LSTM_Clfn_finetuned.py 文件源码

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 self.monitor_op(current, self.best):
                self.best = current
                self.wait = 0
            else:
                if (self.wait == self.patience - 1) and self.anneal:
                        print 'Halving Learning Rate...'
                        K.set_value(self.model.optimizer.lr, K.get_value(self.model.optimizer.lr)/2)

                elif self.wait >= self.patience:
                    print('Epoch %d: early stopping' % (epoch))
                    self.model.stop_training = True
                self.wait += 1