Documentation changes to adhere to new doc generator

Change: 147382677

Author: aselle

tensorflow/contrib/linalg/__init__.py

@@ -12,34 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""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.
 
 @@LinearOperator
-
-### Individual operators
-
 @@LinearOperatorDiag
 @@LinearOperatorIdentity
 @@LinearOperatorScaledIdentity
 @@LinearOperatorMatrix
 @@LinearOperatorTriL
 @@LinearOperatorUDVHUpdate
-
-### Transformations and Combinations of operators
-
 @@LinearOperatorComposition
-
 """
 from __future__ import absolute_import
 from __future__ import division

tensorflow/contrib/losses/__init__.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Ops for building neural network losses."""
+"""Ops for building neural network losses. See @{$python/contrib.losses}."""
 
 from __future__ import absolute_import
 from __future__ import division

tensorflow/contrib/metrics/__init__.py

@@ -12,90 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""##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.
 
 @@streaming_accuracy
 @@streaming_mean
@@ -130,18 +49,11 @@
 @@streaming_true_negatives_at_thresholds
 @@streaming_true_positives
 @@streaming_true_positives_at_thresholds
-
 @@auc_using_histogram
-
 @@accuracy
-
 @@aggregate_metrics
 @@aggregate_metric_map
-
 @@confusion_matrix
-
-## Set `Ops`
-
 @@set_difference
 @@set_intersection
 @@set_size

tensorflow/contrib/opt/__init__.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""opt: A module containing optimization routines."""
+"""A module containing optimization routines."""
 
 from __future__ import absolute_import
 from __future__ import division

tensorflow/contrib/opt/python/training/moving_average_optimizer.py

@@ -12,39 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""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().
-"""
+"""Moving average optimizer."""
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -60,7 +29,39 @@
 
 
 class MovingAverageOptimizer(optimizer.Optimizer):
- """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().
+ """
 
  def __init__(self, opt, average_decay=0.9999, num_updates=None,
  sequential_update=True):

tensorflow/contrib/rnn/__init__.py

@@ -12,26 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""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.
 
 @@RNNCell
-
-## Core RNN Cells for use with TensorFlow's core RNN methods
-
 @@BasicRNNCell
 @@BasicLSTMCell
 @@GRUCell
 @@LSTMCell
 @@LayerNormBasicLSTMCell
-
-## Classes storing split `RNNCell` state
-
 @@LSTMStateTuple
-
-## Core RNN Cell wrappers (RNNCells that wrap other RNNCells)
-
 @@MultiRNNCell
 @@LSTMBlockWrapper
 @@DropoutWrapper
@@ -40,32 +29,17 @@
 @@OutputProjectionWrapper
 @@DeviceWrapper
 @@ResidualWrapper
-
-### Block RNNCells
 @@LSTMBlockCell
 @@GRUBlockCell
-
-### Fused RNNCells
 @@FusedRNNCell
 @@FusedRNNCellAdaptor
 @@TimeReversedFusedRNN
 @@LSTMBlockFusedCell
-
-### LSTM-like cells
 @@CoupledInputForgetGateLSTMCell
 @@TimeFreqLSTMCell
 @@GridLSTMCell
-
-### RNNCell wrappers
 @@AttentionCellWrapper
 @@CompiledWrapper
-
-
-## Recurrent Neural Networks
-
-TensorFlow provides a number of methods for constructing Recurrent Neural
-Networks.
-
 @@static_rnn
 @@static_state_saving_rnn
 @@static_bidirectional_rnn