Really? There are certainly frameworks that you can use to achieve this performance in that amount of code, but I would be really interested to see that in vanilla JS/Python.
```js
const dotSign = (v1, v2) => v1.reduce((prev, x, i) => prev + x * v2[i], 1) > 0 ? 1 : -1
module.exports = (data, weights = Array(data[0].content.length).fill(0)) => {
for(const {label, content} of data) {
const delta = (label - dotSign(content, weights)) / 2;
weights = weights.map((x, i) => x + delta * content[i]);
}
return { perceive: vector => dotSign(vector, weights), weights };
}
I can upload an electron app that does this with mnist if interested.
MNIST has 60k training samples and 10k test samples. Are you using only 400 of them? Is 95% the accuracy on the test samples or on the same set of training samples? I believe when we talk about MNIST accuracy, we always refer to the accuracy on the 10k test samples.