tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.variance(name='variance') Variance.

tf.contrib.distributions.Multinomial.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.learn.infer(restore_checkpoint_path, output_dict, feed_dict=None) Restore graph from restore_checkpoint_path and run output_dict tensors. If restore_checkpoint_path is supplied, restore from checkpoint. Otherwise, init all variables. Args: restore_checkpoint_path: A string containing the path to a checkpoint to restore. output_dict: A dict mapping string names to Tensor objects to run. Tensors must all be from the same graph. feed_dict: dict object mapping Tensor objects to input

tf.contrib.losses.compute_weighted_loss(losses, weight=1.0) Computes the weighted loss. Args: losses: A tensor of size [batch_size, d1, ... dN]. weight: A tensor of size [1] or [batch_size, d1, ... dK] where K < N. Returns: A scalar Tensor that returns the weighted loss. Raises: ValueError: If the weight is None or the shape is not compatible with the losses shape or if the number of dimensions (rank) of either losses or weight is missing.

tf.contrib.distributions.BernoulliWithSigmoidP.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.diag_part(input, name=None) Returns the diagonal part of the tensor. This operation returns a tensor with the diagonal part of the input. The diagonal part is computed as follows: Assume input has dimensions [D1,..., Dk, D1,..., Dk], then the output is a tensor of rank k with dimensions [D1,..., Dk] where: diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]. For example: # 'input' is [[1, 0, 0, 0] [0, 2, 0, 0] [0, 0, 3, 0] [0, 0, 0, 4]] tf.diag_p

tf.contrib.distributions.Uniform.is_continuous

tf.image.random_contrast(image, lower, upper, seed=None) Adjust the contrast of an image by a random factor. Equivalent to adjust_contrast() but uses a contrast_factor randomly picked in the interval [lower, upper]. Args: image: An image tensor with 3 or more dimensions. lower: float. Lower bound for the random contrast factor. upper: float. Upper bound for the random contrast factor. seed: A Python integer. Used to create a random seed. See set_random_seed for behavior. Returns: The con

tf.contrib.distributions.TransformedDistribution.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from TransformedDistribution: Implements p(g(y)) / det|J(g(y))|, where g is the inverse of transform. Also raises a ValueError if inverse was not provided to the distribution and y was not returned from sample. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) +

tf.contrib.training.SequenceQueueingStateSaver.prefetch_op The op used to prefetch new data into the state saver. Running it once enqueues one new input example into the state saver. The first time this gets called, it additionally creates the prefetch_op. Subsequent calls simply return the previously created prefetch_op. It should be run in a separate thread via e.g. a QueueRunner. Returns: An Operation that performs prefetching.