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baseline.py
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baseline.py
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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.
# ==============================================================================
"""Baseline estimators.
Baseline estimators are bias-only estimators that can be used for debugging
and as simple baselines.
Example:
```
# Build BaselineClassifier
classifier = BaselineClassifier(n_classes=3)
# Input builders
def input_fn_train(): # returns x, y (where y represents label's class index).
pass
def input_fn_eval(): # returns x, y (where y represents label's class index).
pass
# Fit model.
classifier.train(input_fn=input_fn_train)
# Evaluate cross entropy between the test and train labels.
loss = classifier.evaluate(input_fn=input_fn_eval)["loss"]
# predict outputs the probability distribution of the classes as seen in
# training.
predictions = classifier.predict(new_samples)
```
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from tensorflow.python.estimator import estimator
from tensorflow.python.estimator.canned import head as head_lib
from tensorflow.python.estimator.canned import optimizers
from tensorflow.python.feature_column import feature_column as feature_column_lib
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops.losses import losses
from tensorflow.python.training import training_util
from tensorflow.python.util.tf_export import estimator_export
# The default learning rate of 0.3 is a historical artifact of the initial
# implementation, but seems a reasonable choice.
_LEARNING_RATE = 0.3
def _get_weight_column_key(weight_column):
if weight_column is None:
return None
if isinstance(weight_column, six.string_types):
return weight_column
if not isinstance(weight_column, feature_column_lib._NumericColumn): # pylint: disable=protected-access
raise TypeError('Weight column must be either a string or _NumericColumn.'
' Given type: {}.'.format(type(weight_column)))
return weight_column.key()
def _baseline_logit_fn_builder(num_outputs, weight_column=None):
"""Function builder for a baseline logit_fn.
Args:
num_outputs: Number of outputs for the model.
weight_column: A string or a `_NumericColumn` created by
`tf.feature_column.numeric_column` defining feature column representing
weights. It will be multiplied by the loss of the example.
Returns:
A logit_fn (see below).
"""
def baseline_logit_fn(features):
"""Baseline model logit_fn.
The baseline model simply learns a bias, so the output logits are a
`Variable` with one weight for each output that learns the bias for the
corresponding output.
Args:
features: The first item returned from the `input_fn` passed to `train`,
`evaluate`, and `predict`. This should be a single `Tensor` or dict with
`Tensor` values.
Returns:
A `Tensor` representing the logits.
"""
size_checks = []
batch_size = None
weight_column_key = _get_weight_column_key(weight_column)
# The first dimension is assumed to be a batch size and must be consistent
# among all of the features.
for key, feature in features.items():
# Skip weight_column to ensure we don't add size checks to it.
# These would introduce a dependency on the weight at serving time.
if key == weight_column_key:
continue
first_dim = array_ops.shape(feature)[0]
if batch_size is None:
batch_size = first_dim
else:
size_checks.append(check_ops.assert_equal(batch_size, first_dim))
with ops.control_dependencies(size_checks):
with variable_scope.variable_scope('baseline'):
bias = variable_scope.get_variable('bias', shape=[num_outputs],
initializer=init_ops.Zeros)
return math_ops.multiply(bias, array_ops.ones([batch_size,
num_outputs]))
return baseline_logit_fn
def _baseline_model_fn(features, labels, mode, head, optimizer,
weight_column=None, config=None):
"""Model_fn for baseline models.
Args:
features: `Tensor` or dict of `Tensor` (depends on data passed to `train`).
labels: `Tensor` of labels that are compatible with the `Head` instance.
mode: Defines whether this is training, evaluation or prediction.
See `ModeKeys`.
head: A `Head` instance.
optimizer: String, `tf.Optimizer` object, or callable that creates the
optimizer to use for training. If not specified, will use `FtrlOptimizer`
with a default learning rate of 0.3.
weight_column: A string or a `_NumericColumn` created by
`tf.feature_column.numeric_column` defining feature column representing
weights. It will be multiplied by the loss of the example.
config: `RunConfig` object to configure the runtime settings.
Raises:
KeyError: If weight column is specified but not present.
ValueError: If features is an empty dictionary.
Returns:
An `EstimatorSpec` instance.
"""
del config # Unused.
logit_fn = _baseline_logit_fn_builder(head.logits_dimension, weight_column)
logits = logit_fn(features)
def train_op_fn(loss):
opt = optimizers.get_optimizer_instance(
optimizer, learning_rate=_LEARNING_RATE)
return opt.minimize(loss, global_step=training_util.get_global_step())
return head.create_estimator_spec(
features=features,
mode=mode,
logits=logits,
labels=labels,
train_op_fn=train_op_fn)
@estimator_export('estimator.BaselineClassifier')
class BaselineClassifier(estimator.Estimator):
"""A classifier that can establish a simple baseline.
This classifier ignores feature values and will learn to predict the average
value of each label. For single-label problems, this will predict the
probability distribution of the classes as seen in the labels. For multi-label
problems, this will predict the fraction of examples that are positive for
each class.
Example:
```python
# Build BaselineClassifier
classifier = BaselineClassifier(n_classes=3)
# Input builders
def input_fn_train: # returns x, y (where y represents label's class index).
pass
def input_fn_eval: # returns x, y (where y represents label's class index).
pass
# Fit model.
classifier.train(input_fn=input_fn_train)
# Evaluate cross entropy between the test and train labels.
loss = classifier.evaluate(input_fn=input_fn_eval)["loss"]
# predict outputs the probability distribution of the classes as seen in
# training.
predictions = classifier.predict(new_samples)
```
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`.
@compatibility(eager)
Estimators can be used while eager execution is enabled. Note that `input_fn`
and all hooks are executed inside a graph context, so they have to be written
to be compatible with graph mode. Note that `input_fn` code using `tf.data`
generally works in both graph and eager modes.
@end_compatibility
"""
def __init__(self,
model_dir=None,
n_classes=2,
weight_column=None,
label_vocabulary=None,
optimizer='Ftrl',
config=None,
loss_reduction=losses.Reduction.SUM):
"""Initializes a BaselineClassifier instance.
Args:
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.
n_classes: number of label classes. Default is binary classification.
It must be greater than 1. Note: Class labels are integers representing
the class index (i.e. values from 0 to n_classes-1). For arbitrary
label values (e.g. string labels), convert to class indices first.
weight_column: A string or a `_NumericColumn` created by
`tf.feature_column.numeric_column` defining feature column representing
weights. It will be multiplied by the loss of the example.
label_vocabulary: Optional list of strings with size `[n_classes]`
defining the label vocabulary. Only supported for `n_classes` > 2.
optimizer: String, `tf.Optimizer` object, or callable that creates the
optimizer to use for training. If not specified, will use
`FtrlOptimizer` with a default learning rate of 0.3.
config: `RunConfig` object to configure the runtime settings.
loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how
to reduce training loss over batch. Defaults to `SUM`.
Returns:
A `BaselineClassifier` estimator.
Raises:
ValueError: If `n_classes` < 2.
"""
if n_classes == 2:
head = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( # pylint: disable=protected-access
weight_column=weight_column,
label_vocabulary=label_vocabulary,
loss_reduction=loss_reduction)
else:
head = head_lib._multi_class_head_with_softmax_cross_entropy_loss( # pylint: disable=protected-access
n_classes, weight_column=weight_column,
label_vocabulary=label_vocabulary,
loss_reduction=loss_reduction)
def _model_fn(features, labels, mode, config):
return _baseline_model_fn(
features=features,
labels=labels,
mode=mode,
head=head,
optimizer=optimizer,
weight_column=weight_column,
config=config)
super(BaselineClassifier, self).__init__(
model_fn=_model_fn,
model_dir=model_dir,
config=config)
@estimator_export('estimator.BaselineRegressor')
class BaselineRegressor(estimator.Estimator):
"""A regressor that can establish a simple baseline.
This regressor ignores feature values and will learn to predict the average
value of each label.
Example:
```python
# Build BaselineRegressor
regressor = BaselineRegressor()
# Input builders
def input_fn_train: # returns x, y (where y is the label).
pass
def input_fn_eval: # returns x, y (where y is the label).
pass
# Fit model.
regressor.train(input_fn=input_fn_train)
# Evaluate squared-loss between the test and train targets.
loss = regressor.evaluate(input_fn=input_fn_eval)["loss"]
# predict outputs the mean value seen during training.
predictions = regressor.predict(new_samples)
```
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`.
@compatibility(eager)
Estimators can be used while eager execution is enabled. Note that `input_fn`
and all hooks are executed inside a graph context, so they have to be written
to be compatible with graph mode. Note that `input_fn` code using `tf.data`
generally works in both graph and eager modes.
@end_compatibility
"""
def __init__(self,
model_dir=None,
label_dimension=1,
weight_column=None,
optimizer='Ftrl',
config=None,
loss_reduction=losses.Reduction.SUM):
"""Initializes a BaselineRegressor instance.
Args:
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.
label_dimension: Number of regression targets per example. This is the
size of the last dimension of the labels and logits `Tensor` objects
(typically, these have shape `[batch_size, label_dimension]`).
weight_column: A string or a `_NumericColumn` created by
`tf.feature_column.numeric_column` defining feature column representing
weights. It will be multiplied by the loss of the example.
optimizer: String, `tf.Optimizer` object, or callable that creates the
optimizer to use for training. If not specified, will use
`FtrlOptimizer` with a default learning rate of 0.3.
config: `RunConfig` object to configure the runtime settings.
loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how
to reduce training loss over batch. Defaults to `SUM`.
Returns:
A `BaselineRegressor` estimator.
"""
head = head_lib._regression_head( # pylint: disable=protected-access
label_dimension=label_dimension,
weight_column=weight_column,
loss_reduction=loss_reduction)
def _model_fn(features, labels, mode, config):
return _baseline_model_fn(
features=features,
labels=labels,
mode=mode,
head=head,
optimizer=optimizer,
config=config)
super(BaselineRegressor, self).__init__(
model_fn=_model_fn,
model_dir=model_dir,
config=config)