seq2seq.py

# Encoder
source = Input(shape=(None,), dtype='int32', name='source')
embedded = Embedding(output_dim=128, input_dim=train.source_vocab_size(), mask_zero=True)(source)
last_hid = LSTM(output_dim=128)(embedded)

# Decoder
repeated = RepeatVector(train.target.padded.shape[1])(last_hid)
decoder = LSTM(output_dim=128, return_sequences=True)(repeated)
output = TimeDistributed(Dense(output_dim=train.target_vocab_size(), activation='softmax'))(decoder)
model = Model([source], output=[output])