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.

void tensorflow::TensorShape::RemoveDim(int d) Removes dimension d from the TensorShape. REQUIRES: 0 <= d < dims()

tf.neg(x, name=None) Computes numerical negative value element-wise. I.e., (y = -x). Args: x: A Tensor or SparseTensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor or SparseTensor, respectively. Has the same type as x.

tf.contrib.learn.monitors.SummarySaver.post_step(step, session)

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__()

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mean(name='mean') Mean.

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.