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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.
# ==============================================================================
"""TensorFlow estimator for Linear and DNN joined training models."""
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 dnn_linear_combined as dnn_linear_combined_lib
from tensorflow.python.ops import nn
class DNNLinearCombinedEstimator(estimator.Estimator):
"""An estimator for TensorFlow Linear and DNN joined models with custom head.
Note: This estimator is also known as wide-n-deep.
Example:
```python
numeric_feature = numeric_column(...)
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(...)
categorical_feature_a_emb = embedding_column(
categorical_column=categorical_feature_a, ...)
categorical_feature_b_emb = embedding_column(
categorical_column=categorical_feature_b, ...)
estimator = DNNLinearCombinedEstimator(
head=tf.contrib.estimator.multi_label_head(n_classes=3),
# wide settings
linear_feature_columns=[categorical_feature_a_x_categorical_feature_b],
linear_optimizer=tf.train.FtrlOptimizer(...),
# deep settings
dnn_feature_columns=[
categorical_feature_a_emb, categorical_feature_b_emb,
numeric_feature],
dnn_hidden_units=[1000, 500, 100],
dnn_optimizer=tf.train.ProximalAdagradOptimizer(...))
# To apply L1 and L2 regularization, you can set dnn_optimizer to:
tf.train.ProximalAdagradOptimizer(
learning_rate=0.1,
l1_regularization_strength=0.001,
l2_regularization_strength=0.001)
# To apply learning rate decay, you can set dnn_optimizer to a callable:
lambda: tf.AdamOptimizer(
learning_rate=tf.exponential_decay(
learning_rate=0.1,
global_step=tf.get_global_step(),
decay_steps=10000,
decay_rate=0.96)
# It is the same for linear_optimizer.
# Input builders
def input_fn_train: # returns x, y
pass
estimator.train(input_fn=input_fn_train, steps=100)
def input_fn_eval: # returns x, y
pass
metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10)
def input_fn_predict: # returns x, None
pass
predictions = estimator.predict(input_fn=input_fn_predict)
```
Input of `train` and `evaluate` should have following features,
otherwise there will be a `KeyError`:
* for each `column` in `dnn_feature_columns` + `linear_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 is calculated by using mean squared error.
@compatibility(eager)
Estimators are not compatible with eager execution.
@end_compatibility
"""
def __init__(self,
head,
model_dir=None,
linear_feature_columns=None,
linear_optimizer='Ftrl',
dnn_feature_columns=None,
dnn_optimizer='Adagrad',
dnn_hidden_units=None,
dnn_activation_fn=nn.relu,
dnn_dropout=None,
input_layer_partitioner=None,
config=None):
"""Initializes a DNNLinearCombinedEstimator instance.
Args:
head: A `_Head` instance constructed with a method such as
`tf.contrib.estimator.multi_label_head`.
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.
linear_feature_columns: An iterable containing all the feature columns
used by linear part of the model. All items in the set must be
instances of classes derived from `FeatureColumn`.
linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the linear part of the model. Can also be a string (one of 'Adagrad',
'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to FTRL
optimizer.
dnn_feature_columns: An iterable containing all the feature columns used
by deep part of the model. All items in the set must be instances of
classes derived from `FeatureColumn`.
dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the deep part of the model. Can also be a string (one of 'Adagrad',
'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to Adagrad
optimizer.
dnn_hidden_units: List of hidden units per layer. All layers are fully
connected.
dnn_activation_fn: Activation function applied to each layer. If None,
will use `tf.nn.relu`.
dnn_dropout: When not None, the probability we will drop out
a given coordinate.
input_layer_partitioner: Partitioner for input layer. Defaults to
`min_max_variable_partitioner` with `min_slice_size` 64 << 20.
config: RunConfig object to configure the runtime settings.
Raises:
ValueError: If both linear_feature_columns and dnn_features_columns are
empty at the same time.
"""
linear_feature_columns = linear_feature_columns or []
dnn_feature_columns = dnn_feature_columns or []
self._feature_columns = (
list(linear_feature_columns) + list(dnn_feature_columns))
if not self._feature_columns:
raise ValueError('Either linear_feature_columns or dnn_feature_columns '
'must be defined.')
def _model_fn(features, labels, mode, config):
return dnn_linear_combined_lib._dnn_linear_combined_model_fn( # pylint: disable=protected-access
features=features,
labels=labels,
mode=mode,
head=head,
linear_feature_columns=linear_feature_columns,
linear_optimizer=linear_optimizer,
dnn_feature_columns=dnn_feature_columns,
dnn_optimizer=dnn_optimizer,
dnn_hidden_units=dnn_hidden_units,
dnn_activation_fn=dnn_activation_fn,
dnn_dropout=dnn_dropout,
input_layer_partitioner=input_layer_partitioner,
config=config)
super(DNNLinearCombinedEstimator, self).__init__(
model_fn=_model_fn, model_dir=model_dir, config=config)
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