问题描述
Caffe不仅可以打印整体准确度，而且还可以按类显示准确度.
Caffe can not only print overall accuracy, but also per-class accuracy.
在Keras日志中，只有整体准确性.对我来说，很难计算出单独的班级准确性.
In Keras log, there's only overall accuracy. It's hard for me to calculate the separate class accuracy.
0s-损失:0.0495-acc:0.9818-val_loss:0.0519-val_acc:0.9796
0s - loss: 0.0495 - acc: 0.9818 - val_loss: 0.0519 - val_acc: 0.9796
Epoch 169/200
Epoch 169/200
0s-损失:0.0519-acc:0.9796-val_loss:0.0496-val_acc:0.9815
0s - loss: 0.0519 - acc: 0.9796 - val_loss: 0.0496 - val_acc: 0.9815
Epoch 170/200
Epoch 170/200
0s-损失:0.0496-acc:0.9815-val_loss:0.0514-val_acc:0.9801
0s - loss: 0.0496 - acc: 0.9815 - val_loss: 0.0514 - val_acc: 0.9801
知道如何在keras中输出每个类别的准确性的人吗?
Anybody who knows how to output per-class accuracy in keras?
推荐答案
精度&召回是用于多类分类的更有用的方法(请参见定义).遵循Keras的 MNIST CNN 示例(10类分类)，您可以使用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))
这是结果:
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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