Python util.get_logits_and_probs函数代码示例

  def __init__(
      self,
      logits=None,
      probs=None,
      dtype=dtypes.int32,
      validate_args=False,
      allow_nan_stats=True,
      name="OneHotCategorical"):
    """Initialize OneHotCategorical distributions using class log-probabilities.

    Args:
      logits: An N-D `Tensor`, `N >= 1`, representing the log probabilities of a
        set of Categorical distributions. The first `N - 1` dimensions index
        into a batch of independent distributions and the last dimension
        represents a vector of logits for each class. Only one of `logits` or
        `probs` should be passed in.
      probs: An N-D `Tensor`, `N >= 1`, representing the probabilities of a set
        of Categorical distributions. The first `N - 1` dimensions index into a
        batch of independent distributions and the last dimension represents a
        vector of probabilities for each class. Only one of `logits` or `probs`
        should be passed in.
      dtype: The type of the event samples (default: int32).
      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.
    """
    parameters = dict(locals())
    with ops.name_scope(name, values=[logits, probs]) as name:
      self._logits, self._probs = distribution_util.get_logits_and_probs(
          name=name, logits=logits, probs=probs, validate_args=validate_args,
          multidimensional=True)

      logits_shape_static = self._logits.get_shape().with_rank_at_least(1)
      if logits_shape_static.ndims is not None:
        self._batch_rank = ops.convert_to_tensor(
            logits_shape_static.ndims - 1,
            dtype=dtypes.int32,
            name="batch_rank")
      else:
        with ops.name_scope(name="batch_rank"):
          self._batch_rank = array_ops.rank(self._logits) - 1

      with ops.name_scope(name="event_size"):
        self._event_size = array_ops.shape(self._logits)[-1]

    super(OneHotCategorical, self).__init__(
        dtype=dtype,
        reparameterization_type=distribution.NOT_REPARAMETERIZED,
        validate_args=validate_args,
        allow_nan_stats=allow_nan_stats,
        parameters=parameters,
        graph_parents=[self._logits,
                       self._probs],
        name=name)

  def __init__(self,
               total_count,
               logits=None,
               probs=None,
               validate_args=False,
               allow_nan_stats=True,
               name="Multinomial"):
    """Initialize a batch of Multinomial distributions.

    Args:
      total_count: Non-negative floating point tensor with shape broadcastable
        to `[N1,..., Nm]` with `m >= 0`. Defines this as a batch of
        `N1 x ... x Nm` different Multinomial distributions. Its components
        should be equal to integer values.
      logits: Floating point tensor representing unnormalized log-probabilities
        of a positive event with shape broadcastable to
        `[N1,..., Nm, K]` `m >= 0`, and the same dtype as `total_count`. Defines
        this as a batch of `N1 x ... x Nm` different `K` class Multinomial
        distributions. Only one of `logits` or `probs` should be passed in.
      probs: Positive floating point tensor with shape broadcastable to
        `[N1,..., Nm, K]` `m >= 0` and same dtype as `total_count`. Defines
        this as a batch of `N1 x ... x Nm` different `K` class Multinomial
        distributions. `probs`'s components in the last portion of its shape
        should sum to `1`. 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.
    """
    parameters = locals()
    with ops.name_scope(name, values=[total_count, logits, probs]):
      self._total_count = ops.convert_to_tensor(total_count, name="total_count")
      if validate_args:
        self._total_count = (
            distribution_util.embed_check_nonnegative_integer_form(
                self._total_count))
      self._logits, self._probs = distribution_util.get_logits_and_probs(
          logits=logits,
          probs=probs,
          multidimensional=True,
          validate_args=validate_args,
          name=name)
      self._mean_val = self._total_count[..., array_ops.newaxis] * self._probs
    super(Multinomial, self).__init__(
        dtype=self._probs.dtype,
        reparameterization_type=distribution.NOT_REPARAMETERIZED,
        validate_args=validate_args,
        allow_nan_stats=allow_nan_stats,
        parameters=parameters,
        graph_parents=[self._total_count,
                       self._logits,
                       self._probs],
        name=name)

  def __init__(self,
               total_count,
               logits=None,
               probs=None,
               validate_args=False,
               allow_nan_stats=True,
               name="NegativeBinomial"):
    """Construct NegativeBinomial distributions.

    Args:
      total_count: Non-negative floating-point `Tensor` with shape
        broadcastable to `[B1,..., Bb]` with `b >= 0` and the same dtype as
        `probs` or `logits`. Defines this as a batch of `N1 x ... x Nm`
        different Negative Binomial distributions. In practice, this represents
        the number of negative Bernoulli trials to stop at (the `total_count`
        of failures), but this is still a valid distribution when
        `total_count` is a non-integer.
      logits: Floating-point `Tensor` with shape broadcastable to
        `[B1, ..., Bb]` where `b >= 0` indicates the number of batch dimensions.
        Each entry represents logits for the probability of success for
        independent Negative Binomial distributions and must be in the open
        interval `(-inf, inf)`. Only one of `logits` or `probs` should be
        specified.
      probs: Positive floating-point `Tensor` with shape broadcastable to
        `[B1, ..., Bb]` where `b >= 0` indicates the number of batch dimensions.
        Each entry represents the probability of success for independent
        Negative Binomial distributions and must be in the open interval
        `(0, 1)`. Only one of `logits` or `probs` should be specified.
      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.
    """

    parameters = dict(locals())
    with ops.name_scope(name, values=[total_count, logits, probs]) as name:
      self._logits, self._probs = distribution_util.get_logits_and_probs(
          logits, probs, validate_args=validate_args, name=name)
      with ops.control_dependencies(
          [check_ops.assert_positive(total_count)] if validate_args else []):
        self._total_count = array_ops.identity(total_count)

    super(NegativeBinomial, self).__init__(
        dtype=self._probs.dtype,
        reparameterization_type=distribution.NOT_REPARAMETERIZED,
        validate_args=validate_args,
        allow_nan_stats=allow_nan_stats,
        parameters=parameters,
        graph_parents=[self._total_count, self._probs, self._logits],
        name=name)

  def __init__(self,
               total_count,
               logits=None,
               probs=None,
               validate_args=False,
               allow_nan_stats=True,
               name="Binomial"):
    """Initialize a batch of Binomial distributions.

    Args:
      total_count: Non-negative floating point tensor with shape broadcastable
        to `[N1,..., Nm]` with `m >= 0` and the same dtype as `probs` or
        `logits`. Defines this as a batch of `N1 x ...  x Nm` different Binomial
        distributions. Its components should be equal to integer values.
      logits: Floating point tensor representing the log-odds of a
        positive event with shape broadcastable to `[N1,..., Nm]` `m >= 0`, and
        the same dtype as `total_count`. Each entry represents logits for the
        probability of success for independent Binomial distributions. Only one
        of `logits` or `probs` should be passed in.
      probs: Positive floating point tensor with shape broadcastable to
        `[N1,..., Nm]` `m >= 0`, `probs in [0, 1]`. Each entry represents the
        probability of success for independent Binomial distributions. 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.
    """
    parameters = locals()
    with ops.name_scope(name, values=[total_count, logits, probs]):
      self._total_count = self._maybe_assert_valid_total_count(
          ops.convert_to_tensor(total_count, name="total_count"),
          validate_args)
      self._logits, self._probs = distribution_util.get_logits_and_probs(
          logits=logits,
          probs=probs,
          validate_args=validate_args,
          name=name)
    super(Binomial, self).__init__(
        dtype=self._probs.dtype,
        reparameterization_type=distribution.NOT_REPARAMETERIZED,
        validate_args=validate_args,
        allow_nan_stats=allow_nan_stats,
        parameters=parameters,
        graph_parents=[self._total_count,
                       self._logits,
                       self._probs],
        name=name)

  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.name_scope(name, values=[logits, probs, temperature]) as name:
      with tf.control_dependencies([tf.assert_positive(temperature)]
                                   if validate_args else []):
        self._temperature = tf.identity(temperature, name="temperature")
      self._logits, self._probs = distribution_util.get_logits_and_probs(
          logits=logits, probs=probs, validate_args=validate_args)
      super(RelaxedBernoulli, self).__init__(
          distribution=Logistic(
              self._logits / self._temperature,
              1. / self._temperature,
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              name=name + "/Logistic"),
          bijector=Sigmoid(validate_args=validate_args),
          validate_args=validate_args,
          name=name)
    self._parameters = parameters

  def __init__(self,
               logits=None,
               probs=None,
               validate_args=False,
               allow_nan_stats=True,
               name="Geometric"):
    """Construct Geometric distributions.

    Args:
      logits: Floating-point `Tensor` with shape `[B1, ..., Bb]` where `b >= 0`
        indicates the number of batch dimensions. Each entry represents logits
        for the probability of success for independent Geometric distributions
        and must be in the range `(-inf, inf]`. Only one of `logits` or `probs`
        should be specified.
      probs: Positive floating-point `Tensor` with shape `[B1, ..., Bb]`
        where `b >= 0` indicates the number of batch dimensions. Each entry
        represents the probability of success for independent Geometric
        distributions and must be in the range `(0, 1]`. Only one of `logits`
        or `probs` should be specified.
      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.
    """

    parameters = dict(locals())
    with ops.name_scope(name, values=[logits, probs]) as name:
      self._logits, self._probs = distribution_util.get_logits_and_probs(
          logits, probs, validate_args=validate_args, name=name)

      with ops.control_dependencies(
          [check_ops.assert_positive(self._probs)] if validate_args else []):
        self._probs = array_ops.identity(self._probs, name="probs")

    super(Geometric, self).__init__(
        dtype=self._probs.dtype,
        reparameterization_type=distribution.NOT_REPARAMETERIZED,
        validate_args=validate_args,
        allow_nan_stats=allow_nan_stats,
        parameters=parameters,
        graph_parents=[self._probs, self._logits],
        name=name)

  def __init__(self,
               logits=None,
               probs=None,
               dtype=dtypes.int32,
               validate_args=False,
               allow_nan_stats=True,
               name="Bernoulli"):
    """Construct Bernoulli distributions.

    Args:
      logits: An N-D `Tensor` representing the log-odds of a `1` event. Each
        entry in the `Tensor` parametrizes an independent Bernoulli 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 `1`
        event. Each entry in the `Tensor` parameterizes an independent
        Bernoulli distribution. Only one of `logits` or `probs` should be passed
        in.
      dtype: The type of the event samples. Default: `int32`.
      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 p and logits are passed, or if neither are passed.
    """
    parameters = dict(locals())
    with ops.name_scope(name) as name:
      self._logits, self._probs = distribution_util.get_logits_and_probs(
          logits=logits,
          probs=probs,
          validate_args=validate_args,
          name=name)
    super(Bernoulli, self).__init__(
        dtype=dtype,
        reparameterization_type=distribution.NOT_REPARAMETERIZED,
        validate_args=validate_args,
        allow_nan_stats=allow_nan_stats,
        parameters=parameters,
        graph_parents=[self._logits, self._probs],
        name=name)