tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.name

tf.contrib.rnn.GRUBlockCell.__init__(cell_size) Initialize the Block GRU cell. Args: cell_size: int, GRU cell size.

tf.contrib.distributions.Bernoulli.validate_args Python boolean indicated possibly expensive checks are enabled.

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

tf.asin(x, name=None) Computes asin of x element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.distributions.Exponential.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.distribution

tf.contrib.distributions.InverseGamma.entropy(name='entropy') Shanon entropy in nats. Additional documentation from InverseGamma: This is defined to be entropy = alpha - log(beta) + log(Gamma(alpha)) + (1-alpha)digamma(alpha) where digamma(alpha) is the digamma function.

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

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.name