def __init__(self,
loc,
scale,
validate_args=False,
allow_nan_stats=True,
name='LogLogistic'):
"""Construct a log-logistic distribution.
The LogLogistic distribution models positive-valued random variables
whose logarithm is a logistic distribution with location `loc` and
scale `scale`. It is constructed as the exponential
transformation of a Logistic distribution.
Args:
loc: Floating-point `Tensor`; the location of the underlying logistic
distribution(s).
scale: Floating-point `Tensor`; the scale of the underlying logistic
distribution(s).
validate_args: Python `bool`, default `False`. Whether to validate input
with asserts. If `validate_args` is `False`, and the inputs are invalid,
correct behavior is not guaranteed.
allow_nan_stats: Python `bool`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: The name to give Ops created by the initializer.
"""
parameters = dict(locals())
with tf.name_scope(name) as name:
super(LogLogistic, self).__init__(distribution=logistic.Logistic(
loc=loc, scale=scale, allow_nan_stats=allow_nan_stats),
bijector=exp_bijector.Exp(),
validate_args=validate_args,
parameters=parameters,
name=name)
def _make_mixture_dist(self, component_logits, locs, scales):
"""Builds a mixture of quantized logistic distributions.
Args:
component_logits: 4D `Tensor` of logits for the Categorical distribution
over Quantized Logistic mixture components. Dimensions are `[batch_size,
height, width, num_logistic_mix]`.
locs: 4D `Tensor` of location parameters for the Quantized Logistic
mixture components. Dimensions are `[batch_size, height, width,
num_logistic_mix, num_channels]`.
scales: 4D `Tensor` of location parameters for the Quantized Logistic
mixture components. Dimensions are `[batch_size, height, width,
num_logistic_mix, num_channels]`.
Returns:
dist: A quantized logistic mixture `tfp.distribution` over the input data.
"""
mixture_distribution = categorical.Categorical(logits=component_logits)
# Convert distribution parameters for pixel values in
# `[self._low, self._high]` for use with `QuantizedDistribution`
locs = self._low + 0.5 * (self._high - self._low) * (locs + 1.)
scales *= 0.5 * (self._high - self._low)
logistic_dist = quantized_distribution.QuantizedDistribution(
distribution=transformed_distribution.TransformedDistribution(
distribution=logistic.Logistic(loc=locs, scale=scales),
bijector=shift.Shift(shift=tf.cast(-0.5, self.dtype))),
low=self._low, high=self._high)
dist = mixture_same_family.MixtureSameFamily(
mixture_distribution=mixture_distribution,
components_distribution=independent.Independent(
logistic_dist, reinterpreted_batch_ndims=1))
return independent.Independent(dist, reinterpreted_batch_ndims=2)
def _sample_channels(self,
component_logits,
locs,
scales,
coeffs=None,
seed=None):
"""Sample a single pixel-iteration and apply channel conditioning.
Args:
component_logits: 4D `Tensor` of logits for the Categorical distribution
over Quantized Logistic mixture components. Dimensions are `[batch_size,
height, width, num_logistic_mix]`.
locs: 4D `Tensor` of location parameters for the Quantized Logistic
mixture components. Dimensions are `[batch_size, height, width,
num_logistic_mix, num_channels]`.
scales: 4D `Tensor` of location parameters for the Quantized Logistic
mixture components. Dimensions are `[batch_size, height, width,
num_logistic_mix, num_channels]`.
coeffs: 4D `Tensor` of coefficients for the linear dependence among color
channels, or `None` if there is only one channel. Dimensions are
`[batch_size, height, width, num_logistic_mix, num_coeffs]`, where
`num_coeffs = num_channels * (num_channels - 1) // 2`.
seed: `int`, random seed.
Returns:
samples: 4D `Tensor` of sampled image data with autoregression among
channels. Dimensions are `[batch_size, height, width, num_channels]`.
"""
num_channels = self.event_shape[-1]
# sample mixture components once for the entire pixel
component_dist = categorical.Categorical(logits=component_logits)
mask = tf.one_hot(indices=component_dist.sample(seed=seed),
depth=self._num_logistic_mix)
mask = tf.cast(mask[..., tf.newaxis], self.dtype)
# apply mixture component mask and separate out RGB parameters
masked_locs = tf.reduce_sum(locs * mask, axis=-2)
loc_tensors = tf.split(masked_locs, num_channels, axis=-1)
masked_scales = tf.reduce_sum(scales * mask, axis=-2)
scale_tensors = tf.split(masked_scales, num_channels, axis=-1)
if coeffs is not None:
num_coeffs = num_channels * (num_channels - 1) // 2
masked_coeffs = tf.reduce_sum(coeffs * mask, axis=-2)
coef_tensors = tf.split(masked_coeffs, num_coeffs, axis=-1)
channel_samples = []
coef_count = 0
for i in range(num_channels):
loc = loc_tensors[i]
for c in channel_samples:
loc += c * coef_tensors[coef_count]
coef_count += 1
logistic_samp = logistic.Logistic(
loc=loc, scale=scale_tensors[i]).sample(seed=seed)
logistic_samp = tf.clip_by_value(logistic_samp, -1., 1.)
channel_samples.append(logistic_samp)
return tf.concat(channel_samples, axis=-1)
def __init__(self,
temperature,
logits=None,
probs=None,
validate_args=False,
allow_nan_stats=True,
name="RelaxedBernoulli"):
"""Construct RelaxedBernoulli distributions.
Args:
temperature: An 0-D `Tensor`, representing the temperature
of a set of RelaxedBernoulli distributions. The temperature should be
positive.
logits: An N-D `Tensor` representing the log-odds
of a positive event. Each entry in the `Tensor` parametrizes
an independent RelaxedBernoulli distribution where the probability of an
event is sigmoid(logits). Only one of `logits` or `probs` should be
passed in.
probs: An N-D `Tensor` representing the probability of a positive event.
Each entry in the `Tensor` parameterizes an independent Bernoulli
distribution. Only one of `logits` or `probs` should be passed in.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: If both `probs` and `logits` are passed, or if neither.
"""
parameters = dict(locals())
with tf.compat.v1.name_scope(
name, values=[logits, probs, temperature]) as name:
dtype = dtype_util.common_dtype([logits, probs, temperature], tf.float32)
self._temperature = tf.convert_to_tensor(
value=temperature, name="temperature", dtype=dtype)
if validate_args:
with tf.control_dependencies(
[tf.compat.v1.assert_positive(temperature)]):
self._temperature = tf.identity(self._temperature)
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits, probs=probs, validate_args=validate_args, dtype=dtype)
super(RelaxedBernoulli, self).__init__(
distribution=logistic.Logistic(
self._logits / self._temperature,
1. / self._temperature,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name + "/Logistic"),
bijector=sigmoid_bijector.Sigmoid(validate_args=validate_args),
validate_args=validate_args,
name=name)
self._parameters = parameters
def _transformed_logistic(self):
logistic_scale = tf.math.reciprocal(self._temperature)
logits_parameter = self._logits_parameter_no_checks()
logistic_loc = logits_parameter * logistic_scale
return transformed_distribution.TransformedDistribution(
distribution=logistic.Logistic(
logistic_loc,
logistic_scale,
allow_nan_stats=self.allow_nan_stats),
bijector=sigmoid_bijector.Sigmoid())
def __init__(self,
temperature,
logits=None,
probs=None,
validate_args=False,
allow_nan_stats=True,
name='RelaxedBernoulli'):
"""Construct RelaxedBernoulli distributions.
Args:
temperature: A `Tensor`, representing the temperature of a set of
RelaxedBernoulli distributions. The temperature values should be
positive.
logits: An N-D `Tensor` representing the log-odds
of a positive event. Each entry in the `Tensor` parametrizes
an independent RelaxedBernoulli distribution where the probability of an
event is sigmoid(logits). Only one of `logits` or `probs` should be
passed in.
probs: An N-D `Tensor` representing the probability of a positive event.
Each entry in the `Tensor` parameterizes an independent Bernoulli
distribution. Only one of `logits` or `probs` should be passed in.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: If both `probs` and `logits` are passed, or if neither.
"""
parameters = dict(locals())
with tf.name_scope(name) as name:
dtype = dtype_util.common_dtype([logits, probs, temperature],
tf.float32)
self._temperature = tensor_util.convert_nonref_to_tensor(
temperature, name='temperature', dtype=dtype)
self._probs = tensor_util.convert_nonref_to_tensor(probs,
name='probs',
dtype=dtype)
self._logits = tensor_util.convert_nonref_to_tensor(logits,
name='logits',
dtype=dtype)
if logits is None:
logits_parameter = tfp_util.DeferredTensor(
lambda x: tf.math.log(x) - tf.math.log1p(-x), self._probs)
else:
logits_parameter = self._logits
shape = tf.broadcast_static_shape(logits_parameter.shape,
self._temperature.shape)
logistic_scale = tfp_util.DeferredTensor(tf.math.reciprocal,
self._temperature)
logistic_loc = tfp_util.DeferredTensor(
lambda x: x * logistic_scale, logits_parameter, shape=shape)
self._transformed_logistic = (
transformed_distribution.TransformedDistribution(
distribution=logistic.Logistic(
logistic_loc,
logistic_scale,
allow_nan_stats=allow_nan_stats,
name=name + '/Logistic'),
bijector=sigmoid_bijector.Sigmoid()))
super(RelaxedBernoulli,
self).__init__(dtype=dtype,
reparameterization_type=reparameterization.
FULLY_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
name=name)
