[inglor]

Hey, sure - here's 9 lines of code that implement a perceptron. I get ~95% of precision after a second with 400 samples.

```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.

[attractivechaos]

> I get ~95% of precision after a second with 400 samples.

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.

[anentropic]

how long does does it take to get from 95% -> 98.8% though?

[lunula]

This. You can also get near 98% accuracy with vw and one quadratic interaction over the pixel space. It takes seconds. But that last .8%, forget about ever getting there with infinite training time.

[inglor]

About 10 seconds and 10 times the samples does that.

[quinnftw]

Is this not a binary perceptron? How do you plan to classify all 10 digits with this?

[inglor]

I cheated here (this _is_ a binary perceptron), but the conversion is super easy with one-vs-rest or one-vs-one (training all pairs and then doing pairwise comparison until we find the one that matches), that would still take ~20 seconds to train.