def test_enqueue_dequeue(self):
"""Test enqueueing a dequeueing from a NeuralQueueCell.
"""
input_values = np.array([[[[1.0, 0.0, 0.0]],
[[0.0, 1.0, 0.0]],
[[0.0, 0.0, 1.0]]]])
expected_values = np.array([[[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]]])
expected_read_strengths = np.array([
[[[0.0], [1.0], [0.0], [0.0], [0.0], [0.0]]]])
expected_write_strengths = np.array([
[[[0.0], [0.0], [0.0], [1.0], [0.0], [0.0]]]])
expected_front = np.array([[[0.0, 1.0, 0.0]]])
queue = neural_stack.NeuralQueueCell(8, 6, 3)
rnn_input = tf.constant(input_values, dtype=tf.float32)
(outputs, state) = tf.nn.dynamic_rnn(cell=queue,
inputs=rnn_input,
time_major=False,
dtype=tf.float32)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
_, state_vals = sess.run([outputs, state])
(_, queue_front, values, read_strengths, write_strengths) = state_vals
self.assertAllClose(expected_front, queue_front)
self.assertAllClose(expected_values, values)
self.assertAllClose(expected_read_strengths, read_strengths)
self.assertAllClose(expected_write_strengths, write_strengths)
def test_enqueue_dequeue(self):
"""Test enqueueing a dequeueing from a NeuralQueueCell.
The sequence of operations is:
enqueue([1.0, 0.0, 0.0])
enqueue([0.0, 1.0, 0.0])
dequeue()
"""
input_values = np.array([[[[1.0, 0.0, 0.0]],
[[0.0, 1.0, 0.0]],
[[0.0, 0.0, 1.0]]]])
expected_values = np.array([[[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]]])
expected_read_strengths = np.array([
[[[0.0], [1.0], [0.0], [0.0], [0.0], [0.0]]]])
expected_write_strengths = np.array([
[[[0.0], [0.0], [0.0], [1.0], [0.0], [0.0]]]])
expected_front = np.array([[[0.0, 1.0, 0.0]]])
batch_size = 1
num_units = 8
embedding_size = 3
memory_size = 6
queue = neural_stack.NeuralQueueCell(num_units, memory_size, embedding_size)
rnn_input = tf.constant(input_values, dtype=tf.float32)
queue_zero_state = tf.zeros([batch_size, num_units])
controller_outputs = queue.call_controller(None, None, queue_zero_state,
batch_size)
assert_controller_shapes(self, controller_outputs,
queue.get_controller_shape(batch_size))
(outputs, state) = tf.nn.dynamic_rnn(cell=queue,
inputs=rnn_input,
time_major=False,
dtype=tf.float32)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
_, state_vals = sess.run([outputs, state])
(_, queue_front, values, read_strengths, write_strengths) = state_vals
self.assertAllClose(expected_values, values)
self.assertAllClose(expected_write_strengths, write_strengths)
self.assertAllClose(expected_read_strengths, read_strengths)
self.assertAllClose(expected_front, queue_front)