model.py

from keras.models import Model, Input
from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional
import keras as k
from keras_contrib.layers import CRF

input = Input(shape=(672,))
word_embedding_size = 150

# Embedding Layer
model = Embedding(input_dim=n_words, output_dim=word_embedding_size, input_length=672)(input)

# BI-LSTM Layer
model = Bidirectional(LSTM(units=word_embedding_size, 
                           return_sequences=True, 
                           dropout=0.5, 
                           recurrent_dropout=0.5, 
                           kernel_initializer=k.initializers.he_normal()))(model)
model = LSTM(units=word_embedding_size * 2, 
             return_sequences=True, 
             dropout=0.5, 
             recurrent_dropout=0.5, 
             kernel_initializer=k.initializers.he_normal())(model)

# TimeDistributed Layer
model = TimeDistributed(Dense(n_tags, activation="relu"))(model)  

# CRF Layer
crf = CRF(n_tags)

out = crf(model)  # output
model = Model(input, out)