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) {:#BernoulliTensor.init}


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) {:#BernoulliWithSigmoidPTensor.init}


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) {:#BetaTensor.init}


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) {:#BetaWithSoftplusABTensor.init}


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) {:#BinomialTensor.init}


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) {:#CategoricalTensor.init}


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) {:#Chi2Tensor.init}


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) {:#Chi2WithAbsDfTensor.init}


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) {:#DirichletTensor.init}


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) {:#DirichletMultinomialTensor.init}


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) {:#ExponentialTensor.init}


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) {:#ExponentialWithSoftplusLamTensor.init}


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) {:#GammaTensor.init}


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) {:#GammaWithSoftplusAlphaBetaTensor.init}


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) {:#InverseGammaTensor.init}


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) {:#InverseGammaWithSoftplusAlphaBetaTensor.init}


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) {:#LaplaceTensor.init}


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) {:#LaplaceWithSoftplusScaleTensor.init}


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) {:#MixtureTensor.init}


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) {:#MultinomialTensor.init}


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) {:#MultivariateNormalCholeskyTensor.init}


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) {:#MultivariateNormalDiagTensor.init}


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) {:#MultivariateNormalDiagPlusVDVTTensor.init}


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) {:#MultivariateNormalDiagWithSoftplusStDevTensor.init}


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) {:#MultivariateNormalFullTensor.init}


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) {:#NormalTensor.init}


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) {:#NormalWithSoftplusSigmaTensor.init}


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) {:#PoissonTensor.init}


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) {:#QuantizedDistributionTensor.init}


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) {:#StudentTTensor.init}


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) {:#StudentTWithAbsDfSoftplusSigmaTensor.init}


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) {:#TransformedDistributionTensor.init}


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) {:#UniformTensor.init}


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) {:#WishartCholeskyTensor.init}


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) {:#WishartFullTensor.init}


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