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

tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Dirichlet.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Dirichlet: Note that the input must be a non-negative tensor with dtype dtype and whose shape can be broadcast with self.alpha. For fixed leading dimensions, the last dimension represents counts for the corresponding Dirichlet distribution in self.alpha. x is only legal if it sums up to one. Args: value: float or double Tensor. name: The na

class tf.contrib.distributions.StudentT Student's t distribution with degree-of-freedom parameter df.

tf.ReaderBase.restore_state(state, name=None) Restore a reader to a previously saved state. Not all Readers support being restored, so this can produce an Unimplemented error. Args: state: A string Tensor. Result of a SerializeState of a Reader with matching type. name: A name for the operation (optional). Returns: The created Operation.

tf.contrib.losses.sum_of_squares(*args, **kwargs) Adds a Sum-of-Squares loss to the training procedure. (deprecated) THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-01. Instructions for updating: Use mean_squared_error. weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the weig

tf.contrib.metrics.set_size(a, validate_indices=True) Compute number of unique elements along last dimension of a. Args: a: SparseTensor, with indices sorted in row-major order. validate_indices: Whether to validate the order and range of sparse indices in a. Returns: int32 Tensor of set sizes. For a ranked n, this is a Tensor with rank n-1, and the same 1st n-1 dimensions as a. Each value is the number of unique elements in the corresponding [0...n-1] dimension of a. Raises: TypeError: I

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.name

static Env* tensorflow::Env::Default() Returns a default environment suitable for the current operating system. Sophisticated users may wish to provide their own Env implementation instead of relying on this default environment. The result of Default() belongs to this library and must never be deleted.

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