RNN model with 3 hidden layers In a paper, it mentioned: ANN, RNN, and LSTM NN are optimized to contain three hidden layers with 1000 hidden units in each layer.

I would like to model the RNN model in Keras. But my code fails in an error!

My code:

model=Sequential()
model.add(SimpleRNN(1000,input_shape=(320,15),activation='relu'))
model.add(SimpleRNN(1000))
model.add(SimpleRNN(1000))
model.add(Dense(1600))


Error:

    ValueError                                Traceback (most recent call last)
&lt;ipython-input-49-ff01ce62eb30&gt; in &lt;module&gt;()
      1 model=Sequential()
      2 model.add(SimpleRNN(1000,input_shape=(320,15),activation='relu'))
----&gt; 3 model.add(SimpleRNN(1000))
      4 model.add(SimpleRNN(1000))
      5 model.add(Dense(1600))
.....
....
...
..
.


ValueError: Input 0 is incompatible with layer simple_rnn_2: expected ndim=3, found ndim=2


How can I code for the RNN model which is optimized to contain three hidden layers with 1000 hidden units in each layer? 

Thank you so much
The shape should be 3d array --> (samples, timesteps, features)

It needs to add return_sequences=True in first two RNN layers.

model=Sequential()
model.add(SimpleRNN(1000,input_shape=(1,320*15),activation='relu', return_sequences=True))
model.add(SimpleRNN(1000, return_sequences=True))
model.add(SimpleRNN(1000))