tf.contrib.distributions.BetaWithSoftplusAB.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.StudentTWithAbsDfSoftplusSigma.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.

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.dtype

virtual Status tensorflow::Session::PRunSetup(const std::vector< string > &input_names, const std::vector< string > &output_names, const std::vector< string > &target_nodes, string *handle) Sets up a graph for partial execution. All future feeds and fetches are specified by input_names and output_names. Returns handle that can be used to perform a sequence of partial feeds and fetches. NOTE: This API is still experimental and may change.

tf.contrib.distributions.Uniform.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

TTypes< T, NDIMS >::UnalignedTensor tensorflow::Tensor::unaligned_shaped(gtl::ArraySlice< int64 > new_sizes)

tf.train.shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, num_threads=1, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None) Creates batches by randomly shuffling tensors. This function adds the following to the current Graph: A shuffling queue into which tensors from tensors are enqueued. A dequeue_many operation to create batches from the queue. A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors.

tf.contrib.framework.get_variables_by_suffix(suffix, scope=None) Gets the list of variables that end with the given suffix. Args: suffix: suffix for filtering the variables to return. scope: an optional scope for filtering the variables to return. Returns: a copied list of variables with the given name and prefix.

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.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)