<p><a href="https://arxiv.org/abs/1706.03762" rel="nofollow noreferrer">All you need is attention</a> will the solution you are looking for. The mechanism will help you to focus on certain features/blocks in your inputs. There is a <a href="https://medium.com/@moshnoi2000/all-you-need-is-attention-computer-vision-edition-dbe7538330a4" rel="nofollow noreferrer">medium post</a> that gives a tutorial of how to apply this mechanism to images. </p>
How do I ignore certain data points when using convolutional LSTM's?
<p>I am looking to predict ocean current vorticity using kinetic energy and sea surface temperature. My data consists of satellite kinetic energy readings and surface temperature readings in the gulf stream region. I plan to use a hybrid neural network which combines a recurrent architecture (LSTM) with a convolutional network model.</p>

<p>My dataset consists of daily kinetic energy and temperature readings from 1996 to 2018, for a total of 8036x80x120 grids. For example, given the <a href="https://i.stack.imgur.com/uOhlS.jpg" rel="nofollow noreferrer">kinetic energy</a>, and <a href="https://i.stack.imgur.com/PVTdo.png" rel="nofollow noreferrer">temperature</a>, I want the NN to predict the <a href="https://i.stack.imgur.com/z9snd.jpg" rel="nofollow noreferrer">vorticity</a>.</p>

<p>My question is, how can I influence my neural network to ignore/discount land terrain and only focus on the ocean data. The land terrain data is stored in my image arrays as NaN values.</p>

<p>I am using PyTorch. </p>