emb_model.py

# model creation
input_layers = {
    colname: tf.keras.layers.Input(name=colname, shape=(1,), dtype=tf.int32)
    for colname in ['user_id', 'movie_id']
}


user_id_input = input_layers['user_id']
movie_id_input = input_layers['movie_id']

user_emb = tf.keras.layers.Embedding(len(user_id_enc.classes_), 64, name='user_emb')(user_id_input)
movie_emb = tf.keras.layers.Embedding(len(movie_id_enc.classes_), 64, name='movie_emb')(movie_id_input)
features = tf.keras.layers.Concatenate()([user_emb, movie_emb])

hidden = tf.keras.layers.Dense(64, activation='relu')(features)
model_out = tf.keras.layers.Dense(1, activation='relu')(hidden)
model = tf.keras.Model(input_layers, model_out)