Other Functions and Classes

class tf.contrib.distributions.BaseDistribution

Simple abstract base class for probability distributions.

Implementations of core distributions to be included in the distributions module should subclass Distribution. This base class may be useful to users that want to fulfill a simpler distribution contract.


tf.contrib.distributions.BaseDistribution.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.BaseDistribution.sample_n(n, seed=None, name='sample')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

class tf.contrib.distributions.BernoulliWithSigmoidP

Bernoulli with p = sigmoid(p).


tf.contrib.distributions.BernoulliWithSigmoidP.__init__(p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='BernoulliWithSigmoidP') {:#BernoulliWithSigmoidP.init}


tf.contrib.distributions.BernoulliWithSigmoidP.allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:
  • allow_nan_stats: Python boolean.

tf.contrib.distributions.BernoulliWithSigmoidP.batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:
  • name: name to give to the op
Returns:
  • batch_shape: Tensor.

tf.contrib.distributions.BernoulliWithSigmoidP.cdf(value, name='cdf')

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.BernoulliWithSigmoidP.dtype

The DType of Tensors handled by this Distribution.


tf.contrib.distributions.BernoulliWithSigmoidP.entropy(name='entropy')

Shanon entropy in nats.


tf.contrib.distributions.BernoulliWithSigmoidP.event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:
  • name: name to give to the op
Returns:
  • event_shape: Tensor.

tf.contrib.distributions.BernoulliWithSigmoidP.get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:
  • batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.BernoulliWithSigmoidP.get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:
  • event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.BernoulliWithSigmoidP.is_continuous


tf.contrib.distributions.BernoulliWithSigmoidP.is_reparameterized


tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf')

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf')

Log probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf')

Log probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function')

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.


tf.contrib.distributions.BernoulliWithSigmoidP.logits


tf.contrib.distributions.BernoulliWithSigmoidP.mean(name='mean')

Mean.


tf.contrib.distributions.BernoulliWithSigmoidP.mode(name='mode')

Mode.


tf.contrib.distributions.BernoulliWithSigmoidP.name

Name prepended to all ops created by this Distribution.


tf.contrib.distributions.BernoulliWithSigmoidP.p


tf.contrib.distributions.BernoulliWithSigmoidP.param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:
  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.
Returns:

dict of parameter name to Tensor shapes.


tf.contrib.distributions.BernoulliWithSigmoidP.param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:
  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().
Returns:

dict of parameter name to TensorShape.

Raises:
  • ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.BernoulliWithSigmoidP.parameters

Dictionary of parameters used by this Distribution.


tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf')

Probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf')

Probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob')

Probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.BernoulliWithSigmoidP.q

1-p.


tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample')

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:
  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.BernoulliWithSigmoidP.sample_n(n, seed=None, name='sample_n')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

tf.contrib.distributions.BernoulliWithSigmoidP.std(name='std')

Standard deviation.


tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function')

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.


tf.contrib.distributions.BernoulliWithSigmoidP.validate_args

Python boolean indicated possibly expensive checks are enabled.


tf.contrib.distributions.BernoulliWithSigmoidP.variance(name='variance')

Variance.


class tf.contrib.distributions.BetaWithSoftplusAB

Beta with softplus transform on a and b.


tf.contrib.distributions.BetaWithSoftplusAB.__init__(a, b, validate_args=False, allow_nan_stats=True, name='BetaWithSoftplusAB') {:#BetaWithSoftplusAB.init}


tf.contrib.distributions.BetaWithSoftplusAB.a

Shape parameter.


tf.contrib.distributions.BetaWithSoftplusAB.a_b_sum

Sum of parameters.


tf.contrib.distributions.BetaWithSoftplusAB.allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:
  • allow_nan_stats: Python boolean.

tf.contrib.distributions.BetaWithSoftplusAB.b

Shape parameter.


tf.contrib.distributions.BetaWithSoftplusAB.batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:
  • name: name to give to the op
Returns:
  • batch_shape: Tensor.

tf.contrib.distributions.BetaWithSoftplusAB.cdf(value, name='cdf')

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.BetaWithSoftplusAB.dtype

The DType of Tensors handled by this Distribution.


tf.contrib.distributions.BetaWithSoftplusAB.entropy(name='entropy')

Shanon entropy in nats.


tf.contrib.distributions.BetaWithSoftplusAB.event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:
  • name: name to give to the op
Returns:
  • event_shape: Tensor.

tf.contrib.distributions.BetaWithSoftplusAB.get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:
  • batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.BetaWithSoftplusAB.get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:
  • event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.BetaWithSoftplusAB.is_continuous


tf.contrib.distributions.BetaWithSoftplusAB.is_reparameterized


tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf')

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.BetaWithSoftplusAB.log_pdf(value, name='log_pdf')

Log probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf')

Log probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function')

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.


tf.contrib.distributions.BetaWithSoftplusAB.mean(name='mean')

Mean.


tf.contrib.distributions.BetaWithSoftplusAB.mode(name='mode')

Mode.


tf.contrib.distributions.BetaWithSoftplusAB.name

Name prepended to all ops created by this Distribution.


tf.contrib.distributions.BetaWithSoftplusAB.param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:
  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.
Returns:

dict of parameter name to Tensor shapes.


tf.contrib.distributions.BetaWithSoftplusAB.param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:
  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().
Returns:

dict of parameter name to TensorShape.

Raises:
  • ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.BetaWithSoftplusAB.parameters

Dictionary of parameters used by this Distribution.


tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf')

Probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf')

Probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob')

Probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample')

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:
  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.BetaWithSoftplusAB.sample_n(n, seed=None, name='sample_n')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

tf.contrib.distributions.BetaWithSoftplusAB.std(name='std')

Standard deviation.


tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function')

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.


tf.contrib.distributions.BetaWithSoftplusAB.validate_args

Python boolean indicated possibly expensive checks are enabled.


tf.contrib.distributions.BetaWithSoftplusAB.variance(name='variance')

Variance.


class tf.contrib.distributions.Chi2WithAbsDf

Chi2 with parameter transform df = floor(abs(df)).


tf.contrib.distributions.Chi2WithAbsDf.__init__(df, validate_args=False, allow_nan_stats=True, name='Chi2WithAbsDf') {:#Chi2WithAbsDf.init}


tf.contrib.distributions.Chi2WithAbsDf.allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:
  • allow_nan_stats: Python boolean.

tf.contrib.distributions.Chi2WithAbsDf.alpha

Shape parameter.


tf.contrib.distributions.Chi2WithAbsDf.batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:
  • name: name to give to the op
Returns:
  • batch_shape: Tensor.

tf.contrib.distributions.Chi2WithAbsDf.beta

Inverse scale parameter.


tf.contrib.distributions.Chi2WithAbsDf.cdf(value, name='cdf')

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Chi2WithAbsDf.df


tf.contrib.distributions.Chi2WithAbsDf.dtype

The DType of Tensors handled by this Distribution.


tf.contrib.distributions.Chi2WithAbsDf.entropy(name='entropy')

Shanon entropy in nats.


tf.contrib.distributions.Chi2WithAbsDf.event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:
  • name: name to give to the op
Returns:
  • event_shape: Tensor.

tf.contrib.distributions.Chi2WithAbsDf.get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:
  • batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.Chi2WithAbsDf.get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:
  • event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.Chi2WithAbsDf.is_continuous


tf.contrib.distributions.Chi2WithAbsDf.is_reparameterized


tf.contrib.distributions.Chi2WithAbsDf.log_cdf(value, name='log_cdf')

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf')

Log probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf')

Log probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Chi2WithAbsDf.log_survival_function(value, name='log_survival_function')

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.


tf.contrib.distributions.Chi2WithAbsDf.mean(name='mean')

Mean.


tf.contrib.distributions.Chi2WithAbsDf.mode(name='mode')

Mode.


tf.contrib.distributions.Chi2WithAbsDf.name

Name prepended to all ops created by this Distribution.


tf.contrib.distributions.Chi2WithAbsDf.param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:
  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.
Returns:

dict of parameter name to Tensor shapes.


tf.contrib.distributions.Chi2WithAbsDf.param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:
  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().
Returns:

dict of parameter name to TensorShape.

Raises:
  • ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.Chi2WithAbsDf.parameters

Dictionary of parameters used by this Distribution.


tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf')

Probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf')

Probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob')

Probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample')

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:
  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.Chi2WithAbsDf.sample_n(n, seed=None, name='sample_n')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

tf.contrib.distributions.Chi2WithAbsDf.std(name='std')

Standard deviation.


tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function')

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.


tf.contrib.distributions.Chi2WithAbsDf.validate_args

Python boolean indicated possibly expensive checks are enabled.


tf.contrib.distributions.Chi2WithAbsDf.variance(name='variance')

Variance.


class tf.contrib.distributions.ExponentialWithSoftplusLam

Exponential with softplus transform on lam.


tf.contrib.distributions.ExponentialWithSoftplusLam.__init__(lam, validate_args=False, allow_nan_stats=True, name='ExponentialWithSoftplusLam') {:#ExponentialWithSoftplusLam.init}


tf.contrib.distributions.ExponentialWithSoftplusLam.allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:
  • allow_nan_stats: Python boolean.

tf.contrib.distributions.ExponentialWithSoftplusLam.alpha

Shape parameter.


tf.contrib.distributions.ExponentialWithSoftplusLam.batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:
  • name: name to give to the op
Returns:
  • batch_shape: Tensor.

tf.contrib.distributions.ExponentialWithSoftplusLam.beta

Inverse scale parameter.


tf.contrib.distributions.ExponentialWithSoftplusLam.cdf(value, name='cdf')

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.ExponentialWithSoftplusLam.dtype

The DType of Tensors handled by this Distribution.


tf.contrib.distributions.ExponentialWithSoftplusLam.entropy(name='entropy')

Shanon entropy in nats.


tf.contrib.distributions.ExponentialWithSoftplusLam.event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:
  • name: name to give to the op
Returns:
  • event_shape: Tensor.

tf.contrib.distributions.ExponentialWithSoftplusLam.get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:
  • batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.ExponentialWithSoftplusLam.get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:
  • event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.ExponentialWithSoftplusLam.is_continuous


tf.contrib.distributions.ExponentialWithSoftplusLam.is_reparameterized


tf.contrib.distributions.ExponentialWithSoftplusLam.lam


tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf')

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf')

Log probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf')

Log probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.ExponentialWithSoftplusLam.log_survival_function(value, name='log_survival_function')

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.


tf.contrib.distributions.ExponentialWithSoftplusLam.mean(name='mean')

Mean.


tf.contrib.distributions.ExponentialWithSoftplusLam.mode(name='mode')

Mode.


tf.contrib.distributions.ExponentialWithSoftplusLam.name

Name prepended to all ops created by this Distribution.


tf.contrib.distributions.ExponentialWithSoftplusLam.param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:
  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.
Returns:

dict of parameter name to Tensor shapes.


tf.contrib.distributions.ExponentialWithSoftplusLam.param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:
  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().
Returns:

dict of parameter name to TensorShape.

Raises:
  • ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.ExponentialWithSoftplusLam.parameters

Dictionary of parameters used by this Distribution.


tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf')

Probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf')

Probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob')

Probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.ExponentialWithSoftplusLam.sample(sample_shape=(), seed=None, name='sample')

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:
  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.ExponentialWithSoftplusLam.sample_n(n, seed=None, name='sample_n')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

tf.contrib.distributions.ExponentialWithSoftplusLam.std(name='std')

Standard deviation.


tf.contrib.distributions.ExponentialWithSoftplusLam.survival_function(value, name='survival_function')

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.


tf.contrib.distributions.ExponentialWithSoftplusLam.validate_args

Python boolean indicated possibly expensive checks are enabled.


tf.contrib.distributions.ExponentialWithSoftplusLam.variance(name='variance')

Variance.


class tf.contrib.distributions.GammaWithSoftplusAlphaBeta

Gamma with softplus transform on alpha and beta.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='GammaWithSoftplusAlphaBeta') {:#GammaWithSoftplusAlphaBeta.init}


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:
  • allow_nan_stats: Python boolean.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.alpha

Shape parameter.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:
  • name: name to give to the op
Returns:
  • batch_shape: Tensor.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.beta

Inverse scale parameter.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf')

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.dtype

The DType of Tensors handled by this Distribution.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.entropy(name='entropy')

Shanon entropy in nats.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:
  • name: name to give to the op
Returns:
  • event_shape: Tensor.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:
  • batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:
  • event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.is_continuous


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.is_reparameterized


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')

Log probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')

Log probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.mean(name='mean')

Mean.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.mode(name='mode')

Mode.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.name

Name prepended to all ops created by this Distribution.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:
  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.
Returns:

dict of parameter name to Tensor shapes.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:
  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().
Returns:

dict of parameter name to TensorShape.

Raises:
  • ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.parameters

Dictionary of parameters used by this Distribution.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf')

Probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf')

Probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob')

Probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:
  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.std(name='std')

Standard deviation.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.validate_args

Python boolean indicated possibly expensive checks are enabled.


tf.contrib.distributions.GammaWithSoftplusAlphaBeta.variance(name='variance')

Variance.


class tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta

Inverse Gamma with softplus applied to alpha and beta.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='InverseGammaWithSoftplusAlphaBeta') {:#InverseGammaWithSoftplusAlphaBeta.init}


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:
  • allow_nan_stats: Python boolean.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.alpha

Shape parameter.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:
  • name: name to give to the op
Returns:
  • batch_shape: Tensor.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.beta

Scale parameter.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf')

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.dtype

The DType of Tensors handled by this Distribution.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.entropy(name='entropy')

Shanon entropy in nats.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:
  • name: name to give to the op
Returns:
  • event_shape: Tensor.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:
  • batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:
  • event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.is_continuous


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.is_reparameterized


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')

Log probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')

Log probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.mean(name='mean')

Mean.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.mode(name='mode')

Mode.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.name

Name prepended to all ops created by this Distribution.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:
  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.
Returns:

dict of parameter name to Tensor shapes.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:
  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().
Returns:

dict of parameter name to TensorShape.

Raises:
  • ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.parameters

Dictionary of parameters used by this Distribution.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf')

Probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf')

Probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob')

Probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:
  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.std(name='std')

Standard deviation.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.validate_args

Python boolean indicated possibly expensive checks are enabled.


tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.variance(name='variance')

Variance.


class tf.contrib.distributions.LaplaceWithSoftplusScale

Laplace with softplus applied to scale.


tf.contrib.distributions.LaplaceWithSoftplusScale.__init__(loc, scale, validate_args=False, allow_nan_stats=True, name='LaplaceWithSoftplusScale') {:#LaplaceWithSoftplusScale.init}


tf.contrib.distributions.LaplaceWithSoftplusScale.allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:
  • allow_nan_stats: Python boolean.

tf.contrib.distributions.LaplaceWithSoftplusScale.batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:
  • name: name to give to the op
Returns:
  • batch_shape: Tensor.

tf.contrib.distributions.LaplaceWithSoftplusScale.cdf(value, name='cdf')

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.LaplaceWithSoftplusScale.dtype

The DType of Tensors handled by this Distribution.


tf.contrib.distributions.LaplaceWithSoftplusScale.entropy(name='entropy')

Shanon entropy in nats.


tf.contrib.distributions.LaplaceWithSoftplusScale.event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:
  • name: name to give to the op
Returns:
  • event_shape: Tensor.

tf.contrib.distributions.LaplaceWithSoftplusScale.get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:
  • batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.LaplaceWithSoftplusScale.get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:
  • event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.LaplaceWithSoftplusScale.is_continuous


tf.contrib.distributions.LaplaceWithSoftplusScale.is_reparameterized


tf.contrib.distributions.LaplaceWithSoftplusScale.loc

Distribution parameter for the location.


tf.contrib.distributions.LaplaceWithSoftplusScale.log_cdf(value, name='log_cdf')

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf')

Log probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf')

Log probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.LaplaceWithSoftplusScale.log_survival_function(value, name='log_survival_function')

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.


tf.contrib.distributions.LaplaceWithSoftplusScale.mean(name='mean')

Mean.


tf.contrib.distributions.LaplaceWithSoftplusScale.mode(name='mode')

Mode.


tf.contrib.distributions.LaplaceWithSoftplusScale.name

Name prepended to all ops created by this Distribution.


tf.contrib.distributions.LaplaceWithSoftplusScale.param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:
  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.
Returns:

dict of parameter name to Tensor shapes.


tf.contrib.distributions.LaplaceWithSoftplusScale.param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:
  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().
Returns:

dict of parameter name to TensorShape.

Raises:
  • ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.LaplaceWithSoftplusScale.parameters

Dictionary of parameters used by this Distribution.


tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf')

Probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf')

Probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob')

Probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.LaplaceWithSoftplusScale.sample(sample_shape=(), seed=None, name='sample')

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:
  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.LaplaceWithSoftplusScale.sample_n(n, seed=None, name='sample_n')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

tf.contrib.distributions.LaplaceWithSoftplusScale.scale

Distribution parameter for scale.


tf.contrib.distributions.LaplaceWithSoftplusScale.std(name='std')

Standard deviation.


tf.contrib.distributions.LaplaceWithSoftplusScale.survival_function(value, name='survival_function')

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.


tf.contrib.distributions.LaplaceWithSoftplusScale.validate_args

Python boolean indicated possibly expensive checks are enabled.


tf.contrib.distributions.LaplaceWithSoftplusScale.variance(name='variance')

Variance.


class tf.contrib.distributions.MultivariateNormalDiagPlusVDVT

The multivariate normal distribution on R^k.

Every batch member of this distribution is defined by a mean and a lightweight covariance matrix C.

Mathematical details

The PDF of this distribution in terms of the mean mu and covariance C is:

f(x) = (2 pi)^(-k/2) |det(C)|^(-1/2) exp(-1/2 (x - mu)^T C^{-1} (x - mu))

For every batch member, this distribution represents k random variables (X_1,...,X_k), with mean E[X_i] = mu[i], and covariance matrix C_{ij} := E[(X_i - mu[i])(X_j - mu[j])]

The user initializes this class by providing the mean mu, and a lightweight definition of C:

C = SS^T = SS = (M + V D V^T) (M + V D V^T)
M is diagonal (k x k)
V = is shape (k x r), typically r << k
D = is diagonal (r x r), optional (defaults to identity).

This allows for O(kr + r^3) pdf evaluation and determinant, and O(kr) sampling and storage (per batch member).

Examples

A single multi-variate Gaussian distribution is defined by a vector of means of length k, and square root of the covariance S = M + V D V^T. Extra leading dimensions, if provided, allow for batches.

# Initialize a single 3-variate Gaussian with covariance square root
# S = M + V D V^T, where V D V^T is a matrix-rank 2 update.
mu = [1, 2, 3.]
diag_large = [1.1, 2.2, 3.3]
v = ... # shape 3 x 2
diag_small = [4., 5.]
dist = tf.contrib.distributions.MultivariateNormalDiagPlusVDVT(
    mu, diag_large, v, diag_small=diag_small)

# Evaluate this on an observation in R^3, returning a scalar.
dist.pdf([-1, 0, 1])

# Initialize a batch of two 3-variate Gaussians.  This time, don't provide
# diag_small.  This means S = M + V V^T.
mu = [[1, 2, 3], [11, 22, 33]]  # shape 2 x 3
diag_large = ... # shape 2 x 3
v = ... # shape 2 x 3 x 1, a matrix-rank 1 update.
dist = tf.contrib.distributions.MultivariateNormalDiagPlusVDVT(
    mu, diag_large, v)

# Evaluate this on a two observations, each in R^3, returning a length two
# tensor.
x = [[-1, 0, 1], [-11, 0, 11]]  # Shape 2 x 3.
dist.pdf(x)

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.__init__(mu, diag_large, v, diag_small=None, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiagPlusVDVT') {:#MultivariateNormalDiagPlusVDVT.init}

Multivariate Normal distributions on R^k.

For every batch member, this distribution represents k random variables (X_1,...,X_k), with mean E[X_i] = mu[i], and covariance matrix C_{ij} := E[(X_i - mu[i])(X_j - mu[j])]

The user initializes this class by providing the mean mu, and a lightweight definition of C:

C = SS^T = SS = (M + V D V^T) (M + V D V^T)
M is diagonal (k x k)
V = is shape (k x r), typically r << k
D = is diagonal (r x r), optional (defaults to identity).
Args:
  • mu: Rank n + 1 floating point tensor with shape [N1,...,Nn, k], n >= 0. The means.
  • diag_large: Optional rank n + 1 floating point tensor, shape [N1,...,Nn, k] n >= 0. Defines the diagonal matrix M.
  • v: Rank n + 1 floating point tensor, shape [N1,...,Nn, k, r] n >= 0. Defines the matrix V.
  • diag_small: Rank n + 1 floating point tensor, shape [N1,...,Nn, k] n >= 0. Defines the diagonal matrix D. Default is None, which means D will be the identity matrix.
  • validate_args: Boolean, 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: Boolean, 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.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:
  • allow_nan_stats: Python boolean.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:
  • name: name to give to the op
Returns:
  • batch_shape: Tensor.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.cdf(value, name='cdf')

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.dtype

The DType of Tensors handled by this Distribution.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.entropy(name='entropy')

Shanon entropy in nats.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:
  • name: name to give to the op
Returns:
  • event_shape: Tensor.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:
  • batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:
  • event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.is_continuous


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.is_reparameterized


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf')

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf')

Log probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf')

Log probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_sigma_det(name='log_sigma_det')

Log of determinant of covariance matrix.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function')

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mean(name='mean')

Mean.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mode(name='mode')

Mode.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mu


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.name

Name prepended to all ops created by this Distribution.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:
  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.
Returns:

dict of parameter name to Tensor shapes.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:
  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().
Returns:

dict of parameter name to TensorShape.

Raises:
  • ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.parameters

Dictionary of parameters used by this Distribution.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf')

Probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf')

Probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob')

Probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample')

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:
  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample_n(n, seed=None, name='sample_n')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sigma

Dense (batch) covariance matrix, if available.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sigma_det(name='sigma_det')

Determinant of covariance matrix.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.std(name='std')

Standard deviation.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function')

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.validate_args

Python boolean indicated possibly expensive checks are enabled.


tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.variance(name='variance')

Variance.


class tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev

MultivariateNormalDiag with diag_stddev = softplus(diag_stddev).


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.__init__(mu, diag_stdev, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiagWithSoftplusStdDev') {:#MultivariateNormalDiagWithSoftplusStDev.init}


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:
  • allow_nan_stats: Python boolean.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:
  • name: name to give to the op
Returns:
  • batch_shape: Tensor.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.cdf(value, name='cdf')

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.dtype

The DType of Tensors handled by this Distribution.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.entropy(name='entropy')

Shanon entropy in nats.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:
  • name: name to give to the op
Returns:
  • event_shape: Tensor.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:
  • batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:
  • event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.is_continuous


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.is_reparameterized


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_cdf(value, name='log_cdf')

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf')

Log probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf')

Log probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_sigma_det(name='log_sigma_det')

Log of determinant of covariance matrix.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_survival_function(value, name='log_survival_function')

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.mean(name='mean')

Mean.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.mode(name='mode')

Mode.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.mu


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.name

Name prepended to all ops created by this Distribution.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:
  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.
Returns:

dict of parameter name to Tensor shapes.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:
  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().
Returns:

dict of parameter name to TensorShape.

Raises:
  • ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.parameters

Dictionary of parameters used by this Distribution.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf')

Probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf')

Probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob')

Probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample(sample_shape=(), seed=None, name='sample')

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:
  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample_n(n, seed=None, name='sample_n')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sigma

Dense (batch) covariance matrix, if available.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sigma_det(name='sigma_det')

Determinant of covariance matrix.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.std(name='std')

Standard deviation.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.survival_function(value, name='survival_function')

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.validate_args

Python boolean indicated possibly expensive checks are enabled.


tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.variance(name='variance')

Variance.


class tf.contrib.distributions.NormalWithSoftplusSigma

Normal with softplus applied to sigma.


tf.contrib.distributions.NormalWithSoftplusSigma.__init__(mu, sigma, validate_args=False, allow_nan_stats=True, name='NormalWithSoftplusSigma') {:#NormalWithSoftplusSigma.init}


tf.contrib.distributions.NormalWithSoftplusSigma.allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:
  • allow_nan_stats: Python boolean.

tf.contrib.distributions.NormalWithSoftplusSigma.batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:
  • name: name to give to the op
Returns:
  • batch_shape: Tensor.

tf.contrib.distributions.NormalWithSoftplusSigma.cdf(value, name='cdf')

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.NormalWithSoftplusSigma.dtype

The DType of Tensors handled by this Distribution.


tf.contrib.distributions.NormalWithSoftplusSigma.entropy(name='entropy')

Shanon entropy in nats.


tf.contrib.distributions.NormalWithSoftplusSigma.event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:
  • name: name to give to the op
Returns:
  • event_shape: Tensor.

tf.contrib.distributions.NormalWithSoftplusSigma.get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:
  • batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.NormalWithSoftplusSigma.get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:
  • event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.NormalWithSoftplusSigma.is_continuous


tf.contrib.distributions.NormalWithSoftplusSigma.is_reparameterized


tf.contrib.distributions.NormalWithSoftplusSigma.log_cdf(value, name='log_cdf')

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.NormalWithSoftplusSigma.log_pdf(value, name='log_pdf')

Log probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.NormalWithSoftplusSigma.log_pmf(value, name='log_pmf')

Log probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.NormalWithSoftplusSigma.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.NormalWithSoftplusSigma.log_survival_function(value, name='log_survival_function')

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.


tf.contrib.distributions.NormalWithSoftplusSigma.mean(name='mean')

Mean.


tf.contrib.distributions.NormalWithSoftplusSigma.mode(name='mode')

Mode.


tf.contrib.distributions.NormalWithSoftplusSigma.mu

Distribution parameter for the mean.


tf.contrib.distributions.NormalWithSoftplusSigma.name

Name prepended to all ops created by this Distribution.


tf.contrib.distributions.NormalWithSoftplusSigma.param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:
  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.
Returns:

dict of parameter name to Tensor shapes.


tf.contrib.distributions.NormalWithSoftplusSigma.param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:
  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().
Returns:

dict of parameter name to TensorShape.

Raises:
  • ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.NormalWithSoftplusSigma.parameters

Dictionary of parameters used by this Distribution.


tf.contrib.distributions.NormalWithSoftplusSigma.pdf(value, name='pdf')

Probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.NormalWithSoftplusSigma.pmf(value, name='pmf')

Probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.NormalWithSoftplusSigma.prob(value, name='prob')

Probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.NormalWithSoftplusSigma.sample(sample_shape=(), seed=None, name='sample')

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:
  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.NormalWithSoftplusSigma.sample_n(n, seed=None, name='sample_n')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

tf.contrib.distributions.NormalWithSoftplusSigma.sigma

Distribution parameter for standard deviation.


tf.contrib.distributions.NormalWithSoftplusSigma.std(name='std')

Standard deviation.


tf.contrib.distributions.NormalWithSoftplusSigma.survival_function(value, name='survival_function')

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.


tf.contrib.distributions.NormalWithSoftplusSigma.validate_args

Python boolean indicated possibly expensive checks are enabled.


tf.contrib.distributions.NormalWithSoftplusSigma.variance(name='variance')

Variance.


class tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma

StudentT with df = floor(abs(df)) and sigma = softplus(sigma).


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.__init__(df, mu, sigma, validate_args=False, allow_nan_stats=True, name='StudentTWithAbsDfSoftplusSigma') {:#StudentTWithAbsDfSoftplusSigma.init}


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:
  • allow_nan_stats: Python boolean.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:
  • name: name to give to the op
Returns:
  • batch_shape: Tensor.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.cdf(value, name='cdf')

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.df

Degrees of freedom in these Student's t distribution(s).


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.dtype

The DType of Tensors handled by this Distribution.


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.entropy(name='entropy')

Shanon entropy in nats.


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:
  • name: name to give to the op
Returns:
  • event_shape: Tensor.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:
  • batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:
  • event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.is_continuous


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.is_reparameterized


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_cdf(value, name='log_cdf')

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pdf(value, name='log_pdf')

Log probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pmf(value, name='log_pmf')

Log probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_survival_function(value, name='log_survival_function')

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.mean(name='mean')

Mean.


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.mode(name='mode')

Mode.


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.mu

Locations of these Student's t distribution(s).


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.name

Name prepended to all ops created by this Distribution.


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:
  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.
Returns:

dict of parameter name to Tensor shapes.


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:
  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().
Returns:

dict of parameter name to TensorShape.

Raises:
  • ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.parameters

Dictionary of parameters used by this Distribution.


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pdf(value, name='pdf')

Probability density function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if not is_continuous.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pmf(value, name='pmf')

Probability mass function.

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
Raises:
  • AttributeError: if is_continuous.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.prob(value, name='prob')

Probability density/mass function (depending on is_continuous).

Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:
  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample(sample_shape=(), seed=None, name='sample')

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:
  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample_n(n, seed=None, name='sample_n')

Generate n samples.

Args:
  • n: Scalar Tensor of type int32 or int64, the number of observations to sample.
  • seed: Python integer seed for RNG
  • name: name to give to the op.
Returns:
  • samples: a Tensor with a prepended dimension (n,).
Raises:
  • TypeError: if n is not an integer type.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sigma

Scaling factors of these Student's t distribution(s).


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.std(name='std')

Standard deviation.


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.survival_function(value, name='survival_function')

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).
Args:
  • value: float or double Tensor.
  • name: The name to give this op.
Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.validate_args

Python boolean indicated possibly expensive checks are enabled.


tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.variance(name='variance')

Variance.