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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.
# ==============================================================================
"""Classification metrics library."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
# TODO(nsilberman): move into metrics/python/ops/
def accuracy(predictions, labels, weights=None, name=None):
"""Computes the percentage of times that predictions matches labels.
Args:
predictions: the predicted values, a `Tensor` whose dtype and shape
matches 'labels'.
labels: the ground truth values, a `Tensor` of any shape and
bool, integer, or string dtype.
weights: None or `Tensor` of float values to reweight the accuracy.
name: A name for the operation (optional).
Returns:
Accuracy `Tensor`.
Raises:
ValueError: if dtypes don't match or
if dtype is not bool, integer, or string.
"""
if not (labels.dtype.is_integer or
labels.dtype in (dtypes.bool, dtypes.string)):
raise ValueError(
'Labels should have bool, integer, or string dtype, not %r' %
labels.dtype)
if not labels.dtype.is_compatible_with(predictions.dtype):
raise ValueError('Dtypes of predictions and labels should match. '
'Given: predictions (%r) and labels (%r)' %
(predictions.dtype, labels.dtype))
with ops.name_scope(name, 'accuracy', values=[predictions, labels]):
is_correct = math_ops.cast(
math_ops.equal(predictions, labels), dtypes.float32)
if weights is not None:
is_correct = math_ops.multiply(is_correct, weights)
num_values = math_ops.multiply(weights, array_ops.ones_like(is_correct))
return math_ops.div(math_ops.reduce_sum(is_correct),
math_ops.reduce_sum(num_values))
return math_ops.reduce_mean(is_correct)
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