autoencoder.py

def create_encoder(latent_dim, cat_dim, window_size, input_dim):
    input_layer = Input(shape=(window_size, input_dim))
    
    code = TimeDistributed(Dense(64, activation='linear'))(input_layer)
    code = Bidirectional(LSTM(128, return_sequences=True))(code)
    code = BatchNormalization()(code)
    code = ELU()(code)
    code = Bidirectional(LSTM(64))(code)
    code = BatchNormalization()(code)
    code = ELU()(code)
    
    cat = Dense(64)(code)
    cat = BatchNormalization()(cat)
    cat = PReLU()(cat)
    cat = Dense(cat_dim, activation='softmax')(cat)
    
    latent_repr = Dense(64)(code)
    latent_repr = BatchNormalization()(latent_repr)
    latent_repr = PReLU()(latent_repr)
    latent_repr = Dense(latent_dim, activation='linear')(latent_repr)
    
    decode = Concatenate()([latent_repr, cat])
    decode = RepeatVector(window_size)(decode)
    decode = Bidirectional(LSTM(64, return_sequences=True))(decode)
    decode = ELU()(decode)
    decode = Bidirectional(LSTM(128, return_sequences=True))(decode)
    decode = ELU()(decode)
    decode = TimeDistributed(Dense(64))(decode)
    decode = ELU()(decode)
    decode = TimeDistributed(Dense(input_dim, activation='linear'))(decode)
    
    error = Subtract()([input_layer, decode])
        
    return Model(input_layer, [decode, latent_repr, cat, error])