def test_non_vector_shape(self):
dims = 2
new_batch_shape = 2
old_batch_shape = [2]
new_batch_shape_ph = (constant_op.constant(np.int32(new_batch_shape))
if self.is_static_shape else
array_ops.placeholder_with_default(
np.int32(new_batch_shape), shape=None))
scale = np.ones(old_batch_shape + [dims], self.dtype)
scale_ph = array_ops.placeholder_with_default(
scale, shape=scale.shape if self.is_static_shape else None)
mvn = mvn_lib.MultivariateNormalDiag(scale_diag=scale_ph)
if self.is_static_shape:
with self.assertRaisesRegexp(ValueError, r".*must be a vector.*"):
batch_reshape_lib.BatchReshape(distribution=mvn,
batch_shape=new_batch_shape_ph,
validate_args=True)
else:
with self.cached_session():
with self.assertRaisesOpError(r".*must be a vector.*"):
batch_reshape_lib.BatchReshape(
distribution=mvn,
batch_shape=new_batch_shape_ph,
validate_args=True).sample().eval()
def test_non_positive_shape(self):
dims = 2
old_batch_shape = [4]
if self.is_static_shape:
# Unknown first dimension does not trigger size check. Note that
# any dimension < 0 is treated statically as unknown.
new_batch_shape = [-1, 0]
else:
new_batch_shape = [-2,
-2] # -2 * -2 = 4, same size as the old shape.
new_batch_shape_ph = (constant_op.constant(np.int32(new_batch_shape))
if self.is_static_shape else
array_ops.placeholder_with_default(
np.int32(new_batch_shape), shape=None))
scale = np.ones(old_batch_shape + [dims], self.dtype)
scale_ph = array_ops.placeholder_with_default(
scale, shape=scale.shape if self.is_static_shape else None)
mvn = mvn_lib.MultivariateNormalDiag(scale_diag=scale_ph)
if self.is_static_shape:
with self.assertRaisesRegexp(ValueError, r".*must be >=-1.*"):
batch_reshape_lib.BatchReshape(distribution=mvn,
batch_shape=new_batch_shape_ph,
validate_args=True)
else:
with self.cached_session():
with self.assertRaisesOpError(r".*must be >=-1.*"):
batch_reshape_lib.BatchReshape(
distribution=mvn,
batch_shape=new_batch_shape_ph,
validate_args=True).sample().eval()
def test_bad_reshape_size(self):
dims = 2
new_batch_shape = [2, 3]
old_batch_shape = [2] # 2 != 2*3
new_batch_shape_ph = (constant_op.constant(np.int32(new_batch_shape))
if self.is_static_shape else
array_ops.placeholder_with_default(
np.int32(new_batch_shape), shape=None))
scale = np.ones(old_batch_shape + [dims], self.dtype)
scale_ph = array_ops.placeholder_with_default(
scale, shape=scale.shape if self.is_static_shape else None)
mvn = mvn_lib.MultivariateNormalDiag(scale_diag=scale_ph)
if self.is_static_shape:
with self.assertRaisesRegexp(
ValueError, (r"`batch_shape` size \(6\) must match "
r"`distribution\.batch_shape` size \(2\)")):
batch_reshape_lib.BatchReshape(distribution=mvn,
batch_shape=new_batch_shape_ph,
validate_args=True)
else:
with self.cached_session():
with self.assertRaisesOpError(r"Shape sizes do not match."):
batch_reshape_lib.BatchReshape(
distribution=mvn,
batch_shape=new_batch_shape_ph,
validate_args=True).sample().eval()
def test_broadcasting_explicitly_unsupported(self):
old_batch_shape = [4]
new_batch_shape = [1, 4, 1]
rate_ = self.dtype([1, 10, 2, 20])
rate = array_ops.placeholder_with_default(
rate_, shape=old_batch_shape if self.is_static_shape else None)
poisson_4 = poisson_lib.Poisson(rate)
new_batch_shape_ph = (constant_op.constant(np.int32(new_batch_shape))
if self.is_static_shape else
array_ops.placeholder_with_default(
np.int32(new_batch_shape), shape=None))
poisson_141_reshaped = batch_reshape_lib.BatchReshape(
poisson_4, new_batch_shape_ph, validate_args=True)
x_4 = self.dtype([2, 12, 3, 23])
x_114 = self.dtype([2, 12, 3, 23]).reshape(1, 1, 4)
if self.is_static_shape:
with self.assertRaisesRegexp(NotImplementedError,
"too few batch and event dims"):
poisson_141_reshaped.log_prob(x_4)
with self.assertRaisesRegexp(NotImplementedError,
"unexpected batch and event shape"):
poisson_141_reshaped.log_prob(x_114)
return
with self.assertRaisesOpError("too few batch and event dims"):
with self.cached_session():
poisson_141_reshaped.log_prob(x_4).eval()
with self.assertRaisesOpError("unexpected batch and event shape"):
with self.cached_session():
poisson_141_reshaped.log_prob(x_114).eval()
def make_mvn(self, dims, new_batch_shape, old_batch_shape):
new_batch_shape_ph = (constant_op.constant(np.int32(new_batch_shape))
if self.is_static_shape else
array_ops.placeholder_with_default(
np.int32(new_batch_shape), shape=None))
scale = np.ones(old_batch_shape + [dims], self.dtype)
scale_ph = array_ops.placeholder_with_default(
scale, shape=scale.shape if self.is_static_shape else None)
mvn = mvn_lib.MultivariateNormalDiag(scale_diag=scale_ph)
reshape_mvn = batch_reshape_lib.BatchReshape(
distribution=mvn,
batch_shape=new_batch_shape_ph,
validate_args=True)
return mvn, reshape_mvn
def make_normal(self, new_batch_shape, old_batch_shape):
new_batch_shape_ph = (constant_op.constant(np.int32(new_batch_shape))
if self.is_static_shape else
array_ops.placeholder_with_default(
np.int32(new_batch_shape), shape=None))
scale = self.dtype(
0.5 + np.arange(np.prod(old_batch_shape)).reshape(old_batch_shape))
scale_ph = array_ops.placeholder_with_default(
scale, shape=scale.shape if self.is_static_shape else None)
normal = normal_lib.Normal(loc=self.dtype(0), scale=scale_ph)
reshape_normal = batch_reshape_lib.BatchReshape(
distribution=normal,
batch_shape=new_batch_shape_ph,
validate_args=True)
return normal, reshape_normal
def make_wishart(self, dims, new_batch_shape, old_batch_shape):
new_batch_shape_ph = (constant_op.constant(np.int32(new_batch_shape))
if self.is_static_shape else
array_ops.placeholder_with_default(
np.int32(new_batch_shape), shape=None))
scale = self.dtype([
[[1., 0.5], [0.5, 1.]],
[[0.5, 0.25], [0.25, 0.75]],
])
scale = np.reshape(np.concatenate([scale, scale], axis=0),
old_batch_shape + [dims, dims])
scale_ph = array_ops.placeholder_with_default(
scale, shape=scale.shape if self.is_static_shape else None)
wishart = wishart_lib.WishartFull(df=5, scale=scale_ph)
reshape_wishart = batch_reshape_lib.BatchReshape(
distribution=wishart,
batch_shape=new_batch_shape_ph,
validate_args=True)
return wishart, reshape_wishart
