BayesFlow Stochastic Tensors (contrib)

[TOC]

Classes and helper functions for creating Stochastic Tensors.

StochasticTensor objects wrap Distribution objects. Their values may be samples from the underlying distribution, or the distribution mean (as governed by value_type). These objects provide a loss method for use when sampling from a non-reparameterized distribution. The lossmethod is used in conjunction with stochastic_graph.surrogate_loss to produce a single differentiable loss in stochastic graphs having both continuous and discrete stochastic nodes.

Stochastic Tensor Classes

class tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor

Base Class for Tensor-like objects that emit stochastic values.

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.__init__()

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.graph

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.loss(sample_loss)

Returns the term to add to the surrogate loss.

This method is called by surrogate_loss. The input sample_loss should have already had stop_gradient applied to it. This is because the surrogate_loss usually provides a Monte Carlo sample term of the form differentiable_surrogate * sample_loss where sample_loss is considered constant with respect to the input for purposes of the gradient.

Args:

• sample_loss: Tensor, sample loss downstream of this StochasticTensor.

Returns:

Either None or a Tensor.

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.name

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.value(name=None)

class tf.contrib.bayesflow.stochastic_tensor.StochasticTensor

StochasticTensor is a BaseStochasticTensor backed by a distribution.

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.__init__(dist_cls, name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

Construct a StochasticTensor.

StochasticTensor will instantiate a distribution from dist_cls and dist_args and its value method will return the same value each time it is called. What value is returned is controlled by the dist_value_type (defaults to SampleAndReshapeValue).

Some distributions' sample functions are not differentiable (e.g. a sample from a discrete distribution like a Bernoulli) and so to differentiate wrt parameters upstream of the sample requires a gradient estimator like the score function estimator. This is accomplished by passing a differentiable loss_fn to the StochasticTensor, which defaults to a function whose derivative is the score function estimator. Calling stochastic_graph.surrogate_loss(final_losses) will call loss() on every StochasticTensor upstream of final losses.

loss() will return None for StochasticTensors backed by reparameterized distributions; it will also return None if the value type is MeanValueType or if loss_fn=None.

Args:

• dist_cls: a Distribution class.
• name: a name for this StochasticTensor and its ops.
• dist_value_type: a _StochasticValueType, which will determine what the value of this StochasticTensor will be. If not provided, the value type set with the value_type context manager will be used.
• loss_fn: callable that takes (dt, dt.value(), influenced_loss), where dt is this StochasticTensor, and returns a Tensor loss. By default, loss_fn is the score_function, or more precisely, the integral of the score function, such that when the gradient is taken, the score function results. See the stochastic_gradient_estimators module for additional loss functions and baselines.
• **dist_args: keyword arguments to be passed through to dist_cls on construction.

Raises:

• TypeError: if dist_cls is not a Distribution.
• TypeError: if loss_fn is not callable.

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.graph

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.name

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value_type

Stochastic Tensor Value Types

class tf.contrib.bayesflow.stochastic_tensor.MeanValue

tf.contrib.bayesflow.stochastic_tensor.MeanValue.__init__(stop_gradient=False)

tf.contrib.bayesflow.stochastic_tensor.MeanValue.declare_inputs(unused_stochastic_tensor, unused_inputs_dict)

tf.contrib.bayesflow.stochastic_tensor.MeanValue.popped_above(unused_value_type)

tf.contrib.bayesflow.stochastic_tensor.MeanValue.pushed_above(unused_value_type)

tf.contrib.bayesflow.stochastic_tensor.MeanValue.stop_gradient

class tf.contrib.bayesflow.stochastic_tensor.SampleValue

Draw n samples along a new outer dimension.

This ValueType draws n samples from StochasticTensors run within its context, increasing the rank by one along a new outer dimension.

Example:

mu = tf.zeros((2,3))
sigma = tf.ones((2, 3))
with sg.value_type(sg.SampleValue(n=4)):
  dt = sg.DistributionTensor(
    distributions.Normal, mu=mu, sigma=sigma)
# draws 4 samples each with shape (2, 3) and concatenates
assertEqual(dt.value().get_shape(), (4, 2, 3))

tf.contrib.bayesflow.stochastic_tensor.SampleValue.__init__(n=1, stop_gradient=False)

Sample n times and concatenate along a new outer dimension.

Args:

• n: A python integer or int32 tensor. The number of samples to take.
• stop_gradient: If True, StochasticTensors' values are wrapped in stop_gradient, to avoid backpropagation through.

tf.contrib.bayesflow.stochastic_tensor.SampleValue.declare_inputs(unused_stochastic_tensor, unused_inputs_dict)

tf.contrib.bayesflow.stochastic_tensor.SampleValue.n

tf.contrib.bayesflow.stochastic_tensor.SampleValue.popped_above(unused_value_type)

tf.contrib.bayesflow.stochastic_tensor.SampleValue.pushed_above(unused_value_type)

tf.contrib.bayesflow.stochastic_tensor.SampleValue.stop_gradient

class tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue

Ask the StochasticTensor for n samples and reshape the result.

Sampling from a StochasticTensor increases the rank of the value by 1 (because each sample represents a new outer dimension).

This ValueType requests n samples from StochasticTensors run within its context that the outer two dimensions are reshaped to intermix the samples with the outermost (usually batch) dimension.

Example:

# mu and sigma are both shaped (2, 3)
mu = [[0.0, -1.0, 1.0], [0.0, -1.0, 1.0]]
sigma = tf.constant([[1.1, 1.2, 1.3], [1.1, 1.2, 1.3]])

with sg.value_type(sg.SampleAndReshapeValue(n=2)):
  dt = sg.DistributionTensor(
      distributions.Normal, mu=mu, sigma=sigma)

# sample(2) creates a (2, 2, 3) tensor, and the two outermost dimensions
# are reshaped into one: the final value is a (4, 3) tensor.
dt_value = dt.value()
assertEqual(dt_value.get_shape(), (4, 3))

dt_value_val = sess.run([dt_value])[0]  # or e.g. run([tf.identity(dt)])[0]
assertEqual(dt_value_val.shape, (4, 3))

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.__init__(n=1, stop_gradient=False)

Sample n times and reshape the outer 2 axes so rank does not change.

Args:

• n: A python integer or int32 tensor. The number of samples to take.
• stop_gradient: If True, StochasticTensors' values are wrapped in stop_gradient, to avoid backpropagation through.

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.declare_inputs(unused_stochastic_tensor, unused_inputs_dict)

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.n

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.popped_above(unused_value_type)

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.pushed_above(unused_value_type)

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.stop_gradient

tf.contrib.bayesflow.stochastic_tensor.value_type(dist_value_type)

Creates a value type context for any StochasticTensor created within.

Typical usage:

with sg.value_type(sg.MeanValue(stop_gradients=True)):
  dt = sg.DistributionTensor(distributions.Normal, mu=mu, sigma=sigma)

In the example above, dt.value() (or equivalently, tf.identity(dt)) will be the mean value of the Normal distribution, i.e., mu (possibly broadcasted to the shape of sigma). Furthermore, because the MeanValue was marked with stop_gradients=True, this value will have been wrapped in a stop_gradients call to disable any possible backpropagation.

Args:

• dist_value_type: An instance of MeanValue, SampleAndReshapeValue, or any other stochastic value type.

Yields:

A context for StochasticTensor objects that controls the value created when they are initialized.

Raises:

• TypeError: if dist_value_type is not an instance of a stochastic value type.

tf.contrib.bayesflow.stochastic_tensor.get_current_value_type()

Automatically Generated StochasticTensors

class tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor

BernoulliTensor is a StochasticTensor backed by the distribution Bernoulli.

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.graph

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.name

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor

BernoulliWithSigmoidPTensor is a StochasticTensor backed by the distribution BernoulliWithSigmoidP.

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.graph

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.name

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.BetaTensor

BetaTensor is a StochasticTensor backed by the distribution Beta.

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.graph

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.name

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor

BetaWithSoftplusABTensor is a StochasticTensor backed by the distribution BetaWithSoftplusAB.

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.graph

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.name

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.BinomialTensor

BinomialTensor is a StochasticTensor backed by the distribution Binomial.

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.graph

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.name

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor

CategoricalTensor is a StochasticTensor backed by the distribution Categorical.

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.graph

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.name

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor

Chi2Tensor is a StochasticTensor backed by the distribution Chi2.

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.distribution

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.dtype

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.graph

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.name

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor

Chi2WithAbsDfTensor is a StochasticTensor backed by the distribution Chi2WithAbsDf.

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.graph

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.name

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.DirichletTensor

DirichletTensor is a StochasticTensor backed by the distribution Dirichlet.

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.graph

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.name

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor

DirichletMultinomialTensor is a StochasticTensor backed by the distribution DirichletMultinomial.

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.graph

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.name

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor

ExponentialTensor is a StochasticTensor backed by the distribution Exponential.

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.graph

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.name

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor

ExponentialWithSoftplusLamTensor is a StochasticTensor backed by the distribution ExponentialWithSoftplusLam.

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.graph

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.name

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.GammaTensor

GammaTensor is a StochasticTensor backed by the distribution Gamma.

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.graph

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.name

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor

GammaWithSoftplusAlphaBetaTensor is a StochasticTensor backed by the distribution GammaWithSoftplusAlphaBeta.

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.graph

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.name

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor

InverseGammaTensor is a StochasticTensor backed by the distribution InverseGamma.

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.graph

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.name

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor

InverseGammaWithSoftplusAlphaBetaTensor is a StochasticTensor backed by the distribution InverseGammaWithSoftplusAlphaBeta.

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.graph

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.name

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor

LaplaceTensor is a StochasticTensor backed by the distribution Laplace.

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.graph

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.name

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor

LaplaceWithSoftplusScaleTensor is a StochasticTensor backed by the distribution LaplaceWithSoftplusScale.

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.graph

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.name

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.MixtureTensor

MixtureTensor is a StochasticTensor backed by the distribution Mixture.

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.name

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor

MultinomialTensor is a StochasticTensor backed by the distribution Multinomial.

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.name

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor

MultivariateNormalCholeskyTensor is a StochasticTensor backed by the distribution MultivariateNormalCholesky.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.name

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor

MultivariateNormalDiagTensor is a StochasticTensor backed by the distribution MultivariateNormalDiag.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.name

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor

MultivariateNormalDiagPlusVDVTTensor is a StochasticTensor backed by the distribution MultivariateNormalDiagPlusVDVT.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.name

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor

MultivariateNormalDiagWithSoftplusStDevTensor is a StochasticTensor backed by the distribution MultivariateNormalDiagWithSoftplusStDev.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.name

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor

MultivariateNormalFullTensor is a StochasticTensor backed by the distribution MultivariateNormalFull.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.name

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.NormalTensor

NormalTensor is a StochasticTensor backed by the distribution Normal.

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.graph

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.name

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor

NormalWithSoftplusSigmaTensor is a StochasticTensor backed by the distribution NormalWithSoftplusSigma.

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.graph

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.name

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.PoissonTensor

PoissonTensor is a StochasticTensor backed by the distribution Poisson.

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.graph

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.name

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor

QuantizedDistributionTensor is a StochasticTensor backed by the distribution QuantizedDistribution.

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.graph

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.name

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.StudentTTensor

StudentTTensor is a StochasticTensor backed by the distribution StudentT.

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.graph

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.name

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor

StudentTWithAbsDfSoftplusSigmaTensor is a StochasticTensor backed by the distribution StudentTWithAbsDfSoftplusSigma.

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.graph

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.name

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor

TransformedDistributionTensor is a StochasticTensor backed by the distribution TransformedDistribution.

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.graph

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.name

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.UniformTensor

UniformTensor is a StochasticTensor backed by the distribution Uniform.

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.graph

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.name

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor

WishartCholeskyTensor is a StochasticTensor backed by the distribution WishartCholesky.

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.graph

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.name

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor

WishartFullTensor is a StochasticTensor backed by the distribution WishartFull.

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.graph

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.name

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.value_type

Other Functions and Classes

class tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor

A StochasticTensor with an observed value.

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.__init__(dist_cls, value, name=None, **dist_args)

Construct an ObservedStochasticTensor.

ObservedStochasticTensor will instantiate a distribution from dist_cls and dist_args but use the provided value instead of sampling from the distribution. The provided value argument must be appropriately shaped to have come from the constructed distribution.

Args:

• dist_cls: a Distribution class.
• value: a Tensor containing the observed value
• name: a name for this ObservedStochasticTensor and its ops.
• **dist_args: keyword arguments to be passed through to dist_cls on construction.

Raises:

• TypeError: if dist_cls is not a Distribution.
• ValueError: if value is not compatible with the distribution.

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.graph

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.loss(final_loss, name=None)

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.name

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.value_type