diff options
Diffstat (limited to 'tensorflow/python/layers/base.py')
| -rw-r--r-- | tensorflow/python/layers/base.py | 25 |
1 files changed, 10 insertions, 15 deletions
diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py index c71e8382e9..db608aa79a 100644 --- a/tensorflow/python/layers/base.py +++ b/tensorflow/python/layers/base.py @@ -220,7 +220,7 @@ class Layer(object): Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed - when calling a layer. Hence, when reusing a same layer on + when calling a layer. Hence, when reusing the same layer on different inputs `a` and `b`, some entries in `layer.updates` may be dependent on `a` and some on `b`. This method automatically keeps track of dependencies. @@ -294,9 +294,9 @@ class Layer(object): """Add loss tensor(s), potentially dependent on layer inputs. Some losses (for instance, activity regularization losses) may be dependent - on the inputs passed when calling a layer. Hence, when reusing a same layer - on different inputs `a` and `b`, some entries in `layer.losses` may be - dependent on `a` and some on `b`. This method automatically keeps track + on the inputs passed when calling a layer. Hence, when reusing the same + layer on different inputs `a` and `b`, some entries in `layer.losses` may + be dependent on `a` and some on `b`. This method automatically keeps track of dependencies. The `get_losses_for` method allows to retrieve the losses relevant to a @@ -401,11 +401,10 @@ class Layer(object): """ return input_shape - def _make_unique_name(self, name_uid_map=None, avoid_names=None, - namespace=''): + def _make_unique_name(self, name_uid_map=None, avoid_names=None): base_name = _to_snake_case(self.__class__.__name__) name = _unique_layer_name(base_name, name_uid_map=name_uid_map, - avoid_names=avoid_names, namespace=namespace) + avoid_names=avoid_names) return (name, base_name) def _set_scope(self, scope=None): @@ -642,7 +641,7 @@ class Layer(object): for output in output_list: with ops.name_scope('ActivityRegularizer'): activity_regularization = self._activity_regularizer(output) - self.add_loss(activity_regularization, inputs=inputs) + self.add_loss(activity_regularization) if not in_deferred_mode: # TODO(fchollet): consider how masking will work with deferred mode. @@ -2371,7 +2370,7 @@ def _get_default_graph_uid_map(): return name_uid_map -def _unique_layer_name(name, name_uid_map=None, avoid_names=None, namespace=''): +def _unique_layer_name(name, name_uid_map=None, avoid_names=None): """Makes a layer name (or arbitrary string) unique within a TensorFlow graph. Arguments: @@ -2380,9 +2379,6 @@ def _unique_layer_name(name, name_uid_map=None, avoid_names=None, namespace=''): names. If None (default), uses a per-Graph dictionary. avoid_names: An optional set or dict with names which should not be used. If None (default) does not avoid any names. - namespace: Gets a name which is unique within the (graph, namespace). Layers - which are not Networks use a blank namespace and so get graph-global - names. Returns: Unique string name. @@ -2400,7 +2396,6 @@ def _unique_layer_name(name, name_uid_map=None, avoid_names=None, namespace=''): avoid_names = set() proposed_name = None while proposed_name is None or proposed_name in avoid_names: - name_key = (namespace, name) - name_uid_map[name_key] += 1 - proposed_name = name + '_' + str(name_uid_map[name_key]) + name_uid_map[name] += 1 + proposed_name = name + '_' + str(name_uid_map[name]) return proposed_name |