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One can read everywhere on internet or in books that in convoluted neural networks, between convolution layers and the first fully connected layer, you should flatten your data.

I managed to understand that Dense layer (=first fully connected layer) requires 1d (= flattened = linearized) data.

However, I failed to figure out WHY dense layer specificaly requires 1d data.

Could you share your explanation if you have a didactical one?

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    $\begingroup$ It’s just a way to visualize what’s going on that gets coded into the software. You can take a 2D array to a fully-connected layer with no issue (in terms of the math, not necessarily the software). Try drawing it out. This drawing might help you understand what’s going on in a convolutional neural network. You could draw the layer fully-connected with no issue. $\endgroup$
    – Dave
    Jul 5 at 16:52
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Requiring a fully connected layer to only accept one dimensional (a vector) makes for a consistent interface between layers. Strict inputs makes the the code more straightforward. Otherwise a fully interconnected layer might have to accept arbitrary inputs (e.g., n-dimensional).

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  • $\begingroup$ Hello @Brian Spiering! Thanks for your answer. I am not entirely sure to understand what you mean by "Strict inputs makes the code more straifghforward". Could you clarify ? Thank you ! $\endgroup$ Jul 6 at 8:18

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