Input Shape Error in Second-layer (but not first) of Keras LSTM

Question

I am trying to build an LSTM model, working off the documentation example at https://keras.io/layers/recurrent/

from keras.models import Sequential
from kera         


        

Answer 1

Thanks to patyork for answering this on Github:

the second LSTM layer is not getting a 3D input that it expects (with a shape of (batch_size, timesteps, features). This is because the first LSTM layer has (by fortune of default values) return_sequences=False, meaning it only output the last feature set at time t-1 which is of shape (batch_size, 32), or 2 dimensions that doesn't include time.

So to offer a code example of how to use a stacked LSTM to achieve many-to-one (return_sequences=False) sequence classification, just make sure to use return_sequences=True on the intermediate layers like this:

model = Sequential()
model.add(LSTM(32, input_dim=64, input_length=10, return_sequences=True))
model.add(LSTM(24, return_sequences=True))
model.add(LSTM(16, return_sequences=True))
model.add(LSTM(1,  return_sequences=False))

model.compile(optimizer = 'RMSprop', loss = 'categorical_crossentropy')

(no errors)