d03644cb2140ea0c92f2360407d26a23f6e68c54,art/detection/features_unittest.py,TestFeatures,test_attention_map,#TestFeatures#,130

Before Change


        nb_classes = 10

        // compute the attention map using only Keras
        model = load_model("./tests/model.h5")

        STRIDES = 4
        WINDOW_WIDTH = 8

After Change

Italian Trulli
In pattern: SUPERPATTERN

Frequency: 3

Non-data size: 3

Instances


Project Name: IBM/adversarial-robustness-toolbox
Commit Name: d03644cb2140ea0c92f2360407d26a23f6e68c54
Time: 2018-11-29
Author: ambrish.rawat@ie.ibm.com
File Name: art/detection/features_unittest.py
Class Name: TestFeatures
Method Name: test_attention_map


Project Name: IBM/adversarial-robustness-toolbox
Commit Name: d03644cb2140ea0c92f2360407d26a23f6e68c54
Time: 2018-11-29
Author: ambrish.rawat@ie.ibm.com
File Name: art/detection/features_unittest.py
Class Name: TestFeatures
Method Name: test_mean_class_dist_fv


Project Name: IBM/adversarial-robustness-toolbox
Commit Name: d03644cb2140ea0c92f2360407d26a23f6e68c54
Time: 2018-11-29
Author: ambrish.rawat@ie.ibm.com
File Name: art/detection/features_unittest.py
Class Name: TestFeatures
Method Name: test_saliency_map