code2emb.py

# extract encoder
encoder_model = extract_encoder_model(seq2seq_Model)

# Freeze Encoder Model
for l in encoder_model.layers:
    l.trainable = False

#### Build Model Architecture For Fine-Tuning ####
encoder_inputs = Input(shape=(doc_length,), name='Encoder-Input')
enc_out = encoder_model(encoder_inputs)

# first dense layer with batch norm
x = Dense(500, activation='relu')(enc_out)
x = BatchNormalization(name='bn-1')(x)
# dense layer for output
out = Dense(500)(x)

# keras model object
code2emb_model = Model([encoder_inputs], out)