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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Linear estimator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.estimator import estimator
from tensorflow.python.estimator.canned import linear as linear_lib
class LinearEstimator(estimator.Estimator):
"""An estimator for TensorFlow linear models with user-specified head.
Example:
```python
categorical_column_a = categorical_column_with_hash_bucket(...)
categorical_column_b = categorical_column_with_hash_bucket(...)
categorical_feature_a_x_categorical_feature_b = crossed_column(...)
# Estimator using the default optimizer.
estimator = LinearEstimator(
head=tf.contrib.estimator.multi_label_head(n_classes=3),
feature_columns=[categorical_column_a,
categorical_feature_a_x_categorical_feature_b])
# Or estimator using an optimizer with a learning rate decay.
estimator = LinearEstimator(
head=tf.contrib.estimator.multi_label_head(n_classes=3),
feature_columns=[categorical_column_a,
categorical_feature_a_x_categorical_feature_b],
optimizer=lambda: tf.train.FtrlOptimizer(
learning_rate=tf.exponential_decay(
learning_rate=0.1,
global_step=tf.get_global_step(),
decay_steps=10000,
decay_rate=0.96))
# Or estimator using the FTRL optimizer with regularization.
estimator = LinearEstimator(
head=tf.contrib.estimator.multi_label_head(n_classes=3),
feature_columns=[categorical_column_a,
categorical_feature_a_x_categorical_feature_b])
optimizer=tf.train.FtrlOptimizer(
learning_rate=0.1,
l1_regularization_strength=0.001
))
def input_fn_train: # returns x, y (where y represents label's class index).
...
estimator.train(input_fn=input_fn_train, steps=100)
def input_fn_eval: # returns x, y (where y represents label's class index).
...
metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10)
def input_fn_predict: # returns x, None
...
predictions = estimator.predict(input_fn=input_fn_predict)
```
Input of `train` and `evaluate` should have following features,
otherwise there will be a `KeyError`:
* if `weight_column` is not `None`, a feature with
`key=weight_column` whose value is a `Tensor`.
* for each `column` in `feature_columns`:
- if `column` is a `_CategoricalColumn`, a feature with `key=column.name`
whose `value` is a `SparseTensor`.
- if `column` is a `_WeightedCategoricalColumn`, two features: the first
with `key` the id column name, the second with `key` the weight column
name. Both features' `value` must be a `SparseTensor`.
- if `column` is a `_DenseColumn`, a feature with `key=column.name`
whose `value` is a `Tensor`.
Loss and predicted output are determined by the specified head.
@compatibility(eager)
Estimators are not compatible with eager execution.
@end_compatibility
"""
def __init__(self,
head,
feature_columns,
model_dir=None,
optimizer='Ftrl',
config=None,
partitioner=None,
sparse_combiner='sum'):
"""Initializes a `LinearEstimator` instance.
Args:
head: A `_Head` instance constructed with a method such as
`tf.contrib.estimator.multi_label_head`.
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
optimizer: An instance of `tf.Optimizer` used to train the model. Can also
be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or
callable. Defaults to FTRL optimizer.
config: `RunConfig` object to configure the runtime settings.
partitioner: Optional. Partitioner for input layer.
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}.
"""
def _model_fn(features, labels, mode, config):
return linear_lib._linear_model_fn( # pylint: disable=protected-access
features=features,
labels=labels,
mode=mode,
head=head,
feature_columns=tuple(feature_columns or []),
optimizer=optimizer,
partitioner=partitioner,
config=config,
sparse_combiner=sparse_combiner)
super(LinearEstimator, self).__init__(
model_fn=_model_fn, model_dir=model_dir, config=config)
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