tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from DirichletMultinomial: For each batch of counts [n_1,...,n_k], P[counts] is the probability that after sampling n draws from this Dirichlet 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 combina

tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob') 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: prob: a Tensor of shape

tf.contrib.learn.read_batch_record_features(file_pattern, batch_size, features, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name='dequeue_record_examples') Reads TFRecord, queues, batches and parses Example proto. See more detailed description in read_examples. Args: file_pattern: List of files or pattern of file paths containing Example records. See tf.gfile.Glob for pattern rules. batch_size: An int or scalar Tensor specifying th

tf.contrib.distributions.StudentT.__init__(df, mu, sigma, validate_args=False, allow_nan_stats=True, name='StudentT') Construct Student's t distributions. The distributions have degree of freedom df, mean mu, and scale sigma. The parameters df, mu, and sigma must be shaped in a way that supports broadcasting (e.g. df + mu + sigma is a valid operation). Args: df: Floating point tensor, the degrees of freedom of the distribution(s). df must contain only positive values. mu: Floating point tens

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.distributions.Normal.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 un

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

tf.contrib.distributions.Poisson.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.contrib.bayesflow.stochastic_tensor.GammaTensor.input_dict

tf.contrib.learn.monitors.GraphDump.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.