Add layers to a pretrained model wihout creating a sequential model

Question

I am using a pretrained Resnet50 (from the tensorflow.keras.applications package) and finetune it for multilabel classification (with 2 classes), and I'd like to extract the Saliency maps from the finetuned model.

To make a classifier, i add 2 dense layers to the Resnet model, creating a new sequential model as follow :

    self.model = tf.keras.Sequential([
        resnet50,
        layers.Dense(1024, activation='relu', name='hidden_layer'),
        layers.Dense(2, activation='sigmoid', name='output')
    ])

but my problem is that the resnet50 becomes a "single layer", like each layer is no more accessible : the model summary only contains 3 layers. I'd like to know if there is a way to add layers to a functional model without creating a sequential model, in order to be able to access each layer of the resnet model.

Thank you in advance,