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

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

tf.errors.PermissionDeniedError.__init__(node_def, op, message) Creates a PermissionDeniedError.

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

tf.contrib.distributions.LaplaceWithSoftplusScale.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.crf.crf_sequence_score(inputs, tag_indices, sequence_lengths, transition_params) Computes the unnormalized score for a tag sequence. Args: inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials to use as input to the CRF layer. tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we compute the unnormalized score. sequence_lengths: A [batch_size] vector of true sequence lengths. transition_params: A [num_tags, num_tags] transition matrix.

tf.image.adjust_contrast(images, contrast_factor) Adjust contrast of RGB or grayscale images. This is a convenience method that converts an RGB image to float representation, adjusts its contrast, and then converts it back to the original data type. If several adjustments are chained it is advisable to minimize the number of redundant conversions. images is a tensor of at least 3 dimensions. The last 3 dimensions are interpreted as [height, width, channels]. The other dimensions only represent

tf.contrib.distributions.MultivariateNormalCholesky.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.WishartCholesky.cholesky_input_output_matrices Boolean indicating if Tensor input/outputs are Cholesky factorized.

tf.image.per_image_whitening(image) Linearly scales image to have zero mean and unit norm. This op computes (x - mean) / adjusted_stddev, where mean is the average of all values in image, and adjusted_stddev = max(stddev, 1.0/sqrt(image.NumElements())). stddev is the standard deviation of all values in image. It is capped away from zero to protect against division by 0 when handling uniform images. Note that this implementation is limited: * It only whitens based on the statistics of an indivi