def test_tas_for_tensors(self):
a = tf.reshape(tf.range(20), [5, 4])
tensors = [a, (a, ExampleTuple(a, a))]
tas = nested_utils.tas_for_tensors(tensors, 5)
nest.assert_same_structure(tensors, tas)
# We can't pass TensorArrays to sess.run so instead we turn then back into
# tensors to check that they were created correctly.
stacked = nested_utils.map_nested(lambda x: x.stack(), tas)
with self.test_session() as sess:
gt, out = sess.run([tensors, stacked])
gt = nest.flatten(gt)
out = nest.flatten(out)
# Check that the tas were created correctly.
for x, y in zip(gt, out):
self.assertAllClose(x, y)
def test_tas_for_tensors(self):
a = tf.reshape(tf.range(20), [5, 4])
tensors = [a, (a, ExampleTuple(a, a))]
tas = nested_utils.tas_for_tensors(tensors, 5)
nest.assert_same_structure(tensors, tas)
# We can't pass TensorArrays to sess.run so instead we turn then back into
# tensors to check that they were created correctly.
stacked = nested_utils.map_nested(lambda x: x.stack(), tas)
with self.test_session() as sess:
gt, out = sess.run([tensors, stacked])
gt = nest.flatten(gt)
out = nest.flatten(out)
# Check that the tas were created correctly.
for x, y in zip(gt, out):
self.assertAllClose(x, y)
def relaxed_resampling(log_weights,
states,
num_particles,
batch_size,
temperature=0.5,
random_seed=None):
"""Resample states with relaxed resampling.
Draw soft "ancestors" using the Gumbel-Softmax distribution.
Args:
log_weights: A [num_particles, batch_size] Tensor representing a batch
of batch_size logits for num_particles-ary Categorical distribution.
states: A nested list of [batch_size * num_particles, d] Tensors that will
be resampled from the groups of every num_particles-th row.
num_particles: The number of particles/samples.
batch_size: The batch size.
temperature: The temperature used for the relaxed one hot distribution.
random_seed: The random seed to pass to the resampling operations in
the particle filter. Mainly useful for testing.
Returns:
resampled_states: A nested list of [batch_size * num_particles, d]
Tensors resampled via multinomial sampling.
"""
# log_weights are [num_particles, batch_size], so we transpose to get a
# set of batch_size distributions over [0, num_particles).
resampling_parameters = tf.transpose(log_weights, perm=[1, 0])
resampling_dist = tf.contrib.distributions.RelaxedOneHotCategorical(
temperature, logits=resampling_parameters)
# Sample num_particles samples from the distribution, resulting in a
# [num_particles, batch_size, num_particles] Tensor that represents a set of
# [num_particles, batch_size] blending weights. The dimensions represent
# [particle index, batch index, blending weight index].
ancestors = resampling_dist.sample(sample_shape=num_particles,
seed=random_seed)
def map_fn(tensor):
return _blend_tensor(ancestors, tensor, num_particles, batch_size)
resampled_states = nested.map_nested(map_fn, states)
return resampled_states
def relaxed_resampling(log_weights, states, num_particles, batch_size,
temperature=0.5, random_seed=None):
"""Resample states with relaxed resampling.
Draw soft "ancestors" using the Gumbel-Softmax distribution.
Args:
log_weights: A [num_particles, batch_size] Tensor representing a batch
of batch_size logits for num_particles-ary Categorical distribution.
states: A nested list of [batch_size * num_particles, d] Tensors that will
be resampled from the groups of every num_particles-th row.
num_particles: The number of particles/samples.
batch_size: The batch size.
temperature: The temperature used for the relaxed one hot distribution.
random_seed: The random seed to pass to the resampling operations in
the particle filter. Mainly useful for testing.
Returns:
resampled_states: A nested list of [batch_size * num_particles, d]
Tensors resampled via multinomial sampling.
"""
# log_weights are [num_particles, batch_size], so we transpose to get a
# set of batch_size distributions over [0, num_particles).
resampling_parameters = tf.transpose(log_weights, perm=[1, 0])
resampling_dist = tf.contrib.distributions.RelaxedOneHotCategorical(
temperature,
logits=resampling_parameters)
# Sample num_particles samples from the distribution, resulting in a
# [num_particles, batch_size, num_particles] Tensor that represents a set of
# [num_particles, batch_size] blending weights. The dimensions represent
# [particle index, batch index, blending weight index].
ancestors = resampling_dist.sample(sample_shape=num_particles,
seed=random_seed)
def map_fn(tensor):
return _blend_tensor(ancestors, tensor, num_particles, batch_size)
resampled_states = nested.map_nested(map_fn, states)
return resampled_states
def test_map_nested_works_on_flat_lists(self):
"""Check that map_nested works with a flat list."""
original = [1, 2, 3]
expected = [2, 3, 4]
out = nested_utils.map_nested(lambda x: x + 1, original)
self.assertEqual(expected, out)
def test_map_nested_works_on_single_objects(self):
"""Check that map_nested works with raw objects."""
original = 1
expected = 2
out = nested_utils.map_nested(lambda x: x + 1, original)
self.assertEqual(expected, out)
def test_map_nested_works_on_nested_structures(self):
"""Check that map_nested works with nested structures."""
original = [1, (2, 3.2, (4., ExampleTuple(5, 6)))]
expected = [2, (3, 4.2, (5., ExampleTuple(6, 7)))]
out = nested_utils.map_nested(lambda x: x + 1, original)
self.assertEqual(expected, out)
def test_map_nested_works_on_flat_lists(self): """Check that map_nested works with a flat list.""" original = [1, 2, 3] expected = [2, 3, 4] out = nested_utils.map_nested(lambda x: x+1, original) self.assertEqual(expected, out)
def test_map_nested_works_on_single_objects(self): """Check that map_nested works with raw objects.""" original = 1 expected = 2 out = nested_utils.map_nested(lambda x: x+1, original) self.assertEqual(expected, out)
def test_map_nested_works_on_nested_structures(self): """Check that map_nested works with nested structures.""" original = [1, (2, 3.2, (4., ExampleTuple(5, 6)))] expected = [2, (3, 4.2, (5., ExampleTuple(6, 7)))] out = nested_utils.map_nested(lambda x: x+1, original) self.assertEqual(expected, out)