3a3dc678e50e60f9cf05e95e992ab873046c1b8f,cifar10.py,,,#,14

Before Change



yPreds = model.predict(testX)
yPred = np.argmax(yPreds, axis=1)
yTrue = testY

accuracy = metrics.accuracy_score(yTrue, yPred) * 100
error = 100 - accuracy
print("Accuracy : ", accuracy)
print("Error : ", error)

After Change



scores = model.evaluate_generator(test_generator.flow(testX, testY, nb_epoch), testX.shape[0])
print("Accuracy = %f" % (100 * scores[1]))
print("Error = %f" % (100 - 100 * scores[1]))

Italian Trulli
In pattern: SUPERPATTERN

Frequency: 3

Non-data size: 4

Instances


Project Name: titu1994/DenseNet
Commit Name: 3a3dc678e50e60f9cf05e95e992ab873046c1b8f
Time: 2016-12-05
Author: titu1994@gmail.com
File Name: cifar10.py
Class Name:
Method Name:


Project Name: scikit-learn-contrib/DESlib
Commit Name: 85d5c30d2186d07857d1f0fb7c269eb08d2b7d79
Time: 2018-04-07
Author: rafaelmenelau@gmail.com
File Name: deslib/des/des_clustering.py
Class Name: DESClustering
Method Name: fit


Project Name: pfnet/optuna
Commit Name: 8f36532df3a2dcc9558bc9b574dd435362e6141e
Time: 2019-08-29
Author: stephane.couvreur.sueron@gmail.com
File Name: examples/keras_simple.py
Class Name:
Method Name: objective