diff options
Diffstat (limited to 'tensorflow/contrib/kfac/python/ops/optimizer.py')
| -rw-r--r-- | tensorflow/contrib/kfac/python/ops/optimizer.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/tensorflow/contrib/kfac/python/ops/optimizer.py b/tensorflow/contrib/kfac/python/ops/optimizer.py index 03b9da7933..38605259b5 100644 --- a/tensorflow/contrib/kfac/python/ops/optimizer.py +++ b/tensorflow/contrib/kfac/python/ops/optimizer.py @@ -72,7 +72,7 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): (Higher damping means the update looks more like a standard gradient update - see Tikhonov regularization.) layer_collection: The layer collection object, which holds the fisher - blocks, kronecker factors, and losses associated with the + blocks, Kronecker factors, and losses associated with the graph. The layer_collection cannot be modified after KfacOptimizer's initialization. var_list: Optional list or tuple of variables to train. Defaults to the @@ -99,7 +99,7 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): placement_strategy: string, Device placement strategy used when creating covariance variables, covariance ops, and inverse ops. (Default: `None`) - **kwargs: Arguments to be passesd to specific placement + **kwargs: Arguments to be passed to specific placement strategy mixin. Check `placement.RoundRobinPlacementMixin` for example. Raises: @@ -120,7 +120,7 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): self._estimation_mode = estimation_mode self._colocate_gradients_with_ops = colocate_gradients_with_ops - # The below parameters are required only if damping needs to be adapated. + # The below parameters are required only if damping needs to be adapted. # These parameters can be set by calling # set_damping_adaptation_params() explicitly. self._damping_adaptation_decay = 0.95 @@ -574,7 +574,7 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer): """Wrapper function for `self._compute_qmodel_hyperparams`. Constructs a list of preconditioned gradients and variables. Also creates a - op to asssign the computed q model change to `self._q_model_change`. + op to assign the computed q model change to `self._q_model_change`. Args: grads_and_vars: List of (gradient, variable) pairs. |