tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None) Outputs random values from a normal distribution. Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. mean: A 0-D Tensor or Python value of type dtype. The mean of the normal distribution. stddev: A 0-D Tensor or Python value of type dtype. The standard deviation of the normal distribution. dtype: The type of the output. seed: A Python integer. Used to create a random seed

tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float or double Tenso

tf.contrib.distributions.Multinomial.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Multinomial: For each batch of counts [n_1,...,n_k], P[counts] is the probability that after sampling n draws from this Multinomial distribution, the number of draws falling in class j is n_j. Note that different sequences of draws can result in the same counts, thus the probability includes a combinatorial coefficient. Note that input "cou

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

tf.contrib.learn.TensorFlowRNNRegressor.export(*args, **kwargs) Exports inference graph into given dir. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed

tf.image.non_max_suppression(boxes, scores, max_output_size, iou_threshold=None, name=None) Greedily selects a subset of bounding boxes in descending order of score, pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absol

tf.decode_raw(bytes, out_type, little_endian=None, name=None) Reinterpret the bytes of a string as a vector of numbers. Args: bytes: A Tensor of type string. All the elements must have the same length. out_type: A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.int64. little_endian: An optional bool. Defaults to True. Whether the input bytes are in little-endian order. Ignored for out_type values that are stored in a single byte like uint8. name: A name for

tf.contrib.distributions.StudentT.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float or double Te

tf.image.random_hue(image, max_delta, seed=None) Adjust the hue of an RGB image by a random factor. Equivalent to adjust_hue() but uses a delta randomly picked in the interval [-max_delta, max_delta]. max_delta must be in the interval [0, 0.5]. Args: image: RGB image or images. Size of the last dimension must be 3. max_delta: float. Maximum value for the random delta. seed: An operation-specific seed. It will be used in conjunction with the graph-level seed to determine the real seeds that

tf.contrib.distributions.Bernoulli.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.