Understanding keras model.summary()

Question

I am trying to understand the model.summary() in keras, I have the code as:

model = Sequential([
        Dense(3,activation='relu',input_shape=(6,)),
        Dense(3,activation='relu'),
        Dense(1),
    ])
model.compile(optimizer='adam',
                 loss='mean_squared_error',
                  metrics=['mae','mape','mse','cosine']
                 )

And when I print(model.summary()) I get output as

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_16 (Dense)             (None, 3)                 21        
_________________________________________________________________
dense_17 (Dense)             (None, 3)                 12        
_________________________________________________________________
dense_18 (Dense)             (None, 1)                 4         
=================================================================
Total params: 37
Trainable params: 37
Non-trainable params: 0
_________________________________________________________________
None

I cannot understand the meaning of dense_16, dense_17 and dense_18 with respect to my described model input layers.

Answer 1

Those are just the names of the layer that were autogenerated by Keras. To name layers manually, pass a keyword argument name='my_custon_name' to each layer that you want to name. Note that layer names must be unique inside a model.

Layer names are useful for debugging and to get specific layers in code, for example using model.get_layer(layer_name).