Update generated Python Op docs.

Change: 147388400

12 files changed, 76 insertions, 634 deletions

diff --git a/tensorflow/g3doc/api_docs/python/contrib.linalg.md b/tensorflow/g3doc/api_docs/python/contrib.linalg.md
index 2eb11e6766..2060e85211 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.linalg.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.linalg.md
@@ -3,18 +3,7 @@
 # Linear Algebra (contrib)
 [TOC]
 
-Linear algebra libraries for TensorFlow.
-
-## `LinearOperator`
-
-Subclasses of `LinearOperator` provide a access to common methods on a
-(batch) matrix, without the need to materialize the matrix.  This allows:
-
-* Matrix free computations
-* Different operators to take advantage of special strcture, while providing a
-  consistent API to users.
-
-### Base class
+Linear algebra libraries. See the @{$python/contrib.linalg} guide.
 
 - - -
 
@@ -574,9 +563,6 @@ Return a dense (batch) matrix representing this operator.
 
 
 
-
-### Individual operators
-
 - - -
 
 ### `class tf.contrib.linalg.LinearOperatorDiag` {#LinearOperatorDiag}
@@ -3884,9 +3870,6 @@ If this operator is `A = L + U D V^H`, this is the `V`.
 
 
 
-
-### Transformations and Combinations of operators
-
 - - -
 
 ### `class tf.contrib.linalg.LinearOperatorComposition` {#LinearOperatorComposition}
diff --git a/tensorflow/g3doc/api_docs/python/contrib.losses.md b/tensorflow/g3doc/api_docs/python/contrib.losses.md
index 7e3bada502..eef457bd1a 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.losses.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.losses.md
@@ -3,7 +3,7 @@
 # Losses (contrib)
 [TOC]
 
-Ops for building neural network losses.
+Ops for building neural network losses. See @{$python/contrib.losses}.
 
 ## Other Functions and Classes
 - - -
diff --git a/tensorflow/g3doc/api_docs/python/contrib.metrics.md b/tensorflow/g3doc/api_docs/python/contrib.metrics.md
index f7f02dd7d1..f11fd9d193 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.metrics.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.metrics.md
@@ -3,90 +3,9 @@
 # Metrics (contrib)
 [TOC]
 
-##Ops for evaluation metrics and summary statistics.
+Ops for evaluation metrics and summary statistics.
 
-### API
-
-This module provides functions for computing streaming metrics: metrics computed
-on dynamically valued `Tensors`. Each metric declaration returns a
-"value_tensor", an idempotent operation that returns the current value of the
-metric, and an "update_op", an operation that accumulates the information
-from the current value of the `Tensors` being measured as well as returns the
-value of the "value_tensor".
-
-To use any of these metrics, one need only declare the metric, call `update_op`
-repeatedly to accumulate data over the desired number of `Tensor` values (often
-each one is a single batch) and finally evaluate the value_tensor. For example,
-to use the `streaming_mean`:
-
-```python
-value = ...
-mean_value, update_op = tf.contrib.metrics.streaming_mean(values)
-sess.run(tf.local_variables_initializer())
-
-for i in range(number_of_batches):
-  print('Mean after batch %d: %f' % (i, update_op.eval())
-print('Final Mean: %f' % mean_value.eval())
-```
-
-Each metric function adds nodes to the graph that hold the state necessary to
-compute the value of the metric as well as a set of operations that actually
-perform the computation. Every metric evaluation is composed of three steps
-
-* Initialization: initializing the metric state.
-* Aggregation: updating the values of the metric state.
-* Finalization: computing the final metric value.
-
-In the above example, calling streaming_mean creates a pair of state variables
-that will contain (1) the running sum and (2) the count of the number of samples
-in the sum.  Because the streaming metrics use local variables,
-the Initialization stage is performed by running the op returned
-by `tf.local_variables_initializer()`. It sets the sum and count variables to
-zero.
-
-Next, Aggregation is performed by examining the current state of `values`
-and incrementing the state variables appropriately. This step is executed by
-running the `update_op` returned by the metric.
-
-Finally, finalization is performed by evaluating the "value_tensor"
-
-In practice, we commonly want to evaluate across many batches and multiple
-metrics. To do so, we need only run the metric computation operations multiple
-times:
-
-```python
-labels = ...
-predictions = ...
-accuracy, update_op_acc = tf.contrib.metrics.streaming_accuracy(
-    labels, predictions)
-error, update_op_error = tf.contrib.metrics.streaming_mean_absolute_error(
-    labels, predictions)
-
-sess.run(tf.local_variables_initializer())
-for batch in range(num_batches):
-  sess.run([update_op_acc, update_op_error])
-
-accuracy, mean_absolute_error = sess.run([accuracy, mean_absolute_error])
-```
-
-Note that when evaluating the same metric multiple times on different inputs,
-one must specify the scope of each metric to avoid accumulating the results
-together:
-
-```python
-labels = ...
-predictions0 = ...
-predictions1 = ...
-
-accuracy0 = tf.contrib.metrics.accuracy(labels, predictions0, name='preds0')
-accuracy1 = tf.contrib.metrics.accuracy(labels, predictions1, name='preds1')
-```
-
-Certain metrics, such as streaming_mean or streaming_accuracy, can be weighted
-via a `weights` argument. The `weights` tensor must be the same size as the
-labels and predictions tensors and results in a weighted average of the metric.
-
-## Metric `Ops`
+See the @{$python/contrib.metrics} guide.
 
 - - -
 
@@ -1696,7 +1615,6 @@ If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
 
 
 
-
 - - -
 
 ### `tf.contrib.metrics.auc_using_histogram(boolean_labels, scores, score_range, nbins=100, collections=None, check_shape=True, name=None)` {#auc_using_histogram}
@@ -1738,7 +1656,6 @@ numbers of bins and comparing results.
 *  <b>`update_op`</b>: `Op`, when run, updates internal histograms.
 
 
-
 - - -
 
 ### `tf.contrib.metrics.accuracy(predictions, labels, weights=None)` {#accuracy}
@@ -1765,7 +1682,6 @@ Computes the percentage of times that predictions matches labels.
               if dtype is not bool, integer, or string.
 
 
-
 - - -
 
 ### `tf.contrib.metrics.aggregate_metrics(*value_update_tuples)` {#aggregate_metrics}
@@ -1822,7 +1738,6 @@ and update ops when the list of metrics is long. For example:
   names to update ops.
 
 
-
 - - -
 
 ### `tf.contrib.metrics.confusion_matrix(labels, predictions, num_classes=None, dtype=tf.int32, name=None, weights=None)` {#confusion_matrix}
@@ -1830,9 +1745,6 @@ and update ops when the list of metrics is long. For example:
 Deprecated. Use tf.confusion_matrix instead.
 
 
-
-## Set `Ops`
-
 - - -
 
 ### `tf.contrib.metrics.set_difference(a, b, aminusb=True, validate_indices=True)` {#set_difference}
diff --git a/tensorflow/g3doc/api_docs/python/contrib.opt.md b/tensorflow/g3doc/api_docs/python/contrib.opt.md
index 12d789af83..e93e3f4571 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.opt.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.opt.md
@@ -3,7 +3,7 @@
 # Optimization (contrib)
 [TOC]
 
-opt: A module containing optimization routines.
+A module containing optimization routines.
 
 ## Other Functions and Classes
 - - -
@@ -71,7 +71,38 @@ executing commands to control a `Session`.
 
 ### `class tf.contrib.opt.MovingAverageOptimizer` {#MovingAverageOptimizer}
 
-Optimizer wrapper that maintains a moving average of parameters.
+Optimizer that computes a moving average of the variables.
+
+Empirically it has been found that using the moving average of the trained
+parameters of a deep network is better than using its trained parameters
+directly. This optimizer allows you to compute this moving average and swap
+the variables at save time so that any code outside of the training loop will
+use by default the averaged values instead of the original ones.
+
+Example of usage:
+
+```python
+
+// Encapsulate your favorite optimizer (here the momentum one)
+// inside the MovingAverageOptimizer.
+opt = tf.train.MomentumOptimizer(learning_rate, FLAGS.momentum)
+opt = tf.contrib.opt.MovingAverageOptimizer(opt)
+// Then create your model and all its variables.
+model = build_model()
+// Add the training op that optimizes using opt.
+// This needs to be called before swapping_saver().
+opt.minimize(cost, var_list)
+// Then create your saver like this:
+saver = opt.swapping_saver()
+// Pass it to your training loop.
+    slim.learning.train(
+        model,
+        ...
+        saver=saver)
+```
+
+Note that for evaluation, the normal saver should be used instead of
+swapping_saver().
 - - -
 
 #### `tf.contrib.opt.MovingAverageOptimizer.__init__(opt, average_decay=0.9999, num_updates=None, sequential_update=True)` {#MovingAverageOptimizer.__init__}
diff --git a/tensorflow/g3doc/api_docs/python/contrib.rnn.md b/tensorflow/g3doc/api_docs/python/contrib.rnn.md
index 6482ec4b01..6107639c95 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.rnn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.rnn.md
@@ -3,9 +3,7 @@
 # RNN and Cells (contrib)
 [TOC]
 
-Module for constructing RNN Cells and additional RNN operations.
-
-## Base interface for all RNN Cells
+RNN Cells and additional RNN operations. See @{$python/contrib.rnn} guide.
 
 - - -
 
@@ -97,9 +95,6 @@ the shapes `[batch_size x s]` for each s in `state_size`.
 
 
 
-
-## Core RNN Cells for use with TensorFlow's core RNN methods
-
 - - -
 
 ### `class tf.contrib.rnn.BasicRNNCell` {#BasicRNNCell}
@@ -507,9 +502,6 @@ the shapes `[batch_size x s]` for each s in `state_size`.
 
 
 
-
-## Classes storing split `RNNCell` state
-
 - - -
 
 ### `class tf.contrib.rnn.LSTMStateTuple` {#LSTMStateTuple}
@@ -569,9 +561,6 @@ Alias for field number 1
 
 
 
-
-## Core RNN Cell wrappers (RNNCells that wrap other RNNCells)
-
 - - -
 
 ### `class tf.contrib.rnn.MultiRNNCell` {#MultiRNNCell}
@@ -1137,8 +1126,6 @@ the shapes `[batch_size x s]` for each s in `state_size`.
 
 
 
-
-### Block RNNCells
 - - -
 
 ### `class tf.contrib.rnn.LSTMBlockCell` {#LSTMBlockCell}
@@ -1300,8 +1287,6 @@ the shapes `[batch_size x s]` for each s in `state_size`.
 
 
 
-
-### Fused RNNCells
 - - -
 
 ### `class tf.contrib.rnn.FusedRNNCell` {#FusedRNNCell}
@@ -1487,8 +1472,6 @@ Number of units in this cell (output dimension).
 
 
 
-
-### LSTM-like cells
 - - -
 
 ### `class tf.contrib.rnn.CoupledInputForgetGateLSTMCell` {#CoupledInputForgetGateLSTMCell}
@@ -1865,8 +1848,6 @@ the shapes `[batch_size x s]` for each s in `state_size`.
 
 
 
-
-### RNNCell wrappers
 - - -
 
 ### `class tf.contrib.rnn.AttentionCellWrapper` {#AttentionCellWrapper}
@@ -2012,13 +1993,6 @@ the shapes `[batch_size x s]` for each s in `state_size`.
 
 
 
-
-
-## Recurrent Neural Networks
-
-TensorFlow provides a number of methods for constructing Recurrent Neural
-Networks.
-
 - - -
 
 ### `tf.contrib.rnn.static_rnn(cell, inputs, initial_state=None, dtype=None, sequence_length=None, scope=None)` {#static_rnn}
diff --git a/tensorflow/g3doc/api_docs/python/functional_ops.md b/tensorflow/g3doc/api_docs/python/functional_ops.md
index 02338eb97e..f81af171b1 100644
--- a/tensorflow/g3doc/api_docs/python/functional_ops.md
+++ b/tensorflow/g3doc/api_docs/python/functional_ops.md
@@ -7,12 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 
 [TOC]
 
-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.
 
 - - -
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.opt.MovingAverageOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.opt.MovingAverageOptimizer.md
index 88f68a4b85..582deec24d 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.opt.MovingAverageOptimizer.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.opt.MovingAverageOptimizer.md
@@ -1,4 +1,35 @@
-Optimizer wrapper that maintains a moving average of parameters.
+Optimizer that computes a moving average of the variables.
+
+Empirically it has been found that using the moving average of the trained
+parameters of a deep network is better than using its trained parameters
+directly. This optimizer allows you to compute this moving average and swap
+the variables at save time so that any code outside of the training loop will
+use by default the averaged values instead of the original ones.
+
+Example of usage:
+
+```python
+
+// Encapsulate your favorite optimizer (here the momentum one)
+// inside the MovingAverageOptimizer.
+opt = tf.train.MomentumOptimizer(learning_rate, FLAGS.momentum)
+opt = tf.contrib.opt.MovingAverageOptimizer(opt)
+// Then create your model and all its variables.
+model = build_model()
+// Add the training op that optimizes using opt.
+// This needs to be called before swapping_saver().
+opt.minimize(cost, var_list)
+// Then create your saver like this:
+saver = opt.swapping_saver()
+// Pass it to your training loop.
+    slim.learning.train(
+        model,
+        ...
+        saver=saver)
+```
+
+Note that for evaluation, the normal saver should be used instead of
+swapping_saver().
 - - -
 
 #### `tf.contrib.opt.MovingAverageOptimizer.__init__(opt, average_decay=0.9999, num_updates=None, sequential_update=True)` {#MovingAverageOptimizer.__init__}
diff --git a/tensorflow/g3doc/api_docs/python/histogram_ops.md b/tensorflow/g3doc/api_docs/python/histogram_ops.md
index 912712fb02..e9fa732e60 100644
--- a/tensorflow/g3doc/api_docs/python/histogram_ops.md
+++ b/tensorflow/g3doc/api_docs/python/histogram_ops.md
@@ -3,7 +3,7 @@
 # Histograms
 [TOC]
 
-## Histograms
+Histograms. Please see @{$python/histogram_ops} guide.
 
 - - -
 
diff --git a/tensorflow/g3doc/api_docs/python/image.md b/tensorflow/g3doc/api_docs/python/image.md
index baef42db05..8d233dcadb 100644
--- a/tensorflow/g3doc/api_docs/python/image.md
+++ b/tensorflow/g3doc/api_docs/python/image.md
@@ -7,19 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 
 [TOC]
 
-## Encoding and Decoding
-
-TensorFlow provides Ops to decode and encode JPEG and PNG formats.  Encoded
-images are represented by scalar string Tensors, decoded images by 3-D uint8
-tensors of shape `[height, width, channels]`. (PNG also supports uint16.)
-
-The encode and decode Ops apply to one image at a time.  Their input and output
-are all of variable size.  If you need fixed size images, pass the output of
-the decode Ops to one of the cropping and resizing Ops.
-
-Note: The PNG encode and decode Ops support RGBA, but the conversions Ops
-presently only support RGB, HSV, and GrayScale. Presently, the alpha channel has
-to be stripped from the image and re-attached using slicing ops.
+Image processing and decoding ops. See the @{$python/image} guide.
 
 - - -
 
@@ -44,7 +32,6 @@ convert $src.gif -coalesce $dst.gif
   4-D with shape `[num_frames, height, width, 3]`. RGB order
 
 
-
 - - -
 
 ### `tf.image.decode_jpeg(contents, channels=None, ratio=None, fancy_upscaling=None, try_recover_truncated=None, acceptable_fraction=None, dct_method=None, name=None)` {#decode_jpeg}
@@ -150,7 +137,6 @@ in function of the number of channels in `image`:
   A `Tensor` of type `string`. 0-D. JPEG-encoded image.
 
 
-
 - - -
 
 ### `tf.image.decode_png(contents, channels=None, dtype=None, name=None)` {#decode_png}
@@ -215,7 +201,6 @@ the smallest output, but is slower.
   A `Tensor` of type `string`. 0-D. PNG-encoded image.
 
 
-
 - - -
 
 ### `tf.image.decode_image(contents, channels=None, name=None)` {#decode_image}
@@ -245,29 +230,6 @@ files.
     images.
 
 
-
-## Resizing
-
-The resizing Ops accept input images as tensors of several types.  They always
-output resized images as float32 tensors.
-
-The convenience function [`resize_images()`](#resize_images) supports both 4-D
-and 3-D tensors as input and output.  4-D tensors are for batches of images,
-3-D tensors for individual images.
-
-Other resizing Ops only support 4-D batches of images as input:
-[`resize_area`](#resize_area), [`resize_bicubic`](#resize_bicubic),
-[`resize_bilinear`](#resize_bilinear),
-[`resize_nearest_neighbor`](#resize_nearest_neighbor).
-
-Example:
-
-```python
-# Decode a JPG image and resize it to 299 by 299 using default method.
-image = tf.image.decode_jpeg(...)
-resized_image = tf.image.resize_images(image, [299, 299])
-```
-
 - - -
 
 ### `tf.image.resize_images(images, size, method=0, align_corners=False)` {#resize_images}
@@ -312,7 +274,6 @@ the same as `size`.  To avoid distortions see
   `[new_height, new_width, channels]`.
 
 
-
 - - -
 
 ### `tf.image.resize_area(images, size, align_corners=None, name=None)` {#resize_area}
@@ -419,9 +380,6 @@ Resize `images` to `size` using nearest neighbor interpolation.
   `[batch, new_height, new_width, channels]`.
 
 
-
-## Cropping
-
 - - -
 
 ### `tf.image.resize_image_with_crop_or_pad(image, target_height, target_width)` {#resize_image_with_crop_or_pad}
@@ -455,7 +413,6 @@ dimension.
   `[target_height, target_width, channels]`
 
 
-
 - - -
 
 ### `tf.image.central_crop(image, central_fraction)` {#central_crop}
@@ -616,7 +573,6 @@ The argument `normalized` and `centered` controls how the windows are built:
   glimpse_height, glimpse_width, channels]`.
 
 
-
 - - -
 
 ### `tf.image.crop_and_resize(image, boxes, box_ind, crop_size, method=None, extrapolation_value=None, name=None)` {#crop_and_resize}
@@ -670,9 +626,6 @@ result is a 4-D tensor `[num_boxes, crop_height, crop_width, depth]`.
   A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
 
 
-
-## Flipping, Rotating and Transposing
-
 - - -
 
 ### `tf.image.flip_up_down(image)` {#flip_up_down}
@@ -726,7 +679,6 @@ dimension, which is `height`.  Otherwise output the image as-is.
 *  <b>`ValueError`</b>: if the shape of `image` not supported.
 
 
-
 - - -
 
 ### `tf.image.flip_left_right(image)` {#flip_left_right}
@@ -780,7 +732,6 @@ second dimension, which is `width`.  Otherwise output the image as-is.
 *  <b>`ValueError`</b>: if the shape of `image` not supported.
 
 
-
 - - -
 
 ### `tf.image.transpose_image(image)` {#transpose_image}
@@ -804,7 +755,6 @@ See also `transpose()`.
 *  <b>`ValueError`</b>: if the shape of `image` not supported.
 
 
-
 - - -
 
 ### `tf.image.rot90(image, k=1, name=None)` {#rot90}
@@ -824,38 +774,6 @@ Rotate an image counter-clockwise by 90 degrees.
 
 
 
-## Converting Between Colorspaces.
-
-Image ops work either on individual images or on batches of images, depending on
-the shape of their input Tensor.
-
-If 3-D, the shape is `[height, width, channels]`, and the Tensor represents one
-image. If 4-D, the shape is `[batch_size, height, width, channels]`, and the
-Tensor represents `batch_size` images.
-
-Currently, `channels` can usefully be 1, 2, 3, or 4. Single-channel images are
-grayscale, images with 3 channels are encoded as either RGB or HSV. Images
-with 2 or 4 channels include an alpha channel, which has to be stripped from the
-image before passing the image to most image processing functions (and can be
-re-attached later).
-
-Internally, images are either stored in as one `float32` per channel per pixel
-(implicitly, values are assumed to lie in `[0,1)`) or one `uint8` per channel
-per pixel (values are assumed to lie in `[0,255]`).
-
-TensorFlow can convert between images in RGB or HSV. The conversion functions
-work only on float images, so you need to convert images in other formats using
-[`convert_image_dtype`](#convert-image-dtype).
-
-Example:
-
-```python
-# Decode an image and convert it to HSV.
-rgb_image = tf.image.decode_png(...,  channels=3)
-rgb_image_float = tf.image.convert_image_dtype(rgb_image, tf.float32)
-hsv_image = tf.image.rgb_to_hsv(rgb_image)
-```
-
 - - -
 
 ### `tf.image.rgb_to_grayscale(images, name=None)` {#rgb_to_grayscale}
@@ -898,7 +816,6 @@ last dimension of the output is 3, containing the RGB value of the pixels.
   The converted grayscale image(s).
 
 
-
 - - -
 
 ### `tf.image.hsv_to_rgb(images, name=None)` {#hsv_to_rgb}
@@ -949,7 +866,6 @@ corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue.
   A `Tensor`. Has the same type as `images`. `images` converted to HSV.
 
 
-
 - - -
 
 ### `tf.image.convert_image_dtype(image, dtype, saturate=False, name=None)` {#convert_image_dtype}
@@ -985,18 +901,6 @@ effect on casts between floats, or on casts that increase the type's range).
   `image`, converted to `dtype`.
 
 
-
-## Image Adjustments
-
-TensorFlow provides functions to adjust images in various ways: brightness,
-contrast, hue, and saturation.  Each adjustment can be done with predefined
-parameters or with random parameters picked from predefined intervals. Random
-adjustments are often useful to expand a training set and reduce overfitting.
-
-If several adjustments are chained it is advisable to minimize the number of
-redundant conversions by first converting the images to the most natural data
-type and representation (RGB or HSV).
-
 - - -
 
 ### `tf.image.adjust_brightness(image, delta)` {#adjust_brightness}
@@ -1053,7 +957,6 @@ interval `[-max_delta, max_delta)`.
 *  <b>`ValueError`</b>: if `max_delta` is negative.
 
 
-
 - - -
 
 ### `tf.image.adjust_contrast(images, contrast_factor)` {#adjust_contrast}
@@ -1115,7 +1018,6 @@ picked in the interval `[lower, upper]`.
 *  <b>`ValueError`</b>: if `upper <= lower` or if `lower < 0`.
 
 
-
 - - -
 
 ### `tf.image.adjust_hue(image, delta, name=None)` {#adjust_hue}
@@ -1176,7 +1078,6 @@ picked in the interval `[-max_delta, max_delta]`.
 *  <b>`ValueError`</b>: if `max_delta` is invalid.
 
 
-
 - - -
 
 ### `tf.image.adjust_gamma(image, gamma=1, gain=1)` {#adjust_gamma}
@@ -1208,7 +1109,6 @@ Performs Gamma Correction on the input image.
   [1] http://en.wikipedia.org/wiki/Gamma_correction
 
 
-
 - - -
 
 ### `tf.image.adjust_saturation(image, saturation_factor, name=None)` {#adjust_saturation}
@@ -1267,7 +1167,6 @@ picked in the interval `[lower, upper]`.
 *  <b>`ValueError`</b>: if `upper <= lower` or if `lower < 0`.
 
 
-
 - - -
 
 ### `tf.image.per_image_standardization(image)` {#per_image_standardization}
@@ -1296,9 +1195,6 @@ away from zero to protect against division by 0 when handling uniform images.
 *  <b>`ValueError`</b>: if the shape of 'image' is incompatible with this function.
 
 
-
-## Working with Bounding Boxes
-
 - - -
 
 ### `tf.image.draw_bounding_boxes(images, boxes, name=None)` {#draw_bounding_boxes}
@@ -1474,9 +1370,6 @@ false and no bounding boxes are supplied, an error is raised.
     Provide as input to `tf.image.draw_bounding_boxes`.
 
 
-
-## Denoising
-
 - - -
 
 ### `tf.image.total_variation(images, name=None)` {#total_variation}
diff --git a/tensorflow/g3doc/api_docs/python/io_ops.md b/tensorflow/g3doc/api_docs/python/io_ops.md
index fad7a6f12e..458480c876 100644
--- a/tensorflow/g3doc/api_docs/python/io_ops.md
+++ b/tensorflow/g3doc/api_docs/python/io_ops.md
@@ -7,11 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 
 [TOC]
 
-## 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.
 
 - - -
 
@@ -70,10 +66,6 @@ A placeholder op that passes through `input` when its output is not fed.
   A placeholder tensor that defaults to `input` if it is not fed.
 
 
-
-For feeding `SparseTensor`s which are composite type,
-there is a convenience function:
-
 - - -
 
 ### `tf.sparse_placeholder(dtype, shape=None, name=None)` {#sparse_placeholder}
@@ -120,13 +112,6 @@ with tf.Session() as sess:
   evaluated directly.
 
 
-
-## 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).
-
 - - -
 
 ### `class tf.ReaderBase` {#ReaderBase}
@@ -1213,12 +1198,6 @@ Whether the Reader implementation can serialize its state.
 
 
 
-
-## Converting
-
-TensorFlow provides several operations that you can use to convert various data
-formats into tensors.
-
 - - -
 
 ### `tf.decode_csv(records, record_defaults, field_delim=None, name=None)` {#decode_csv}
@@ -1274,18 +1253,6 @@ Reinterpret the bytes of a string as a vector of numbers.
   of `bytes` divided by the number of bytes to represent `out_type`.
 
 
-
-- - -
-
-### 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).
-
 - - -
 
 ### `class tf.VarLenFeature` {#VarLenFeature}
@@ -1831,15 +1798,6 @@ Example-parsing ops.
   to the respective element of `json_examples`.
 
 
-
-## 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).
-
 - - -
 
 ### `class tf.QueueBase` {#QueueBase}
@@ -2373,9 +2331,6 @@ an int64 scalar (for `enqueue`) or an int64 vector (for `enqueue_many`).
 
 
 
-
-## Conditional Accumulators
-
 - - -
 
 ### `class tf.ConditionalAccumulatorBase` {#ConditionalAccumulatorBase}
@@ -2815,9 +2770,6 @@ Once successful, the following actions are also triggered:
 
 
 
-
-## Dealing with the filesystem
-
 - - -
 
 ### `tf.matching_files(pattern, name=None)` {#matching_files}
@@ -2876,18 +2828,6 @@ Writes contents to the file at input filename. Creates file if not existing.
   The created Operation.
 
 
-
-## 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.
-
 - - -
 
 ### `tf.train.match_filenames_once(pattern, name=None)` {#match_filenames_once}
@@ -3085,27 +3025,6 @@ Note: if `num_epochs` is not `None`, this function creates local counter
   will fail with an assertion if string_tensor becomes a null tensor.
 
 
-
-### 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.
-
 - - -
 
 ### `tf.train.batch(tensors, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None)` {#batch}
diff --git a/tensorflow/g3doc/api_docs/python/math_ops.md b/tensorflow/g3doc/api_docs/python/math_ops.md
index 8a4a117907..c6e68117db 100644
--- a/tensorflow/g3doc/api_docs/python/math_ops.md
+++ b/tensorflow/g3doc/api_docs/python/math_ops.md
@@ -7,13 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 
 [TOC]
 
-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.
 
 - - -
 
@@ -376,12 +370,6 @@ of corresponding 3-element vectors is cross-multiplied independently.
   Pairwise cross product of the vectors in `a` and `b`.
 
 
-
-## Basic Math Functions
-
-TensorFlow provides several operations that you can use to add basic
-mathematical functions to your graph.
-
 - - -
 
 ### `tf.add_n(inputs, name=None)` {#add_n}
@@ -1155,12 +1143,6 @@ rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.]
   A `Tensor`. Has the same type as `x`.
 
 
-
-## Matrix Math Functions
-
-TensorFlow provides several operations that you can use to add linear algebra
-functions on matrices to your graph.
-
 - - -
 
 ### `tf.diag(diagonal, name=None)` {#diag}
@@ -1330,7 +1312,6 @@ tf.transpose(x, perm=[0, 2, 1]) ==> [[[1  4]
   A transposed `Tensor`.
 
 
-
 - - -
 
 ### `tf.eye(num_rows, num_columns=None, batch_shape=None, dtype=tf.float32, name=None)` {#eye}
@@ -1597,7 +1578,6 @@ tf.matrix_transpose(x) ==> [[1 4]
 *  <b>`ValueError`</b>: If `a` is determined statically to have `rank < 2`.
 
 
-
 - - -
 
 ### `tf.matmul(a, b, transpose_a=False, transpose_b=False, adjoint_a=False, adjoint_b=False, a_is_sparse=False, b_is_sparse=False, name=None)` {#matmul}
@@ -1691,7 +1671,6 @@ c = tf.matmul(a, b) => [[[ 94 100]
     are both set to True.
 
 
-
 - - -
 
 ### `tf.norm(tensor, ord='euclidean', axis=None, keep_dims=False, name=None)` {#norm}
@@ -2148,13 +2127,6 @@ arguments here is `s`, `u`, `v` when `compute_uv` is `True`, as opposed to
 @end_compatibility
 
 
-
-
-## Tensor Math Function
-
-TensorFlow provides operations that you can use to add tensor functions to your
-graph.
-
 - - -
 
 ### `tf.tensordot(a, b, axes, name=None)` {#tensordot}
@@ -2212,13 +2184,6 @@ In general, `order(c) = order(a) + order(b) - 2*len(axes[0])`.
     tensor.
 
 
-
-
-## Complex Number Functions
-
-TensorFlow provides several operations that you can use to add complex number
-functions to your graph.
-
 - - -
 
 ### `tf.complex(real, imag, name=None)` {#complex}
@@ -2352,12 +2317,6 @@ If `input` is already real, it is returned unchanged.
   A `Tensor` of type `float32` or `float64`.
 
 
-
-## Fourier Transform Functions
-
-TensorFlow provides several operations that you can use to add discrete
-Fourier transform functions to your graph.
-
 - - -
 
 ### `tf.fft(input, name=None)` {#fft}
@@ -2500,12 +2459,6 @@ Compute the inverse 3-dimensional discrete Fourier Transform over the inner-most
   @end_compatibility
 
 
-
-## Reduction
-
-TensorFlow provides several operations that you can use to perform
-common math computations that reduce various dimensions of a tensor.
-
 - - -
 
 ### `tf.reduce_sum(input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None)` {#reduce_sum}
@@ -2870,7 +2823,6 @@ tf.count_nonzero(x, [0, 1]) ==> 3
   The reduced tensor (number of nonzero values).
 
 
-
 - - -
 
 ### `tf.accumulate_n(inputs, shape=None, tensor_dtype=None, name=None)` {#accumulate_n}
@@ -2914,7 +2866,6 @@ tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)
   cannot be inferred.
 
 
-
 - - -
 
 ### `tf.einsum(equation, *inputs)` {#einsum}
@@ -2992,12 +2943,6 @@ This function behaves like `numpy.einsum`, but does not support:
     - the input shapes are inconsistent along a particular axis.
 
 
-
-## Scan
-
-TensorFlow provides several operations that you can use to perform scans
-(running totals) across one axis of a tensor.
-
 - - -
 
 ### `tf.cumsum(x, axis=0, exclusive=False, reverse=False, name=None)` {#cumsum}
@@ -3090,28 +3035,6 @@ tf.cumprod([a, b, c], exclusive=True, reverse=True) ==> [b * c, c, 1]
   A `Tensor`. Has the same type as `x`.
 
 
-
-## 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]]
-```
-
 - - -
 
 ### `tf.segment_sum(data, segment_ids, name=None)` {#segment_sum}
@@ -3281,7 +3204,6 @@ values summed.
   has size `k`, the number of segments.
 
 
-
 - - -
 
 ### `tf.unsorted_segment_sum(data, segment_ids, num_segments, name=None)` {#unsorted_segment_sum}
@@ -3364,7 +3286,6 @@ If the maximum is empty for a given segment ID `i`, it outputs the smallest poss
   has size `num_segments`.
 
 
-
 - - -
 
 ### `tf.sparse_segment_sum(data, indices, segment_ids, name=None)` {#sparse_segment_sum}
@@ -3477,15 +3398,6 @@ of segments.
   has size `k`, the number of segments.
 
 
-
-
-## 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.
-
 - - -
 
 ### `tf.argmin(input, axis=None, name=None, dimension=None)` {#argmin}
@@ -3526,7 +3438,6 @@ Returns the index with the largest value across axes of a tensor.
   A `Tensor` of type `int64`.
 
 
-
 - - -
 
 ### `tf.setdiff1d(x, y, index_dtype=tf.int32, name=None)` {#setdiff1d}
@@ -3658,7 +3569,6 @@ idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
 *  <b>`idx`</b>: A `Tensor` of type `out_idx`. 1-D.
 
 
-
 - - -
 
 ### `tf.edit_distance(hypothesis, truth, normalize=True, name='edit_distance')` {#edit_distance}
@@ -3727,7 +3637,6 @@ output ==> [[inf, 1.0],  # (0,0): no truth, (0,1): no hypothesis
 *  <b>`TypeError`</b>: If either `hypothesis` or `truth` are not a `SparseTensor`.
 
 
-
 - - -
 
 ### `tf.invert_permutation(x, name=None)` {#invert_permutation}
diff --git a/tensorflow/g3doc/api_docs/python/nn.md b/tensorflow/g3doc/api_docs/python/nn.md
index c348155bd7..cda8959391 100644
--- a/tensorflow/g3doc/api_docs/python/nn.md
+++ b/tensorflow/g3doc/api_docs/python/nn.md
@@ -7,16 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 
 [TOC]
 
-## Activation Functions.
-
-The activation ops provide different types of nonlinearities for use in neural
-networks.  These include smooth nonlinearities (`sigmoid`, `tanh`, `elu`,
-`softplus`, and `softsign`), continuous but not everywhere differentiable
-functions (`relu`, `relu6`, `crelu` and `relu_x`), and random regularization
-(`dropout`).
-
-All activation ops apply componentwise, and produce a tensor of the same
-shape as the input tensor.
+## Neural network support. See the @{$python/nn} guide.
 
 - - -
 
@@ -242,78 +233,6 @@ Computes hyperbolic tangent of `x` element-wise.
   `x.dtype != qint32` otherwise the return type is `quint8`.
 
 
-
-## Convolution
-
-The convolution ops sweep a 2-D filter over a batch of images, applying the
-filter to each window of each image of the appropriate size.  The different
-ops trade off between generic vs. specific filters:
-
-* `conv2d`: Arbitrary filters that can mix channels together.
-* `depthwise_conv2d`: Filters that operate on each channel independently.
-* `separable_conv2d`: A depthwise spatial filter followed by a pointwise filter.
-
-Note that although these ops are called "convolution", they are strictly
-speaking "cross-correlation" since the filter is combined with an input window
-without reversing the filter.  For details, see [the properties of
-cross-correlation](https://en.wikipedia.org/wiki/Cross-correlation#Properties).
-
-The filter is applied to image patches of the same size as the filter and
-strided according to the `strides` argument.  `strides = [1, 1, 1, 1]` applies
-the filter to a patch at every offset, `strides = [1, 2, 2, 1]` applies the
-filter to every other image patch in each dimension, etc.
-
-Ignoring channels for the moment, and assume that the 4-D `input` has shape
-`[batch, in_height, in_width, ...]` and the 4-D `filter` has shape
-`[filter_height, filter_width, ...]`, then the spatial semantics of the
-convolution ops are as follows: first, according to the padding scheme chosen
-as `'SAME'` or `'VALID'`, the output size and the padding pixels are computed.
-For the `'SAME'` padding, the output height and width are computed as:
-
-    out_height = ceil(float(in_height) / float(strides[1]))
-    out_width  = ceil(float(in_width) / float(strides[2]))
-
-and the padding on the top and left are computed as:
-
-    pad_along_height = max((out_height - 1) * strides[1] +
-                        filter_height - in_height, 0)
-    pad_along_width = max((out_width - 1) * strides[2] +
-                       filter_width - in_width, 0)
-    pad_top = pad_along_height // 2
-    pad_bottom = pad_along_height - pad_top
-    pad_left = pad_along_width // 2
-    pad_right = pad_along_width - pad_left
-
-
-Note that the division by 2 means that there might be cases when the padding on
-both sides (top vs bottom, right vs left) are off by one. In this case, the
-bottom and right sides always get the one additional padded pixel. For example,
-when `pad_along_height` is 5, we pad 2 pixels at the top and 3 pixels at the
-bottom. Note that this is different from existing libraries such as cuDNN and
-Caffe, which explicitly specify the number of padded pixels and always pad the
-same number of pixels on both sides.
-
-For the `'VALID`' padding, the output height and width are computed as:
-
-    out_height = ceil(float(in_height - filter_height + 1) / float(strides[1]))
-    out_width  = ceil(float(in_width - filter_width + 1) / float(strides[2]))
-
-and the padding values are always zero. The output is then computed as
-
-    output[b, i, j, :] =
-        sum_{di, dj} input[b, strides[1] * i + di - pad_top,
-                           strides[2] * j + dj - pad_left, ...] *
-                     filter[di, dj, ...]
-
-where any value outside the original input image region are considered zero (
-i.e. we pad zero values around the border of the image).
-
-Since `input` is 4-D, each `input[b, i, j, :]` is a vector.  For `conv2d`, these
-vectors are multiplied by the `filter[di, dj, :, :]` matrices to produce new
-vectors.  For `depthwise_conv_2d`, each scalar component `input[b, i, j, k]`
-is multiplied by a vector `filter[di, dj, k]`, and all the vectors are
-concatenated.
-
 - - -
 
 ### `tf.nn.convolution(input, filter, padding, strides=None, dilation_rate=None, name=None, data_format=None)` {#convolution}
@@ -1132,22 +1051,6 @@ Computes the gradients of depthwise convolution with respect to the input.
   w.r.t. the input of the convolution.
 
 
-
-## Pooling
-
-The pooling ops sweep a rectangular window over the input tensor, computing a
-reduction operation for each window (average, max, or max with argmax).  Each
-pooling op uses rectangular windows of size `ksize` separated by offset
-`strides`.  For example, if `strides` is all ones every window is used, if
-`strides` is all twos every other window is used in each dimension, etc.
-
-In detail, the output is
-
-    output[i] = reduce(value[strides * i:strides * i + ksize])
-
-where the indices also take into consideration the padding values. Please refer
-to the `Convolution` section for details about the padding calculation.
-
 - - -
 
 ### `tf.nn.avg_pool(value, ksize, strides, padding, data_format='NHWC', name=None)` {#avg_pool}
@@ -1515,44 +1418,6 @@ simply transposed as follows:
 *  <b>`ValueError`</b>: if arguments are invalid.
 
 
-
-## Morphological filtering
-
-Morphological operators are non-linear filters used in image processing.
-
-[Greyscale morphological dilation
-](https://en.wikipedia.org/wiki/Dilation_(morphology))
-is the max-sum counterpart of standard sum-product convolution:
-
-    output[b, y, x, c] =
-        max_{dy, dx} input[b,
-                           strides[1] * y + rates[1] * dy,
-                           strides[2] * x + rates[2] * dx,
-                           c] +
-                     filter[dy, dx, c]
-
-The `filter` is usually called structuring function. Max-pooling is a special
-case of greyscale morphological dilation when the filter assumes all-zero
-values (a.k.a. flat structuring function).
-
-[Greyscale morphological erosion
-](https://en.wikipedia.org/wiki/Erosion_(morphology))
-is the min-sum counterpart of standard sum-product convolution:
-
-    output[b, y, x, c] =
-        min_{dy, dx} input[b,
-                           strides[1] * y - rates[1] * dy,
-                           strides[2] * x - rates[2] * dx,
-                           c] -
-                     filter[dy, dx, c]
-
-Dilation and erosion are dual to each other. The dilation of the input signal
-`f` by the structuring signal `g` is equal to the negation of the erosion of
-`-f` by the reflected `g`, and vice versa.
-
-Striding and padding is carried out in exactly the same way as in standard
-convolution. Please refer to the `Convolution` section for details.
-
 - - -
 
 ### `tf.nn.dilation2d(input, filter, strides, rates, padding, name=None)` {#dilation2d}
@@ -1797,12 +1662,6 @@ can be combined into a single `with_space_to_batch` operation as follows:
 *  <b>`ValueError`</b>: if `spatial_dims` are invalid.
 
 
-
-## Normalization
-
-Normalization is useful to prevent neurons from saturating when inputs may
-have varying scale, and to aid generalization.
-
 - - -
 
 ### `tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None)` {#l2_normalize}
@@ -2101,13 +1960,6 @@ This op is deprecated. See `tf.nn.batch_normalization`.
    A batch-normalized `t`.
 
 
-
-## Losses
-
-The loss ops measure error between two tensors, or between a tensor and zero.
-These can be used for measuring accuracy of a network in a regression task
-or for regularization purposes (weight decay).
-
 - - -
 
 ### `tf.nn.l2_loss(t, name=None)` {#l2_loss}
@@ -2177,11 +2029,6 @@ loss is
 *  <b>`ValueError`</b>: If `log_input` and `targets` do not have the same shape.
 
 
-
-## Classification
-
-TensorFlow provides several operations that help you perform classification.
-
 - - -
 
 ### `tf.nn.sigmoid_cross_entropy_with_logits(_sentinel=None, labels=None, logits=None, name=None)` {#sigmoid_cross_entropy_with_logits}
@@ -2446,12 +2293,6 @@ the implementation uses
 *  <b>`ValueError`</b>: If `logits` and `targets` do not have the same shape.
 
 
-
-## Embeddings
-
-TensorFlow provides library support for looking up values in embedding
-tensors.
-
 - - -
 
 ### `tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None, validate_indices=True, max_norm=None)` {#embedding_lookup}
@@ -2590,13 +2431,6 @@ is the sum of the size of params along dimension 0.
 *  <b>`ValueError`</b>: If combiner is not one of {"mean", "sqrtn", "sum"}.
 
 
-
-## Recurrent Neural Networks
-
-TensorFlow provides a number of methods for constructing Recurrent
-Neural Networks.  Most accept an `RNNCell`-subclassed object
-(see the documentation for `tf.contrib.rnn`).
-
 - - -
 
 ### `tf.nn.dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None)` {#dynamic_rnn}
@@ -2962,9 +2796,6 @@ outputs = outputs_ta.stack()
     a `callable`.
 
 
-
-## Connectionist Temporal Classification (CTC)
-
 - - -
 
 ### `tf.nn.ctc_loss(labels, inputs, sequence_length, preprocess_collapse_repeated=False, ctc_merge_repeated=True, time_major=True)` {#ctc_loss}
@@ -3159,12 +2990,6 @@ is `A B B B B`, the return value is:
       sequence log-probabilities.
 
 
-
-## Evaluation
-
-The evaluation ops are useful for measuring the performance of a network.
-They are typically used at evaluation time.
-
 - - -
 
 ### `tf.nn.top_k(input, k=1, sorted=True, name=None)` {#top_k}
@@ -3235,24 +3060,6 @@ $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
   A `Tensor` of type `bool`. Computed Precision at `k` as a `bool Tensor`.
 
 
-
-## Candidate Sampling
-
-Do you want to train a multiclass or multilabel model with thousands
-or millions of output classes (for example, a language model with a
-large vocabulary)?  Training with a full Softmax is slow in this case,
-since all of the classes are evaluated for every training example.
-Candidate Sampling training algorithms can speed up your step times by
-only considering a small randomly-chosen subset of contrastive classes
-(called candidates) for each batch of training examples.
-
-See our
-[Candidate Sampling Algorithms Reference](../../extras/candidate_sampling.pdf)
-
-### Sampled Loss Functions
-
-TensorFlow provides the following sampled loss functions for faster training.
-
 - - -
 
 ### `tf.nn.nce_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=False, partition_strategy='mod', name='nce_loss')` {#nce_loss}
@@ -3366,12 +3173,6 @@ Also see Section 3 of [Jean et al., 2014](http://arxiv.org/abs/1412.2007)
   A `batch_size` 1-D tensor of per-example sampled softmax losses.
 
 
-
-### Candidate Samplers
-
-TensorFlow provides the following samplers for randomly sampling candidate
-classes when using one of the sampled loss functions above.
-
 - - -
 
 ### `tf.nn.uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)` {#uniform_candidate_sampler}
@@ -3617,9 +3418,6 @@ compute them approximately.
     of each of `sampled_candidates`.
 
 
-
-### Miscellaneous candidate sampling utilities
-
 - - -
 
 ### `tf.nn.compute_accidental_hits(true_classes, sampled_candidates, num_true, seed=None, name=None)` {#compute_accidental_hits}
@@ -3668,9 +3466,6 @@ target classes as noise classes for the same example.
     Each value is `-FLOAT_MAX`.
 
 
-
-### Quantization ops
-
 - - -
 
 ### `tf.nn.quantized_conv2d(input, filter, min_input, max_input, min_filter, max_filter, strides, padding, out_type=None, name=None)` {#quantized_conv2d}