浅谈keras中自定义二分类任务评价指标metrics的方法以及代码

import tensorflow as tf

#精确率评价指标
def metric_precision(y_true,y_pred): 
 TP=tf.reduce_sum(y_true*tf.round(y_pred))
 TN=tf.reduce_sum((1-y_true)*(1-tf.round(y_pred)))
 FP=tf.reduce_sum((1-y_true)*tf.round(y_pred))
 FN=tf.reduce_sum(y_true*(1-tf.round(y_pred)))
 precision=TP/(TP+FP)
 return precision

#召回率评价指标
def metric_recall(y_true,y_pred): 
 TP=tf.reduce_sum(y_true*tf.round(y_pred))
 TN=tf.reduce_sum((1-y_true)*(1-tf.round(y_pred)))
 FP=tf.reduce_sum((1-y_true)*tf.round(y_pred))
 FN=tf.reduce_sum(y_true*(1-tf.round(y_pred)))
 recall=TP/(TP+FN)
 return recall

#F1-score评价指标
def metric_F1score(y_true,y_pred): 
 TP=tf.reduce_sum(y_true*tf.round(y_pred))
 TN=tf.reduce_sum((1-y_true)*(1-tf.round(y_pred)))
 FP=tf.reduce_sum((1-y_true)*tf.round(y_pred))
 FN=tf.reduce_sum(y_true*(1-tf.round(y_pred)))
 precision=TP/(TP+FP)
 recall=TP/(TP+FN)
 F1score=2*precision*recall/(precision+recall)
 return F1score

#编译阶段引用自定义评价指标示例
model.compile(optimizer='adam',
	 loss='binary_crossentropy',
	 metrics=['accuracy',
	 		metric_precision,
	 		metric_recall,
	 		metric_F1score])

fro<b>本文来源gao@!dai!ma.com搞$$代^@码5网@</b>m sklearn.model_selection import StratifiedKFold
import numpy

seed = 7 # 随机种子
numpy.random.seed(seed) # 生成固定的随机数
num_k = 5 # 多少折

# 整个数据集(自己定义)
X = 
Y = 

kfold = StratifiedKFold(n_splits=num_k, shuffle=True, random_state=seed) # 分层K折，保证类别比例一致

cvscores = []
for train, test in kfold.split(X, Y):

	# 可以用sequential或者function的方式建模（自己定义）
	model = 
 model.compile() # 自定义
 
	# 模型训练
 model.fit(X[train], Y[train], epochs=150, batch_size=10, verbose=0)
 
 # 模型测试
 scores = model.evaluate(X[test], Y[test], verbose=0)
 
 print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100)) # 打印出验证集准确率
 
 cvscores.append(scores[1] * 100)
 
print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores), numpy.std(cvscores))) # 输出k-fold的模型平均和标准差结果