Implementing Seq2Seq in Keras with State Transfer and Teacher Forcing

keras_encoder_decoder.py

from keras.models import Model
from keras.layers import Input, LSTM, Dense, TimeDistributed

inputs = Input(batch_shape=(8,16,1024))
layer = LSTM(256, return_state=True, return_sequences=True)
outputs = layer(inputs)

# TODO Add Teacher Forcing
output, state = outputs[0], outputs[1:]
output = LSTM(256)(output, initial_state=state)
model = Model(inputs, output)
model.summary()