tf.WholeFileReader.read(queue, name=None) Returns the next record (key, value pair) produced by a reader. Will dequeue a work unit from queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). Args: queue: A Queue or a mutable string Tensor representing a handle to a Queue, with string work items. name: A name for the operation (optional). Returns: A tuple of Tensors (key, value). key: A string scalar Tensor. value: A

tf.WholeFileReader.num_records_produced(name=None) Returns the number of records this reader has produced. This is the same as the number of Read executions that have succeeded. Args: name: A name for the operation (optional). Returns: An int64 Tensor.

class tf.WholeFileReader A Reader that outputs the entire contents of a file as a value. To use, enqueue filenames in a Queue. The output of Read will be a filename (key) and the contents of that file (value). See ReaderBase for supported methods.

tf.VarLenFeature.__new__(_cls, dtype) Create new instance of VarLenFeature(dtype,)

tf.where(input, name=None) Returns locations of true values in a boolean tensor. This operation returns the coordinates of true elements in input. The coordinates are returned in a 2-D tensor where the first dimension (rows) represents the number of true elements, and the second dimension (columns) represents the coordinates of the true elements. Keep in mind, the shape of the output tensor can vary depending on how many true values there are in input. Indices are output in row-major order. Fo

tf.VarLenFeature.__getstate__() Exclude the OrderedDict from pickling

tf.VarLenFeature.__repr__() Return a nicely formatted representation string

class tf.VarLenFeature Configuration for parsing a variable-length input feature. Fields: dtype: Data type of input.

tf.unsorted_segment_sum(data, segment_ids, num_segments, 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 the sum is over tuples j... such that segment_ids[j...] == i. Unlike SegmentSum, segment_ids need not be sorted and need not cover all values in the full range of valid values. If the sum is empty for a given segment ID i, output[i] = 0. num_segments

tf.VarLenFeature.__getnewargs__() Return self as a plain tuple. Used by copy and pickle.