tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf') Log probability density function. 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. Raises: TypeError: if not is_continuous.

tf.contrib.training.NextQueuedSequenceBatch.context A dict mapping keys of input_context to batched context. Returns: A dict mapping keys of input_context to tensors. If we had at input: context["name"].get_shape() == [d1, d2, ...] then for this property: context["name"].get_shape() == [batch_size, d1, d2, ...]

tf.constant(value, dtype=None, shape=None, name='Const') Creates a constant tensor. The resulting tensor is populated with values of type dtype, as specified by arguments value and (optionally) shape (see examples below). The argument value can be a constant value, or a list of values of type dtype. If value is a list, then the length of the list must be less than or equal to the number of elements implied by the shape argument (if specified). In the case where the list length is less than the

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.input_dict

TTypes< T >::ConstScalar tensorflow::Tensor::scalar() const

tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.graph_editor.select_ops(*args, **kwargs) Helper to select operations. Args: *args: list of 1) regular expressions (compiled or not) or 2) (array of) tf.Operation. tf.Tensor instances are silently ignored. **kwargs: 'graph': tf.Graph in which to perform the regex query.This is required when using regex. 'positive_filter': an elem if selected only if positive_filter(elem) is True. This is optional. 'restrict_ops_regex': a regular expression is ignored if it doesn't start with the su

tf.contrib.distributions.Dirichlet.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float or double T

tf.contrib.losses.mean_pairwise_squared_error(*args, **kwargs) Adds a pairwise-errors-squared loss to the training procedure. (deprecated) THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-01. Instructions for updating: Use mean_pairwise_squared_error. Unlike the sum_of_squares loss, which is a measure of the differences between corresponding elements of predictions and targets, sum_of_pairwise_squares is a measure of the differences between pairs of corresponding elements of predi

tf.ReaderBase.reader_ref Op that implements the reader.