Hey I love this but watch out with this kind of stuff. And by stuff I mean training RNNs to do classification on very small (100s) sets of images. (Also you didn't mention your train/test split?). Which just means, get more data and make a smaller network by likely re-using a trained network.

I highly recommend the paper "Understanding deep learning requires rethinking generalization" (https://arxiv.org/abs/1611.03530), which shows how amazing some RNNs are at memorizing pure noise.

Would love to see different applications of DL on historical visual art: GANNs making new paintings in different styles, mapping meaning-space between artists, style transfer, "is this painting done by this artist?" ...