# [week 1 Programming Assignment #2] ZeroPadding2D with padding 3, input shape of 64 x 64 x 3

Hi,

I have a doubt regarding the last assignment of Week 1.

When defining the model, we are required to specify an input shape of 64 x 64 x 3.

However, it is unclear to me how to do this. Obviously, I have looked at the documentation (tf.keras.layers.ZeroPadding2D  |  TensorFlow Core v2.4.1) and Googled quite a bit, but would appreciate some pointer or some understanding of what is going on because I have been stuck here for the whole afternoon.

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Solved. For future reference: it goes as an argument inside the tfl.ZeroPadding2D function

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Hi vaccam1,

input_shape can be used as a parameter in ZeroPadding2D. Unfortunately this is not mentioned in the documentation.

I hope this brightens your afternoon!

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Hi vaccam1,

Thanks and good luck with the rest of the course!

Done, thanks and take care

Hi @reinoudbosch, It will help me to sleep now…!!

@nomi This info will also help to grab some pakoras that have become cold as I was stuck here for a while now. Thanks @reinoudbosch

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But how can you explain this usage of a class
`y = tf.keras.layers.ZeroPadding2D(padding=1)(x)`

taken from tensorflow documentation

I mean : ZeroPadding2D is a class, so with any class (say ‘y’), we can use : y(some args) to instantiate an object of the class type.
But what does this → y(some args)(something) mean?

@ashish_learns you will understand this while doing assignment of functional model. Actually it is like pipeline, in `y = tf.keras.layers.ZeroPadding2D(padding=1)(x)` assume x as a input that come from previous layer and in next layer, let suppose `z = tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=None, padding='valid')(y)` will be placed on x.

Okay.
I have been using Python for more than 4 years and have never come across this type of programming construct, where apart from the usual () for instantiating object of a class, we also have an extra () just after this —> tf.keras.layers.ZeroPadding2D(padding=1)(x)

@ashish_learns yeah me too. I have also seen this format in only tensorflow.