def testEventShapeStatic(self):
"""Check shape methods when shape is statically known."""
shape_in = tensor_shape.TensorShape([
6,
])
shape_out = tensor_shape.TensorShape([2, 3])
bijector_static = Reshape(event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
# test that forward_ and inverse_event_shape do sensible things
# when shapes are statically known.
self.assertEqual(bijector_static.forward_event_shape(shape_in),
shape_out)
self.assertEqual(bijector_static.inverse_event_shape(shape_out),
shape_in)
with self.test_session() as sess:
(
shape_out_static_,
shape_in_static_,
) = sess.run((
bijector_static.forward_event_shape_tensor(shape_in),
bijector_static.inverse_event_shape_tensor(shape_out),
))
self.assertAllEqual(shape_out, shape_out_static_)
self.assertAllEqual(shape_in, shape_in_static_)
def testEventShape(self):
shape_in_static = tensor_shape.TensorShape([2, 3])
shape_out_static = tensor_shape.TensorShape([
6,
])
bijector = Reshape(event_shape_out=shape_out_static,
event_shape_in=shape_in_static,
validate_args=True)
# test that forward_ and inverse_event_shape do sensible things
# when shapes are statically known.
self.assertEqual(bijector.forward_event_shape(shape_in_static),
shape_out_static)
self.assertEqual(bijector.inverse_event_shape(shape_out_static),
shape_in_static)
def testEventShape(self):
shape_in_static = tensor_shape.TensorShape([2, 3])
shape_out_static = tensor_shape.TensorShape([6,])
bijector = Reshape(
event_shape_out=shape_out_static,
event_shape_in=shape_in_static, validate_args=True)
# test that forward_ and inverse_event_shape do sensible things
# when shapes are statically known.
self.assertEqual(
bijector.forward_event_shape(shape_in_static),
shape_out_static)
self.assertEqual(
bijector.inverse_event_shape(shape_out_static),
shape_in_static)
def testEventShapeDynamic(self):
"""Check shape methods with static ndims but dynamic shape."""
shape_in = tensor_shape.TensorShape([
6,
])
shape_in_partial = tensor_shape.TensorShape([
None,
])
shape_in_ph = array_ops.placeholder(shape=[
1,
], dtype=dtypes.int32)
shape_out = tensor_shape.TensorShape([2, 3])
shape_out_partial = tensor_shape.TensorShape([None, None])
shape_out_ph = array_ops.placeholder(shape=[
2,
], dtype=dtypes.int32)
bijector = Reshape(event_shape_out=shape_out_ph,
event_shape_in=shape_in_ph,
validate_args=True)
# if event shapes are not statically available, should
# return partially-specified TensorShapes.
self.assertAllEqual(
bijector.forward_event_shape(shape_in).as_list(),
shape_out_partial.as_list())
self.assertAllEqual(
bijector.inverse_event_shape(shape_out).as_list(),
shape_in_partial.as_list())
# using the _tensor methods, we should always get a fully-specified
# result since these are evaluated at graph runtime.
with self.test_session() as sess:
(shape_out_, shape_in_) = sess.run((
bijector.forward_event_shape_tensor(shape_in),
bijector.inverse_event_shape_tensor(shape_out),
),
feed_dict={
shape_in_ph: shape_in,
shape_out_ph: shape_out,
})
self.assertAllEqual(shape_out, shape_out_)
self.assertAllEqual(shape_in, shape_in_)