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| author |  Rohan Jain <rohanj@google.com> | 2018-09-26 22:00:22 -0700 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-09-26 22:03:37 -0700 |
| commit | a40cfd42e20d7e4520c1306666c9dfee97eb0a2e (patch) | |
| tree | 380100ade305a7b1fe8e7baa7eed2197daf1eabb /tensorflow/contrib/estimator | |
| parent | 941e757a2364bb2e7cf41b8d980d7639849c6c5d (diff) | |
Automated rollback of commit e00d7744dbab5c73e4d8ffa8a7d361f7b2dcefff
PiperOrigin-RevId: 214721004
Diffstat (limited to 'tensorflow/contrib/estimator')
| -rw-r--r-- | tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py | 19 |
1 files changed, 13 insertions, 6 deletions
diff --git a/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py b/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py index 3fd9f12c61..5faf0aacfe 100644 --- a/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py +++ b/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py @@ -75,7 +75,9 @@ def make_input_layer_with_layer_annotations(original_input_layer): weight_collections=None, trainable=True, cols_to_vars=None, - cols_to_output_tensors=None): + scope=None, + cols_to_output_tensors=None, + from_template=False): """Returns a dense `Tensor` as input layer based on given `feature_columns`. Generally a single example in training data is described with @@ -111,9 +113,12 @@ def make_input_layer_with_layer_annotations(original_input_layer): 'some_variable:0' shape=(5, 10), <tf.Variable 'some_variable:1' shape=(5, 10)]} If a column creates no variables, its value will be an empty list. + scope: A name or variable scope to use cols_to_output_tensors: If not `None`, must be a dictionary that will be filled with a mapping from '_FeatureColumn' to the associated output `Tensor`s. + from_template: True if the method is being instantiated from a + `make_template`. Returns: A `Tensor` which represents input layer of a model. Its shape @@ -131,7 +136,9 @@ def make_input_layer_with_layer_annotations(original_input_layer): weight_collections=weight_collections, trainable=trainable, cols_to_vars=cols_to_vars, - cols_to_output_tensors=local_cols_to_output_tensors) + scope=scope, + cols_to_output_tensors=local_cols_to_output_tensors, + from_template=from_template) if cols_to_output_tensors is not None: cols_to_output_tensors = local_cols_to_output_tensors @@ -296,9 +303,9 @@ def DNNClassifierWithLayerAnnotations( # pylint: disable=invalid-name def _model_fn(features, labels, mode, config): with _monkey_patch( - feature_column_lib, 'input_layer', + feature_column_lib, '_internal_input_layer', make_input_layer_with_layer_annotations( - feature_column_lib.input_layer)): + feature_column_lib._internal_input_layer)): # pylint: disable=protected-access return original.model_fn(features, labels, mode, config) return estimator.Estimator( @@ -417,9 +424,9 @@ def DNNRegressorWithLayerAnnotations( # pylint: disable=invalid-name def _model_fn(features, labels, mode, config): with _monkey_patch( - feature_column_lib, 'input_layer', + feature_column_lib, '_internal_input_layer', make_input_layer_with_layer_annotations( - feature_column_lib.input_layer)): + feature_column_lib._internal_input_layer)): # pylint: disable=protected-access return original.model_fn(features, labels, mode, config) return estimator.Estimator( |