Update generated Python Op docs.

Change: 133459813

2 files changed, 36 insertions, 4 deletions

diff --git a/tensorflow/g3doc/api_docs/python/contrib.layers.md b/tensorflow/g3doc/api_docs/python/contrib.layers.md
index 52bb907e30..670c122b2f 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.layers.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.layers.md
@@ -870,6 +870,21 @@ Optimize weights given a loss.
 
 Given loss and parameters for optimizer, returns a training op.
 
+Various ways of passing optimizers, include:
+  - string, name of the optimizer like 'SGD', 'Adam', see OPTIMIZER_CLS_NAMES
+      for full list. E.g. `optimize_loss(..., optimizer='Adam')`.
+  - function, takes learning rate `Tensor` as argument and must return
+      `Optimizer` instance. E.g. `optimize_loss(...,
+      optimizer=lambda lr: tf.train.MomentumOptimizer(lr, momentum=0.5))`.
+    Alternatively, if `learning_rate` is `None`, the function takes no
+    arguments. E.g. `optimize_loss(..., learning_rate=None,
+      optimizer=lambda: tf.train.MomentumOptimizer(0.5, momentum=0.5))`.
+  - class, subclass of `Optimizer` that takes only one required argument -
+      learning rate, such as AdamOptimizer, AdagradOptimizer.
+      E.g. `optimize_loss(..., optimizer=tf.train.AdagradOptimizer)`.
+  - object, instance of subclass of `Optimizer`.
+      E.g., `optimizer_loss(..., optimizer=tf.train.AdagradOptimizer(0.5))`.
+
 ##### Args:
 
 
@@ -881,8 +896,9 @@ Given loss and parameters for optimizer, returns a training op.
                'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant.
              class should be sub-class of tf.Optimizer that implements
                `compute_gradients` and `apply_gradients` functions.
-             optimizer instance should be instantion of tf.Optimizer sub-class
-               and have `compute_gradients` and `apply_gradients` functions.
+             optimizer instance should be instantion of `tf.Optimizer`
+               sub-class and have `compute_gradients` and `apply_gradients`
+               functions.
 *  <b>`gradient_noise_scale`</b>: float or None, adds 0-mean normal noise scaled by this
                         value.
 *  <b>`gradient_multipliers`</b>: dict of variables or variable names to floats.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.optimize_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.optimize_loss.md
index 8a8490ea45..3213b3e8ff 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.optimize_loss.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.optimize_loss.md
@@ -2,6 +2,21 @@
 
 Given loss and parameters for optimizer, returns a training op.
 
+Various ways of passing optimizers, include:
+  - string, name of the optimizer like 'SGD', 'Adam', see OPTIMIZER_CLS_NAMES
+      for full list. E.g. `optimize_loss(..., optimizer='Adam')`.
+  - function, takes learning rate `Tensor` as argument and must return
+      `Optimizer` instance. E.g. `optimize_loss(...,
+      optimizer=lambda lr: tf.train.MomentumOptimizer(lr, momentum=0.5))`.
+    Alternatively, if `learning_rate` is `None`, the function takes no
+    arguments. E.g. `optimize_loss(..., learning_rate=None,
+      optimizer=lambda: tf.train.MomentumOptimizer(0.5, momentum=0.5))`.
+  - class, subclass of `Optimizer` that takes only one required argument -
+      learning rate, such as AdamOptimizer, AdagradOptimizer.
+      E.g. `optimize_loss(..., optimizer=tf.train.AdagradOptimizer)`.
+  - object, instance of subclass of `Optimizer`.
+      E.g., `optimizer_loss(..., optimizer=tf.train.AdagradOptimizer(0.5))`.
+
 ##### Args:
 
 
@@ -13,8 +28,9 @@ Given loss and parameters for optimizer, returns a training op.
                'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant.
              class should be sub-class of tf.Optimizer that implements
                `compute_gradients` and `apply_gradients` functions.
-             optimizer instance should be instantion of tf.Optimizer sub-class
-               and have `compute_gradients` and `apply_gradients` functions.
+             optimizer instance should be instantion of `tf.Optimizer`
+               sub-class and have `compute_gradients` and `apply_gradients`
+               functions.
 *  <b>`gradient_noise_scale`</b>: float or None, adds 0-mean normal noise scaled by this
                         value.
 *  <b>`gradient_multipliers`</b>: dict of variables or variable names to floats.