tf.InteractiveSession.__init__(target='', graph=None, config=None) Creates a new interactive TensorFlow session. If no graph argument is specified when constructing the session, the default graph will be launched in the session. If you are using more than one graph (created with tf.Graph() in the same process, you will have to use different sessions for each graph, but each graph can be used in multiple sessions. In this case, it is often clearer to pass the graph to be launched explicitly to

tf.contrib.distributions.ExponentialWithSoftplusLam.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.Categorical.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.

tf.contrib.training.batch_sequences_with_states(input_key, input_sequences, input_context, input_length, initial_states, num_unroll, batch_size, num_threads=3, capacity=1000, allow_small_batch=True, pad=True, name=None) Creates batches of segments of sequential input. This method creates a SequenceQueueingStateSaver (SQSS) and adds it to the queuerunners. It returns a NextQueuedSequenceBatch. It accepts one example at a time identified by a unique input_key. input_sequence is a dict with value

tf.contrib.distributions.Binomial.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.Laplace.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

Thread* tensorflow::EnvWrapper::StartThread(const ThreadOptions &thread_options, const string &name, std::function< void()> fn) override Returns a new thread that is running fn() and is identified (for debugging/performance-analysis) by "name". Caller takes ownership of the result and must delete it eventually (the deletion will block until fn() stops running).

tf.contrib.distributions.Chi2WithAbsDf.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 =

tf.contrib.learn.LinearClassifier.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=False) Runs inference to determine the class probability predictions.

tf.contrib.distributions.Chi2WithAbsDf.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.