def add_batch_normalization(model_path):
    model = load_model(model_path)
    weights = model.get_weights()
    dense_idx = [index for index,layer in enumerate(model.layers) if type(layer) is Dense][-1] #get indices for dense layers
    x = model.layers[dense_idx -1].output
    new_model = Model(inputs = model.input, outputs = x)
    x= BatchNormalization()(new_model.output)
    x = Dense(2048, activation='relu')(x)
    x =BatchNormalization()(x)
    x = Dropout(.10)(x)
    x=  Dense(512, activation='relu')(x)
    x= BatchNormalization()(x)
    predictions = Dense(num_of_classes, activation='softmax')(x)
    new_model = Model(inputs= new_model.input, outputs=predictions)
    print(new_model.summary())
    model.set_weights(weights)
    return new_model