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

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