tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.BetaWithSoftplusAB.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.QuantizedDistribution.std(name='std') Standard deviation.

tf.contrib.metrics.accuracy(predictions, labels, weights=None) Computes the percentage of times that predictions matches labels. Args: predictions: the predicted values, a Tensor whose dtype and shape matches 'labels'. labels: the ground truth values, a Tensor of any shape and bool, integer, or string dtype. weights: None or Tensor of float values to reweight the accuracy. Returns: Accuracy Tensor. Raises: ValueError: if dtypes don't match or if dtype is not bool, integer, or string.

tf.contrib.learn.monitors.PrintTensor.every_n_post_step(step, session) Callback after a step is finished or end() is called. Args: step: int, the current value of the global step. session: Session object.

tf.fill(dims, value, name=None) Creates a tensor filled with a scalar value. This operation creates a tensor of shape dims and fills it with value. For example: # Output tensor has shape [2, 3]. fill([2, 3], 9) ==> [[9, 9, 9] [9, 9, 9]] Args: dims: A Tensor of type int32. 1-D. Represents the shape of the output tensor. value: A Tensor. 0-D (scalar). Value to fill the returned tensor. name: A name for the operation (optional). Returns: A Tensor. Has the same type a

tf.contrib.crf.crf_unary_score(tag_indices, sequence_lengths, inputs) Computes the unary scores of tag sequences. Args: tag_indices: A [batch_size, max_seq_len] matrix of tag indices. sequence_lengths: A [batch_size] vector of true sequence lengths. inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials. Returns: unary_scores: A [batch_size] vector of unary scores.

tf.contrib.metrics.streaming_sparse_precision_at_k(*args, **kwargs) Computes precision@k of the predictions with respect to sparse labels. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). If class_id is specified, we calculate precision by considering only the entries in the batch for which

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.name

tf.OpError.__str__()