How to output per-class accuracy in Keras？

python machine-learning keras neural-network conv-neural-network
spider picture spider · Aug 29, 2017 · Viewed 16.3k times · Source

Caffe can not only print overall accuracy, but also per-class accuracy.

In Keras log, there's only overall accuracy. It's hard for me to calculate the separate class accuracy.

Epoch 168/200

0s - loss: 0.0495 - acc: 0.9818 - val_loss: 0.0519 - val_acc: 0.9796

Epoch 169/200

0s - loss: 0.0519 - acc: 0.9796 - val_loss: 0.0496 - val_acc: 0.9815

Epoch 170/200

0s - loss: 0.0496 - acc: 0.9815 - val_loss: 0.0514 - val_acc: 0.9801

Anybody who knows how to output per-class accuracy in keras?

Answer

desertnaut picture desertnaut · Aug 29, 2017

Precision & recall are more useful measures for multi-class classification (see definitions). Following the Keras MNIST CNN example (10-class classification), you can get the per-class measures using classification_report from sklearn.metrics:

from sklearn.metrics import classification_report
import numpy as np

Y_test = np.argmax(y_test, axis=1) # Convert one-hot to index
y_pred = model.predict_classes(x_test)
print(classification_report(Y_test, y_pred))

Here is the result:

         precision    recall  f1-score   support

      0       0.99      1.00      1.00       980
      1       0.99      0.99      0.99      1135
      2       1.00      0.99      0.99      1032
      3       0.99      0.99      0.99      1010
      4       0.98      1.00      0.99       982
      5       0.99      0.99      0.99       892
      6       1.00      0.99      0.99       958
      7       0.97      1.00      0.99      1028
      8       0.99      0.99      0.99       974
      9       0.99      0.98      0.99      1009

avg / total   0.99      0.99      0.99     10000

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