如何在 Keras 中输出 per-class 的准确率? [英] How to output per-class accuracy in Keras？

问题描述

Caffe 不仅可以打印整体准确度，还可以打印每类准确度.

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

在 Keras 日志中，只有整体准确性.我很难计算单独的类别准确率.

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

有人知道如何在 keras 中输出每类的准确率吗?

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

推荐答案

Precision &召回是多类分类更有用的度量(参见定义).遵循 Keras MNIST CNN 示例(10 级分类)，你可以使用 classification_report 从 sklearn.metrics:

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))

结果如下:

         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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