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

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

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_sigma_det(name='log_sigma_det') Log of determinant of covariance matrix.

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.

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

tf.OpError.__str__()

tf.SparseTensor.__truediv__(sp_x, y) Internal helper function for 'sp_t / dense_t'.

tf.contrib.distributions.Categorical.mode(name='mode') Mode.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.mu Locations of these Student's t distribution(s).