tf.contrib.learn.monitors.LoggingTrainable.end(session=None)

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.Dirichlet.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 T

tf.contrib.learn.monitors.EveryN.every_n_step_end(step, outputs) Callback after every n'th step finished. This callback provides access to the tensors/ops evaluated at this step, including the additional tensors for which evaluation was requested in step_begin. In addition, the callback has the opportunity to stop training by returning True. This is useful for early stopping, for example. Args: step: int, the current value of the global step. outputs: dict mapping string values representing

tf.diag(diagonal, name=None) Returns a diagonal tensor with a given diagonal values. Given a diagonal, this operation returns a tensor with the diagonal and everything else padded with zeros. The diagonal is computed as follows: Assume diagonal has dimensions [D1,..., Dk], then the output is a tensor of rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where: output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik] and 0 everywhere else. For example: # 'diagonal' is [1, 2, 3, 4] tf.diag(diagonal)

tf.contrib.distributions.Dirichlet.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.Mixture.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.learn.BaseEstimator.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 altogeth

tf.zeta(x, q, name=None) Compute the Hurwitz zeta function \(\zeta(x, q)\). The Hurwitz zeta function is defined as: \zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x} Args: x: A Tensor. Must be one of the following types: float32, float64. q: 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.distributions.WishartFull.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.