def testBatchDimensionNotRequired(self):
classes = 5
# Probs must be a tensor, since we pass it directly to _verify_input.
probs = tf.constant([1.0 / classes] * classes)
# Make sure that these vals/labels pairs don't throw any runtime exceptions.
legal_input_pairs = [
(np.zeros([2, 3]), [x % classes for x in range(2)]), # batch dim 2
(np.zeros([4,
15]), [x % classes for x in range(4)]), # batch dim 4
(np.zeros([10,
1]), [x % classes for x in range(10)]), # batch dim 10
]
# Set up graph with placeholders.
vals_ph = tf.placeholder(tf.float32) # completely undefined shape
labels_ph = tf.placeholder(tf.int32) # completely undefined shape
val_tf, labels_tf, _ = sampling_ops._verify_input( # pylint: disable=protected-access
[vals_ph], labels_ph, [probs])
# Run graph to make sure there are no shape-related runtime errors.
for vals, labels in legal_input_pairs:
with self.test_session() as sess:
sess.run([val_tf, labels_tf],
feed_dict={
vals_ph: vals,
labels_ph: labels
})
def testRuntimeAssertionFailures(self):
valid_probs = [0.2] * 5
valid_labels = [1, 2, 3]
vals = [tf.zeros([3, 1])]
illegal_labels = [
[0, -1, 1], # classes must be nonnegative
[5, 1, 1], # classes must be less than number of classes
[2, 3], # data and label batch size must be the same
]
illegal_probs = [
[0.1] * 5, # probabilities must sum to one
[-0.5, 0.5, 0.5, 0.4, 0.1], # probabilities must be non-negative
]
# Set up graph with illegal label vector.
label_ph = tf.placeholder(tf.int32, shape=[None])
probs_ph = tf.placeholder(tf.float32, shape=[5]) # shape must be defined
val_tf, lbl_tf, prob_tf = sampling_ops._verify_input( # pylint: disable=protected-access
vals, label_ph, [probs_ph]
)
for illegal_label in illegal_labels:
# Run session that should fail.
with self.test_session() as sess:
with self.assertRaises(tf.errors.InvalidArgumentError):
sess.run([val_tf, lbl_tf], feed_dict={label_ph: illegal_label, probs_ph: valid_probs})
for illegal_prob in illegal_probs:
# Run session that should fail.
with self.test_session() as sess:
with self.assertRaises(tf.errors.InvalidArgumentError):
sess.run([prob_tf], feed_dict={label_ph: valid_labels, probs_ph: illegal_prob})
def testBatchDimensionNotRequired(self):
classes = 5
# Probs must be a tensor, since we pass it directly to _verify_input.
probs = constant_op.constant([1.0 / classes] * classes)
# Make sure that these vals/labels pairs don't throw any runtime exceptions.
legal_input_pairs = [
(np.zeros([2, 3]), [x % classes for x in range(2)]), # batch dim 2
(np.zeros([4, 15]), [x % classes for x in range(4)]), # batch dim 4
(np.zeros([10, 1]), [x % classes for x in range(10)]), # batch dim 10
]
# Set up graph with placeholders.
vals_ph = array_ops.placeholder(
dtypes.float32) # completely undefined shape
labels_ph = array_ops.placeholder(
dtypes.int32) # completely undefined shape
val_tf, labels_tf, _ = sampling_ops._verify_input( # pylint: disable=protected-access
[vals_ph], labels_ph, [probs])
# Run graph to make sure there are no shape-related runtime errors.
for vals, labels in legal_input_pairs:
with self.test_session() as sess:
sess.run([val_tf, labels_tf],
feed_dict={vals_ph: vals,
labels_ph: labels})
def testRuntimeAssertionFailures(self):
valid_probs = [.2] * 5
valid_labels = [1, 2, 3]
vals = [tf.zeros([3, 1])]
illegal_labels = [
[0, -1, 1], # classes must be nonnegative
[5, 1, 1], # classes must be less than number of classes
[2, 3], # data and label batch size must be the same
]
illegal_probs = [
[.1] * 5, # probabilities must sum to one
[-.5, .5, .5, .4, .1], # probabilities must be non-negative
]
# Set up graph with illegal label vector.
label_ph = tf.placeholder(tf.int32, shape=[None])
probs_ph = tf.placeholder(tf.float32,
shape=[5]) # shape must be defined
val_tf, lbl_tf, prob_tf = sampling_ops._verify_input( # pylint: disable=protected-access
vals, label_ph, [probs_ph])
for illegal_label in illegal_labels:
# Run session that should fail.
with self.test_session() as sess:
with self.assertRaises(tf.errors.InvalidArgumentError):
sess.run([val_tf, lbl_tf],
feed_dict={
label_ph: illegal_label,
probs_ph: valid_probs
})
for illegal_prob in illegal_probs:
# Run session that should fail.
with self.test_session() as sess:
with self.assertRaises(tf.errors.InvalidArgumentError):
sess.run([prob_tf],
feed_dict={
label_ph: valid_labels,
probs_ph: illegal_prob
})
def testMultiThreadedEstimateDataDistribution(self):
num_classes = 10
# Set up graph.
random_seed.set_random_seed(1234)
label = math_ops.cast(
math_ops.round(random_ops.random_uniform([1]) * num_classes),
dtypes_lib.int32)
prob_estimate = sampling_ops._estimate_data_distribution( # pylint: disable=protected-access
label, num_classes)
# Check that prob_estimate is well-behaved in a multithreaded context.
_, _, [prob_estimate] = sampling_ops._verify_input( # pylint: disable=protected-access
[], label, [prob_estimate])
# Use queues to run multiple threads over the graph, each of which
# fetches `prob_estimate`.
queue = data_flow_ops.FIFOQueue(
capacity=25,
dtypes=[prob_estimate.dtype],
shapes=[prob_estimate.get_shape()])
enqueue_op = queue.enqueue([prob_estimate])
queue_runner_impl.add_queue_runner(
queue_runner_impl.QueueRunner(queue, [enqueue_op] * 25))
out_tensor = queue.dequeue()
# Run the multi-threaded session.
with self.cached_session() as sess:
# Need to initialize variables that keep running total of classes seen.
variables.global_variables_initializer().run()
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(coord=coord)
for _ in range(25):
sess.run([out_tensor])
coord.request_stop()
coord.join(threads)
def testMultiThreadedEstimateDataDistribution(self):
num_classes = 10
# Set up graph.
random_seed.set_random_seed(1234)
label = math_ops.cast(
math_ops.round(random_ops.random_uniform([1]) * num_classes),
dtypes_lib.int32)
prob_estimate = sampling_ops._estimate_data_distribution( # pylint: disable=protected-access
label, num_classes)
# Check that prob_estimate is well-behaved in a multithreaded context.
_, _, [prob_estimate] = sampling_ops._verify_input( # pylint: disable=protected-access
[], label, [prob_estimate])
# Use queues to run multiple threads over the graph, each of which
# fetches `prob_estimate`.
queue = data_flow_ops.FIFOQueue(capacity=25,
dtypes=[prob_estimate.dtype],
shapes=[prob_estimate.get_shape()])
enqueue_op = queue.enqueue([prob_estimate])
queue_runner_impl.add_queue_runner(
queue_runner_impl.QueueRunner(queue, [enqueue_op] * 25))
out_tensor = queue.dequeue()
# Run the multi-threaded session.
with self.test_session() as sess:
# Need to initialize variables that keep running total of classes seen.
variables.global_variables_initializer().run()
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(coord=coord)
for _ in range(25):
sess.run([out_tensor])
coord.request_stop()
coord.join(threads)
