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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?
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).
