Update docs.

Change: 147383879

tensorflow/python/ops/functional_ops.py

@@ -13,12 +13,7 @@
 # limitations under the License.
 # =============================================================================
 
-"""Functional operations.
-
-## Higher Order Operators
-
-TensorFlow provides several higher order operators to simplify the common
-map-reduce programming patterns.
+"""Functional operations. See the @{$python/functional_ops} guide.
 
 @@map_fn
 @@foldl

tensorflow/python/ops/histogram_ops.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 # pylint: disable=g-short-docstring-punctuation
-"""## Histograms
+"""Histograms. Please see @{$python/histogram_ops} guide.
 
 @@histogram_fixed_width
 """

tensorflow/python/ops/io_ops.py

@@ -14,52 +14,19 @@
 # ==============================================================================
 
 # pylint: disable=line-too-long
-"""## Placeholders
-
-TensorFlow provides a placeholder operation that must be fed with data
-on execution.  For more info, see the section on [Feeding
-data](../../how_tos/reading_data/index.md#feeding).
+"""Inputs and Readers. See the @{$python/io_ops} guide.
 
 @@placeholder
 @@placeholder_with_default
-
-For feeding `SparseTensor`s which are composite type,
-there is a convenience function:
-
 @@sparse_placeholder
-
-## Readers
-
-TensorFlow provides a set of Reader classes for reading data formats.
-For more information on inputs and readers, see [Reading
-data](../../how_tos/reading_data/index.md).
-
 @@ReaderBase
 @@TextLineReader
 @@WholeFileReader
 @@IdentityReader
 @@TFRecordReader
 @@FixedLengthRecordReader
-
-## Converting
-
-TensorFlow provides several operations that you can use to convert various data
-formats into tensors.
-
 @@decode_csv
 @@decode_raw
-
-- - -
-
-### Example protocol buffer
-
-TensorFlow's [recommended format for training
-examples](../../how_tos/reading_data/index.md#standard-tensorflow-format)
-is serialized `Example` protocol buffers, [described
-here](https://www.tensorflow.org/code/tensorflow/core/example/example.proto).
-They contain `Features`, [described
-here](https://www.tensorflow.org/code/tensorflow/core/example/feature.proto).
-
 @@VarLenFeature
 @@FixedLenFeature
 @@FixedLenSequenceFeature
@@ -68,71 +35,23 @@
 @@parse_single_example
 @@parse_tensor
 @@decode_json_example
-
-## Queues
-
-TensorFlow provides several implementations of 'Queues', which are
-structures within the TensorFlow computation graph to stage pipelines
-of tensors together. The following describe the basic Queue interface
-and some implementations.  To see an example use, see [Threading and
-Queues](../../how_tos/threading_and_queues/index.md).
-
 @@QueueBase
 @@FIFOQueue
 @@PaddingFIFOQueue
 @@RandomShuffleQueue
 @@PriorityQueue
-
-## Conditional Accumulators
-
 @@ConditionalAccumulatorBase
 @@ConditionalAccumulator
 @@SparseConditionalAccumulator
-
-## Dealing with the filesystem
-
 @@matching_files
 @@read_file
 @@write_file
-
-## Input pipeline
-
-TensorFlow functions for setting up an input-prefetching pipeline.
-Please see the [reading data how-to](../../how_tos/reading_data/index.md)
-for context.
-
-### Beginning of an input pipeline
-
-The "producer" functions add a queue to the graph and a corresponding
-`QueueRunner` for running the subgraph that fills that queue.
-
 @@match_filenames_once
 @@limit_epochs
 @@input_producer
 @@range_input_producer
 @@slice_input_producer
 @@string_input_producer
-
-### Batching at the end of an input pipeline
-
-These functions add a queue to the graph to assemble a batch of
-examples, with possible shuffling.  They also add a `QueueRunner` for
-running the subgraph that fills that queue.
-
-Use [`batch`](#batch) or [`batch_join`](#batch_join) for batching
-examples that have already been well shuffled.  Use
-[`shuffle_batch`](#shuffle_batch) or
-[`shuffle_batch_join`](#shuffle_batch_join) for examples that would
-benefit from additional shuffling.
-
-Use [`batch`](#batch) or [`shuffle_batch`](#shuffle_batch) if you want a
-single thread producing examples to batch, or if you have a
-single subgraph producing examples but you want to run it in *N* threads
-(where you increase *N* until it can keep the queue full).  Use
-[`batch_join`](#batch_join) or [`shuffle_batch_join`](#shuffle_batch_join)
-if you have *N* different subgraphs producing examples to batch and you
-want them run by *N* threads. Use `maybe_*` to enqueue conditionally.
-
 @@batch
 @@maybe_batch
 @@batch_join

tensorflow/python/ops/math_ops.py

@@ -12,13 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Note: Elementwise binary operations in TensorFlow follow [numpy-style
-broadcasting](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html).
-
-## Arithmetic Operators
-
-TensorFlow provides several operations that you can use to add basic arithmetic
-operators to your graph.
+"""Basic arithmetic operators. See the @{$python/math_ops} guide.
 
 @@add
 @@subtract
@@ -35,12 +29,6 @@
 @@floormod
 @@mod
 @@cross
-
-## Basic Math Functions
-
-TensorFlow provides several operations that you can use to add basic
-mathematical functions to your graph.
-
 @@add_n
 @@abs
 @@negative
@@ -77,26 +65,17 @@
 @@polygamma
 @@betainc
 @@rint
-
-## Matrix Math Functions
-
-TensorFlow provides several operations that you can use to add linear algebra
-functions on matrices to your graph.
-
 @@diag
 @@diag_part
 @@trace
 @@transpose
-
 @@eye
 @@matrix_diag
 @@matrix_diag_part
 @@matrix_band_part
 @@matrix_set_diag
 @@matrix_transpose
-
 @@matmul
-
 @@norm
 @@matrix_determinant
 @@matrix_inverse
@@ -109,43 +88,17 @@
 @@self_adjoint_eig
 @@self_adjoint_eigvals
 @@svd
-
-
-## Tensor Math Function
-
-TensorFlow provides operations that you can use to add tensor functions to your
-graph.
-
 @@tensordot
-
-
-## Complex Number Functions
-
-TensorFlow provides several operations that you can use to add complex number
-functions to your graph.
-
 @@complex
 @@conj
 @@imag
 @@real
-
-## Fourier Transform Functions
-
-TensorFlow provides several operations that you can use to add discrete
-Fourier transform functions to your graph.
-
 @@fft
 @@ifft
 @@fft2d
 @@ifft2d
 @@fft3d
 @@ifft3d
-
-## Reduction
-
-TensorFlow provides several operations that you can use to perform
-common math computations that reduce various dimensions of a tensor.
-
 @@reduce_sum
 @@reduce_prod
 @@reduce_min
@@ -155,70 +108,26 @@
 @@reduce_any
 @@reduce_logsumexp
 @@count_nonzero
-
 @@accumulate_n
-
 @@einsum
-
-## Scan
-
-TensorFlow provides several operations that you can use to perform scans
-(running totals) across one axis of a tensor.
-
 @@cumsum
 @@cumprod
-
-## Segmentation
-
-TensorFlow provides several operations that you can use to perform common
-math computations on tensor segments.
-Here a segmentation is a partitioning of a tensor along
-the first dimension, i.e. it  defines a mapping from the first dimension onto
-`segment_ids`. The `segment_ids` tensor should be the size of
-the first dimension, `d0`, with consecutive IDs in the range `0` to `k`,
-where `k<d0`.
-In particular, a segmentation of a matrix tensor is a mapping of rows to
-segments.
-
-For example:
-
-```python
-c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
-tf.segment_sum(c, tf.constant([0, 0, 1]))
-  ==>  [[0 0 0 0]
-        [5 6 7 8]]
-```
-
 @@segment_sum
 @@segment_prod
 @@segment_min
 @@segment_max
 @@segment_mean
-
 @@unsorted_segment_sum
 @@unsorted_segment_max
-
 @@sparse_segment_sum
 @@sparse_segment_mean
 @@sparse_segment_sqrt_n
-
-
-## Sequence Comparison and Indexing
-
-TensorFlow provides several operations that you can use to add sequence
-comparison and index extraction to your graph. You can use these operations to
-determine sequence differences and determine the indexes of specific values in
-a tensor.
-
 @@argmin
 @@argmax
-
 @@setdiff1d
 @@where
 @@unique
-
 @@edit_distance
-
 @@invert_permutation
 """
 from __future__ import absolute_import