Python KERA能否在自定义指标中使用sklearn来创建微观f1_分数

我在stackoverflow中找到了一个版本

from keras import backend as K

def f1(y_true, y_pred):
    def recall(y_true, y_pred):
        """Recall metric.

        Only computes a batch-wise average of recall.

        Computes the recall, a metric for multi-label classification of
        how many relevant items are selected.
        """
        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 precision(y_true, y_pred):
        """Precision metric.

        Only computes a batch-wise average of precision.

        Computes the precision, a metric for multi-label classification of
        how many selected items are relevant.
        """
        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
    precision = precision(y_true, y_pred)
    recall = recall(y_true, y_pred)
    return 2*((precision*recall)/(precision+recall+K.epsilon()))


model.compile(loss='binary_crossentropy',
          optimizer= "adam",
          metrics=[f1])

但我可以使用sklearn f1_分数创建自定义指标吗？

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我想使用f1_分数宏和f1_分数微的平均值，有人能帮我吗？谢谢

我认为您可以在每批培训中使用上面显示的代码。因为它正在计算每个批次的F1分数，您可以在终端中看到打印的日志

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1/13[=>………]-预计到达时间：4s-损失：0.2646-f1:0.2927

2/13[=>………]-预计到达时间：4s-损失：0.2664-f1:0.1463

13/13[========================================================================7s 505ms/步-损耗：0.2615-f1:0.1008-val\u损耗：0.2887-val\u f1:0.1464

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如果您使用fit方法并希望计算每个历元的F1，则应尝试编写如下代码

class Metrics(Callback):
'''
Defined your personal callback
'''
def on_train_begin(self, logs={}):
    self.val_f1s = []
    self.val_recalls = []
    self.val_precisions = []

def on_epoch_end(self, epoch, logs={}):
    #         val_predict = (np.asarray(self.model.predict(self.validation_data[0]))).round()
    val_predict = np.argmax(np.asarray(self.model.predict(self.validation_data[0])), axis=1)
    #         val_targ = self.validation_data[1]
    val_targ = np.argmax(self.validation_data[1], axis=1)
    _val_f1 = f1_score(val_targ, val_predict, average='macro')
    #         _val_recall = recall_score(val_targ, val_predict)
    #         _val_precision = precision_score(val_targ, val_predict)
    self.val_f1s.append(_val_f1)
    #         self.val_recalls.append(_val_recall)
    #         self.val_precisions.append(_val_precision)
    #         print('— val_f1: %f — val_precision: %f — val_recall %f' %(_val_f1, _val_precision, _val_recall))
    print(' — val_f1:', _val_f1)
    return

使用回调拟合方法

metrics = Metrics()

model.fit_generator(generator=generator_train,
                          steps_per_epoch=len(generator_train),
                          validation_data=generator_val,
                          validation_steps=len(generator_val),
                          epochs=epochs,
                          callbacks=[metrics])

有一些提示需要注意： 如果使用fit_generator（）方法进行训练，则只能使用显示的代码。另外，如果使用fit（）方法，可以尝试回调函数

都在那儿