50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
|
# 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.
|
||
|
|
# ==============================================================================
|
||
|
|
|
||
|
|
from __future__ import absolute_import
|
||
|
|
from __future__ import division
|
||
|
|
from __future__ import print_function
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
|
||
|
|
def accuracy(logits, labels):
|
||
|
|
"""
|
||
|
|
Return accuracy of the array of logits (or label predictions) wrt the labels
|
||
|
|
:param logits: this can either be logits, probabilities, or a single label
|
||
|
|
:param labels: the correct labels to match against
|
||
|
|
:return: the accuracy as a float
|
||
|
|
"""
|
||
|
|
assert len(logits) == len(labels)
|
||
|
|
|
||
|
|
if len(np.shape(logits)) > 1:
|
||
|
|
# Predicted labels are the argmax over axis 1
|
||
|
|
predicted_labels = np.argmax(logits, axis=1)
|
||
|
|
else:
|
||
|
|
# Input was already labels
|
||
|
|
assert len(np.shape(logits)) == 1
|
||
|
|
predicted_labels = logits
|
||
|
|
|
||
|
|
# Check against correct labels to compute correct guesses
|
||
|
|
correct = np.sum(predicted_labels == labels.reshape(len(labels)))
|
||
|
|
|
||
|
|
# Divide by number of labels to obtain accuracy
|
||
|
|
accuracy = float(correct) / len(labels)
|
||
|
|
|
||
|
|
# Return float value
|
||
|
|
return accuracy
|
||
|
|
|
||
|
|
|