diff options
Diffstat (limited to 'tensorflow/python/estimator/canned/linear.py')
| -rw-r--r-- | tensorflow/python/estimator/canned/linear.py | 34 |
1 files changed, 27 insertions, 7 deletions
diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py index e22df849e5..58a7160348 100644 --- a/tensorflow/python/estimator/canned/linear.py +++ b/tensorflow/python/estimator/canned/linear.py @@ -66,13 +66,15 @@ def _compute_fraction_of_zero(cols_to_vars): return nn.zero_fraction(array_ops.concat(all_weight_vars, axis=0)) -def _linear_logit_fn_builder(units, feature_columns): +def _linear_logit_fn_builder(units, feature_columns, sparse_combiner='sum'): """Function builder for a linear logit_fn. Args: units: An int indicating the dimension of the logit layer. feature_columns: An iterable containing all the feature columns used by the model. + sparse_combiner: A string specifying how to reduce if a categorical column + is multivalent. One of "mean", "sqrtn", and "sum". Returns: A logit_fn (see below). @@ -95,6 +97,7 @@ def _linear_logit_fn_builder(units, feature_columns): features=features, feature_columns=feature_columns, units=units, + sparse_combiner=sparse_combiner, cols_to_vars=cols_to_vars) bias = cols_to_vars.pop('bias') if units > 1: @@ -111,7 +114,7 @@ def _linear_logit_fn_builder(units, feature_columns): def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer, - partitioner, config): + partitioner, config, sparse_combiner='sum'): """A model_fn for linear models that use a gradient-based optimizer. Args: @@ -126,6 +129,8 @@ def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer, optimizer to use for training. If `None`, will use a FTRL optimizer. partitioner: Partitioner for variables. config: `RunConfig` object to configure the runtime settings. + sparse_combiner: A string specifying how to reduce if a categorical column + is multivalent. One of "mean", "sqrtn", and "sum". Returns: An `EstimatorSpec` instance. @@ -153,7 +158,8 @@ def _linear_model_fn(features, labels, mode, head, feature_columns, optimizer, partitioner=partitioner): logit_fn = _linear_logit_fn_builder( - units=head.logits_dimension, feature_columns=feature_columns) + units=head.logits_dimension, feature_columns=feature_columns, + sparse_combiner=sparse_combiner) logits = logit_fn(features=features) return head.create_estimator_spec( @@ -255,7 +261,8 @@ class LinearClassifier(estimator.Estimator): config=None, partitioner=None, warm_start_from=None, - loss_reduction=losses.Reduction.SUM): + loss_reduction=losses.Reduction.SUM, + sparse_combiner='sum'): """Construct a `LinearClassifier` estimator object. Args: @@ -295,6 +302,11 @@ class LinearClassifier(estimator.Estimator): and Tensor names are unchanged. loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + sparse_combiner: A string specifying how to reduce if a categorical column + is multivalent. One of "mean", "sqrtn", and "sum" -- these are + effectively different ways to do example-level normalization, which can + be useful for bag-of-words features. for more details, see + @{tf.feature_column.linear_model$linear_model}. Returns: A `LinearClassifier` estimator. @@ -323,7 +335,8 @@ class LinearClassifier(estimator.Estimator): feature_columns=tuple(feature_columns or []), optimizer=optimizer, partitioner=partitioner, - config=config) + config=config, + sparse_combiner=sparse_combiner) super(LinearClassifier, self).__init__( model_fn=_model_fn, @@ -422,7 +435,8 @@ class LinearRegressor(estimator.Estimator): config=None, partitioner=None, warm_start_from=None, - loss_reduction=losses.Reduction.SUM): + loss_reduction=losses.Reduction.SUM, + sparse_combiner='sum'): """Initializes a `LinearRegressor` instance. Args: @@ -454,6 +468,11 @@ class LinearRegressor(estimator.Estimator): and Tensor names are unchanged. loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + sparse_combiner: A string specifying how to reduce if a categorical column + is multivalent. One of "mean", "sqrtn", and "sum" -- these are + effectively different ways to do example-level normalization, which can + be useful for bag-of-words features. for more details, see + @{tf.feature_column.linear_model$linear_model}. """ head = head_lib._regression_head( # pylint: disable=protected-access label_dimension=label_dimension, weight_column=weight_column, @@ -469,7 +488,8 @@ class LinearRegressor(estimator.Estimator): feature_columns=tuple(feature_columns or []), optimizer=optimizer, partitioner=partitioner, - config=config) + config=config, + sparse_combiner=sparse_combiner) super(LinearRegressor, self).__init__( model_fn=_model_fn, |