print(confusion_matrix(ConY, ConX)) '''
[27684 6886 3837]
[ 5214 56485 4297]
[ 4782 6429 23873]
'''
print(classification_report(ConY, ConX)) ''' precision recall f1-score support
0.0 0.73 0.72 0.73 38407
1.0 0.81 0.86 0.83 65996
2.0 0.75 0.68 0.71 35084
accuracy 0.77 139487
macro avg 0.76 0.75 0.76 139487
weighted avg 0.77 0.77 0.77 139487
https://blog.csdn.net/weixin_44863328/article/details/107303773
use an EarlyStopping callback:
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
model.fit(x, y, validation_split=0.2, callbacks=[early_stopping])
https://stackoverflow.com/questions/53242875/accuracy-decreasing-with-higher-epochs
T
batch size
frequency
alpha
learning rate
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