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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/

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Bot the name and input_shape come from the Layer class which Conv2D inherited. In the doc you provide, they are implicitly in **kwargs

| improve this answer | |
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  • $\begingroup$ I found the name parmeter but input_shape no $\endgroup$ – baddy Aug 7 at 9:39

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