def test_when_x_has_two_larger_larger_batch_rank_than_batch_rank_arg(self):
batch_shape = [4, 5]
x = self._rng.rand(2, 3, 4, 5, 6)
for static_batch_shape in [
tf.TensorShape(batch_shape), tf.TensorShape(None)]:
with self.test_session():
mat = operator_pd._flip_vector_to_matrix(
x, batch_shape, static_batch_shape)
mat_v = mat.eval()
self.assertAllEqual((4, 5, 6, 2*3), mat_v.shape)
def test_when_batch_shape_requires_reshape_of_vector_batch_shape(self):
batch_shape = [5, 4]
x = self._rng.rand(3, 4, 5, 6) # Note x has (4,5) and batch_shape is (5, 4)
for static_batch_shape in [
tf.TensorShape(batch_shape), tf.TensorShape(None)]:
with self.test_session():
mat = operator_pd._flip_vector_to_matrix(
x, batch_shape, static_batch_shape)
mat_v = mat.eval()
self.assertAllEqual((5, 4, 6, 3), mat_v.shape)
def test_when_x_batch_rank_is_same_as_batch_rank_arg(self):
batch_shape = [4, 5]
x = self._rng.rand(4, 5, 6)
for static_batch_shape in [
tf.TensorShape(batch_shape), tf.TensorShape(None)]:
with self.test_session():
mat = operator_pd._flip_vector_to_matrix(
x, batch_shape, static_batch_shape)
mat_v = mat.eval()
expected_mat_v = x.reshape(x.shape + (1,))
self.assertAllEqual(expected_mat_v, mat_v)
def test_when_x_has_two_larger_larger_batch_rank_than_batch_rank_arg(self):
batch_shape = [4, 5]
x = self._rng.rand(2, 3, 4, 5, 6)
for static_batch_shape in [
tf.TensorShape(batch_shape),
tf.TensorShape(None)
]:
with self.test_session():
mat = operator_pd._flip_vector_to_matrix(
x, batch_shape, static_batch_shape)
mat_v = mat.eval()
self.assertAllEqual((4, 5, 6, 2 * 3), mat_v.shape)
def test_when_batch_shape_requires_reshape_of_vector_batch_shape(self):
batch_shape = [5, 4]
x = self._rng.rand(3, 4, 5,
6) # Note x has (4,5) and batch_shape is (5, 4)
for static_batch_shape in [
tf.TensorShape(batch_shape),
tf.TensorShape(None)
]:
with self.test_session():
mat = operator_pd._flip_vector_to_matrix(
x, batch_shape, static_batch_shape)
mat_v = mat.eval()
self.assertAllEqual((5, 4, 6, 3), mat_v.shape)
def test_when_x_batch_rank_is_same_as_batch_rank_arg(self):
batch_shape = [4, 5]
x = self._rng.rand(4, 5, 6)
for static_batch_shape in [
tf.TensorShape(batch_shape),
tf.TensorShape(None)
]:
with self.test_session():
mat = operator_pd._flip_vector_to_matrix(
x, batch_shape, static_batch_shape)
mat_v = mat.eval()
expected_mat_v = x.reshape(x.shape + (1, ))
self.assertAllEqual(expected_mat_v, mat_v)
