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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.
# ==============================================================================
"""Functions to bridge `Distribution`s and `tf.contrib.learn.estimator` APIs."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.learn.python.learn.estimators.head import _compute_weighted_loss
from tensorflow.contrib.learn.python.learn.estimators.head import _RegressionHead
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
__all__ = [
"estimator_head_distribution_regression",
]
def estimator_head_distribution_regression(make_distribution_fn,
label_dimension=1,
logits_dimension=None,
label_name=None,
weight_column_name=None,
enable_centered_bias=False,
head_name=None):
"""Creates a `Head` for regression under a generic distribution.
Args:
make_distribution_fn: Python `callable` which returns a `tf.Distribution`
instance created using only logits.
label_dimension: Number of regression labels per example. This is the size
of the last dimension of the labels `Tensor` (typically, this has shape
`[batch_size, label_dimension]`).
logits_dimension: Number of logits per example. This is the size of the last
dimension of the logits `Tensor` (typically, this has shape
`[batch_size, logits_dimension]`).
Default value: `label_dimension`.
label_name: Python `str`, name of the key in label `dict`. Can be `None` if
label is a `Tensor` (single headed models).
weight_column_name: Python `str` defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
enable_centered_bias: Python `bool`. If `True`, estimator will learn a
centered bias variable for each class. Rest of the model structure learns
the residual after centered bias.
head_name: Python `str`, name of the head. Predictions, summary and metrics
keys are suffixed by `"/" + head_name` and the default variable scope is
`head_name`.
Returns:
An instance of `Head` for generic regression.
"""
return _DistributionRegressionHead(
make_distribution_fn=make_distribution_fn,
label_dimension=label_dimension,
logits_dimension=logits_dimension,
label_name=label_name,
weight_column_name=weight_column_name,
enable_centered_bias=enable_centered_bias,
head_name=head_name)
class _DistributionRegressionHead(_RegressionHead):
"""Creates a _RegressionHead instance from an arbitray `Distribution`."""
def __init__(self,
make_distribution_fn,
label_dimension,
logits_dimension=None,
label_name=None,
weight_column_name=None,
enable_centered_bias=False,
head_name=None):
"""`Head` for regression.
Args:
make_distribution_fn: Python `callable` which returns a `tf.Distribution`
instance created using only logits.
label_dimension: Number of regression labels per example. This is the
size of the last dimension of the labels `Tensor` (typically, this has
shape `[batch_size, label_dimension]`).
logits_dimension: Number of logits per example. This is the size of the
last dimension of the logits `Tensor` (typically, this has shape
`[batch_size, logits_dimension]`).
Default value: `label_dimension`.
label_name: Python `str`, name of the key in label `dict`. Can be `None`
if label is a tensor (single headed models).
weight_column_name: Python `str` defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
enable_centered_bias: Python `bool`. If `True`, estimator will learn a
centered bias variable for each class. Rest of the model structure
learns the residual after centered bias.
head_name: Python `str`, name of the head. Predictions, summary and
metrics keys are suffixed by `"/" + head_name` and the default variable
scope is `head_name`.
Raises:
TypeError: if `make_distribution_fn` is not `callable`.
"""
if not callable(make_distribution_fn):
raise TypeError("`make_distribution_fn` must be a callable function.")
self._distributions = {}
self._make_distribution_fn = make_distribution_fn
def static_value(x):
"""Returns the static value of a `Tensor` or `None`."""
return tensor_util.constant_value(ops.convert_to_tensor(x))
def concat_vectors(*args):
"""Concatenates input vectors, statically if possible."""
args_ = [static_value(x) for x in args]
if any(vec is None for vec in args_):
return array_ops.concat(args, axis=0)
return [val for vec in args_ for val in vec]
def loss_fn(labels, logits, weights=None):
"""Returns the loss of using `logits` to predict `labels`."""
d = self.distribution(logits)
labels_batch_shape = labels.shape.with_rank_at_least(1)[:-1]
labels_batch_shape = (
labels_batch_shape.as_list() if labels_batch_shape.is_fully_defined()
else array_ops.shape(labels)[:-1])
labels = array_ops.reshape(
labels,
shape=concat_vectors(labels_batch_shape, d.event_shape_tensor()))
return _compute_weighted_loss(
loss_unweighted=-d.log_prob(labels),
weight=weights)
def link_fn(logits):
"""Returns the inverse link function at `logits`."""
# Note: What the API calls a "link function" is really the inverse-link
# function, i.e., the "mean".
d = self.distribution(logits)
return d.mean()
super(_DistributionRegressionHead, self).__init__(
label_dimension=label_dimension,
loss_fn=loss_fn,
link_fn=link_fn,
logits_dimension=logits_dimension,
label_name=label_name,
weight_column_name=weight_column_name,
enable_centered_bias=enable_centered_bias,
head_name=head_name)
@property
def distributions(self):
"""Returns all distributions created by `DistributionRegressionHead`."""
return self._distributions
def distribution(self, logits, name=None):
"""Retrieves a distribution instance, parameterized by `logits`.
Args:
logits: `float`-like `Tensor` representing the parameters of the
underlying distribution.
name: The Python `str` name to given to this op.
Default value: "distribution".
Returns:
distribution: `tf.Distribution` instance parameterized by `logits`.
"""
with ops.name_scope(name, "distribution", [logits]):
d = self._distributions.get(logits, None)
if d is None:
d = self._make_distribution_fn(logits)
self._distributions[logits] = d
return d
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