keras 自定义 metrics

keras 自定义 metrics

 

ilufei2019  2018-11-26 14:36:00 浏览2698

• 函数
 
• mean

 

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自定义 Metrics

在 keras 中操作的均为 Tensor 对象，因此，需要定义操作 Tensor 的函数来操作所有输出结果，定义好函数之后，直接将其放在 model.compile 函数 metrics 中即可生效：

def precision(y_true, y_pred): # Calculates the precision true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + K.epsilon()) return precision def recall(y_true, y_pred): # Calculates the recall true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return recall def fbeta_score(y_true, y_pred, beta=1): # Calculates the F score, the weighted harmonic mean of precision and recall. if beta < 0: raise ValueError('The lowest choosable beta is zero (only precision).') # If there are no true positives, fix the F score at 0 like sklearn. if K.sum(K.round(K.clip(y_true, 0, 1))) == 0: return 0 p = precision(y_true, y_pred) r = recall(y_true, y_pred) bb = beta ** 2 fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon()) return fbeta_score def fmeasure(y_true, y_pred): # Calculates the f-measure, the harmonic mean of precision and recall. return fbeta_score(y_true, y_pred, beta=1) 

使用方法如下：

model.compile( 
    optimizer=Adam(), 
    loss='binary_crossentropy', metrics = ['accuracy', fmeasure, recall, precision]) 

参考

custom metrics for binary classification in Keras