diff --git a/lib/model/seq2seq.py b/lib/model/seq2seq.py
index 7b36122..7b137d6 100644
--- a/lib/model/seq2seq.py
+++ b/lib/model/seq2seq.py
@@ -24,14 +24,18 @@ def __init__(self, config):
         else:
             devices = list('/gpu:' + x for x in config.devices)
 
+        if config.ensemble:
+            inputs = Input(shape=(None,))
+            # TODO: set indices dynamically
+            reconstructed_inputs = Reshape((128,), input_shape=(config.dataset_size,))(inputs)
+            encoder_inputs = Lambda(lambda x: x[:, :50])(reconstructed_inputs)
+            decoder_inputs = Lambda(lambda x: x[:, 50:])(reconstructed_inputs)
+
         with tf.device(devices[0]):
             initial_weights = RandomUniform(minval=-0.08, maxval=0.08, seed=config.seed)
-            encoder_inputs = Input(shape=(None,))
-            reconstructed_inputs = Reshape((128,), input_shape=(config.dataset_size,))
             encoder_embedding = Embedding(config.source_vocab_size, config.embedding_dim,
                                           weights=[config.source_embedding_map], trainable=False)
-            # TODO: set indices dynamically
-            encoder_inputs = Lambda(lambda x: x[:, :50])(reconstructed_inputs)
+            if not config.ensemble: encoder_inputs = Input(shape=(None, ))
             encoder_embedded = encoder_embedding(encoder_inputs)
 
             if recurrent_unit == 'lstm':
@@ -50,7 +54,7 @@ def __init__(self, config):
                 encoder_states = [state_h]
 
         with tf.device(devices[1]):
-            decoder_inputs = Input(shape=(None,))
+            if not config.ensemble: decoder_inputs = Input(shape=(None, ))
             decoder_embedding = Embedding(config.target_vocab_size, config.embedding_dim,
                                           weights=[config.target_embedding_map], trainable=False)
             decoder_embedded = decoder_embedding(decoder_inputs)
@@ -70,6 +74,12 @@ def __init__(self, config):
                         decoder)  # Use the final encoder state as context
                 decoder_outputs, decoder_states = decoder[0], decoder[1]
 
+            # if config.ensemble:
+            #     decoder_reshape = Reshape((128, self.config.target_vocab_size)) #?
+            #     decoder_slice = Lambda(lambda x: x[:, 50:, :])
+            #     decoder_outputs = decoder_reshape(decoder_outputs)
+            #     decoder_outputs = decoder_slice(decoder_outputs)
+
             decoder_dense = Dense(config.target_vocab_size, activation='softmax')
             decoder_outputs = decoder_dense(decoder_outputs)