tensorflowconv-neural-networkrelu

Is there any difference between relu as an activation function or a layer?

Is there any difference between relu as an activation function or a layer? For example

Conv2D(filters=8, kernel_size=(3, 3), activation='relu',padding='SAME', name='conv_2')

or

Conv2D(filters=8, kernel_size=(3, 3),padding='SAME', name='conv_2'),
ReLU()
Solution

  • No practical difference, except on the latter you could assign/set parameters to the Relu()*. In the first case, I believe it uses the default parameters.

    *https://www.tensorflow.org/api_docs/python/tf/keras/layers/ReLU