def test_set_shape_error(self):
spec = CustomTypeSpec([3, None], tf.int32)
kt = keras_tensor.KerasTensor(spec)
with self.assertRaisesRegex(
ValueError, "Keras requires TypeSpec to have a `with_shape` method"
):
kt.set_shape([3, 3])
def test_missing_shape_error(self):
spec = CustomTypeSpec(None, tf.int32)
del spec.shape
with self.assertRaisesRegex(
ValueError,
"KerasTensor only supports TypeSpecs that have a shape field; .*",
):
keras_tensor.KerasTensor(spec)
def test_set_shape(self, spec, new_shape, expected_shape):
kt = keras_tensor.KerasTensor(spec)
kt.set_shape(new_shape)
if expected_shape is None:
self.assertIsNone(kt.type_spec.shape.rank)
else:
self.assertEqual(kt.type_spec.shape.as_list(), expected_shape)
self.assertTrue(kt.type_spec.is_compatible_with(spec))
def test_missing_dtype_error(self):
spec = CustomTypeSpec(None, tf.int32)
del spec.dtype
kt = keras_tensor.KerasTensor(spec)
with self.assertRaisesRegex(
AttributeError,
"KerasTensor wraps TypeSpec .* which does not have a dtype."):
kt.dtype # pylint: disable=pointless-statement
def test_wrong_shape_type_error(self):
spec = CustomTypeSpec(None, tf.int32)
spec.shape = "foo"
with self.assertRaisesRegex(
TypeError,
"KerasTensor requires that wrapped TypeSpec's shape is a "
"TensorShape; .*"):
keras_tensor.KerasTensor(spec)
def test_wrong_dtype_type_error(self):
spec = CustomTypeSpec(None, tf.int32)
spec.dtype = "foo"
kt = keras_tensor.KerasTensor(spec)
with self.assertRaisesRegex(
TypeError,
"KerasTensor requires that wrapped TypeSpec's dtype is a DType; .*",
):
kt.dtype
def test_repr_and_string(self):
kt = keras_tensor.KerasTensor(
type_spec=tf.TensorSpec(shape=(1, 2, 3), dtype=tf.float32))
expected_str = ("KerasTensor(type_spec=TensorSpec(shape=(1, 2, 3), "
"dtype=tf.float32, name=None))")
expected_repr = "<KerasTensor: shape=(1, 2, 3) dtype=float32>"
self.assertEqual(expected_str, str(kt))
self.assertEqual(expected_repr, repr(kt))
kt = keras_tensor.KerasTensor(type_spec=tf.TensorSpec(shape=(2, ),
dtype=tf.int32),
inferred_value=[2, 3])
expected_str = ("KerasTensor(type_spec=TensorSpec(shape=(2,), "
"dtype=tf.int32, name=None), inferred_value=[2, 3])")
expected_repr = (
"<KerasTensor: shape=(2,) dtype=int32 inferred_value=[2, 3]>")
self.assertEqual(expected_str, str(kt))
self.assertEqual(expected_repr, repr(kt))
kt = keras_tensor.KerasTensor(
type_spec=tf.SparseTensorSpec(shape=(1, 2, 3), dtype=tf.float32))
expected_str = ("KerasTensor(type_spec=SparseTensorSpec("
"TensorShape([1, 2, 3]), tf.float32))")
expected_repr = ("<KerasTensor: type_spec=SparseTensorSpec("
"TensorShape([1, 2, 3]), tf.float32)>")
self.assertEqual(expected_str, str(kt))
self.assertEqual(expected_repr, repr(kt))
inp = layers.Input(shape=(3, 5))
kt = layers.Dense(10)(inp)
expected_str = (
"KerasTensor(type_spec=TensorSpec(shape=(None, 3, 10), "
"dtype=tf.float32, name=None), name='dense/BiasAdd:0', "
"description=\"created by layer 'dense'\")")
expected_repr = (
"<KerasTensor: shape=(None, 3, 10) dtype=float32 (created "
"by layer 'dense')>")
self.assertEqual(expected_str, str(kt))
self.assertEqual(expected_repr, repr(kt))
kt = tf.reshape(kt, shape=(3, 5, 2))
expected_str = (
"KerasTensor(type_spec=TensorSpec(shape=(3, 5, 2), dtype=tf.float32, "
"name=None), name='tf.reshape/Reshape:0', description=\"created "
"by layer 'tf.reshape'\")")
expected_repr = (
"<KerasTensor: shape=(3, 5, 2) dtype=float32 (created "
"by layer 'tf.reshape')>")
self.assertEqual(expected_str, str(kt))
self.assertEqual(expected_repr, repr(kt))
kts = tf.unstack(kt)
for i in range(3):
expected_str = (
"KerasTensor(type_spec=TensorSpec(shape=(5, 2), dtype=tf.float32, "
"name=None), name='tf.unstack/unstack:%s', description=\"created "
"by layer 'tf.unstack'\")" % (i, ))
expected_repr = ("<KerasTensor: shape=(5, 2) dtype=float32 "
"(created by layer 'tf.unstack')>")
self.assertEqual(expected_str, str(kts[i]))
self.assertEqual(expected_repr, repr(kts[i]))
def test_set_shape_equals_expected_shape(self):
# Tests b/203201161: DenseSpec has both a _shape and a _shape_tuple
# field, and we need to be sure both get updated.
kt = keras_tensor.KerasTensor(tf.TensorSpec([8, None], tf.int32))
kt.set_shape([8, 3])
self.assertEqual(kt.type_spec, tf.TensorSpec([8, 3], tf.int32))
def test_shape(self, spec, expected_shape):
kt = keras_tensor.KerasTensor(spec)
self.assertEqual(kt.shape.as_list(), expected_shape)
