用于不带all()的多标签分类的keras自定义指标

from keras import backend as K
import tensorflow as tf

def custom_metric(y_true, y_pred):
    # 计算预测结果中的正类别数量
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    
    # 计算真实标签中的正类别数量
    actual_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    
    # 计算准确率
    precision = true_positives / (actual_positives + K.epsilon())
    
    return precision

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=[custom_metric])

model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))