如何防止Keras顺序模型的过度拟合？

Question

I am already adding dropout regularization. I am trying to build a multiclass text classification multilayer perceptron model. My model:

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')])

My 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

I am getting:

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

The validation accuracy is ~20% less than the accuracy, and the validation loss is way higher than the training loss.

I am already using dropout regularization, and using epochs = 1000, batch size = 512 and early stopping on val_loss.

Any suggestions?