forked from 626_privacy/tensorflow_privacy
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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def accuracy(logits, labels):
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"""
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Return accuracy of the array of logits (or label predictions) wrt the labels
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:param logits: this can either be logits, probabilities, or a single label
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:param labels: the correct labels to match against
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:return: the accuracy as a float
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"""
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assert len(logits) == len(labels)
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if len(np.shape(logits)) > 1:
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# Predicted labels are the argmax over axis 1
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predicted_labels = np.argmax(logits, axis=1)
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else:
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# Input was already labels
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assert len(np.shape(logits)) == 1
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predicted_labels = logits
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# Check against correct labels to compute correct guesses
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correct = np.sum(predicted_labels == labels.reshape(len(labels)))
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# Divide by number of labels to obtain accuracy
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accuracy = float(correct) / len(labels)
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# Return float value
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return accuracy
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