def testConditionallyEnqueueAndBatchTypes(self):
tensor = tf.constant(1.0)
keep_input = tf.constant(True)
batch_size = 4
# Check that output types are the same for 1 and 2-length input lists.
output1 = sampling_ops._conditional_batch([tensor], keep_input, batch_size) # pylint: disable=protected-access
output2 = sampling_ops._conditional_batch( # pylint: disable=protected-access
[tensor, tensor], keep_input, batch_size)
self.assertEqual(type(output1), type(output2))
def testConditionallyEnqueueAndBatchTypes(self):
tensor = tf.constant(1.0)
accept_prob = tensor - 1
batch_size = 4
# Check that output types are the same for 1 and 2-length input lists.
output1 = sampling_ops._conditional_batch([tensor], accept_prob, batch_size) # pylint: disable=protected-access
output2 = sampling_ops._conditional_batch( # pylint: disable=protected-access
[tensor, tensor], accept_prob, batch_size)
self.assertEqual(type(output1), type(output2))
def testConditionallyEnqueueAndBatch(self):
tf.set_random_seed(1234)
tensor = tf.cond(
tf.greater(.5, tf.random_uniform([])),
lambda: tf.constant(1.0),
lambda: tf.constant(2.0))
keep_input = tf.equal(tensor, 2.0)
batch_size = 4
# Set up the test graph.
[batch] = sampling_ops._conditional_batch([tensor], keep_input, batch_size) # pylint: disable=protected-access
# Check conditional operation.
with self.test_session():
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
batch_np = batch.eval()
coord.request_stop()
coord.join(threads)
# Check that all elements in batch come from tensors with acceptance prob
# 1, so that none come from acceptance prob 0.
self.assertListEqual(list(batch_np), [2.0] * batch_size)
def testConditionallyEnqueueAndBatch(self):
tf.set_random_seed(1234)
tensor = tf.cond(
tf.greater(.5, tf.random_uniform([])),
lambda: tf.constant(1.0),
lambda: tf.constant(2.0))
accept_prob = tensor - 1
batch_size = 4
# Set up the test graph.
[batch] = sampling_ops._conditional_batch([tensor], accept_prob, batch_size) # pylint: disable=protected-access
# Check conditional operation.
with self.test_session():
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
batch_np = batch.eval()
coord.request_stop()
coord.join(threads)
# Check that all elements in batch come from tensors with acceptance prob
# 1, so that none come from acceptance prob 0.
self.assertListEqual(list(batch_np), [2.0] * batch_size)
def testConditionallyEnqueueAndBatch(self):
random_seed.set_random_seed(1234)
tensor = control_flow_ops.cond(
math_ops.greater(.5, random_ops.random_uniform([])),
lambda: constant_op.constant(1.0), lambda: constant_op.constant(2.0))
keep_input = math_ops.equal(tensor, 2.0)
batch_size = 4
# Set up the test graph.
[batch] = sampling_ops._conditional_batch([tensor], keep_input, batch_size) # pylint: disable=protected-access
# Check conditional operation.
with self.test_session():
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(coord=coord)
batch_np = batch.eval()
coord.request_stop()
coord.join(threads)
# Check that all elements in batch come from tensors with acceptance prob
# 1, so that none come from acceptance prob 0.
self.assertListEqual(list(batch_np), [2.0] * batch_size)
