Running a trained LSTM network is VERY slow (around 15ms for each result)


## _What_ is wrong?

Running a trained LSTM network on each input is VERY slow.

## _Where_ does it happen?

Just running node on my machine. See the code below.

## _How_ do we replicate the issue?

Here is a code that reproduce the issue

```js
const brain = require('brain.js')

const net = new brain.recurrent.LSTM()

const trainingOptions = {
// Defaults values --> expected validation
iterations: 200, // the maximum times to iterate the training data --> number greater than 0
errorThresh: 0.005, // the acceptable error percentage from training data --> number between 0 and 1
log: true, // true to use console.log, when a function is supplied it is used --> Either true or a function
logPeriod: 100, // iterations between logging out --> number greater than 0
learningRate: 0.3, // scales with delta to effect training rate --> number between 0 and 1
// Momentum is missing in the type declaration. See : https://github.com/BrainJS/brain.js/issues/806
// momentum: 0.1, // scales with next layer's change value --> number between 0 and 1
callbackPeriod: 10, // the number of iterations through the training data between callback calls --> number greater than 0
timeout: Infinity // the max number of milliseconds to train for --> number greater than 0
}

for (let loop = 0; loop < 15; loop++) {
net.train(
[
{ input: 'I feel great about the world!', output: 'happy' },
{ input: 'The world is a terrible place!', output: 'sad' }
],
trainingOptions
)
let oldDate = Date.now()
for (let i = 1; i <= 3000; i++) {
net.run('I feel great about the world!')
if (i % 1000 === 0) {
console.log('Each run took about', (Date.now() - oldDate) / 1000, 'ms to complete')
oldDate = Date.now()
}
}
}
```

Here is a sample output:


Sometimes, with some training, you get a better result. Check out the last 2 runs

```
iterations: 0, training error: 5919581.560087977
iterations: 100, training error: 0.10638371212248059
Each run took about 12.506 ms to complete
Each run took about 13.651 ms to complete
Each run took about 14.936 ms to complete
iterations: 0, training error: 0.08807550982775042
iterations: 100, training error: 0.07916010305806237
Each run took about 13.474 ms to complete
Each run took about 13.946 ms to complete
Each run took about 13.448 ms to complete
iterations: 0, training error: 0.07403161373261806
iterations: 100, training error: 0.07549036517869813
Each run took about 13.23 ms to complete
Each run took about 13.455 ms to complete
Each run took about 13.073 ms to complete
iterations: 0, training error: 0.07460482166846667
iterations: 100, training error: 0.07202732715101895
Each run took about 12.981 ms to complete
Each run took about 13.769 ms to complete
Each run took about 16.111 ms to complete
iterations: 0, training error: 0.0656084483947608
iterations: 100, training error: 0.06511340031555182
Each run took about 14.251 ms to complete
Each run took about 14.39 ms to complete
Each run took about 15.322 ms to complete
iterations: 0, training error: 0.06458603058643311
iterations: 100, training error: 0.06371472513073857
Each run took about 0.384 ms to complete
Each run took about 0.462 ms to complete
Each run took about 0.437 ms to complete
iterations: 0, training error: 0.17024263005272064
iterations: 100, training error: 0.10909141758992613
Each run took about 2.994 ms to complete
Each run took about 3.489 ms to complete
Each run took about 3.322 ms to complete
```

## Expected behavior (i.e. solution)

From my understanding, trained networks should be quite fast at doing what they were trained to do. Here, the training seems faster than running a case.

## Version information

### Nodejs: 14.19.3

### Browser: N/A

### Brain.js: 2.0.0-beta.15

## _How_ important is this (1-5)?

5

## Other Comments

I see that many people complain about training being slow, but I think that this is expected. However, I have never heard about running a trained network being slow. It goes against my understanding of neural nets.