tf.QueueBase.enqueue_many(vals, name=None) Enqueues zero or more elements to this queue. This operation slices each component tensor along the 0th dimension to make multiple queue elements. All of the tensors in vals must have the same size in the 0th dimension. If the queue is full when this operation executes, it will block until all of the elements have been enqueued. At runtime, this operation may raise an error if the queue is closed before or during its execution. If the queue is closed

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.

tf.contrib.distributions.Normal.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.QuantizedDistribution.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Additional documentation from QuantizedDistribution: For whole numbers y, cdf(y) := P[Y <= y] = 1, if y >= upper_cutoff, = 0, if y < lower_cutoff, = P[X <= y], otherwise. Since Y only has mass at whole numbers, P[Y <= y] = P[Y <= floor(y)]. This dictates th

tf.contrib.graph_editor.get_forward_walk_ops(seed_ops, inclusive=True, within_ops=None, stop_at_ts=(), control_outputs=None) Do a forward graph walk and return all the visited ops. Args: 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. inclusive: if True the given seed_ops are also part of the resulting set. within_ops: an iterable of tf.Operation whithin which the

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

tf.contrib.framework.VariableDeviceChooser.__init__(num_tasks=0, job_name='ps', device_type='CPU', device_index=0) Initialize VariableDeviceChooser. Usage: To use with 2 parameter servers: VariableDeviceChooser(2) To use without parameter servers: VariableDeviceChooser() VariableDeviceChooser(device_type='GPU') # For GPU placement Args: num_tasks: number of tasks. job_name: String, a name for the parameter server job. device_type: Optional device type string (e.g. "CPU" or "GPU") device_in

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.conj(x, name=None) Returns the complex conjugate of a complex number. Given a tensor input of complex numbers, this operation returns a tensor of complex numbers that are the complex conjugate of each element in input. The complex numbers in input must be of the form \(a + bj\), where a is the real part and b is the imaginary part. The complex conjugate returned by this operation is of the form \(a - bj\). For example: # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] tf.conj(input) ==>

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.