tf.contrib.distributions.Gamma.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

class tf.contrib.distributions.Gamma The Gamma distribution with parameter alpha and beta. The parameters are the shape and inverse scale parameters alpha, beta. The PDF of this distribution is: pdf(x) = (beta^alpha)(x^(alpha-1))e^(-x*beta)/Gamma(alpha), x > 0 and the CDF of this distribution is: cdf(x) = GammaInc(alpha, beta * x) / Gamma(alpha), x > 0 where GammaInc is the incomplete lower Gamma function. WARNING: This distribution may draw 0-valued samples for small alpha values. See

class tf.PriorityQueue A queue implementation that dequeues elements in prioritized order. See tf.QueueBase for a description of the methods on this class.

tf.maximum(x, y, name=None) Returns the max of x and y (i.e. x > y ? x : y) element-wise. NOTE: Maximum supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.learn.TensorFlowRNNClassifier.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 remove

tf.contrib.distributions.Chi2WithAbsDf.entropy(name='entropy') Shanon entropy in nats. Additional documentation from Gamma: This is defined to be entropy = alpha - log(beta) + log(Gamma(alpha)) + (1-alpha)digamma(alpha) where digamma(alpha) is the digamma function.

tf.contrib.distributions.BernoulliWithSigmoidP.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

tf.contrib.distributions.Chi2.is_reparameterized

tf.contrib.framework.has_arg_scope(func) Checks whether a func has been decorated with @add_arg_scope or not. Args: func: function to check. Returns: a boolean.

tf.contrib.learn.DNNRegressor.weights_