You need to add return_sequences=True
to the first layer so that its output tensor has ndim=3
(i.e. batch size, timesteps, hidden state).
Please see the following example:
# expected input data shape: (batch_size, timesteps, data_dim)
model = Sequential()
model.add(LSTM(32, return_sequences=True,
input_shape=(timesteps, data_dim))) # returns a sequence of vectors of dimension 32
model.add(LSTM(32, return_sequences=True)) # returns a sequence of vectors of dimension 32
model.add(LSTM(32)) # return a single vector of dimension 32
model.add(Dense(10, activation='softmax'))
From: https://keras.io/getting-started/sequential-model-guide/ (search for “stacked lstm”)