I am trying to understand the architecture of my keras model implemented by the sequential model.
Here is a piece of the code :
model = Sequential([
#block1
layers.Conv2D(nfilter,(3,3),padding="same",name="block1_conv1",input_shape=(64,64,3)),
layers.Activation("relu"),
layers.BatchNormalization(), .......
My question is why the two parameters input_shape and name are declared in the layer conv2D while they are not included in the set of defined parmeters for Conv2D() in this link https://keras.io/api/layers/convolution_layers/convolution2d/