keras自定义评价函数
注：不知道是否正确
示例一：
```
import keras.backend as K
from keras import Sequential
from keras.layers import Dense
import numpy as np
def getPrecision(y_true, y_pred):
TP = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))#TP
N = (-1)*K.sum(K.round(K.clip(y_true-K.ones_like(y_true), -1, 0)))#N
TN=K.sum(K.round(K.clip((y_true-K.ones_like(y_true))*(y_pred-K.ones_like(y_pred)), 0, 1)))#TN
FP=N-TN
precision = TP / (TP + FP + K.epsilon())#TT/P
return precision
def getRecall(y_true, y_pred):
TP = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))#TP
P=K.sum(K.round(K.clip(y_true, 0, 1)))
FN = P-TP #FN=P-TP
recall = TP / (TP + FN + K.epsilon())#TP/(TP+FN)
return recall
model.compile(optimizer="sgd", loss="categorical_crossentropy",metrics=["acc",getRecall,getPrecision])
```
来源：
示例二：
```
from keras import backend as K
def Precision(y_true, y_pred):
"""精确率"""
tp= K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) # true positives
pp= K.sum(K.round(K.clip(y_pred, 0, 1))) # predicted positives
precision = tp/ (pp+ K.epsilon())
return precision
def Recall(y_true, y_pred):
"""召回率"""
tp = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) # true positives
pp = K.sum(K.round(K.clip(y_true, 0, 1))) # possible positives
recall = tp / (pp + K.epsilon())
return recall
def F1(y_true, y_pred):
"""F1-score"""
precision = Precision(y_true, y_pred)
recall = Recall(y_true, y_pred)
f1 = 2 * ((precision * recall) / (precision + recall + K.epsilon()))
return f1
```
来源：
