如何防止Keras順序模型的過度擬合？

Question

我已經添加了輟學正則化。 我正在嘗試建立一個多類文本分類多層感知器模型。 我的模特：

model = Sequential([
                Dropout(rate=0.2, input_shape=features),
                Dense(units=64, activation='relu'),
                Dropout(rate=0.2),
                Dense(units=64, activation='relu'),
                Dropout(rate=0.2),
                Dense(units=16, activation='softmax')])

我的model.summary（）：

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dropout_1 (Dropout)          (None, 20000)             0
_________________________________________________________________
dense_1 (Dense)              (None, 64)                1280064
_________________________________________________________________
dropout_2 (Dropout)          (None, 64)                0
_________________________________________________________________
dense_2 (Dense)              (None, 64)                4160
_________________________________________________________________
dropout_3 (Dropout)          (None, 64)                0
_________________________________________________________________
dense_3 (Dense)              (None, 16)                1040
=================================================================
Total params: 1,285,264
Trainable params: 1,285,264
Non-trainable params: 0
_________________________________________________________________
None
Train on 6940 samples, validate on 1735 samples

我正進入（狀態：

Epoch 16/1000
 - 4s - loss: 0.4926 - acc: 0.8719 - val_loss: 1.2640 - val_acc: 0.6640
Validation accuracy: 0.6639769498140736, loss: 1.2639631692545559

驗證准確性比准確性低約20％，並且驗證損失遠高於訓練損失。

我已經在使用dropout正則化，並使用epochs = 1000，批處理大小= 512並在val_loss上提前停止。

有什么建議么？