我的损失函数如下：

loss = np.sum(np.square(A-B))

如何编写一个辅助函数来实现像Keras中的“提前停止”功能？

目的：

如果损失值上升或波动不大，则停止并获取A和B的值。

回答：

我查看了Keras的源代码，发现了提前停止的实现代码。我基于此创建了自己的回调函数：

class EarlyStoppingByLossVal(Callback):    def __init__(self, monitor='val_loss', value=0.00001, verbose=0):        super(Callback, self).__init__()        self.monitor = monitor        self.value = value        self.verbose = verbose    def on_epoch_end(self, epoch, logs={}):        current = logs.get(self.monitor)        if current is None:            warnings.warn("Early stopping requires %s available!" % self.monitor, RuntimeWarning)        if current < self.value:            if self.verbose > 0:                print("Epoch %05d: early stopping THR" % epoch)            self.model.stop_training = True

使用方法如下：

callbacks = [    EarlyStoppingByLossVal(monitor='val_loss', value=0.00001, verbose=1),    # EarlyStopping(monitor='val_loss', patience=2, verbose=0),    ModelCheckpoint(        kfold_weights_path, monitor='val_loss', save_best_only=True,         verbose=0    )]model.fit(    X_train.astype('float32'), Y_train,    batch_size=batch_size, nb_epoch=nb_epoch,    shuffle=True, verbose=1, validation_data=(X_valid, Y_valid),    callbacks=callbacks)