1 files changed, 40 insertions, 4 deletions
diff --git a/tensorflow/contrib/opt/python/training/weight_decay_optimizers.py b/tensorflow/contrib/opt/python/training/weight_decay_optimizers.py
index 8aa40aeb45..b9cf40eb7b 100644
--- a/tensorflow/contrib/opt/python/training/weight_decay_optimizers.py
+++ b/tensorflow/contrib/opt/python/training/weight_decay_optimizers.py
@@ -19,13 +19,13 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.framework import ops
-from tensorflow.python.training import optimizer
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.ops import state_ops
 from tensorflow.python.training import adam
 from tensorflow.python.training import momentum as momentum_opt
+from tensorflow.python.training import optimizer
 from tensorflow.python.util.tf_export import tf_export
-from tensorflow.python.ops import state_ops
-from tensorflow.python.ops import resource_variable_ops
 
 
 class DecoupledWeightDecayExtension(object):
@@ -65,7 +65,7 @@ class DecoupledWeightDecayExtension(object):
     Args:
       weight_decay: A `Tensor` or a floating point value, the factor by which
         a variable is decayed in the update step.
-      decay_var_list: Optional list or tuple or set of `Variable` objects to
+      **kwargs: Optional list or tuple or set of `Variable` objects to
         decay.
     """
     self._decay_var_list = None  # is set in minimize or apply_gradients
@@ -85,6 +85,28 @@ class DecoupledWeightDecayExtension(object):
     If decay_var_list is None, all variables in var_list are decayed.
 
     For more information see the documentation of Optimizer.minimize.
+
+    Args:
+      loss: A `Tensor` containing the value to minimize.
+      global_step: Optional `Variable` to increment by one after the
+        variables have been updated.
+      var_list: Optional list or tuple of `Variable` objects to update to
+        minimize `loss`.  Defaults to the list of variables collected in
+        the graph under the key `GraphKeys.TRAINABLE_VARIABLES`.
+      gate_gradients: How to gate the computation of gradients.  Can be
+        `GATE_NONE`, `GATE_OP`, or  `GATE_GRAPH`.
+      aggregation_method: Specifies the method used to combine gradient terms.
+        Valid values are defined in the class `AggregationMethod`.
+      colocate_gradients_with_ops: If True, try colocating gradients with
+        the corresponding op.
+      name: Optional name for the returned operation.
+      grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`.
+      decay_var_list: Optional list of decay variables.
+
+    Returns:
+      An Operation that updates the variables in `var_list`.  If `global_step`
+      was not `None`, that operation also increments `global_step`.
+
     """
     self._decay_var_list = set(decay_var_list) if decay_var_list else False
     return super(DecoupledWeightDecayExtension, self).minimize(
@@ -103,6 +125,19 @@ class DecoupledWeightDecayExtension(object):
     are decayed.
 
     For more information see the documentation of Optimizer.apply_gradients.
+
+    Args:
+      grads_and_vars: List of (gradient, variable) pairs as returned by
+        `compute_gradients()`.
+      global_step: Optional `Variable` to increment by one after the
+        variables have been updated.
+      name: Optional name for the returned operation.  Default to the
+        name passed to the `Optimizer` constructor.
+      decay_var_list: Optional list of decay variables.
+
+    Returns:
+      An `Operation` that applies the specified gradients. If `global_step`
+      was not None, that operation also increments `global_step`.
     """
     self._decay_var_list = set(decay_var_list) if decay_var_list else False
     return super(DecoupledWeightDecayExtension, self).apply_gradients(
@@ -197,6 +232,7 @@ def extend_with_decoupled_weight_decay(base_optimizer):
     A new optimizer class that inherits from DecoupledWeightDecayExtension
     and base_optimizer.
   """
+
   class OptimizerWithDecoupledWeightDecay(DecoupledWeightDecayExtension,
                                           base_optimizer):
     """Base_optimizer with decoupled weight decay.