如何在Keras中将ModelCheckpoint与自定义指标一起使用?

是的，有可能.

按照文档中的说明定义自定义指标:

Define the custom metrics as described in the documentation:

import keras.backend as K

def mean_pred(y_true, y_pred):
    return K.mean(y_pred)

model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy', mean_pred])

要检查所有可用的指标，请执行以下操作:

To check all available metrics:

print(model.metrics_names)
> ['loss', 'acc', 'mean_pred']

通过monitor将度量标准名称传递给ModelCheckpoint.如果要在验证中计算指标，请使用val_前缀.

Pass the metric name to ModelCheckpoint through monitor. If you want the metric calculated in the validation, use the val_ prefix.

ModelCheckpoint(weights.{epoch:02d}-{val_mean_pred:.2f}.hdf5,
                monitor='val_mean_pred',
                save_best_only=True,
                save_weights_only=True,
                mode='max',
                period=1)

请勿将mode='auto'用于自定义指标.理解为什么此处.

Don't use mode='auto' for custom metrics. Understand why here.

我为什么要回答自己的问题?检查此..