If you visualize your Keras model, you will see something like this. It says the name of your model is "sequential_1" and you've got a couple of Dense layers with some trainable parameters.
Every layer of the Keras model has a unique name. e.g. "dense_1", "dense_2" etc.
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 128) 100480
_________________________________________________________________
dense_2 (Dense) (None, 64) 8256
_________________________________________________________________
dense_3 (Dense) (None, 32) 2080
_________________________________________________________________
dense_4 (Dense) (None, 1) 33
=================================================================
Total params: 110,849
Trainable params: 110,849
Non-trainable params: 0
_________________________________________________________________
Keras has a function for getting a layer with this unique name. So you need just to call that function and pass a name for the layer.
layer = model.get_layer('dense_1')
Also, a Keras model's layer has some properties inside of it. Like the input, output, weights, parameters, etc. Take a look at this doc. The are other discussions in this platform about the layers of Keras model.