FAQ: Image Classification - Adding Convolutional Layers to Your Model

This community-built FAQ covers the “Adding Convolutional Layers to Your Model” exercise from the lesson “Image Classification”.

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This exercise can be found in the following Codecademy content:

Build Deep Learning Models with TensorFlow

FAQs on the exercise Adding Convolutional Layers to Your Model

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I dont understand how the 8 newly created “images” after the first 8 5x5 filters is processed afterwards.

codecademy is quoting “The number of filters used in the previous layer becomes the number of channels that we input into the next!”

so how do i pass in 8 channels into 4 filters in the next step? Does every filter go on every channel from the input or is the volume just moved from the front to the back, thus not every filter is applied on every input-channel?

Andrew Ng states in his course (https://www.coursera.org/lecture/convolutional-neural-networks/convolutions-over-volume-ctQZz), that the amount of filters used must match the amount of channels from the input, which is not the case in this example here (8!=4).

Any help is highly appreciated! Thanks :slight_smile: