I understand how convolution works but I don’t get how 1D convolutions are applied to 2D data.

In this example you can see a 2D convolution in a 2D data.

But how it would be if was a 1D convolution?

Just a 1D kernel sliding in the same way? And if the stride was 2?Thank you!

**Answer**

Let $x_1, …,x_n $ be a sequence of vectors (e.g., word vectors). Applying a convolutional layer is equivalent to applying the same weight matrices to all n-grams, where $n$ is the height of your filter. E.g., if $n=3$, you can visualize it as follows:

For a slightly more mathematical explanation, you can check out

*Ji Young Lee, Franck Dernoncourt. “Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks“. NAACL 2016*. section 2.1.2:

**Attribution***Source : Link , Question Author : Gustavo , Answer Author : Franck Dernoncourt*