joint_optimisation.py 文件源码

def on_epoch_end(self, epoch, logs={}):
        current = self.monitor(self.previous_weights, self.model.get_weights())
        self.previous_weights = self.model.get_weights()
        if current is None:
            warnings.warn('Early stopping requires %s available!' %
                          (self.monitor), RuntimeWarning)

        if np.less(current, self.threshold_value):
            if current == 0:
                self.model.stop_training = True
                if self.verbose > 0:
                    print('Epoch %05d: early stopping: ratio weights = 0' % (epoch))
            elif self.wait >= self.patience:
                if self.verbose > 0:
                    print('Epoch %05d: early stopping: ratio weights below %.4f' % (epoch, self.threshold_value))
                self.model.stop_training = True
            self.wait += 1
        else:
            self.wait = 0