tf.contrib.graph_editor.ops(*args, **kwargs) Helper to select operations. Args: *args: list of 1) regular expressions (compiled or not) or 2) (array of) tf.Operation. tf.Tensor instances are silently ignored. **kwargs: 'graph': tf.Graph in which to perform the regex query.This is required when using regex. 'positive_filter': an elem if selected only if positive_filter(elem) is True. This is optional. 'restrict_ops_regex': a regular expression is ignored if it doesn't start with the substring

tf.contrib.learn.monitors.ExportMonitor.__init__(*args, **kwargs) Initializes ExportMonitor. (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 both become required args. Args: every_n_steps: Run monitor every N steps. export_dir: str, f

tf.contrib.graph_editor.SubGraphView.remap_default(remove_input_map=True, remove_output_map=True) Remap the inputs and/or outputs to the default mapping. Args: remove_input_map: if True the input map is reset to the default one. remove_output_map: if True the output map is reset to the default one. Returns: A new modified instance of the original subgraph view with its input and/or output mapping reset to the default one.

tf.contrib.distributions.LaplaceWithSoftplusScale.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from TransformedDistribution: Implements (log o p o g)(y) - (log o det o J o 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: log_prob:

tf.reduce_any(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the "logical or" of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is r

tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_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.distributions.Gamma.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is und

tf.contrib.distributions.Normal.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from _MultivariateNormalOperatorPD: x is a batch vector with compatible shape if x is a Tensor whose shape can be broadcast up to either: self.batch_shape + self.event_shape or [M1,...,Mm] + self.batch_shape + self.event_shape Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob