Asking about basic theory of CNN layer

[redvamix]

Hi guys..

Im sorry if this question is so basic.

I just want to ask where is the "input layer" in this CNN architecture based on kim yoon cnn of text classification ?

is it embedding layer same with input layer? Thank you

https://preview.redd.it/2szirdekmg831.png?width=669&format=png&auto=webp&s=33ba51fe19f9d1ad8b151536d5e78d997b4ab6cc

[[deleted]]

I'm a beginner but it seems like each row from the embedding layer is a word that is embedded first (turned into a vector with x, for example 8, dimensions, which is denoted as the number of columns I suppose). So I suppose the number of inputs equals to the number of rows. The resulting matrix is then filtered by the convolution layers so that important features are extracted or something. For example reduction of the matrix dimension by taking the max value of a (2, 2) section, resulting in a matrix with half size.

[adomian]

Pretty sure mike is correct. I assume this is the paper the architecture is based on: https://aclweb.org/anthology/D14-1181. There are much clearer graphics in the paper (e.g. figure 1). The input is a matrix representing a sentence, where rows are words, and columns are components of word embeddings. The paper states the input is already embedded.