tf.contrib.distributions.Binomial.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Binomial: For each batch member of counts value, P[counts] is the probability that after sampling n draws from this Binomial distribution, the number of successes is k. Note that different sequences of draws can result in the same counts, thus the probability includes a combinatorial coefficient. value must be a non-negative tensor with dtype

tf.segment_sum(data, segment_ids, name=None) Computes the sum along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that \(output_i = \sum_j data_j\) where sum is over j such that segment_ids[j] == i. Args: data: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. segment_ids: A Tensor. Must be one of the following types: in

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.clone(name=None, **dist_args)

tf.contrib.graph_editor.get_walks_union_ops(forward_seed_ops, backward_seed_ops, forward_inclusive=True, backward_inclusive=True, within_ops=None, control_inputs=False, control_outputs=None, control_ios=None) Return the union of a foward and a backward walk. Args: forward_seed_ops: an iterable of operations from which the forward graph walk starts. If a list of tensors is given instead, the seed_ops are set to be the consumers of those tensors. backward_seed_ops: an iterable of operations fr

tf.contrib.distributions.ExponentialWithSoftplusLam.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.

Status tensorflow::Env::NewReadOnlyMemoryRegionFromFile(const string &fname, std::unique_ptr< ReadOnlyMemoryRegion > *result) Creates a readonly region of memory with the file context. On success, it returns a pointer to read-only memory region from the content of file fname. The ownership of the region is passed to the caller. On failure stores nullptr in *result and returns non-OK. The returned memory region can be accessed from many threads in parallel. The ownership of the return

tf.contrib.distributions.Dirichlet.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.distributions.InverseGammaWithSoftplusAlphaBeta.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.MultivariateNormalCholesky.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.learn.Estimator.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 altogether.