b"a", b"b", b"c", b"d", b"e", b"f", b"g"])
self.assertAllEqual(shape, [10, 1])
def testStringSplitOnSetEmptyToken(self):
strings = ["", " a", "b ", " c", " ", " d ", ". e", "f .", " .g. ", " ."]
with self.cached_session():
tokens = string_ops.string_split(strings, delimiter=" .")
indices, values, shape = self.evaluate(tokens)
self.assertAllEqual(
indices,
[[1, 0], [2, 0], [3, 0], [5, 0], [6, 0], [7, 0], [8, 0]])
self.assertAllEqual(values, [b"a", b"b", b"c", b"d", b"e", b"f", b"g"])
self.assertAllEqual(shape, [10, 1])
@test_util.run_deprecated_v1
def testStringSplitWithDelimiter(self):
strings = ["hello|world", "hello world"]
with self.cached_session():
self.assertRaises(
ValueError, string_ops.string_split, strings, delimiter=["|", ""])
self.assertRaises(
ValueError, string_ops.string_split, strings, delimiter=["a"])
tokens = string_ops.string_split(strings, delimiter="|")
indices, values, shape = self.evaluate(tokens)
self.assertAllEqual(indices, [[0, 0], [0, 1], [1, 0]])
self.assertAllEqual(values, [b"hello", b"world", b"hello world"])
self.assertAllEqual(shape, [2, 2])
tokens = string_ops.string_split(strings, delimiter="| ")
indices, values, shape = self.evaluate(tokens)
self.assertAllEqual(indices, [[0, 0], [0, 1], [1, 0], [1, 1]])
self.assertAllEqual(values, [b"hello", b"world", b"hello", b"world"])
self.assertAllEqual(shape, [2, 2])
@test_util.run_deprecated_v1
def testStringSplitWithDelimiterTensor(self):
strings = ["hello|world", "hello world"]
with self.cached_session() as sess:
delimiter = array_ops.placeholder(dtypes.string)
tokens = string_ops.string_split(strings, delimiter=delimiter)
with self.assertRaises(errors_impl.InvalidArgumentError):
sess.run(tokens, feed_dict={delimiter: ["a", "b"]})
with self.assertRaises(errors_impl.InvalidArgumentError):
sess.run(tokens, feed_dict={delimiter: ["a"]})
indices, values, shape = sess.run(tokens, feed_dict={delimiter: "|"})
self.assertAllEqual(indices, [[0, 0], [0, 1], [1, 0]])
self.assertAllEqual(values, [b"hello", b"world", b"hello world"])
self.assertAllEqual(shape, [2, 2])
@test_util.run_deprecated_v1
def testStringSplitWithDelimitersTensor(self):
strings = ["hello.cruel,world", "hello cruel world"]
with self.cached_session() as sess:
delimiter = array_ops.placeholder(dtypes.string)
tokens = string_ops.string_split(strings, delimiter=delimiter)
with self.assertRaises(errors_impl.InvalidArgumentError):
sess.run(tokens, feed_dict={delimiter: ["a", "b"]})
with self.assertRaises(errors_impl.InvalidArgumentError):
sess.run(tokens, feed_dict={delimiter: ["a"]})
indices, values, shape = sess.run(tokens, feed_dict={delimiter: ".,"})
self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], [1, 0]])
self.assertAllEqual(values,
[b"hello", b"cruel", b"world", b"hello cruel world"])
self.assertAllEqual(shape, [2, 3])
def testStringSplitWithNoSkipEmpty(self):
strings = ["#a", "b#", "#c#"]
with self.cached_session():
tokens = string_ops.string_split(strings, "#", skip_empty=False)
indices, values, shape = self.evaluate(tokens)
self.assertAllEqual(indices, [[0, 0], [0, 1],
[1, 0], [1, 1],
[2, 0], [2, 1], [2, 2]])
self.assertAllEqual(values, [b"", b"a", b"b", b"", b"", b"c", b""])
self.assertAllEqual(shape, [3, 3])
with self.cached_session():
tokens = string_ops.string_split(strings, "#")
indices, values, shape = self.evaluate(tokens)
self.assertAllEqual(values, [b"a", b"b", b"c"])
self.assertAllEqual(indices, [[0, 0], [1, 0], [2, 0]])
self.assertAllEqual(shape, [3, 1])
@parameterized.named_parameters([
dict(
testcase_name="RaggedResultType",
source=[b"pigs on the wing", b"animals"],
result_type="RaggedTensor",
expected=[[b"pigs", b"on", b"the", b"wing"], [b"animals"]]),
dict(
testcase_name="SparseResultType",
source=[b"pigs on the wing", b"animals"],
result_type="SparseTensor",
expected=sparse_tensor.SparseTensorValue(
[[0, 0], [0, 1], [0, 2], [0, 3], [1, 0]],
[b"pigs", b"on", b"the", b"wing", b"animals"], [2, 4])),
dict(
testcase_name="DefaultResultType",
source=[b"pigs on the wing", b"animals"],
expected=sparse_tensor.SparseTensorValue(
[[0, 0], [0, 1], [0, 2], [0, 3], [1, 0]],
[b"pigs", b"on", b"the", b"wing", b"animals"], [2, 4])),
dict(
testcase_name="BadResultType",
source=[b"pigs on the wing", b"animals"],
result_type="BouncyTensor",
error="result_type must be .*"),
dict(
testcase_name="WithSepAndAndSkipEmpty",
source=[b"+hello+++this+is+a+test"],
sep="+",
skip_empty=False,
result_type="RaggedTensor",
expected=[[b"", b"hello", b"", b"", b"this", b"is", b"a", b"test"]]),
dict(
testcase_name="WithDelimiter",
source=[b"hello world"],
delimiter="l",
result_type="RaggedTensor",
expected=[[b"he", b"o wor", b"d"]]),
])
def testRaggedStringSplitWrapper(self,
source,
sep=None,
skip_empty=True,
delimiter=None,
result_type="SparseTensor",
expected=None,
error=None):
if error is not None:
with self.assertRaisesRegex(ValueError, error):
ragged_string_ops.string_split(source, sep, skip_empty, delimiter,
result_type)
if expected is not None:
result = ragged_string_ops.string_split(source, sep, skip_empty,
delimiter, result_type)
if isinstance(expected, sparse_tensor.SparseTensorValue):
self.assertAllEqual(result.indices, expected.indices)
self.assertAllEqual(result.values, expected.values)
self.assertAllEqual(result.dense_shape, expected.dense_shape)
else:
self.assertAllEqual(result, expected)
def test_feature_layer_cpu(self):
# Inputs.
vocabulary_size = 3
input_a = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 1)),
values=(2, 0, 1),
dense_shape=(2, 2))
input_b = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
# example 2, ids []
indices=((0, 0), (1, 0), (1, 1)),
values=(2, 0, 1),
dense_shape=(3, 2))
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
)
def _initializer(shape, dtype, partition_info):
self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
self.assertEqual(dtypes.float32, dtype)
self.assertIsNone(partition_info)
return embedding_values
# Expected lookup result, using combiner='mean'.
expected_lookups_a = (
# example 0:
(7., 11.), # ids [2], embedding = [7, 11]
# example 1:
(2., 3.5
), # ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
)
expected_lookups_b = (
# example 0:
(
(7., 11.),
(0., 0.),
), # ids [2], embedding = [[7, 11], [0, 0]]
# example 1:
(
(1., 2.),
(3., 5.),
), # ids [0, 1], embedding = [[1, 2], [3, 5]]
# example 2:
(
(0., 0.),
(0., 0.),
), # ids [], embedding = [[0, 0], [0, 0]]
)
# Build columns.
categorical_column_a = fc_lib.categorical_column_with_identity(
key='aaa', num_buckets=vocabulary_size)
categorical_column_b = fc_lib.sequence_categorical_column_with_identity(
key='bbb', num_buckets=vocabulary_size)
embedding_column_a, embedding_column_b = tpu_fc.shared_embedding_columns_v2(
[categorical_column_a, categorical_column_b],
dimension=embedding_dimension,
initializer=_initializer,
max_sequence_lengths=[0, 2])
# Provide sparse input and get dense result.
dense_features = fc_lib.DenseFeatures([embedding_column_a])
sequence_features = fc_lib.SequenceFeatures([embedding_column_b])
embedding_lookup_a = dense_features(input_features)
embedding_lookup_b = sequence_features(input_features)
# Assert expected embedding variable and lookups.
global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
self.assertItemsEqual(('aaa_bbb_shared_embedding:0', ),
tuple([v.name for v in global_vars]))
embedding_var = global_vars[0]
with _initialized_session():
self.assertAllEqual(embedding_values, embedding_var.eval())
self.assertAllEqual(expected_lookups_a, embedding_lookup_a.eval())
self.assertAllEqual(expected_lookups_b,
embedding_lookup_b[0].eval())
def _make_sparse_tensor(indices, values, dense_shape, dtype=np.int32):
return sparse_tensor.SparseTensorValue(np.array(indices, np.int64),
np.array(values, dtype),
np.array(dense_shape, np.int64))
class OptionalTest(test_base.DatasetTestBase, parameterized.TestCase):
def testFromValue(self):
opt = optional_ops.Optional.from_value(constant_op.constant(37.0))
self.assertTrue(self.evaluate(opt.has_value()))
self.assertEqual(37.0, self.evaluate(opt.get_value()))
def testFromStructuredValue(self):
opt = optional_ops.Optional.from_value({
"a":
constant_op.constant(37.0),
"b": (constant_op.constant(["Foo"]), constant_op.constant("Bar"))
})
self.assertTrue(self.evaluate(opt.has_value()))
self.assertEqual({
"a": 37.0,
"b": ([b"Foo"], b"Bar")
}, self.evaluate(opt.get_value()))
def testFromSparseTensor(self):
st_0 = sparse_tensor.SparseTensorValue(indices=np.array([[0]]),
values=np.array([0],
dtype=np.int64),
dense_shape=np.array([1]))
st_1 = sparse_tensor.SparseTensorValue(
indices=np.array([[0, 0], [1, 1]]),
values=np.array([-1., 1.], dtype=np.float32),
dense_shape=np.array([2, 2]))
opt = optional_ops.Optional.from_value((st_0, st_1))
self.assertTrue(self.evaluate(opt.has_value()))
val_0, val_1 = opt.get_value()
for expected, actual in [(st_0, val_0), (st_1, val_1)]:
self.assertAllEqual(expected.indices,
self.evaluate(actual.indices))
self.assertAllEqual(expected.values, self.evaluate(actual.values))
self.assertAllEqual(expected.dense_shape,
self.evaluate(actual.dense_shape))
def testFromNone(self):
value_structure = structure.TensorStructure(dtypes.float32, [])
opt = optional_ops.Optional.none_from_structure(value_structure)
self.assertTrue(
opt.value_structure.is_compatible_with(value_structure))
self.assertFalse(
opt.value_structure.is_compatible_with(
structure.TensorStructure(dtypes.float32, [1])))
self.assertFalse(
opt.value_structure.is_compatible_with(
structure.TensorStructure(dtypes.int32, [])))
self.assertFalse(self.evaluate(opt.has_value()))
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(opt.get_value())
def testAddN(self):
devices = ["/cpu:0"]
if test_util.is_gpu_available():
devices.append("/gpu:0")
for device in devices:
with ops.device(device):
# With value
opt1 = optional_ops.Optional.from_value((1.0, 2.0))
opt2 = optional_ops.Optional.from_value((3.0, 4.0))
add_tensor = math_ops.add_n(
[opt1._variant_tensor, opt2._variant_tensor])
add_opt = optional_ops._OptionalImpl(add_tensor,
opt1.value_structure)
self.assertAllEqual(self.evaluate(add_opt.get_value()),
(4.0, 6.0))
# Without value
opt_none1 = optional_ops.Optional.none_from_structure(
opt1.value_structure)
opt_none2 = optional_ops.Optional.none_from_structure(
opt2.value_structure)
add_tensor = math_ops.add_n(
[opt_none1._variant_tensor, opt_none2._variant_tensor])
add_opt = optional_ops._OptionalImpl(add_tensor,
opt_none1.value_structure)
self.assertFalse(self.evaluate(add_opt.has_value()))
def testNestedAddN(self):
devices = ["/cpu:0"]
if test_util.is_gpu_available():
devices.append("/gpu:0")
for device in devices:
with ops.device(device):
opt1 = optional_ops.Optional.from_value([1, 2.0])
opt2 = optional_ops.Optional.from_value([3, 4.0])
opt3 = optional_ops.Optional.from_value(
(5.0, opt1._variant_tensor))
opt4 = optional_ops.Optional.from_value(
(6.0, opt2._variant_tensor))
add_tensor = math_ops.add_n(
[opt3._variant_tensor, opt4._variant_tensor])
add_opt = optional_ops._OptionalImpl(add_tensor,
opt3.value_structure)
self.assertEqual(self.evaluate(add_opt.get_value()[0]), 11.0)
inner_add_opt = optional_ops._OptionalImpl(
add_opt.get_value()[1], opt1.value_structure)
self.assertAllEqual(inner_add_opt.get_value(), [4, 6.0])
def testZerosLike(self):
devices = ["/cpu:0"]
if test_util.is_gpu_available():
devices.append("/gpu:0")
for device in devices:
with ops.device(device):
# With value
opt = optional_ops.Optional.from_value((1.0, 2.0))
zeros_tensor = array_ops.zeros_like(opt._variant_tensor)
zeros_opt = optional_ops._OptionalImpl(zeros_tensor,
opt.value_structure)
self.assertAllEqual(self.evaluate(zeros_opt.get_value()),
(0.0, 0.0))
# Without value
opt_none = optional_ops.Optional.none_from_structure(
opt.value_structure)
zeros_tensor = array_ops.zeros_like(opt_none._variant_tensor)
zeros_opt = optional_ops._OptionalImpl(
zeros_tensor, opt_none.value_structure)
self.assertFalse(self.evaluate(zeros_opt.has_value()))
def testNestedZerosLike(self):
devices = ["/cpu:0"]
if test_util.is_gpu_available():
devices.append("/gpu:0")
for device in devices:
with ops.device(device):
opt1 = optional_ops.Optional.from_value(1.0)
opt2 = optional_ops.Optional.from_value(opt1._variant_tensor)
zeros_tensor = array_ops.zeros_like(opt2._variant_tensor)
zeros_opt = optional_ops._OptionalImpl(zeros_tensor,
opt2.value_structure)
inner_zeros_opt = optional_ops._OptionalImpl(
zeros_opt.get_value(), opt1.value_structure)
self.assertEqual(self.evaluate(inner_zeros_opt.get_value()),
0.0)
def testCopyToGPU(self):
if not test_util.is_gpu_available():
self.skipTest("No GPU available")
with ops.device("/cpu:0"):
optional_with_value = optional_ops.Optional.from_value(
(constant_op.constant(37.0), constant_op.constant("Foo"),
constant_op.constant(42)))
optional_none = optional_ops.Optional.none_from_structure(
structure.TensorStructure(dtypes.float32, []))
with ops.device("/gpu:0"):
gpu_optional_with_value = optional_ops._OptionalImpl(
array_ops.identity(optional_with_value._variant_tensor),
optional_with_value.value_structure)
gpu_optional_none = optional_ops._OptionalImpl(
array_ops.identity(optional_none._variant_tensor),
optional_none.value_structure)
gpu_optional_with_value_has_value = gpu_optional_with_value.has_value(
)
gpu_optional_with_value_values = gpu_optional_with_value.get_value(
)
gpu_optional_none_has_value = gpu_optional_none.has_value()
self.assertTrue(self.evaluate(gpu_optional_with_value_has_value))
self.assertEqual((37.0, b"Foo", 42),
self.evaluate(gpu_optional_with_value_values))
self.assertFalse(self.evaluate(gpu_optional_none_has_value))
def testNestedCopyToGPU(self):
if not test_util.is_gpu_available():
self.skipTest("No GPU available")
with ops.device("/cpu:0"):
optional_with_value = optional_ops.Optional.from_value(
(constant_op.constant(37.0), constant_op.constant("Foo"),
constant_op.constant(42)))
optional_none = optional_ops.Optional.none_from_structure(
structure.TensorStructure(dtypes.float32, []))
nested_optional = optional_ops.Optional.from_value(
(optional_with_value._variant_tensor,
optional_none._variant_tensor, 1.0))
with ops.device("/gpu:0"):
gpu_nested_optional = optional_ops._OptionalImpl(
array_ops.identity(nested_optional._variant_tensor),
nested_optional.value_structure)
gpu_nested_optional_has_value = gpu_nested_optional.has_value()
gpu_nested_optional_values = gpu_nested_optional.get_value()
self.assertTrue(self.evaluate(gpu_nested_optional_has_value))
inner_with_value = optional_ops._OptionalImpl(
gpu_nested_optional_values[0], optional_with_value.value_structure)
inner_none = optional_ops._OptionalImpl(gpu_nested_optional_values[1],
optional_none.value_structure)
self.assertEqual((37.0, b"Foo", 42),
self.evaluate(inner_with_value.get_value()))
self.assertFalse(self.evaluate(inner_none.has_value()))
self.assertEqual(1.0, self.evaluate(gpu_nested_optional_values[2]))
def _assertElementValueEqual(self, expected, actual):
if isinstance(expected, dict):
self.assertItemsEqual(list(expected.keys()), list(actual.keys()))
for k in expected.keys():
self._assertElementValueEqual(expected[k], actual[k])
elif isinstance(expected, sparse_tensor.SparseTensorValue):
self.assertAllEqual(expected.indices, actual.indices)
self.assertAllEqual(expected.values, actual.values)
self.assertAllEqual(expected.dense_shape, actual.dense_shape)
else:
self.assertAllEqual(expected, actual)
# pylint: disable=g-long-lambda
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant(37.0),
structure.TensorStructure(dtypes.float32, [])),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[0, 1]],
values=constant_op.constant([0], dtype=dtypes.int32),
dense_shape=[10, 10]),
structure.SparseTensorStructure(dtypes.int32, [10, 10])),
("Nest", lambda: {
"a": constant_op.constant(37.0),
"b": (constant_op.constant(["Foo"]), constant_op.constant("Bar"))
},
structure.NestedStructure({
"a":
structure.TensorStructure(dtypes.float32, []),
"b": (structure.TensorStructure(dtypes.string, [1]),
structure.TensorStructure(dtypes.string, []))
})),
("Optional", lambda: optional_ops.Optional.from_value(37.0),
optional_ops.OptionalStructure(
structure.TensorStructure(dtypes.float32, []))),
)
def testOptionalStructure(self, tf_value_fn, expected_value_structure):
tf_value = tf_value_fn()
opt = optional_ops.Optional.from_value(tf_value)
self.assertTrue(
expected_value_structure.is_compatible_with(opt.value_structure))
self.assertTrue(
opt.value_structure.is_compatible_with(expected_value_structure))
opt_structure = structure.Structure.from_value(opt)
self.assertIsInstance(opt_structure, optional_ops.OptionalStructure)
self.assertTrue(opt_structure.is_compatible_with(opt_structure))
self.assertTrue(
opt_structure._value_structure.is_compatible_with(
expected_value_structure))
self.assertEqual([dtypes.variant], opt_structure._flat_types)
self.assertEqual([tensor_shape.scalar()], opt_structure._flat_shapes)
# All OptionalStructure objects are not compatible with a non-optional
# value.
non_optional_structure = structure.Structure.from_value(
constant_op.constant(42.0))
self.assertFalse(
opt_structure.is_compatible_with(non_optional_structure))
# Assert that the optional survives a round-trip via _from_tensor_list()
# and _to_tensor_list().
round_trip_opt = opt_structure._from_tensor_list(
opt_structure._to_tensor_list(opt))
if isinstance(tf_value, optional_ops.Optional):
self._assertElementValueEqual(
self.evaluate(tf_value.get_value()),
self.evaluate(round_trip_opt.get_value().get_value()))
else:
self._assertElementValueEqual(
self.evaluate(tf_value),
self.evaluate(round_trip_opt.get_value()))
@parameterized.named_parameters(
("Tensor", np.array([1, 2, 3], dtype=np.int32),
lambda: constant_op.constant([4, 5, 6], dtype=dtypes.int32), True),
("SparseTensor",
sparse_tensor.SparseTensorValue(indices=[[0, 0], [1, 1]],
values=np.array([-1., 1.],
dtype=np.float32),
dense_shape=[2, 2]),
lambda: sparse_tensor.SparseTensor(indices=[[0, 1], [1, 0]],
values=[37.0, 42.0],
dense_shape=[2, 2]), False),
("Nest", {
"a":
np.array([1, 2, 3], dtype=np.int32),
"b":
sparse_tensor.SparseTensorValue(indices=[[0, 0], [1, 1]],
values=np.array([-1., 1.],
dtype=np.float32),
dense_shape=[2, 2])
}, lambda: {
"a":
constant_op.constant([4, 5, 6], dtype=dtypes.int32),
"b":
sparse_tensor.SparseTensor(indices=[[0, 1], [1, 0]],
values=[37.0, 42.0],
dense_shape=[2, 2])
}, False),
)
def testIteratorGetNextAsOptional(self, np_value, tf_value_fn,
works_on_gpu):
if not works_on_gpu and test.is_gpu_available():
self.skipTest("Test case not yet supported on GPU.")
ds = dataset_ops.Dataset.from_tensors(np_value).repeat(3)
if context.executing_eagerly():
iterator = dataset_ops.make_one_shot_iterator(ds)
# For each element of the dataset, assert that the optional evaluates to
# the expected value.
for _ in range(3):
next_elem = iterator_ops.get_next_as_optional(iterator)
self.assertIsInstance(next_elem, optional_ops.Optional)
self.assertTrue(
next_elem.value_structure.is_compatible_with(
structure.Structure.from_value(tf_value_fn())))
self.assertTrue(next_elem.has_value())
self._assertElementValueEqual(np_value, next_elem.get_value())
# After exhausting the iterator, `next_elem.has_value()` will evaluate to
# false, and attempting to get the value will fail.
for _ in range(2):
next_elem = iterator_ops.get_next_as_optional(iterator)
self.assertFalse(self.evaluate(next_elem.has_value()))
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(next_elem.get_value())
else:
iterator = dataset_ops.make_initializable_iterator(ds)
next_elem = iterator_ops.get_next_as_optional(iterator)
self.assertIsInstance(next_elem, optional_ops.Optional)
self.assertTrue(
next_elem.value_structure.is_compatible_with(
structure.Structure.from_value(tf_value_fn())))
# Before initializing the iterator, evaluating the optional fails with
# a FailedPreconditionError. This is only relevant in graph mode.
elem_has_value_t = next_elem.has_value()
elem_value_t = next_elem.get_value()
with self.assertRaises(errors.FailedPreconditionError):
self.evaluate(elem_has_value_t)
with self.assertRaises(errors.FailedPreconditionError):
self.evaluate(elem_value_t)
# Now we initialize the iterator.
self.evaluate(iterator.initializer)
# For each element of the dataset, assert that the optional evaluates to
# the expected value.
for _ in range(3):
elem_has_value, elem_value = self.evaluate(
[elem_has_value_t, elem_value_t])
self.assertTrue(elem_has_value)
self._assertElementValueEqual(np_value, elem_value)
# After exhausting the iterator, `next_elem.has_value()` will evaluate to
# false, and attempting to get the value will fail.
for _ in range(2):
self.assertFalse(self.evaluate(elem_has_value_t))
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(elem_value_t)
def testFunctionBoundaries(self):
@def_function.function
def get_optional():
x = constant_op.constant(1.0)
opt = optional_ops.Optional.from_value(x)
# TODO(skyewm): support returning Optionals from functions?
return opt._variant_tensor
# TODO(skyewm): support Optional arguments?
@def_function.function
def consume_optional(opt_tensor):
value_structure = structure.TensorStructure(dtypes.float32, [])
opt = optional_ops._OptionalImpl(opt_tensor, value_structure)
return opt.get_value()
opt_tensor = get_optional()
val = consume_optional(opt_tensor)
self.assertEqual(self.evaluate(val), 1.0)
def testLimitedRetracing(self):
trace_count = [0]
@def_function.function
def f(opt):
trace_count[0] += 1
return opt.get_value()
opt1 = optional_ops.Optional.from_value(constant_op.constant(37.0))
opt2 = optional_ops.Optional.from_value(constant_op.constant(42.0))
for _ in range(10):
self.assertEqual(self.evaluate(f(opt1)), 37.0)
self.assertEqual(self.evaluate(f(opt2)), 42.0)
self.assertEqual(trace_count[0], 1)
def testFromTensorSlicesMixed(self):
"""Test a dataset that represents the slices from a tuple of tensors."""
components = (np.tile(np.array([[1], [2], [3]]), 20),
np.tile(np.array([[12], [13], [14]]), 22),
np.array([37.0, 38.0, 39.0]),
sparse_tensor.SparseTensorValue(
indices=np.array([[0, 0], [1, 0], [2, 0]]),
values=np.array([0, 0, 0]),
dense_shape=np.array([3, 1])),
sparse_tensor.SparseTensorValue(
indices=np.array([[0, 0], [1, 1], [2, 2]]),
values=np.array([1, 2, 3]),
dense_shape=np.array([3, 3])))
iterator = (
dataset_ops.Dataset.from_tensor_slices(components)
.make_initializable_iterator())
init_op = iterator.initializer
get_next = iterator.get_next()
self.assertEqual([
tensor_shape.TensorShape(c.dense_shape[1:])
if sparse_tensor.is_sparse(c) else c.shape[1:] for c in components
], [shape for shape in iterator.output_shapes])
with self.cached_session() as sess:
self.evaluate(init_op)
expected = [
(sparse_tensor.SparseTensorValue(
indices=np.array([[0]]),
values=np.array([0]),
dense_shape=np.array([1])),
sparse_tensor.SparseTensorValue(
indices=np.array([[0]]),
values=np.array([1]),
dense_shape=np.array([3]))),
(sparse_tensor.SparseTensorValue(
indices=np.array([[0]]),
values=np.array([0]),
dense_shape=np.array([1])),
sparse_tensor.SparseTensorValue(
indices=np.array([[1]]),
values=np.array([2]),
dense_shape=np.array([3]))),
(sparse_tensor.SparseTensorValue(
indices=np.array([[0]]),
values=np.array([0]),
dense_shape=np.array([1])),
sparse_tensor.SparseTensorValue(
indices=np.array([[2]]),
values=np.array([3]),
dense_shape=np.array([3]))),
]
for i in range(3):
results = self.evaluate(get_next)
for component, result_component in zip(
(list(zip(*components[:3]))[i] + expected[i]), results):
if sparse_tensor.is_sparse(component):
self.assertSparseValuesEqual(component, result_component)
else:
self.assertAllEqual(component, result_component)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
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()
def dense_to_sparse_non_scalar(tensor):
indices = array_ops.where(
array_ops.ones_like(tensor, dtype=dtypes.bool))
values = array_ops.gather_nd(tensor, indices)
shape = array_ops.shape(tensor, out_type=dtypes.int64)
return sparse_tensor.SparseTensorValue(indices, values, shape)
def make_sparse_fn(i):
return sparse_tensor.SparseTensorValue(
indices=np.array([[0, 0]]),
values=(i * np.array([1])),
dense_shape=np.array([1, 1]))
def test_feature_layer_cpu(self):
# Inputs.
vocabulary_size = 3
sparse_input = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
# example 2, ids []
# example 3, ids [1]
indices=((0, 0), (1, 0), (1, 1), (3, 0)),
values=(2, 0, 1, 1),
dense_shape=(4, 2))
# Embedding variable.
embedding_dimension = 2
embedding_values = (
(1., 2.), # id 0
(3., 5.), # id 1
(7., 11.) # id 2
)
def _initializer(shape, dtype, partition_info):
self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
self.assertEqual(dtypes.float32, dtype)
self.assertIsNone(partition_info)
return embedding_values
# Expected lookup result, using combiner='mean'.
expected_lookups = (
# example 0, ids [2], embedding = [7, 11]
(7., 11.),
# example 1, ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
(2., 3.5),
# example 2, ids [], embedding = [0, 0]
(0., 0.),
# example 3, ids [1], embedding = [3, 5]
(3., 5.),
)
expected_lookups_sequence = (
# example 0, ids [2], embedding = [[7, 11], [0, 0]]
(
(7., 11.),
(0., 0.),
),
# example 1, ids [0, 1], embedding = [[1, 2], [3. 5]]
(
(1., 2.),
(3., 5.),
),
# example 2, ids [], embedding = [0, 0]
(
(0., 0.),
(0., 0.),
),
# example 3, ids [1], embedding = [3, 5]
(
(3., 5.),
(0., 0.),
),
)
# Build columns.
categorical_column = fc_lib.categorical_column_with_identity(
key='aaa', num_buckets=vocabulary_size)
sequence_categorical_column = (
fc_lib.sequence_categorical_column_with_identity(
key='bbb', num_buckets=vocabulary_size))
embedding_column = tpu_fc.embedding_column_v2(
categorical_column,
dimension=embedding_dimension,
initializer=_initializer)
sequence_embedding_column = tpu_fc.embedding_column_v2(
sequence_categorical_column,
dimension=embedding_dimension,
initializer=_initializer,
max_sequence_length=2)
# Provide sparse input and get dense result.
features = {'aaa': sparse_input, 'bbb': sparse_input}
dense_features = fc_lib.DenseFeatures([embedding_column])
sequence_features = fc_lib.SequenceFeatures(
[sequence_embedding_column])
embedding_lookup = dense_features(features)
sequence_embedding_lookup = sequence_features(features)
# Assert expected embedding variable and lookups.
global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
self.assertItemsEqual((
'dense_features/aaa_embedding/embedding_weights:0',
'sequence_features/bbb_embedding/embedding_weights:0',
), tuple([v.name for v in global_vars]))
with _initialized_session():
self.assertAllEqual(embedding_values, global_vars[0].eval())
self.assertAllEqual(expected_lookups, embedding_lookup.eval())
self.assertAllEqual(expected_lookups_sequence,
sequence_embedding_lookup[0].eval())
def _SparseTensorValue_2x3x4(self):
ind = np.array([[0, 0, 1], [0, 1, 0], [0, 1, 2], [1, 0, 3], [1, 1, 1],
[1, 1, 3], [1, 2, 2]])
val = np.array([1, 10, 12, 103, 111, 113, 122])
shape = np.array([2, 3, 4])
return sparse_tensor.SparseTensorValue(ind, val, shape)
def _SparseTensorValue_1x1x1(self):
ind = np.array([[0, 0, 0]]).astype(np.int64)
val = np.array([0]).astype(np.int32)
shape = np.array([3, 4, 5]).astype(np.int64)
return sparse_tensor_lib.SparseTensorValue(ind, val, shape)
def _SparseTensorValue_2x5x6(self):
return sparse_tensor.SparseTensorValue(self._IND_2_5_6, self._VAL_2_5_6,
self._SHP_2_5_6)
def test_dense_features_not_trainable(self):
# Inputs.
vocabulary_size = 3
sparse_input = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
# example 2, ids []
# example 3, ids [1]
indices=((0, 0), (1, 0), (1, 4), (3, 0)),
values=(2, 0, 1, 1),
dense_shape=(4, 5))
# Embedding variable.
embedding_dimension = 2
embedding_values = (
(1., 2.), # id 0
(3., 5.), # id 1
(7., 11.) # id 2
)
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
# Expected lookup result, using combiner='mean'.
expected_lookups = (
# example 0, ids [2], embedding = [7, 11]
(7., 11.),
# example 1, ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
(2., 3.5),
# example 2, ids [], embedding = [0, 0]
(0., 0.),
# example 3, ids [1], embedding = [3, 5]
(3., 5.),
)
# Build columns.
categorical_column = fc.categorical_column_with_identity(
key='aaa', num_buckets=vocabulary_size)
embedding_column = fc.embedding_column(categorical_column,
dimension=embedding_dimension,
initializer=_initializer,
trainable=False)
# Provide sparse input and get dense result.
dense_features = df.DenseFeatures((embedding_column, ))({
'aaa':
sparse_input
})
# Assert expected embedding variable and lookups.
global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
self.assertCountEqual(
('dense_features/aaa_embedding/embedding_weights:0', ),
tuple([v.name for v in global_vars]))
self.assertCountEqual([],
ops.get_collection(
ops.GraphKeys.TRAINABLE_VARIABLES))
self.evaluate(variables_lib.global_variables_initializer())
self.evaluate(lookup_ops.tables_initializer())
self.assertAllEqual(embedding_values, self.evaluate(global_vars[0]))
self.assertAllEqual(expected_lookups, self.evaluate(dense_features))
def _sparse(i):
return sparse_tensor.SparseTensorValue(
indices=[[0]], values=(i * [1]), dense_shape=[1])
def test_embedding_column(self):
vocabulary_size = 3
sparse_input_a = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 1)),
values=(2, 0, 1),
dense_shape=(2, 2))
sparse_input_b = sparse_tensor.SparseTensorValue(
# example 0, ids [1]
# example 1, ids [2, 0]
indices=((0, 0), (1, 0), (1, 1)),
values=(1, 2, 0),
dense_shape=(2, 2))
embedding_dimension_a = 2
embedding_values_a = (
(1., 2.), # id 0
(3., 4.), # id 1
(5., 6.) # id 2
)
embedding_dimension_b = 3
embedding_values_b = (
(11., 12., 13.), # id 0
(14., 15., 16.), # id 1
(17., 18., 19.) # id 2
)
def _get_initializer(embedding_dimension, embedding_values):
def _initializer(shape, dtype, partition_info):
self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
self.assertEqual(dtypes.float32, dtype)
self.assertIsNone(partition_info)
return embedding_values
return _initializer
expected_input_layer = [
# example 0, ids_a [2], ids_b [1]
[[5., 6., 14., 15., 16.], [0., 0., 0., 0., 0.]],
# example 1, ids_a [0, 1], ids_b [2, 0]
[[1., 2., 17., 18., 19.], [3., 4., 11., 12., 13.]],
]
expected_sequence_length = [1, 2]
categorical_column_a = sfc.sequence_categorical_column_with_identity(
key='aaa', num_buckets=vocabulary_size)
embedding_column_a = sfc._sequence_embedding_column(
categorical_column_a, dimension=embedding_dimension_a,
initializer=_get_initializer(embedding_dimension_a, embedding_values_a))
categorical_column_b = sfc.sequence_categorical_column_with_identity(
key='bbb', num_buckets=vocabulary_size)
embedding_column_b = sfc._sequence_embedding_column(
categorical_column_b, dimension=embedding_dimension_b,
initializer=_get_initializer(embedding_dimension_b, embedding_values_b))
input_layer, sequence_length = sfc.sequence_input_layer(
features={
'aaa': sparse_input_a,
'bbb': sparse_input_b,
},
# Test that columns are reordered alphabetically.
feature_columns=[embedding_column_b, embedding_column_a])
global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
self.assertItemsEqual(
('sequence_input_layer/aaa_embedding/embedding_weights:0',
'sequence_input_layer/bbb_embedding/embedding_weights:0'),
tuple([v.name for v in global_vars]))
with monitored_session.MonitoredSession() as sess:
self.assertAllEqual(embedding_values_a, global_vars[0].eval(session=sess))
self.assertAllEqual(embedding_values_b, global_vars[1].eval(session=sess))
self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess))
self.assertAllEqual(
expected_sequence_length, sequence_length.eval(session=sess))
