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# Copyright 2016 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.
# ==============================================================================
"""Classifier class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib import metrics as metrics_lib
from tensorflow.contrib.learn.python.learn.estimators import estimator
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
def _get_classifier_metrics(unused_n_classes):
return {
('accuracy', 'classes'): metrics_lib.streaming_accuracy
}
class Classifier(estimator.Estimator):
"""Classifier single output Estimator.
Given logits generating function, provides class / probabilities heads and
functions to work with them.
"""
CLASS_OUTPUT = 'classes'
PROBABILITY_OUTPUT = 'probabilities'
def __init__(self, model_fn, n_classes, model_dir=None, config=None):
"""Constructor for Classifier.
Args:
model_fn: (targets, predictions, mode) -> logits, loss, train_op
n_classes: Number of classes
model_dir: Base directory for output data
config: Configuration object (optional)
"""
self._n_classes = n_classes
self._logits_fn = model_fn
super(Classifier, self).__init__(model_fn=self._classifier_model,
model_dir=model_dir, config=config)
def evaluate(self, x=None, y=None, input_fn=None, batch_size=None,
steps=None, metrics=None):
metrics = metrics or _get_classifier_metrics(self._n_classes)
return super(Classifier, self).evaluate(x=x, y=y, input_fn=input_fn,
batch_size=batch_size,
steps=steps, metrics=metrics)
def predict(self, x=None, input_fn=None, batch_size=None, as_iterable=False):
"""Returns predicted classes for given features.
Args:
x: Matrix of shape [n_samples, n_features...]. Can be iterator that
returns arrays of features. The training input samples for fitting the
model. If set, `input_fn` must be `None`.
input_fn: Input function. If set, `x` and 'batch_size' must be `None`.
batch_size: Override default batch size. If set, 'input_fn' must be
'None'.
as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns:
A numpy array of predicted classes (or an iterable of predicted classes if
as_iterable is True).
Raises:
ValueError: If x and input_fn are both provided or both `None`.
"""
predictions = super(Classifier, self).predict(
x=x, input_fn=input_fn, batch_size=batch_size, as_iterable=as_iterable,
outputs=[self.CLASS_OUTPUT])
if as_iterable:
return (p[self.CLASS_OUTPUT] for p in predictions)
else:
return predictions[self.CLASS_OUTPUT]
def predict_proba(
self, x=None, input_fn=None, batch_size=None, as_iterable=False):
"""Returns predicted probabilty distributions for given features.
Args:
x: Matrix of shape [n_samples, n_features...]. Can be iterator that
returns arrays of features. The training input samples for fitting the
model. If set, `input_fn` must be `None`.
input_fn: Input function. If set, `x` and 'batch_size' must be `None`.
batch_size: Override default batch size. If set, 'input_fn' must be
'None'.
as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns:
A numpy array of predicted probability distributions (or an iterable of
predicted probability distributions if as_iterable is True).
Raises:
ValueError: If x and input_fn are both provided or both `None`.
"""
predictions = super(Classifier, self).predict(
x=x, input_fn=input_fn, batch_size=batch_size, as_iterable=as_iterable,
outputs=[self.PROBABILITY_OUTPUT])
if as_iterable:
return (p[self.PROBABILITY_OUTPUT] for p in predictions)
else:
return predictions[self.PROBABILITY_OUTPUT]
def _classifier_model(self, features, targets, mode):
logits, loss, train_op = self._logits_fn(features, targets, mode)
return {
'classes': math_ops.argmax(logits, len(logits.get_shape()) - 1),
'probabilities': nn.softmax(logits)
}, loss, train_op
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