def _parameter_control_dependencies(self, is_init):
if not self.validate_args:
return []
sample_shape = tf.concat(
[self._batch_shape_tensor(), self._event_shape_tensor()], axis=0)
low = None if self._low is None else tf.convert_to_tensor(self._low)
high = None if self._high is None else tf.convert_to_tensor(self._high)
assertions = []
if self._low is not None and is_init != tensor_util.is_ref(self._low):
low_shape = ps.shape(low)
broadcast_shape = ps.broadcast_shape(sample_shape, low_shape)
assertions.extend(
[distribution_util.assert_integer_form(
low, message='`low` has non-integer components.'),
assert_util.assert_equal(
tf.reduce_prod(broadcast_shape),
tf.reduce_prod(sample_shape),
message=('Shape of `low` adds extra batch dimensions to '
'sample shape.'))])
if self._high is not None and is_init != tensor_util.is_ref(self._high):
high_shape = ps.shape(high)
broadcast_shape = ps.broadcast_shape(sample_shape, high_shape)
assertions.extend(
[distribution_util.assert_integer_form(
high, message='`high` has non-integer components.'),
assert_util.assert_equal(
tf.reduce_prod(broadcast_shape),
tf.reduce_prod(sample_shape),
message=('Shape of `high` adds extra batch dimensions to '
'sample shape.'))])
if (self._low is not None and self._high is not None and
(is_init != (tensor_util.is_ref(self._low)
or tensor_util.is_ref(self._high)))):
assertions.append(assert_util.assert_less(
low, high,
message='`low` must be strictly less than `high`.'))
return assertions
def _calculate_mean_and_var(self, x, axes, keep_dims):
with backend.name_scope('moments'):
# The dynamic range of fp16 is too limited to support the collection of
# sufficient statistics. As a workaround we simply perform the operations
# on 32-bit floats before converting the mean and variance back to fp16
y = tf.cast(x, tf.float32) if x.dtype == tf.float16 else x
replica_ctx = tf.distribute.get_replica_context()
if replica_ctx:
local_sum = tf.reduce_sum(y, axis=axes, keepdims=True)
local_squared_sum = tf.reduce_sum(tf.square(y),
axis=axes,
keepdims=True)
batch_size = tf.cast(tf.shape(y)[axes[0]], tf.float32)
# TODO(b/163099951): batch the all-reduces once we sort out the ordering
# issue for NCCL. We don't have a mechanism to launch NCCL in the same
# order in each replica nowadays, so we limit NCCL to batch all-reduces.
y_sum = replica_ctx.all_reduce(tf.distribute.ReduceOp.SUM,
local_sum)
y_squared_sum = replica_ctx.all_reduce(
tf.distribute.ReduceOp.SUM, local_squared_sum)
global_batch_size = replica_ctx.all_reduce(
tf.distribute.ReduceOp.SUM, batch_size)
axes_vals = [(tf.shape(y))[axes[i]]
for i in range(1, len(axes))]
multiplier = tf.cast(tf.reduce_prod(axes_vals), tf.float32)
multiplier = multiplier * global_batch_size
mean = y_sum / multiplier
y_squared_mean = y_squared_sum / multiplier
# var = E(x^2) - E(x)^2
variance = y_squared_mean - tf.square(mean)
else:
# Compute true mean while keeping the dims for proper broadcasting.
mean = tf.reduce_mean(y, axes, keepdims=True, name='mean')
# sample variance, not unbiased variance
# Note: stop_gradient does not change the gradient that gets
# backpropagated to the mean from the variance calculation,
# because that gradient is zero
variance = tf.reduce_mean(tf.math.squared_difference(
y, tf.stop_gradient(mean)),
axes,
keepdims=True,
name='variance')
if not keep_dims:
mean = tf.compat.v1.squeeze(mean, axes)
variance = tf.compat.v1.squeeze(variance, axes)
if x.dtype == tf.float16:
return (tf.cast(mean,
tf.float16), tf.cast(variance, tf.float16))
else:
return (mean, variance)
def _entropy(self, **kwargs):
if not self.bijector.is_constant_jacobian:
raise NotImplementedError('`entropy` is not implemented.')
if not self.bijector._is_injective: # pylint: disable=protected-access
raise NotImplementedError('`entropy` is not implemented when '
'`bijector` is not injective.')
distribution_kwargs, bijector_kwargs = self._kwargs_split_fn(kwargs)
override_event_shape = tf.convert_to_tensor(self._override_event_shape)
override_batch_shape = tf.convert_to_tensor(self._override_batch_shape)
base_batch_shape_tensor = self.distribution.batch_shape_tensor()
base_event_shape_tensor = self.distribution.event_shape_tensor()
# Suppose Y = g(X) where g is a diffeomorphism and X is a continuous rv. It
# can be shown that:
# H[Y] = H[X] + E_X[(log o abs o det o J o g)(X)].
# If is_constant_jacobian then:
# E_X[(log o abs o det o J o g)(X)] = (log o abs o det o J o g)(c)
# where c can by anything.
entropy = self.distribution.entropy(**distribution_kwargs)
if self._is_maybe_event_override:
# H[X] = sum_i H[X_i] if X_i are mutually independent.
# This means that a reduce_sum is a simple rescaling.
entropy = entropy * tf.cast(tf.reduce_prod(override_event_shape),
dtype=dtype_util.base_dtype(
entropy.dtype))
if self._is_maybe_batch_override:
new_shape = tf.concat([
prefer_static.ones_like(override_batch_shape),
base_batch_shape_tensor
], 0)
entropy = tf.reshape(entropy, new_shape)
multiples = tf.concat([
override_batch_shape,
prefer_static.ones_like(base_batch_shape_tensor)
], 0)
entropy = tf.tile(entropy, multiples)
# Create a dummy event of zeros to pass to
# `bijector.inverse_log_det_jacobian` to extract the constant Jacobian.
event_shape_tensor = self._event_shape_tensor(override_event_shape,
base_event_shape_tensor)
event_ndims = tf.nest.map_structure(prefer_static.rank_from_shape,
event_shape_tensor,
self.event_shape)
dummy = tf.nest.map_structure(prefer_static.zeros, event_shape_tensor,
self.dtype)
ildj = self.bijector.inverse_log_det_jacobian(dummy,
event_ndims=event_ndims,
**bijector_kwargs)
entropy = entropy - tf.cast(ildj, entropy.dtype)
tensorshape_util.set_shape(entropy, self.batch_shape)
return entropy
def get_jacobian_fn_mat(jacobian_fn, ode_fn_vec, state_shape, use_pfor):
"""Returns a wrapper around the user-specified `jacobian_fn` argument.
`jacobian_fn` is an optional argument that can either be a constant `Tensor`
or a function of the form `jacobian_fn(time, state)`. This function returns a
wrapper `jacobian_fn_mat(time, state_vec)` whose second argument and output
are 1 and 2-D `Tensor`s, respectively, corresponding reshaped versions of
`state` and `jacobian_fn(time, state)`.
Args:
jacobian_fn: User-specified `jacobian_fn` passed to `solve`.
ode_fn_vec: User-specified `ode_fn` passed to `solve`.
state_shape: The shape of the second argument and output of `ode_fn`.
use_pfor: User-specified `use_pfor` passed to `solve`.
Returns:
The wrapper described above.
"""
if jacobian_fn is None:
def automatic_jacobian_fn_mat(time, state_vec):
with tf.GradientTape(watch_accessed_variables=False,
persistent=not use_pfor) as tape:
tape.watch(state_vec)
outputs = ode_fn_vec(time, state_vec)
jacobian_mat = tape.jacobian(outputs,
state_vec,
experimental_use_pfor=use_pfor)
if jacobian_mat is None:
return tf.zeros([tf.size(state_vec)] * 2,
dtype=state_vec.dtype)
return jacobian_mat
return automatic_jacobian_fn_mat
if not callable(jacobian_fn):
constant_jacobian_mat = tf.reshape(
tf.convert_to_tensor(jacobian_fn),
[-1, tf.reduce_prod(state_shape)])
def constant_jacobian_fn_mat(*_):
return constant_jacobian_mat
return constant_jacobian_fn_mat
def jacobian_fn_mat(time, state_vec):
state = tf.reshape(state_vec, state_shape)
jacobian_mat = tf.reshape(jacobian_fn(time, state),
[-1, tf.size(state)])
return jacobian_mat
return jacobian_fn_mat
def _event_shape_tensor(self):
event_sizes = tf.nest.map_structure(tensorshape_util.num_elements,
self._distribution.event_shape)
if any(s is None for s in tf.nest.flatten(event_sizes)):
event_sizes = tf.nest.map_structure(
lambda static_size, shape_tensor: # pylint: disable=g-long-lambda
(tf.reduce_prod(shape_tensor)
if static_size is None else static_size),
event_sizes,
self._distribution.event_shape_tensor())
return tf.reduce_sum(tf.nest.flatten(event_sizes))[tf.newaxis]
def _get_leftmost_dim_size(x, name=None):
"""Returns the size of the left most dimension, statically if possible."""
with tf.name_scope(name or 'get_leftmost_dim_size'):
x = tf.convert_to_tensor(value=x, name='x')
if x.shape.ndims is None:
# If tf.shape(x) is scalar, the [:1] will produce the empty list, whose
# reduce_prod is 1 as desired. Otherwise, the [:1] will select the first
# dimension, and reduce_prod will not alter it.
return tf.reduce_prod(input_tensor=tf.shape(input=x)[:1])
if x.shape.ndims == 0:
return 1
leftmost = tf.compat.dimension_value(x.shape[0])
return leftmost if leftmost is not None else tf.shape(input=x)[0]
def make_2d(tensor, split_dim):
"""Reshapes an N-dimensional tensor into a 2D tensor.
Dimensions before (excluding) and after (including) `split_dim` are grouped
together.
Args:
tensor: a tensor of shape `(d0, ..., d(N-1))`.
split_dim: an integer from 1 to N-1, index of the dimension to group
dimensions before (excluding) and after (including).
Returns:
Tensor of shape
`(d0 * ... * d(split_dim-1), d(split_dim) * ... * d(N-1))`.
"""
shape = tf.compat.v1.shape(tensor)
in_dims = shape[:split_dim]
out_dims = shape[split_dim:]
in_size = tf.reduce_prod(in_dims)
out_size = tf.reduce_prod(out_dims)
return tf.reshape(tensor, (in_size, out_size))
def _mean(self):
with tf.control_dependencies(self._runtime_assertions):
probs = self._marginal_hidden_probs()
# probs :: num_steps batch_shape num_states
means = self._observation_distribution.mean()
# means :: observation_batch_shape[:-1] num_states
# observation_event_shape
means_shape = tf.concat([
self.batch_shape_tensor(), [self._num_states],
self._observation_distribution.event_shape_tensor()
],
axis=0)
means = tf.broadcast_to(means, means_shape)
# means :: batch_shape num_states observation_event_shape
observation_event_shape = (
self._observation_distribution.event_shape_tensor())
batch_size = tf.reduce_prod(self.batch_shape_tensor())
flat_probs_shape = [self._num_steps, batch_size, self._num_states]
flat_means_shape = [
batch_size, self._num_states,
tf.reduce_prod(observation_event_shape)
]
flat_probs = tf.reshape(probs, flat_probs_shape)
# flat_probs :: num_steps batch_size num_states
flat_means = tf.reshape(means, flat_means_shape)
# flat_means :: batch_size num_states observation_event_size
flat_mean = tf.einsum("ijk,jkl->jil", flat_probs, flat_means)
# flat_mean :: batch_size num_steps observation_event_size
unflat_mean_shape = tf.concat([
self.batch_shape_tensor(), [self._num_steps],
observation_event_shape
],
axis=0)
# returns :: batch_shape num_steps observation_event_shape
return tf.reshape(flat_mean, unflat_mean_shape)
def _finish_prob_for_one_fiber(self, y, x, ildj, event_ndims,
**distribution_kwargs):
"""Finish computation of prob on one element of the inverse image."""
x = self._maybe_rotate_dims(x, rotate_right=True)
prob = self.distribution.prob(x, **distribution_kwargs)
if self._is_maybe_event_override:
prob = tf.reduce_prod(prob, axis=self._reduce_event_indices)
prob = prob * tf.exp(tf.cast(ildj, prob.dtype))
if self._is_maybe_event_override and isinstance(event_ndims, int):
tensorshape_util.set_shape(
prob,
tf.broadcast_static_shape(
tensorshape_util.with_rank_at_least(y.shape, 1)[:-event_ndims],
self.batch_shape))
return prob
def _mean(self):
observation_distribution = self.observation_distribution
batch_shape = self.batch_shape_tensor()
num_states = self.transition_distribution.batch_shape_tensor()[-1]
probs = self._marginal_hidden_probs()
# probs :: num_steps batch_shape num_states
means = observation_distribution.mean()
# means :: observation_batch_shape[:-1] num_states
# observation_event_shape
means_shape = tf.concat([
batch_shape, [num_states],
observation_distribution.event_shape_tensor()
],
axis=0)
means = tf.broadcast_to(means, means_shape)
# means :: batch_shape num_states observation_event_shape
observation_event_shape = (
observation_distribution.event_shape_tensor())
batch_size = tf.reduce_prod(batch_shape)
flat_probs_shape = [self._num_steps, batch_size, num_states]
flat_means_shape = [
batch_size, num_states,
tf.reduce_prod(observation_event_shape)
]
flat_probs = tf.reshape(probs, flat_probs_shape)
# flat_probs :: num_steps batch_size num_states
flat_means = tf.reshape(means, flat_means_shape)
# flat_means :: batch_size num_states observation_event_size
flat_mean = tf.einsum('ijk,jkl->jil', flat_probs, flat_means)
# flat_mean :: batch_size num_steps observation_event_size
unflat_mean_shape = tf.concat(
[batch_shape, [self._num_steps], observation_event_shape], axis=0)
# returns :: batch_shape num_steps observation_event_shape
return tf.reshape(flat_mean, unflat_mean_shape)
def test_docstring_example(self):
# Produce the first 1000 members of the Halton sequence in 3 dimensions.
num_results = 1000
dim = 3
sample, params = random.halton.sample(dim,
num_results=num_results,
seed=127)
# Evaluate the integral of x_1 * x_2^2 * x_3^3 over the three dimensional
# hypercube.
powers = tf.range(1., limit=dim + 1)
integral = tf.reduce_mean(
input_tensor=tf.reduce_prod(input_tensor=sample**powers, axis=-1))
true_value = 1. / tf.reduce_prod(input_tensor=powers + 1.)
# Produces a relative absolute error of 1.7%.
self.assertAllClose(self.evaluate(integral),
self.evaluate(true_value),
rtol=0.02)
# Now skip the first 1000 samples and recompute the integral with the next
# thousand samples. The sequence_indices argument can be used to do this.
sequence_indices = tf.range(start=1000,
limit=1000 + num_results,
dtype=tf.int32)
sample_leaped, _ = random.halton.sample(
dim,
sequence_indices=sequence_indices,
randomization_params=params)
integral_leaped = tf.reduce_mean(input_tensor=tf.reduce_prod(
input_tensor=sample_leaped**powers, axis=-1))
self.assertAllClose(self.evaluate(integral_leaped),
self.evaluate(true_value),
rtol=0.05)
def _make_flatten_unflatten_fns_tf(batch_shape):
"""Returns functions to flatten and unflatten a batch shape."""
batch_shape = tf.cast(batch_shape, dtype=tf.int32)
batch_rank = batch_shape.shape[0]
batch_ndims = tf.reduce_prod(batch_shape)
@tf.function
def flatten_fn(x):
flat_shape = tf.concat([[batch_ndims], tf.shape(x)[batch_rank:]], axis=0)
return tf.reshape(x, flat_shape)
@tf.function
def unflatten_fn(x):
full_shape = tf.concat([batch_shape, tf.shape(x)[1:]], axis=0)
return tf.reshape(x, full_shape)
return flatten_fn, unflatten_fn
def _mode(self, samples=None):
# Samples count can vary by batch member. Use map_fn to compute mode for
# each batch separately.
def _get_mode(samples):
_, idx, count = tf.raw_ops.UniqueWithCountsV2(x=samples, axis=[0])
# TODO(b/161402486): Remove this hack for fixing the wrong static shape
# of `idx` in graph mode.
idx = tf.vectorized_map(lambda x: tf.reshape(x, [-1])[0], idx)
# NOTE:
# - `count` has shape `[K]`, where `K` is the number of unique elements,
# and `count[j]` is the number of times the j-th unique element occurs
# in `samples`.
# - `idx` has shape `[samples.shape[0]]`, and `idx[i] == j` means that
# `samples[i]` is equal to the `j`-th unique element.
max_count_idx = tf.argmax(count, output_type=tf.int32)
# Return an index `i` for which `idx[i] == max_count_idx`.
return tf.argmax(tf.cast(tf.math.equal(idx, max_count_idx),
dtype=tf.int32),
output_type=tf.int32)
if samples is None:
samples = tf.convert_to_tensor(self._samples)
num_samples = self._compute_num_samples(samples)
# Flatten samples for each batch.
if self._event_ndims == 0:
flattened_samples = tf.reshape(samples, [-1, num_samples])
mode_shape = self._batch_shape_tensor(samples)
else:
event_size = tf.reduce_prod(self._event_shape_tensor(samples))
mode_shape = ps.concat([
self._batch_shape_tensor(samples),
self._event_shape_tensor(samples)
],
axis=0)
flattened_samples = tf.reshape(samples,
[-1, num_samples, event_size])
indices = tf.map_fn(_get_mode,
flattened_samples,
fn_output_signature=tf.int32)
full_indices = tf.stack([tf.range(tf.shape(indices)[0]), indices],
axis=1)
mode = tf.gather_nd(flattened_samples, full_indices)
return tf.reshape(mode, mode_shape)
def _entropy(self, **kwargs):
if not self.bijector.is_constant_jacobian:
raise NotImplementedError("entropy is not implemented")
if not self.bijector._is_injective: # pylint: disable=protected-access
raise NotImplementedError("entropy is not implemented when "
"bijector is not injective.")
distribution_kwargs, bijector_kwargs = self._kwargs_split_fn(kwargs)
# Suppose Y = g(X) where g is a diffeomorphism and X is a continuous rv. It
# can be shown that:
# H[Y] = H[X] + E_X[(log o abs o det o J o g)(X)].
# If is_constant_jacobian then:
# E_X[(log o abs o det o J o g)(X)] = (log o abs o det o J o g)(c)
# where c can by anything.
entropy = self.distribution.entropy(**distribution_kwargs)
if self._is_maybe_event_override:
# H[X] = sum_i H[X_i] if X_i are mutually independent.
# This means that a reduce_sum is a simple rescaling.
entropy = entropy * tf.cast(
tf.reduce_prod(self._override_event_shape),
dtype=dtype_util.base_dtype(entropy.dtype))
if self._is_maybe_batch_override:
new_shape = tf.concat([
prefer_static.ones_like(self._override_batch_shape),
self.distribution.batch_shape_tensor()
], 0)
entropy = tf.reshape(entropy, new_shape)
multiples = tf.concat([
self._override_batch_shape,
prefer_static.ones_like(self.distribution.batch_shape_tensor())
], 0)
entropy = tf.tile(entropy, multiples)
dummy = prefer_static.zeros(
shape=tf.concat(
[self.batch_shape_tensor(), self.event_shape_tensor()],
0),
dtype=self.dtype)
event_ndims = (
tensorshape_util.rank(self.event_shape)
if tensorshape_util.rank(self.event_shape) is not None else tf.size(
self.event_shape_tensor()))
ildj = self.bijector.inverse_log_det_jacobian(
dummy, event_ndims=event_ndims, **bijector_kwargs)
entropy = entropy - tf.cast(ildj, entropy.dtype)
tensorshape_util.set_shape(entropy, self.batch_shape)
return entropy
def _sample_n(self, n, seed=None):
# Get ids as a [n, batch_size]-shaped matrix, unless batch_shape=[] then get
# ids as a [n]-shaped vector.
distributions = self.poisson_and_mixture_distributions()
dist, mixture_dist = distributions
batch_size = tensorshape_util.num_elements(self.batch_shape)
if batch_size is None:
batch_size = tf.reduce_prod(
self._batch_shape_tensor(distributions=distributions))
# We need to 'sample extra' from the mixture distribution if it doesn't
# already specify a probs vector for each batch coordinate.
# We only support this kind of reduced broadcasting, i.e., there is exactly
# one probs vector for all batch dims or one for each.
mixture_seed, poisson_seed = samplers.split_seed(
seed, salt='PoissonLogNormalQuadratureCompound')
ids = mixture_dist.sample(
sample_shape=concat_vectors(
[n],
distribution_util.pick_vector(
mixture_dist.is_scalar_batch(),
[batch_size],
np.int32([]))),
seed=mixture_seed)
# We need to flatten batch dims in case mixture_dist has its own
# batch dims.
ids = tf.reshape(
ids,
shape=concat_vectors([n],
distribution_util.pick_vector(
self.is_scalar_batch(), np.int32([]),
np.int32([-1]))))
# Stride `quadrature_size` for `batch_size` number of times.
offset = tf.range(
start=0,
limit=batch_size * self._quadrature_size,
delta=self._quadrature_size,
dtype=ids.dtype)
ids = ids + offset
rate = tf.gather(tf.reshape(dist.rate_parameter(), shape=[-1]), ids)
rate = tf.reshape(
rate, shape=concat_vectors([n], self._batch_shape_tensor(
distributions=distributions)))
return samplers.poisson(
shape=[], lam=rate, dtype=self.dtype, seed=poisson_seed)
def _sample_n(self, n, seed=None):
distribution0 = self._get_distribution0()
if self._num_steps is not None:
num_steps = tf.convert_to_tensor(self._num_steps)
num_steps_static = tf.get_static_value(num_steps)
else:
num_steps_static = tensorshape_util.num_elements(
distribution0.event_shape)
if num_steps_static is None:
num_steps = tf.reduce_prod(distribution0.event_shape_tensor())
stateless_seed = samplers.sanitize_seed(seed, salt='Autoregressive')
stateful_seed = None
try:
samples = distribution0.sample(n, seed=stateless_seed)
is_stateful_sampler = False
except TypeError as e:
if ('Expected int for argument' not in str(e)
and TENSOR_SEED_MSG_PREFIX not in str(e)):
raise
msg = (
'Falling back to stateful sampling for `distribution_fn(sample0)` of '
'type `{}`. Please update to use `tf.random.stateless_*` RNGs. '
'This fallback may be removed after 20-Aug-2020. ({})')
warnings.warn(
msg.format(distribution0.name, type(distribution0), str(e)))
stateful_seed = SeedStream(seed, salt='Autoregressive')()
samples = distribution0.sample(n, seed=stateful_seed)
is_stateful_sampler = True
seed = stateful_seed if is_stateful_sampler else stateless_seed
if num_steps_static is not None:
for _ in range(num_steps_static):
# pylint: disable=not-callable
samples = self.distribution_fn(samples).sample(seed=seed)
else:
# pylint: disable=not-callable
samples = tf.foldl(
lambda s, _: self.distribution_fn(s).sample(seed=seed),
elems=tf.range(0, num_steps),
initializer=samples)
return samples
def __call__(self, x):
"""Computes regularization given an input ed.RandomVariable."""
if not isinstance(x, random_variable.RandomVariable):
raise ValueError('Input must be an ed.RandomVariable.')
# variance = (tr( sigma_q + mu_q mu_q^T ) + 2*beta) / (omega + 2*alpha + 2)
trace_covariance = tf.reduce_sum(x.distribution.variance())
trace_mean_outer_product = tf.reduce_sum(x.distribution.mean()**2)
num_weights = tf.cast(tf.reduce_prod(x.shape), x.dtype)
variance = ((trace_covariance + trace_mean_outer_product) +
2. * self.variance_scale)
variance /= num_weights + 2. * self.variance_concentration + 2.
self.stddev = tf.sqrt(variance)
variance_prior = generated_random_variables.InverseGamma(
self.variance_concentration, self.variance_scale)
regularization = super(NormalEmpiricalBayesKLDivergence, self).__call__(x)
regularization -= (self.scale_factor *
variance_prior.distribution.log_prob(variance))
return regularization
def _forward(self, x, **kwargs):
static_event_size = tensorshape_util.num_elements(
tensorshape_util.with_rank_at_least(
x.shape, self._event_ndims)[-self._event_ndims:])
if self._unroll_loop:
if not static_event_size:
raise ValueError(
'The final {} dimensions of `x` must be known at graph '
'construction time if `unroll_loop=True`. `x.shape: {!r}`'.
format(self._event_ndims, x.shape))
y = tf.zeros_like(x, name='y0')
for _ in range(static_event_size):
y = self._bijector_fn(y, **kwargs).forward(x)
return y
event_size = tf.reduce_prod(tf.shape(x)[-self._event_ndims:])
y0 = tf.zeros_like(x, name='y0')
# call the template once to ensure creation
if not tf.executing_eagerly():
_ = self._bijector_fn(y0, **kwargs).forward(y0)
def _loop_body(index, y0):
"""While-loop body for autoregression calculation."""
# Set caching device to avoid re-getting the tf.Variable for every while
# loop iteration.
with tf1.variable_scope(tf1.get_variable_scope()) as vs:
if vs.caching_device is None and not tf.executing_eagerly():
vs.set_caching_device(lambda op: op.device)
bijector = self._bijector_fn(y0, **kwargs)
y = bijector.forward(x)
return index + 1, y
# If the event size is available at graph construction time, we can inform
# the graph compiler of the maximum number of steps. If not,
# static_event_size will be None, and the maximum_iterations argument will
# have no effect.
_, y = tf.while_loop(cond=lambda index, _: index < event_size,
body=_loop_body,
loop_vars=(0, y0),
maximum_iterations=static_event_size)
return y
def update_state(self, data):
if self.input_mean is not None:
raise ValueError(
"Cannot `adapt` a Normalization layer that is initialized with "
"static `mean` and `variance`, "
"you passed mean {} and variance {}.".format(
self.input_mean, self.input_variance
)
)
if not self.built:
raise RuntimeError("`build` must be called before `update_state`.")
data = self._standardize_inputs(data)
data = tf.cast(data, self.adapt_mean.dtype)
batch_mean, batch_variance = tf.nn.moments(data, axes=self._reduce_axis)
batch_shape = tf.shape(data, out_type=self.count.dtype)
if self._reduce_axis:
batch_reduce_shape = tf.gather(batch_shape, self._reduce_axis)
batch_count = tf.reduce_prod(batch_reduce_shape)
else:
batch_count = 1
total_count = batch_count + self.count
batch_weight = tf.cast(batch_count, dtype=self.compute_dtype) / tf.cast(
total_count, dtype=self.compute_dtype
)
existing_weight = 1.0 - batch_weight
total_mean = (
self.adapt_mean * existing_weight + batch_mean * batch_weight
)
# The variance is computed using the lack-of-fit sum of squares
# formula (see
# https://en.wikipedia.org/wiki/Lack-of-fit_sum_of_squares).
total_variance = (
self.adapt_variance + (self.adapt_mean - total_mean) ** 2
) * existing_weight + (
batch_variance + (batch_mean - total_mean) ** 2
) * batch_weight
self.adapt_mean.assign(total_mean)
self.adapt_variance.assign(total_variance)
self.count.assign(total_count)
def _split_and_reshape_event(self, x):
"""Splits and reshapes of a vector-valued event `x`."""
splits = [
tf.maximum(1, tf.reduce_prod(s))
for s in tf.nest.flatten(self._target_density.event_shape)
]
x = tf.nest.pack_sequence_as(self._target_density.event_shape,
tf.split(x, splits, axis=-1))
def _reshape_part(part, dtype, event_shape):
part = tf.cast(part, dtype)
rank = event_shape.rank
if rank == 1:
return part
new_shape = tf.concat([tf.shape(part)[:-1], event_shape], axis=-1)
return tf.reshape(part, tf.cast(new_shape, tf.int32))
x = tf.nest.map_structure(_reshape_part, x, self._target_density.dtype,
self._target_density.event_shape)
return x
def sample(self, sample_shape=(), seed=None, name=None):
with tf.name_scope(name or 'sample'):
# Grab the required number of values from the provided tensors.
sample_shape = dist_util.expand_to_vector(sample_shape)
n = tf.cast(tf.reduce_prod(sample_shape), dtype=tf.int32)
# Check that we're not trying to draw too many samples.
assertions = []
will_overflow_ = tf.get_static_value(n > self.max_num_samples)
if will_overflow_:
raise ValueError(
'Trying to draw {} samples from a '
'`DeterministicEmpirical` instance for which only {} '
'samples were provided.'.format(
tf.get_static_value(n),
tf.get_static_value(self.max_num_samples)))
elif (will_overflow_ is None # Couldn't determine statically.
and self.validate_args):
assertions.append(
tf.debugging.assert_less_equal(
n,
self.max_num_samples,
message='Number of samples to draw '
'from a `DeterministicEmpirical` instance must not exceed the '
'number provided at construction.'))
# Extract the appropriate number of sampled values.
with tf.control_dependencies(assertions):
sampled = tf.nest.map_structure(lambda x: x[:n, ...],
self.values_with_sample_dim)
# Reshape the values to the appropriate sample shape.
return tf.nest.map_structure(
lambda x: tf.reshape(
x, # pylint: disable=g-long-lambda
tf.concat([
tf.cast(sample_shape, tf.int32),
tf.cast(tf.shape(x)[1:], tf.int32)
],
axis=0)),
sampled)
def _entropy(self):
# Use map_fn to compute entropy for each batch separately.
def _get_entropy(samples):
# TODO(b/123985779): Swith to tf.unique_with_counts_v2 when exposed
count = gen_array_ops.unique_with_counts_v2(samples, axis=[0]).count
prob = count / self.num_samples
entropy = tf.reduce_sum(input_tensor=-prob * tf.math.log(prob))
return entropy
# Flatten samples for each batch.
if self._event_ndims == 0:
samples = tf.reshape(self.samples, [-1, self.num_samples])
else:
event_size = tf.reduce_prod(input_tensor=self.event_shape_tensor())
samples = tf.reshape(self.samples, [-1, self.num_samples, event_size])
entropy = tf.map_fn(_get_entropy, samples)
entropy_shape = self.batch_shape_tensor()
if self.dtype.is_floating:
entropy = tf.cast(entropy, self.dtype)
return tf.reshape(entropy, entropy_shape)
def basis(sample_paths):
"""Computes polynomial basis expansion at the given sample points.
Args:
sample_paths: A `Tensor`s of either `flot32` or `float64` dtype and of
shape `[num_samples, dim]` where `dim` has to be statically known.
Returns:
A `Tensor`s of shape `[degree * dim, num_samples]`.
"""
samples = tf.convert_to_tensor(sample_paths)
dim = samples.shape.as_list()[-1]
grid = tf.range(0, degree + 1, dtype=samples.dtype)
samples_centered = samples - tf.math.reduce_mean(samples, axis=0)
samples_centered = tf.expand_dims(samples_centered, -2)
grid = tf.meshgrid(*(dim * [grid]))
grid = tf.reshape(tf.stack(grid, -1), [-1, dim])
# Shape [num_samples, degree * dim]
basis_expansion = tf.reduce_prod(samples_centered**grid, -1)
return tf.transpose(basis_expansion)
def _entropy(self):
samples = tf.convert_to_tensor(self.samples)
num_samples = self._compute_num_samples(samples)
entropy_shape = self._batch_shape_tensor(samples)
# Flatten samples for each batch.
if self._event_ndims == 0:
samples = tf.reshape(samples, [-1, num_samples])
else:
event_size = tf.reduce_prod(self.event_shape_tensor())
samples = tf.reshape(samples, [-1, num_samples, event_size])
# Use map_fn to compute entropy for each batch separately.
def _get_entropy(samples):
count = tf.raw_ops.UniqueWithCountsV2(x=samples, axis=[0]).count
prob = tf.cast(count / num_samples, dtype=self.dtype)
entropy = tf.reduce_sum(-prob * tf.math.log(prob))
return entropy
entropy = tf.map_fn(_get_entropy, samples, dtype=self.dtype)
return tf.reshape(entropy, entropy_shape)
def _sample_n(self, n, seed=None):
# Get ids as a [n, batch_size]-shaped matrix, unless batch_shape=[] then get
# ids as a [n]-shaped vector.
batch_size = self.batch_shape.num_elements()
if batch_size is None:
batch_size = tf.reduce_prod(input_tensor=self.batch_shape_tensor())
# We need to "sample extra" from the mixture distribution if it doesn't
# already specify a probs vector for each batch coordinate.
# We only support this kind of reduced broadcasting, i.e., there is exactly
# one probs vector for all batch dims or one for each.
stream = seed_stream.SeedStream(
seed, salt="PoissonLogNormalQuadratureCompound")
ids = self._mixture_distribution.sample(sample_shape=concat_vectors(
[n],
distribution_util.pick_vector(
self.mixture_distribution.is_scalar_batch(), [batch_size],
np.int32([]))),
seed=stream())
# We need to flatten batch dims in case mixture_distribution has its own
# batch dims.
ids = tf.reshape(ids,
shape=concat_vectors([n],
distribution_util.pick_vector(
self.is_scalar_batch(),
np.int32([]),
np.int32([-1]))))
# Stride `quadrature_size` for `batch_size` number of times.
offset = tf.range(start=0,
limit=batch_size * self._quadrature_size,
delta=self._quadrature_size,
dtype=ids.dtype)
ids += offset
rate = tf.gather(tf.reshape(self.distribution.rate, shape=[-1]), ids)
rate = tf.reshape(rate,
shape=concat_vectors([n], self.batch_shape_tensor()))
return tf.random.poisson(lam=rate,
shape=[],
dtype=self.dtype,
seed=seed)
def basis(sample_paths, time_index):
"""Computes polynomial basis expansion at the given sample points.
Args:
sample_paths: A `Tensor` of either `flaot32` or `float64` dtype and of
either shape `[num_samples, num_times, dim]` or
`[batch_size, num_samples, num_times, dim]`.
time_index: An integer scalar `Tensor` that corresponds to the time
coordinate at which the basis function is computed.
Returns:
A `Tensor`s of shape `[batch_size, (degree + 1)**dim, num_samples]`.
"""
sample_paths = tf.convert_to_tensor(sample_paths,
name="sample_paths")
if sample_paths.shape.rank == 3:
sample_paths = tf.expand_dims(sample_paths, axis=0)
shape = tf.shape(sample_paths)
num_samples = shape[1]
batch_size = shape[0]
dim = sample_paths.shape[-1] # Dimension should statically known
# Shape [batch_size, num_samples, 1, dim]
slice_samples = tf.slice(sample_paths, [0, 0, time_index, 0],
[batch_size, num_samples, 1, dim])
# Shape [batch_size, num_samples, 1, dim]
samples_centered = slice_samples - tf.math.reduce_mean(
slice_samples, axis=1, keepdims=True)
grid = tf.range(degree + 1, dtype=samples_centered.dtype)
# Creates a grid of 'power' expansions, i.e., a `Tensor` of shape
# [(degree + 1)**dim, dim] with entries [k_1, .., k_dim] where
## 0 <= k_i <= dim.
grid = tf.meshgrid(*(dim * [grid]))
# Shape [(degree + 1)**3, dim]
grid = tf.reshape(tf.stack(grid, -1), [-1, dim])
# `samples_centered` has shape [batch_size, num_samples, 1, dim],
# `samples_centered**grid` has shape
# `[batch_size, num_samples, (degree + 1)**dim, dim]`
# so that the output shape is `[batch_size, num_samples, (degree + 1)**dim]`
basis_expansion = tf.reduce_prod(samples_centered**grid, axis=-1)
return tf.transpose(basis_expansion, [0, 2, 1])
def testRank1ResNetV1(self, alpha_initializer, gamma_initializer,
random_sign_init, ensemble_size):
tf.random.set_seed(83922)
dataset_size = 10
batch_size = 6
input_shape = (32, 32, 2
) # TODO(dusenberrymw): (32, 32, 1) doesn't work...
num_classes = 2
features = tf.random.normal((dataset_size, ) + input_shape)
coeffs = tf.random.normal([tf.reduce_prod(input_shape), num_classes])
net = tf.reshape(features, [dataset_size, -1])
logits = tf.matmul(net, coeffs)
labels = tf.random.categorical(logits, 1)
dataset = tf.data.Dataset.from_tensor_slices((features, labels))
dataset = dataset.repeat().shuffle(dataset_size).batch(batch_size)
model = resnet_cifar_model.rank1_resnet_v1(
input_shape=input_shape,
depth=8,
num_classes=num_classes,
width_multiplier=1,
alpha_initializer=alpha_initializer,
gamma_initializer=gamma_initializer,
alpha_regularizer=None,
gamma_regularizer=None,
use_additive_perturbation=False,
ensemble_size=ensemble_size,
random_sign_init=-0.5,
dropout_rate=0.)
model.compile('adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True))
history = model.fit(dataset,
steps_per_epoch=dataset_size // batch_size,
epochs=2)
loss_history = history.history['loss']
self.assertAllGreaterEqual(loss_history, 0.)
def _quasi_uniform(
dim,
sample_shape,
random_type,
dtype,
seed=None,
**kwargs):
"""Quasi random draws from a uniform distribution on [0, 1)."""
# Shape of the output
output_shape = tf.concat([sample_shape] + [[dim]], -1)
# Number of quasi random samples
num_samples = tf.reduce_prod(sample_shape)
# Number of initial low discrepancy sequence numbers to skip
if 'skip' in kwargs:
skip = kwargs['skip']
else:
skip = 0
if random_type == RandomType.SOBOL:
# Shape [num_samples, dim] of the Sobol samples
low_discrepancy_seq = sobol.sample(
dim=dim, num_results=num_samples, skip=skip,
dtype=dtype)
# TODO(b/148005344): Remove after tf.reshape after the bug is fixed
low_discrepancy_seq = tf.reshape(low_discrepancy_seq, [num_samples, dim])
else: # HALTON or HALTON_RANDOMIZED random_dtype
if 'randomization_params' in kwargs:
randomization_params = kwargs['randomization_params']
else:
randomization_params = None
randomized = random_type == RandomType.HALTON_RANDOMIZED
# Shape [num_samples, dim] of the Sobol samples
low_discrepancy_seq, _ = halton.sample(
dim=dim,
sequence_indices=tf.range(skip, skip + num_samples),
randomized=randomized,
randomization_params=randomization_params,
seed=seed,
dtype=dtype)
return tf.reshape(low_discrepancy_seq, output_shape)
def _entropy(self):
samples = tf.convert_to_tensor(self.samples)
num_samples = self._compute_num_samples(samples)
entropy_shape = self._batch_shape_tensor(samples)
# Flatten samples for each batch.
if self._event_ndims == 0:
samples = tf.reshape(samples, [-1, num_samples])
else:
event_size = tf.reduce_prod(self.event_shape_tensor())
samples = tf.reshape(samples, [-1, num_samples, event_size])
# Use map_fn to compute entropy for each batch separately.
def _get_entropy(samples):
# TODO(b/123985779): Switch to tf.unique_with_counts_v2 when exposed
count = gen_array_ops.unique_with_counts_v2(samples, axis=[0]).count
prob = tf.cast(count / num_samples, dtype=self.dtype)
entropy = tf.reduce_sum(-prob * tf.math.log(prob))
return entropy
entropy = tf.map_fn(_get_entropy, samples, dtype=self.dtype)
return tf.reshape(entropy, entropy_shape)
def easom(z):
"""The value of the two dimensional Easom function.
The Easom function is a standard optimization test function. It has
a single global minimum at (pi, pi) which is located inside a deep
funnel. The expression for the function is:
```None
f(x, y) = -cos(x) cos(y) exp(-(x-pi)**2 - (y-pi)**2)
```
Args:
z: `Tensor` of shape [2] and real dtype. The argument at which to
evaluate the function.
Returns:
value: Scalar real `Tensor`. The value of the Easom function at the
supplied argument.
"""
f1 = tf.reduce_prod(tf.cos(z), axis=-1)
f2 = tf.exp(-tf.reduce_sum((z - np.pi)**2, axis=-1))
return -f1 * f2
