def testGraphConstructionFailures(self):
accept_prob_fn = lambda _: constant_op.constant(1.0)
batch_size = 32
# Data must have batch dimension if `enqueue_many` is `True`.
with self.assertRaises(ValueError):
sampling_ops.rejection_sample(
[array_ops.zeros([])], accept_prob_fn, batch_size, enqueue_many=True)
# Batch dimensions should be equal if `enqueue_many` is `True`.
with self.assertRaises(ValueError):
sampling_ops.rejection_sample(
[array_ops.zeros([5, 1]), array_ops.zeros([4, 1])],
accept_prob_fn,
batch_size,
enqueue_many=True)
def testGraphConstructionFailures(self):
accept_prob_fn = lambda _: constant_op.constant(1.0)
batch_size = 32
# Data must have batch dimension if `enqueue_many` is `True`.
with self.assertRaises(ValueError):
sampling_ops.rejection_sample(
[array_ops.zeros([])], accept_prob_fn, batch_size, enqueue_many=True)
# Batch dimensions should be equal if `enqueue_many` is `True`.
with self.assertRaises(ValueError):
sampling_ops.rejection_sample(
[array_ops.zeros([5, 1]), array_ops.zeros([4, 1])],
accept_prob_fn,
batch_size,
enqueue_many=True)
def testRuntimeFailures(self):
prob_ph = array_ops.placeholder(dtypes.float32, [])
accept_prob_fn = lambda _: prob_ph
batch_size = 32
# Set up graph.
random_seed.set_random_seed(1234)
sampling_ops.rejection_sample([array_ops.zeros([])],
accept_prob_fn,
batch_size,
runtime_checks=True,
name='rejection_sample')
prob_tensor = ops.get_default_graph().get_tensor_by_name(
'rejection_sample/prob_with_checks:0')
# Run session that should fail.
with self.cached_session() as sess:
for illegal_prob in [-0.1, 1.1]:
with self.assertRaises(errors_impl.InvalidArgumentError):
sess.run(prob_tensor, feed_dict={prob_ph: illegal_prob})
def testRuntimeFailures(self):
prob_ph = array_ops.placeholder(dtypes.float32, [])
accept_prob_fn = lambda _: prob_ph
batch_size = 32
# Set up graph.
random_seed.set_random_seed(1234)
sampling_ops.rejection_sample(
[array_ops.zeros([])],
accept_prob_fn,
batch_size,
runtime_checks=True,
name='rejection_sample')
prob_tensor = ops.get_default_graph().get_tensor_by_name(
'rejection_sample/prob_with_checks:0')
# Run session that should fail.
with self.test_session() as sess:
for illegal_prob in [-0.1, 1.1]:
with self.assertRaises(errors_impl.InvalidArgumentError):
sess.run(prob_tensor, feed_dict={prob_ph: illegal_prob})
def testNormalBehavior(self):
tensor_list = [
control_flow_ops.cond(
math_ops.greater(.5, random_ops.random_uniform([])),
lambda: constant_op.constant(1.0),
lambda: constant_op.constant(2.0))
]
accept_prob_fn = lambda x: x[0] - 1.0
batch_size = 10
# Set up graph.
sample = sampling_ops.rejection_sample(tensor_list, accept_prob_fn,
batch_size)
with self.cached_session() as sess:
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(coord=coord)
for _ in range(5):
sample_np = sess.run(sample)[0]
self.assertListEqual([2.0] * batch_size, list(sample_np))
coord.request_stop()
coord.join(threads)
def testNormalBehavior(self):
tensor_list = [
control_flow_ops.cond(
math_ops.greater(.5, random_ops.random_uniform([])),
lambda: constant_op.constant(1.0),
lambda: constant_op.constant(2.0))
]
accept_prob_fn = lambda x: x[0] - 1.0
batch_size = 10
# Set up graph.
sample = sampling_ops.rejection_sample(tensor_list, accept_prob_fn,
batch_size)
with self.test_session() as sess:
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(coord=coord)
for _ in range(5):
sample_np = sess.run(sample)[0]
self.assertListEqual([2.0] * batch_size, list(sample_np))
coord.request_stop()
coord.join(threads)