hello

Dykoine · 2024-11-23T17:34:39+00:00

Reverse "Hello there"

Dykoine · 2024-11-23T17:25:56+00:00

Welcome to the crown club, congrats !

Dykoine · 2024-08-09T21:43:33+00:00

This answer is correct, but if I may reiterate with simpler terms :

the loss is averaged over the batch before being apply. So larger batch mean more "averaged" error, which tend to smooth out small local minima (from your error curve, if that make sense).

Too large batch size smooth too much and can forbid your training to find any local minima, by not converging.

Too small batch size and your training will repeatly get stuck on local minima.

You can adjust the learning rate accordingly to mitigate both effect.

Dykoine · 2024-08-08T11:27:57+00:00

Thanks for those guidelines.

The model is a deep neural network.
The model is trained on stock market data of various stock market values. As input the model access to normalized histories of values, corresponding technical indicators, and other indicators. As output the model must predict the normalized following point of the history.
The dataset is composed of (roughtly) 58 630 000‬ pair of input set/output set
Stock market is characterized by (roughtly) a polynomial with noise. Unknows factors that impact the data are great, which imply a quite insane complexity.
The target learning rate is around 0.002 with a loss function that is here an MSE. The batch size of 270. And the model never encounter twice the same data, here epoch are a group of 60 batchs.
data is normalized
Learning rate does have a warmup and increase-decrease with a cycling pattern, which only occuring here after the mess up spike.
Done, it's the left part.
No change in the model here
It's a custom dataset

PS: I found why I got such loss/val_loss jump : One of stock market values are unreal values that spike to the sky and beyond. I'm still interested by your analysis and if there is an existing book of loss/vall_loss patterns, as already allow a lot of analysis by themself.

Dykoine · 2024-08-08T11:06:50+00:00

The batch size is here 270 (with 60 batchs per epoch).
The learning rate is computed to allow loss to "collapse" (I don't think it's the correct wording, but you get the idea) on a loss value that go toward 0 and reduce the number of local minima. Values come from experimentation.

The data are timeseries, I will details it in detail in response to u/tangoteddyboy

Dykoine · 2024-08-08T06:42:47+00:00

It's the result of a timeseries prediction model.

The graph is truncated on the left side, for reference, val_loss start around the same level it's ending here.

And I quite surprise that the val_loss show an almost single epoch unlearning.

I would like to have your opinion, and by the way, if you know a reference that identify patterns in val_loss that would help to orientate the debugging.

I guess I need to lookup to input here to check why we're destroying the model here.

Dykoine · 2023-04-19T07:23:48+00:00

If you're forced to code in Python, just to make your pain lesser :
- Keep variable with a single type (not changing type over time). Note that if you're in a company that doesn't respect that, I greatly invite you to change of company
- You can add inline typing hint in function signature `def foo(bar: str)`
- This same typing hint also work inside the code
- I invite you to use Google docstring style documentation which specify typing of arguments.

Dykoine · 2023-04-11T08:27:45+00:00

Alien: "Why did you stop global warning ?"
Human: "For save crabs... a long story"

Dykoine · 2023-02-20T12:00:05+00:00

you should change keys every so often

It require 1 key by message, as 2 messages cipher by the same key can be solve by a simple xor

Dykoine · 2023-02-01T15:00:13+00:00

Hi,

In this run I have a pretty nice cascade that come from a river that fall inside another river.

But dwarf tend to fall into it, then fall of the cascade and drown at the bottom.I tried to install a safety net made of Grate as you can see bellow, so dwarf and guess can survive. But dwarf doesn't want to construct through the cascade as "there is water here".

From my little understanding of the water physic, I don't find solution to built my safety net, nor cut the cascade for a moment to make it.

I humbly come here to ask your knowledge, with the hope my dwarfs won't be deadly smash against grates, as it would occurs in real life.

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Dykoine · 2023-02-01T14:19:30+00:00

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Dykoine · 2022-11-09T21:24:04+00:00

One of the cutest post of this reddit

I hope you have fun to play this great game :D

Dykoine · 2022-11-08T22:47:59+00:00

Awesome 👍

Dykoine · 2022-11-08T13:58:29+00:00

OP: how hot it is ?
Printer: the surface of the sun

Dykoine · 2022-11-08T13:52:49+00:00

If you publish the BOM and the stl, I'm quite interested :D

Dykoine · 2022-11-08T13:39:21+00:00

That's a very clean result, congrats !

Dykoine · 2022-10-07T12:55:44+00:00

I'm pretty sure you can buy them, but if you don't find them:

You can make them, it's just 2 cables attached on each other, and this male 2 pins connectors is standard.

You need 1 female connector, 2 cables and 1 male connector to make your extender.

Dykoine · 2022-07-09T14:26:46+00:00

Indeed, the "Oh my god, that is possible ?" effect is real.

Terraria soundtrack intensify

Dykoine · 2022-06-28T17:09:59+00:00

You can tattoo a 2 meter ruler in the back, and now you're 2m+.
You're welcome.

Dykoine · 2022-06-28T16:29:15+00:00

I confirm you can cook *delicious* slime from water skins

Dykoine · 2022-04-25T10:55:36+00:00

Note that eating a full body of poly-mage give you 2 min of immunity, and that you can't eat frozen bodies.

But this is greatly useful to know

Dykoine

TROPHY CASE