Chainman: Never give up
dscientist: Never surrender, never look back
Chainman: never gonna give you up
Alemhan: never gonna let you down
ASW: CoC is basically just AP computer science tests lol
dscientist: ok, but what is it? I am in 2 rooms CoC+aLongNumber.
sscoot13: im just learning programming and when i do these clash of codes im just amazed at how compact other peoples code is compared to me lol
bg117: lol same
bg117: literally theirs is one-liner
KiwiTae: sscoot13 patpat
sscoot13: patpat?
walleta: b
walleta: bb
bg117: but i still get 100% for some reason
sscoot13: haha same lol
MatasSiu1E: Ching Chong
bg117: stfu racist
ArtLiteracyStory: yo this is such a good way to learn code
ArtLiteracyStory: So fun
bg117: ya i agree
jrke: i have an issue the vector size in c++ gets unexpectedly very big causing timeouts to me
jrke: why does this happen
ddreams: perhaps you're overwriting it elsewhere
KiwiTae: got a leak somewhere ah~
nicola: :leak:
jrke: are submissions not getting updated?
ddreams: ?
nicola: Which one?
ddreams: is there a problem with the site?
ddreams: some vague problem, somewhere
ddreams: pls help
jrke: for OOC my all battle completed but rank didn't changed at any moment
Hebo: is the site borked or is it just me
nicola: Oops...
jrke: thibaud said in discord that they are dowloadind database after challenge so there can 2 interuptions in service
jrke: "we're downgrading the DB after the challenge. You can expect 2 short interruptions of service"
Hebo: let the bass cannon kick it
ArtLiteracyStory: hello world
MXgms: hi
LinhT.Nguyen: lo world
Peached: brb back to the website
Peached: after like a year
Peached: gonna update my profile
Cybersick: Did you see the code?
Peached: why is the website failing in everything
jrke: they are downloading DB so interuption in service for now
Peached: for how long
jrke: not sure can be done in a min or hour or two hour
Peached: ok
jrke: i don't know that
Peached: imma go get dinner then
jrke: :thumbsup:
AqilM: hallo
AqilM: wie geht`s Ihnen?
AntiSquid: #de
YahyaBahjaoui: hy
YahyaBahjaoui: hey
Westicles: Now the default for code ala mode made it to brone :thinking:
Westicles: bronze
Peached: im back is it done
jacek: yeah, "WAIT" is sufficient for bronzee
zakaria-wina: Hi !
Peached: is the db thing done
b0ba5: knock knock
Peached: come in
Peached: lawl
Westicles: Oh, I see. Took it 3 months to get there though
jacek: wait 6 months and youll get silver
Westicles: I won't hold my breath
AntiSquid: you wouldn't last for more than a few minutes anyway
Alshock: either that or he'd hold it for a very long time
KalamariKing: are you assuming Westicles is mortal
AntiSquid: unless it's a chatbot, it's mortal
Westicles: KalamariKing , nice trick
Westicles: Got in trouble for too much pinging?
AntiSquid: "degenerate furry" :thinking:
jacek:
jacek: :thinking:
Greg_3141: why wont code a la mode load
KalamariKing: anti lol
KalamariKing: Westicles there's a zero-width space, didnt wanna ping
KalamariKing: Westicles
AntiSquid: you can turn off the ping notification westi cles , or you prefer breaded kalamari rings ?
AntiSquid: KalamariRings ping
jetenergy: hey, i have a small question concering "Code your own Automaton". I think im having trouble understanding the question regarding the most powerful word, does anybode know?
KalamariKing: wdym?
jetenergy: in the description they are talking about "chosing the most powerful word" but i have no idea what they mean by powerful, do they mean the word with the most children or the longest word
Uljahn: do you mean "the most likely"?
Westicles: most occurrences, then alphabetical
jetenergy: aha thank you westicles
MSmits: hey guys, I think I'm done with my TTT experimentation. Did a bunch of tests with keras and got some nice results
MSmits: https://imgur.com/a/FjaX0Y4
MSmits: https://imgur.com/a/1tUItJF
BlaiseEbuth: There's points. And curves. Blue and orange.
MSmits: indeed
jacek: lost games?
MSmits: second one is games played vs random player. Bot the NN and the random takes any immediate winning moves automatically.
MSmits: if no winning moves available, the random player randoms and NN uses network
AntiSquid: so what next ?
reCurse: Half of machine learning is about making pretty graphs, so good job
MSmits: thanks :)
AntiSquid: EDA is more than that @_@
MSmits: you can see it overfitting at some point, but i think there's a limit to this as you said reCurse
MSmits: I think generalization is not possible with so little data
MSmits: the bot is trained on 90% of states and the other 10% are validation
jacek: so little data... 100%?
MSmits: but the validation data is just not similar enough to training data
MSmits: because there is so little of it
MSmits: also because it's TTT, you expect it to play perfectly
MSmits: even though you'd never do that in a real game
MSmits: so the bar is too high
AntiSquid: are you sure you need validation data for this ?
MSmits: well if you're practicing it makes sense
MSmits: but if you're just looking for a good bot, then no
MSmits: because all possible data is available
MSmits: so generalization is pointless
reCurse: It's always a good idea to have some sort of clue about generalization yes
MSmits: yeah, for practicing machine learning this was a good idea
MSmits: it was quite obvious that with a network too big, it overfits. Nice learning experience
jacek: nice machine learning experience eh
MSmits: it didnt do that much better than my handcoded one. Adam optimizer was a big boost
AntiSquid: how many hidden layers in the ends ? it just says hidden nodes
reCurse: Probably because your SGD wasn't well tuned enough
MSmits: mean squared error over absolute was also a bit better
MSmits: yeah probably reCurse
MSmits: AntiSquid 1
MSmits: i could get better results sometimes when i added another layer
MSmits: but it's kinda hard to make graphs like these if the number of layers is also a variable
AntiSquid: you add color or shape as 3rd dimension
MSmits: sure, yeah and i would, if TTT was my goal
MSmits: oh
MSmits: i ran into an issue
AntiSquid: for graphs
MSmits: playing games with this network was extremely slow
MSmits: slower than with my handcoded one
reCurse: Python is slow? :o
MSmits: they are both python
AntiSquid: too many nodes
reCurse: Ah
MSmits: network was over 10x slower with Keras
MSmits: even if i had 1 hidden node
MSmits: instead of 20
reCurse: Yeah there's some overhead to API calls
reCurse: Especially if you use GPU
MSmits: no gpu. I think there is a problem with conversion of input to tensor
MSmits: and it does 30 predictions per game
MSmits: 1000 games means 30k predictions
jacek: so whats next step? RL?
reCurse: Maybe, python is a nightmare to profile in my experience
MSmits: jacek not sure, Iwas thinking maybe experimenting with oware supervised learning before trying it solely by selfplay
MSmits: that will require me to have an inferer so thats a lot of work already
MSmits: (in c++ i mean)
MSmits: i have a lot of data from meta mcts, so supervised should be workable
jacek: alright. i started with SL somewhat too
MSmits: expect this to take a month or so though, dont ask me every day :P
MSmits: TTT was easy
AntiSquid: is it ready yet ?
MSmits: lol
reCurse: Automaton2000, please learn to ping MSmits about it every day
Automaton2000: here is the full code
reCurse: Thanks?
MSmits: oh right jacek you taught your NN to predict what your other bot found with a search
MSmits: I remember that
ikustom: bruuh
AntiSquid: 20 blocked calls from the same number in just 1 minute, new record :o
jacek: your mum?
VizGhar: oO
jrke: 3secs per call
jrke: quite fast
Zimtime907: Hi Guys!
Zimtime907: :grinning:
AntiSquid: jacek being a cunt again
jacek: Oo
Zimtime907: Wait what?!:confused:
jacek: typical mod's language
Zimtime907: Ahhhh...
reCurse: Hey no generalization that's just him
Zimtime907: Okay...
Zimtime907: Sure
Zimtime907: Mabye Mabye not
BlaiseEbuth: Yeah don't worry, AntiSquid is a cunt...
reCurse: Ok stop with the unnecessary language
BlaiseEbuth: :speak_no_evil:
reCurse: Some mods we got
BlaiseEbuth: My apologies your Eminence. I didn't want to hurt your chaste ears. :bow:
reCurse: :roll_eyes:
Greg_3141: heard that I could get points in code a la mode just by clicking the submit button
AntiSquid: what the fuck is your problem BlaiseEbuth? dipshit
AntiSquid: jacek no need to be an ass and bring my mum into the convo, your trolling goes too far
reCurse: Geez you need to take a chill pill
BlaiseEbuth: Shhh ! Be kind or reCurse will scold you.
reCurse: I mean come on we ban people on a daily basis for stuff like that
reCurse: Can you please?
Scarfield: +1
AntiSquid: not my fault
reCurse: Right
AntiSquid: stuff like that lol
BlaiseEbuth: I never banned anybody for saying c***t... This chan is so touchy... :unamused:
AntiSquid: deserved
reCurse: Real kids...
AntiSquid: you too now?
AntiSquid: really?
AntiSquid: just let it go
Greg_3141: if you get banned, do you get banned from the chat or the website?
KalamariKing: the mods are arguing about how touchy the channel is... maybe the channel mirrors the mods
jOE: Hi!
AntiSquid: i thought to not kick him for that ... but whatever guess i should have
KalamariKing: Hello
AntiSquid: good point KalamariKing
reCurse: You're right, it's certainly not the kind of channel I want to stick around.
BlaiseEbuth: Probably. That's why I'm not so often here...
AntiSquid: bye
AntiSquid: if you have a problem take it on with guilty stop the drama
reCurse: If you don't see the irony in flying off the handle at a mom joke and calling people c**ts and pretending to be a moderator, I'm not gonna bother :)
AntiSquid: talking from a high horse again
ZarthaxX: peace in chat please :(
AntiSquid: ya better don't i prefer when you don'rt
ZarthaxX: also hi
BlaiseEbuth: :popcorn:
KalamariKing: howdy
ZarthaxX: gucci
MSmits: hi ZarthaxX
ZarthaxX: you kalamari
ZarthaxX: hi smito :O
Scarfield: Hi Zarthie
ZarthaxX: SCARFO!
AntiSquid: you could just not get involved reCurse, nobody forced you too
AntiSquid: simple
KalamariKing: mods fighting + horses = jousting?
reCurse: I'm doing my job actually
AntiSquid: then don't take it on with me
reCurse: You're not doing yours, so I will
ZarthaxX: how is that nn smito
AntiSquid: so he's kicking mud, but good you blame me
AntiSquid: typical
ZarthaxX: scarfo you have been missing dont you? mhm
KalamariKing: take this out like big bois, get out your jousting large-toothpick-things
MSmits: ZarthaxX i am done with TTT. I'll pm you two graphs
AntiSquid: and good you added your own ad hominid and still pretend you did the right thing, keep adding insult to the injury reCurse
Scarfield: havent been on much the last few weeks true, but not missing entirely :)
BlaiseEbuth: Hold on. I gonna grab more popcorn.
Scarfield: tbh its not reBless who is adding insult to injury. but i would appreciate it if this "argument" was over
KalamariKing: alr now I'm being serious why are you two just throwing insults at each other?
either agree to disagree, or just drop it
reCurse: Can you point me to the part where I insulted him?
AntiSquid: trolling ?
ZarthaxX: Scarfield ah ok, missed you mah man
KalamariKing: "you're not doing yours, so I will"
AntiSquid: can't see the problem from your own ego i guess, reCurse
reCurse: Geez looks like you had a beef with me just waiting for an occasion
KalamariKing: anti over here is just popping off
AntiSquid: not at all
Scarfield: you too Zarthoo :kiss:
KalamariKing: reCurse you're not doing much but both of you chill
AntiSquid: i am just saying it as is, thanks for proving my point
QAT: you guys are weird
AntiSquid: thanks QAT, love you too
KalamariKing: chat's not normally so toxic, they usually kick arguments
but up on their high horses, the rules are different apparently
reCurse: Sorry if asking to act decently is 'being on a high horse'
reCurse: I'll leave
Greg_3141: incredible, my lazy chef has been promoted to wood 1
ZarthaxX: Scarfield :hug:
QAT: just take it a discord voice channel and turn the gain on your mics like real men
ZarthaxX: :hugging:
KalamariKing: you don't have to leave, you all should just stop arguing (specifically anti... just shut)
AntiSquid: no it's the way you ask and the timing
AntiSquid: bad timing, missing part of the problem entirely, ya the selective filtering and then the bullshit you throw at me for now reason, pretending to do your job haha
AntiSquid: "self-righteous" perfect
KalamariKing: my guy
KalamariKing: youre beating a dead horse
AntiSquid: he's not dead yet
KalamariKing: youre missing the point
Scarfield: https://imgur.com/a/FjaX0Y4
https://imgur.com/a/1tUItJF ZarthaxX smito shared these ealier about his TTT NN :)
ZarthaxX: he pmed me those just now yes
ZarthaxX: sweet graphs :)
KalamariKing: it might be an american expression AntiSquid but it means drop the argument, he's already gone
JSboss: guys whats your favorite coding soundtrack. i always bump Miles Davis
AntiSquid: i know what it means, was joking KalamariKing @_@
QAT: @jsboss david shawty
Wontonimo: oh, those are nice graphs!
MSmits: hey Wontonimo
MSmits: yeah happy with them. They seem very explainable/unsurprising to someone who knows ML right?
MSmits: or do you see something weird?
KalamariKing: they look pretty good to me
MSmits: it's ridiculously easy to use Keras/tf for this
MSmits: it took me less than a day to convert to that
MSmits: you just have to look up the api calls and such when you try different things. And of course there's the googling what it all means
KalamariKing: see?
KalamariKing: tf makes things easy, plus its all optimized cpp so its much faster
MSmits: the training is
KalamariKing: are you still using numpy datasets tho?
MSmits: doing a single prediction was extremely slow
MSmits: over 10x slower than my handcoded network
KalamariKing: that might be a bit broken...
MSmits: I was using numpy yes
KalamariKing: switch to tf.data
MSmits: probably the conversion from numpy to tensor was slowing me down
MSmits: my handcoded network didnt need to do that
KalamariKing: yeah, it has to do all that behind the scenes
KalamariKing: once it's cached as a tensor (e.x. training) its much faster
MSmits: how do you use tf.data?
KalamariKing: Thats a good question
MSmits: is this usuable in place of numpy arrays?
KalamariKing: I believe so?
Wontonimo: those graphs look great MSmits. Nothing out of the ordinary, and furthermore they show that everything appears "normal" and good.
MSmits: well.. in the end it doesnt matter that much as i will be using a c++ inferer, but it was a bit annoying player 1k TTT games, doing 30k prediction and it taking over 10 mintues
MSmits: i type crap today
MSmits: good to hear Wontonimo
KalamariKing: predictions aren't supposed to take 20 seconds hmm
MSmits: eh no not 20 seconds
MSmits: 30k predictions in 10 minutes i said :0
MSmits: thats 50/second
KalamariKing: I can't do math today
KalamariKing: That sounds better
MSmits: still weird, it didnt matter if i had 1 hidden neuron or 20 either
Wontonimo: in particular, something that is VERY nice to see is that it gets better with more size. That means there is something regularizing your network. Perhaps now that you are using Adam.
MSmits: yeah adam helped a lot and mean square error
MSmits: that second one helped a bit
Wontonimo: also nice to see is that Validation didn't become worse with more size, again something is regularizing your network in a good way.
MSmits: it might be a bit different from your average classification nn though, because I train on all possible states
MSmits: well actually 90% of them
MSmits: 10% are validation
Wontonimo: a similar graph, but instead of size for the X axis, pick one size and replace the X axis with epochs. If the graph looks similar and has those same nice features then you've got a great setup
MSmits: hmm, i will not play games then, will just do the training. That will be fast
MSmits: size 25 seems to be good
Wontonimo: in particular, Validation following test and not exploding near the end. Next test continuously decreasing and not exploding at the end.
MSmits: mmh i should automate this
MSmits: can keras produce a graph of some kind?
MSmits: with an api call?
KalamariKing: kinda
KalamariKing: gimme a sec, switching classes, then I'll tell you what I did
Wontonimo: there is the whole tf thing for monitoring training
Wontonimo: tensorboard
KalamariKing: yeah that
KalamariKing: had to find it again, sorry
MSmits: ah there is also just matplotlif
KalamariKing: tensorboard's pretty ok
MSmits: lib
Wontonimo: here is a tensorboard playground link for later https://colab.research.google.com/github/tensorflow/tensorboard/blob/master/docs/tensorboard_in_notebooks.ipynb
Wontonimo: probably matplotlib will be your quick and easy friend for now
Wontonimo: tensorboard allows you to explore your network values, the graph, tensors, histograms of embeddings etc
MSmits: yeah i think i have it working, just waiting for training to finish
Wontonimo: it takes a bit to get tensorboard going and figure out the controls and it isn't particularly good for automation, but if you do a lot of NN work it is eventually worth the effort to get it going.
KalamariKing: really? I thought tb was quite easy
Voudrais: Oups
An error occurred (#412): "Could not get leaderboard in time: giving up...".
I have this problem, please help me
Wontonimo: i'm dumb
MSmits: mmh fixing bugs with model history
KalamariKing: Voudrais is your connection stable? firewall blocking anything?
Wontonimo: KalamariKing or maybe tb has improved in the while. I just remember the first time using it was painful.
Voudrais: I think yes, this error only happens when I'm clicking "Compete" in menu
KalamariKing: afaik it was just add tb in the callbacks, then call tensorboard --logdir logs in shell
Voudrais: I can't see "Games" section, "Contest" and "Leaderboards" work normally
BlaiseEbuth: Hmm... Same
BlaiseEbuth: There was DB downgrade operations today, perhaps that's not over.
KalamariKing: They were doing db stuff a few hours ago I think
Wontonimo: the trouble i ran into was getting tensorboard to actually display anything
KalamariKing: That shell command was what displayed it
KalamariKing: I can send you a notebook if you want
MSmits: https://imgur.com/a/LwwPmOO
MSmits: this is with batchsize 10 instead of 1, i was impatient
KalamariKing: that's pretty good MSmits
MSmits: yeah I think so
KalamariKing: it learns pretty fast and there isn't much overfititng
Wontonimo: bigger batch size usually results in a network that is more generalized, depending on how your error is calculated.
Wontonimo: Yeah, the graph looks fantastic
Wontonimo: very very well behaved network which avoids the two pitfalls i was talking about earlier
MSmits: and I have learned a way to graph stuff now too! matplotlib is nice!
MSmits: tensorboard will come later i suppose, for now this'll do
KalamariKing: https://colab.research.google.com/drive/1j8hWE8WMDyo7x8lobGESQ_y9_NbHadEg?authuser=1#scrollTo=KBHp6M_zgjp4
KalamariKing: if you want it, thats a basic mnist nn, but it uses tb
MSmits: no access
Wontonimo: There is a noise component in the Validation. You can think of this as learning and unlearning lessons from being overtrained. A lower learning rate and larger batch size may smooth that out.
KalamariKing: oh forgot about that lol
MSmits: hmm ok
MSmits: will try those two things Wontonimo
KalamariKing: https://colab.research.google.com/drive/1j8hWE8WMDyo7x8lobGESQ_y9_NbHadEg?usp=sharing
KalamariKing: there
Wontonimo: you mentioned that your learning rate is "already really low". Learning rates in Adam seem really low, but values as low as 1e-8 can still work
UncertainLeo: lua cool
UncertainLeo: any other language cooln't
MSmits: hmm no it's not that low currently. It's 0.001
MSmits: looks good KalamariKing
MSmits: batchsize from 10 to 20 was worse, trying learning rate 10x smaller now
Wontonimo: Andrew Ng's advice on selecting a learning rate is to just try some x3 and /3 values. What you want is something that takes a couple epochs achieve good results, not too fast, not too slow.
MSmits: ahh, not sure what a couple is though
Wontonimo: 2-3
Wontonimo: not great results, but meaningful progress.
MSmits: I was using 400 before.
MSmits: now 1000 because of low learning rate
MSmits: I dont have that much data, maybe thats why i need more epochs
Wontonimo: but in about 2-3 epochs you can see that it is doing well already, even in the previous graph you sent!
Wontonimo: that's all i'm talking about
MSmits: ohh ok
MSmits: I thought you meant 2-3 to finish training
AqilM: hallo
MSmits: https://imgur.com/a/N2PLCyb
MSmits: with lr 0.0001
Wontonimo: a good sign of a learning rate that is too high is that the error rate decreases and everything looks good, then suddenly out at epoch 20 or something the error explodes and never goes back down, or wildly flops up and down
MSmits: everything else the same
MSmits: ah then it was ok before and still ok i guess
MSmits: I am guessing a wide range of lr's will work
MSmits: this one is a bit too low probably, because it needs too much time to finish
Wontonimo: yes, a wide range will def work. This graph looks suspiciously like the previous but better behaved.
Wontonimo: oh, i didn't notice that
Wontonimo: wow, yeah, that's long.
MSmits: thats because i made the LR 10x smaller :)
Wontonimo: if you are looking for another experiment, may i suggest your original LearningRate and a batch size of 100 or 64
MSmits: hmm ok
MSmits: 64 then
colindillman: what the heck is c++ i cant under stand it
MSmits: https://imgur.com/a/NWwKK8u
MSmits: it seems really good, but i am a little worried about the loss
MSmits: it is much larger
Wontonimo: wow. interesting.
MSmits: this could be a feature of the fact that my "sample" is the entire state space maybe ?
MSmits: batching might be bad in that case?
RoboStac: are you teaching the same data in the same order each time?
MSmits: yes
MSmits: i set random seed to 0 before taking 90% as training
RoboStac: might be worth shuffling it between training steps
Wontonimo: Not the same order unless you set shuffle=False in tf.model.fit
MSmits: shuffle is set to true
RoboStac: ah, ok
MSmits: because that's the default
RoboStac: nm then
Wontonimo: so, something strange is going on here
MSmits: well I will do a 1k game test, maybe it doesn't play worse
Wontonimo: your batch size is 64 and LR is 0.001
MSmits: yes
MSmits: and i have 4520 states as data, 90% training, 10% validation
reCurse: Increasing batch size also typically means increasing learning rate
Wontonimo: ^^ right
MSmits: you mean, you have to increase learning rate for it to work?
reCurse: Not necessary to work
MSmits: no i mean to be as effective
reCurse: But your gradient is more precise so you can take a larger step
MSmits: ahh
reCurse: It's more effective wrt time
reCurse: Because 2x batch takes 2x time
reCurse: But gradient is more accurate
MSmits: maybe it just wasnt done training yet because the lr was too small
reCurse: So you can 2x LR
JakubBiskup: is it just me or multiplayer still not working? :(
reCurse: I have a picture in my mind of the article/paper I remember it from, but finding it back is always such a trouble
MSmits: yeah that solved it reCurse
Wontonimo: awesome
reCurse: It was showing a trajectory
reCurse: With more noise with smaller batch size
reCurse: It made a lot of sense, anyway
reCurse: The funny part is you actually *want* that noise in RL
reCurse: But not in SL
MSmits: to get out of local minimum
MSmits: ?
reCurse: Ok sorry, trying again
reCurse: You want more noise in RL
reCurse: You want less in SL
MSmits: I see
reCurse: Because you don't know if your data is good in RL yet
reCurse: So you want to keep some noise for exploration
reCurse: SL you're assumed to have the perfect data
MSmits: ah yeah
reCurse: 'Stationary target' is the term
MSmits: https://imgur.com/a/r4vgHYY
MSmits: this is with batch 64 and 0.01 LR
MSmits: the end result is similar to what i had with batch 1 and 0.001 LR, maybe a bit less noise
MSmits: trains much faster though :)
Wontonimo: and it seems like validation is a little high? or am I just squinting wrong?
MSmits: well validation is possibly closer to training loss
MSmits: but the absolute validation is about as good as before
MSmits: relatively it seems better because training loss is higher
MSmits: anyways, i should move onto other things. I might mess around with oware for a few days. Not coding NN, but coming up with a way to create a good supervised learning set
Wontonimo: i've mentioned before how I like when training and validation are close. It isn't a universal belief, and there are definitely those in the camp that believe that test should always be able to achieve 0% error.
MSmits: nah it makes sense that they should be close
Wontonimo: sorry, training not test
MSmits: but in the end you just want your validation loss as low as possible
Wontonimo: but you get it
Wontonimo: yeah
MSmits: the weird thing though, is that in the case of my TTT NN, that is not what makes it play better
MSmits: since the 10% validation set is just that... 10%. the 90% training set is most of the gameplaying :)
MSmits: i could just not validate at all and train on 100% :) But that would be boring
reCurse: That's part of why I was saying it's not a good testbed
MSmits: yes i got that
Wontonimo: pro tip : you have to be careful with trying to chase validation. if you keep tweaking and tweaking your design and hyperparameters to minimize validation, then you are effectively overfitting for that metric and you have all the same pitfalls of overfitting again
MSmits: but as you see, generalization is still possible. It is just meaningless in terms of this usecase
KalamariKing: WOO $10 says I failed that math test
MSmits: well maybe you shouldn't be on CG between tests :P
Wontonimo: lol
reCurse: I find it hard to tell if it's generalization or something else on such a small state space
KalamariKing: wontonimo wdym chasing validation?
MSmits: yeah, well... me too, but at least it gets better at predicting a valdiation set while training on a different one
KalamariKing: are you saying you're essentially overfitting to the validation data?
Wontonimo: yes
KalamariKing: if its not training on that data tho, how can it overfit?
reCurse: There can be hidden correlations in training and validation
Wontonimo: if you change a hyperparameter to get a better validation, you have manually-fit
reCurse: That's part of the challenge
Wontonimo: if you automate a process to find the best hyperparameter to minimize validation, you are by definition overfitting to validation
KalamariKing: interesting
KalamariKing: what if you change the validation data per batch tho?
Wontonimo: no, don't do that.
KalamariKing: why?
Wontonimo: unless you have new data
MSmits: btw, reCurse I agree that the result would have been far more meaningful if it were learning to distinguish pants from shirts on mnist data, but I get more motivated by boardgames :)
reCurse: Well it would have been easier to test the basics
reCurse: I get the motivation part
MSmits: agred
KalamariKing: Wontonimo why is changing train/val split per batch a bad idea?
Wontonimo: i think there are some better resources online about stats and train/test/validation split rational than what I could say in just a few short posts here, but in a nutshell, a test should be a test. If you study to the test, it isn't testing if you understand anymore, it is a measure if you memorize
reCurse: I couldn't quite wrap my head around the actual usefulness of test/validation
reCurse: And it seems to be uncommon practice
reCurse: I get the idea you can overfit the hyperparams but does it really matter if you have a good dataset to begin with?
KalamariKing: well for one you can detect overfitting
other than that what's it good for? maybe knowing when its ready for application?
reCurse: Sorry, test vs validation
reCurse: Not test vs training, I totally get that
KalamariKing: validation is testing per epoch right
struct: Is it used for training?
KalamariKing: and/or a similar concept
reCurse: It doesn't help I often see the terms test and validation used interchangeably
Wontonimo: yeah, they are interchanged a lot and that doesn't help
jacek: so your data must be... invalid
jacek: or intest
Wontonimo: but image you've been training an NN, and using 1 test set to see how well it has been doing. During this process you
Wontonimo: change a bunch of configurations, add layers, put in some features
Wontonimo: and keep working till your test results are awesome
Wontonimo: how do you know if you haven't just overfit the test?
reCurse: But that's the part I don't get
jacek: or switch to RL. inifinite data *_*
reCurse: The minute you use a validation set, you're subject to the same overfitting
reCurse: So what's the point? What am I missing?
Wontonimo: you need a 2nd test set that you've NEVER used before. You use it only once. That's it
reCurse: So what's the point?
jacek: you use test set after every epoch and validation after all learning ?
Wontonimo: that's the point
reCurse: I don't get why you would do that
reCurse: Seems like a waste of data
Wontonimo: the 2nd one is the FINAL test, because in actuality all the previous was self-testing
reCurse: Ok but by definition you can't compare it to anything else
reCurse: So what use is it for?
Wontonimo: to see if you f*** up and overfit to your 1st test set.
struct: But wont it still overfit for the firt tests»
Wontonimo: which happens
Wontonimo: A LOT
reCurse: But you have no reference by definition
Wontonimo: what do you mean?
reCurse: So what do you do then? Try to fix it, but you can't compare to the previous result
reCurse: Because if you do you're back to square one
King_Coda: XD
King_Coda: Disney XD
Wontonimo: then you need another test set
reCurse: Who says it's even comparable then
Wontonimo: unseen
ldiaks01: hey
KalamariKing: Hello again King_Coda long time no see
Wontonimo: that reCurse is a very good question, and something that plagues deep learning
reCurse: And what if your dataset is fixed, you keep a validation set and hope you don't screw up?
King_Coda: KalamariKing!
reCurse: I'm not convinced at all on the concept
LazyMammal: reCurse my 2 cents. the once-use-only validation set is prevent the researcher/operator/engineer person from fooling themselves. however much overfit, accuracy or precision NN has on dataset. the human wants to know how much is real. the validation set is that reality check. super important for research papers. still kinda important for setting up pipelines at home. don't want to live in delusion right?
Wontonimo: excellently said
King_Coda: Did LazyMammal just watch Inception or something?
LazyMammal: :D
Scarfield: at least 4 cents :p
MarcFrancisEscasinas: what does int mean
reCurse: Yeah, so you screwed up, so then you have no data left to try again
King_Coda: Integer
King_Coda: Int - eger
MarcFrancisEscasinas: tnx
King_Coda: :stuck_out_tongue:
Wontonimo: and if you didn't do that, then you wouldn't know if you screwed up
KalamariKing: As far as I have gathered...
You use the train data to... train, and to try to get the validation data as high as possible (without training on it). Then you use the test data to make sure you didn't overfit to the val data
KalamariKing: right?
reCurse: I'd think your test set was bad to begin with if that happens
King_Coda: KalamariKing is officially smarter than me
Scarfield: not that i know, but it sounds like its just insurance against a bad test set
King_Coda: I didn't understand a single word of that
LazyMammal: confidence interval and sampling sizes still apply. the validation set should be big enough to ... validate things but no bigger. of course using as much data for train/test as possible. just reserve a tiny bit for illusion bubble popping
reCurse: And who's to say validation data isn't ill-formed either...
AntiSquid: it's true even your validation might not be enough, depends how much data you're missing . how do you decide how much data you're missing unless someone specifically tells you ?
LazyMammal: yep, if your data collection is skewed in some way and validation is drawn from that pool of data then it won't improve your data. ofc
jacek: reCurse this should be better than nothing
jacek: this way you can discard anything because you dont have ground truth
reCurse: Yeah but the minute you use it you're screwed
reCurse: You can't do anything anymore until you find a new one
reCurse: That's insane
jacek: schroedingers data
KalamariKing: I was literally asking if that explanation was correct
KalamariKing: By the way the conversation continued I assume it was right?
Wontonimo: it's not so black and white, do or die reCurse.
reCurse: But that's the distinction between validation and test
LazyMammal: "can't do anything" is limited to things you can say "this has been validated". if you don't care about that answer you never have to ask it.
reCurse: I don't know, I naively think most hyperparameters would have a hard time overfitting to data it never sees
reCurse: Then again I don't work in life-critical domains so
KalamariKing: *sigh* I'm going with correct
Scarfield: xD
Wontonimo: imagine i'm coming up with a theory of motion for heavenly bodies. I have data for 20 planets and moons. I come up with something. Susan says she has data for 5 more that we could test by theory. It doesn't match so i keep tweaking things till it matches. Is it a good theory now? Will Bob has 5 more, and we look at that and it doesn't match. What does that say of my original theory and the tweaks
KalamariKing: Isn't that the point of splitting val and test? so that even if it overfits to val, it still has new data it hasn't seen?
reCurse: Yes but I take the issue with wasting data that you can't even compare or reference
KalamariKing: ok, true, but with a large enough dataset that shouldn't matter
KalamariKing: but with a large enough dataset overfitting *shouldn't* happen
struct: what if it does
Wontonimo: tbh reCurse, I do all this to validate my model, then after it is validated I retrain with ALL THE DATA
struct: how will the hidden dataset help?
AntiSquid: reminds me of particle physics Wontonimo . always a new particle gets added and then the model needs to be rethought, what are your suggestions ?
Wontonimo: so i don't think of it as a waste of data at all.
LazyMammal: http://chat.codingame.com/pastebin/4ee7b1a9-1eb8-4480-a2db-c36d848b831d
KalamariKing: if the patterns are similar enough, can the model overfit to the train data, but also be overfit to the test data it has yet to see? or is that the definition of a good model
LazyMammal: oh, I hit some kind of word limit?
KalamariKing: I think so
KalamariKing: but if you have real-world data like the bot battles, why not go with rl
reCurse: Oh that's an interesting point LazyMammal
reCurse: If you do have a final deadline then sure
reCurse: I can see
Wontonimo: wow, i didn't think this was going to lead to so much discussion on the topic! :D
Wontonimo: cool stuff
reCurse: The only reason I still stick around hehe
Wontonimo: i though it was my dad jokes
Wontonimo: you need a mentor
reCurse: I do? I thought I made enough bad jokes already
jacek:
Wontonimo: AntiSquid, about the particle physicics, it's like the planet thing. if by adding something you need a new formula, then probably the old formula was poor to begin with
Wontonimo: or was the question rhetorical ?
AntiSquid: "then I probably want a reality check before that do-or-die moment" last minute submit reality checks LazyMammal
AntiSquid: no Wontonimo, it's a geniune question, you might find more data later ...
LazyMammal: hehehe, actually that's a good point. 11th hour contest submission has no take-backs. good idea to have confidence about overfitting. so pipeline should have internal reality check. bot arena with old versions is a good start.
reCurse: It doesn't seem like it would affect anything though
reCurse: You already have validation through the final games
Wontonimo: imo, the test is testing if the NN can digest the training data well and make sense of it properly. So if validation passes, then I have no problem retrain on all data.
reCurse: Not like the validation results would give you anything actionable
Wontonimo: validation / test is way more important with medical applications, flight simulators, things were lives are on the line, like UTTT
Wontonimo: okay, maybe not UTTT
LazyMammal: reCurse, action choice is "submit or don't submit". If I fool myself that NN is best-ever-badass and it might still fail purpose as engine-of-fighting-bot. I won't know if I only looked at NN data fit. I also need to look at bot performance. This is analogy of test vs validate but also a real scenario.
KalamariKing: Wontonimo but then can you continue training after that, with no 'new' val data?
reCurse: There is nothing to gain by not submitting though
KalamariKing: Maybe keeping your confidence high
Wontonimo: on CG, yeah, submit
reCurse: I can totally see for a deadline like research paper, critical domains, etc
reCurse: Thanks for that, missed the perspective
reCurse: Still seems like it's overkill for the majority of applications though
Wontonimo: it is
Wontonimo: it's a good habit, like unit testing
reCurse: Oh no don't get me started
LazyMammal: But you could totally lose internet points if you drop 50 ranks with a stinker submission 5 minutes before the contest closing! That's real life stakes!
Wontonimo: KalamariKing - if it is for something serious, absolutely not.
reCurse: I think the validation won't help you much there
reCurse: The arena is the best validation there is
reCurse: See the number of times people complain their local 80% wr bot sinks in the rankings :P
Wontonimo: different things - arena is validation of your bot, validation set is for validating your model which is only part of your bot. Unless you are doing RL, then your bot and model are intertwined
reCurse: Well it seems like acting on the validation in this case would lead you astray more than otherwise
reCurse: shrug
LazyMammal: welp, I can't argue with that. everyone has to choose cost/benefit for themselves. if offline arena is too hard or slow to make then don't do it. a good fallback plan is to split test/train/validate for NN data.
reCurse: Adversarial is pretty different
KalamariKing: Wontonimo I thought not, but if you have a limited dataset, how can you train more
KalamariKing: Thats the issue I have with training on val data when you're done
reCurse: Training on all data seems like a mistake to me
reCurse: Even after validation
Wontonimo: reCurse and I were talking about this yesterday was it? you can have several models, each trained on a different train/test split
Wontonimo: then combine them to vote on the answer
[kirr]: Hi guys
jacek: ensemble model
jacek: eh
Wontonimo: yeah that
reCurse: That's a different issue from validation though?
Wontonimo: "if you have limited data, how can you train more" and "training on all data seems like a mistake". I think ensemble model addresses both (so long as you ignore validation)
reCurse: Ok I didn't see ensemble like training on all data, even though it technically is
reCurse: It's more of an issue of having a training session with all data, then you have zero tools to spot overfit, and it might start overfitting where it didn't before
Wontonimo: i've only done toy ensemble models. it's never come up at work or in production for me
reCurse: If you have separate models trained on partial data like an ensemble, then yes, that won't occur, and you still have a tool to make sure each model is reasonable
reCurse: Maybe not so useful for inference, but for testing that sounds pretty good if you can afford it
Wontonimo: not on CG where you only get 100ms and you want to run 50k simulations
reCurse: Well even in general
Wontonimo: at work, taking x10 as long to increase accuracy by 1% is totally worth it.
reCurse: Oh, then how come you didn't get around to trying that? Are there better methods?
jacek: where you work at
1415495: reusing you the same test sets, remind me of http://imgs.xkcd.com/comics/significant.png
reCurse: Am I naive in thinking the hyperparameters can't overfit that much to test set?
Westicles: Volcanoes got approved
jacek: oO
Wontonimo: How many hyperparameters do you have? 2? maybe not. 120? probably
reCurse: I'd like seeing an actual example where an hyperparameter overfit the test set and bombed the validation test
1415495: reCurse: with a sufficient test sets, and the classical hyperparameters, I don't think that you can overfit the test cases (if they are actually different that the training set)
reCurse: That's my intuition yeah
Wontonimo: the number of layers, size of layers, connection of layers, preprocessing steps, preprocessing thresholds, loss factors are all hyperparameters if you change them.
1415495: https://arxiv.org/abs/1611.03530
1415495: (Understanding deep learning requires rethinking generalization)
Wontonimo: i'm not disagreeing with you reCurse, just stating what i htink when i think hyperparameters
Alshock: is it bad if I butt in and say, uh, hyperparameters? Like magic numbers, but for the magic numbers spaghetti that are NNs?
MSmits: to some (much lesser) degree, if you fit the hyperparameters to get the best result on the validation set, you're using a validationset as a training set.
reCurse: Yeah, I have a hard time thinking changing those would work well on training, test but not validation
MSmits: and i should probably call it test
reCurse: Thanks for the paper fenrir I'll check
jacek: Alshock parameters like learning rate, or the size of NN
MSmits: so I get why you would want a 3rd set in some cases... probably not applicable for us though
reCurse: Parameters are part of the model, hyperparameters are part of how they affect training the parameters.
reCurse: You could also call them metaparameters if it helps
reCurse: IMO
Alshock: tyvm, I figured it was that *tries to get away looking brilliant and knowledgeable*
Wontonimo: lol
Wontonimo: i don't like the term hyperparameter. it's just another parameter
MSmits: you could also use a GA to find the best hyperparameters. That GA will also have parameters
MSmits: hyperhyperparameters
jacek: neat
Wontonimo: your could make a NN to find the best parameters for the GA
MSmits: maybe the same NN the GA is finding hyperparamters for
1415495: about the hyperparameters: an argument that I see is: what is the entropy of your hyperparameters ?, basically if it's entropy is lower than you test cases, I don't see an issue
MSmits: entropy can only increase afaik
jacek: phycisits :unamused:
1415495: otherwise, you have a compressor that can compress down to 1 bit
MSmits: :P
Wontonimo: and my son when talking about his room
1415495: I am talking about Shannon entropy
Alshock: Hey now that's an idea
Alshock: woopsie scrolling
jrke: CG is still very slow
jacek: :scream:
1415495: and in physic: the entropy can go down spontaneously, it is just *so* improbable that it happens on near infinite timescale
jacek: jrke found out the issue with the vector?
MSmits: sounds theoretical to me
jrke: i finally fixed the vector issue
MSmits: like a hypothesis
jacek: what was it
Wontonimo: what was the vector issue?
jrke: the issue was like i push data having .x =- 1 and i further process that in map[data.x][data.y]
jrke: just not pushing that -1 worked
jrke: and made in top silver without any mine
Alshock: map[INTMAX] may not have been your best access ever xD
jrke: my rank 108 in silver but my currrent submission ened in top 15 or 10 in silver cause rank is not getting updated now but CG enhancer says that last battles are from top 15
Alshock: try to bring back the random memory check when you battle against the top 15
1415495: and for overfiting: a nice blog post about GPT-2 and private information leaks: https://bair.berkeley.edu/blog/2020/12/20/lmmem/
Wontonimo: fenrir - from the book "magic of reality" , it is possible that if you put all the parts of a car in a large bag and shook it the car would assemble itself, but it is highly improbably.
1415495: that's the same thing yes
Alshock: The future is adding an output to your NN that is estimating its own level of overfitting
MSmits: ah but that only works if the bag with the car parts is a completely closed system.
MSmits: otherwise there's probably some outside interaction, increasing entropy elsewhere
Alshock: You mean it's actually much more probable than estimated?
MSmits: entropy is mostly a statistical notion though
Alshock: Gotta buy a car and break it down to pieces right now
Alshock: does anyone have a big big bag?
reCurse: Funny fenrir, reminds me of automaton's regurgitating exact sentences sometimes
jacek: Automaton2000 or AutomatonNN
Automaton2000: but you could also use it to improve your skills
Alshock: RIP AutomatonNN
1415495: yes, might be the same king of issue
Alshock: @Automaton2000 What if I have no skills whatsoever?
Automaton2000: was thinking of something else
Alshock: aw ok... :disappointed:
1415495: another interesting paper is https://arxiv.org/abs/1803.03635 (The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks)
reCurse: Yeah I remember that one, still have no idea what to make of it :)
1415495: basically, most of the weight are useless (if not overfitted), but you nee them to be able to train
1415495: (I understood it like that)
AntiSquid: how much did you read fenrir ? papers overall
reCurse: I think it's the driving idea behind teacher/student networks if I'm not mistaken
reCurse: Distillation and all
1415495: a lot, while doing my NN on CSB
1415495: and then I find the domain interesting
reCurse: I find it more applicable when seen as such anyway
Wontonimo: that paper about lottery ticket hypothesis is great.
Wontonimo: it's a fantastic lesson on : larger networks train faster, but once trained they can be smaller.
Wontonimo: once smaller then can't learn much anymore
1415495: reCurse: for distillation yes, but I also remember that training with the teacher output is difficult, so they make them 'softer' at the begining
Wontonimo: so so similar to alzheimer's
1415495: mmh, didn't saw that point, but making me think now
AntiSquid: regarding the example you gave where GPT memorizes personal info, i mean it's accurate real data, it retained information, great, any idea of related topics / papers for this example on how to make the model understand better what values are likely to change at some point ?
Wontonimo: as we lose neural connections we retain most of what we learnt but lose capacity to learn
1415495: AntiSquid: I don't think I saw something about that, no
reCurse: I don't know if it's train faster, or you need a lot more 'space' to learn before you can focus on what matters
reCurse: Like a puzzle
reCurse: You have some little clusters all over the place = more space
reCurse: Then you start assembling = less space
reCurse: If you can't store all the small hints you don't make progress
reCurse: Anyway, maybe just rambling
Wontonimo: yeah, i haven't seen anything either AntiSquid, other than maybe knowledge graph architectures. I don't have links but the idea is that the network only learns how to traverse a knowledge graph and not the data in it.
Wontonimo: you can then change the data
Alshock: we're all just ramblnig anyway, I don't like calling it "space" because in my opinion it's more like actual data. A bit like a "what else could I try?" data
AntiSquid: right Wonto .
Wontonimo: hey, reCurse , here is a great video by the late Professor Winston about larger vs smaller nets https://youtu.be/VrMHA3yX_QI?t=2344 , i've bookmarked the time in the video where he starts to dive into this specific topic
1415495: one big issue related to that is: how do you check against hidden 'trap door'
reCurse: Cool thanks
Wontonimo: he shows that with a small enough network, it can't learn the problem, but it CAN remember if it learnt when it was larger
reCurse: I'll have to try teacher/student at some point
reCurse: Does bother me a lot that a big part of the NN doesn't seem so useful
reCurse: After training
1415495: for RL: becarefull: you must train it on point explored by the student
reCurse: Makes sense
Wontonimo: AntiSquid but i haven't seen anything groudbreaking about about knowledge trees lately
1415495: otherwise, it may have a small loss, but will go where untrained very easily
jacek: teacher/student? so teacher NN labels data and student learns on labelled data?
reCurse: Yeah I still fail to have a reasoning over why it's important to learn near on-policy
reCurse: But that's how it works in practice
1415495: because with a policy, you are accumulating drift over the trajectory
1415495: and if you go where untrained, you have random data
reCurse: jacek: yeah
reCurse: Oh, right.
reCurse: Should have been obvious
jrke: reCurs e so you designed something for training OOC NN
jrke: ?
reCurse: I didn't start anything
reCurse: Just musing
jrke: damn i always forgot question mark after questions
jacek: why
jrke: oh so ran updated
jrke: now 8th in silver
jrke: rank*
reCurse: fenrir: Though I think the part where I had trouble with, since the trained doesn't have much data about those states anymore, shouldn't it be the same?
reCurse: Same issue on both networks
jrke: btw is that me or everyone - does CG enhancer brakes the outfit of ide?
reCurse: So why would training "off-policy" be so much worse than sampling what the trained network knows best
reCurse: I mean, 99% of what it explores
reCurse: Because the latest epochs will be concentrated on what the trajectory it typically takes anyway, so that's where the learning occurs
reCurse: So in theory if you cover the same space on a new network, it should work (but doesn't)
reCurse: Unless some parts of the trained networks survive through epochs of training somehow
1415495: unless the student take exactly the same action that the teacher, you will enter some place not teached, if the student take corrective action and go back where it was trained, no issue, but otherwise it will go somewhere untrained, where anything that it output will be random
Paul_Demanze: does anyone know what is the max amount of code
jrke: 100k chars
reCurse: Yeah my question is the teacher does not go there anymore, so chances are it lost that information as well?
Paul_Demanze: ok thx
reCurse: Oh I think I see
1415495: I mean: if you play the teacher: it won't explore all the possible states
1415495: and so the student won't have seen the state where it want to go
1415495: and won't have learner anything for them
reCurse: Yeah I'm seeing it as a problem of distribution of data now
reCurse: That makes a lot of sense
1415495: so as soon as the student diverge from the teacher, you enter uncharted territory
1415495: (for the student)
reCurse: The student needs to focus on its mistakes but doesn't have nearly enough data to do so
reCurse: I'm happy with that now
reCurse: Thanks
1415495: the teacher may know how to recover, but won't have shown it to the student
1415495: cool
reCurse: Some form of importance sampling maybe :)
1415495: I see it as: due to small inaccuracy in the learn actions the student may end up somewhere where it has never been (or not often enough) teach what to do
1415495: making it do more mistake, that accumulate
reCurse: Yeah it's in the same area, I just have an easier time framing it as "it doesn't see its mistake enough to correct it"
1415495: (but if the student has perfectly generalized what it should be doing, there shouldn't be an issue, but ... ;))
1415495: it's probably the same issue with RL with off policy data which seems damn hard
1415495: (I mean truly off policy, not just 'near offpolicy'
reCurse: Definitely seems like a probability mismatch to me yeah
reCurse: Though off-policy and on-policy seem to be ill-defined as well
reCurse: In papers I read anyway
1415495: I have seen some success in a paper where that had to trained multiple model and then average them
reCurse: Oh SWA?
reCurse: That seems like a common technique in general
1415495: ah no
1415495: I think it was a paper on deepmind
1415495: (but they seems to be multiple about that issue)
1415495: https://arxiv.org/abs/1907.04543v3
reCurse: Hmm ok
MSmits: someone who knows nothing about ML is going to find this conversation about students and teachers very strange
reCurse: Huh?
1415495: the key point seems to be a lot and lot of data, and models ensembling
reCurse: Wait
reCurse: I remember a paper saying the exact opposite am I hallucinating
1415495: ah the wonder of research ;)
reCurse: Arguing that Q-learning was not really off policy
1415495: it depends on what king of data you train your q function
jacek: mandela effect eh
reCurse: Because models trained on their own experience replay outperformed those that trained on foreign ones
reCurse: Or something like that...
1415495: on policy basically means using sample from running your policy (and where your NN has not yet changed)
reCurse: Well off-policy vs on-policy is a 'gradient'
1415495: the more it has changed, the more off policy
reCurse: You're still closer to on-policy if it's the model that generated the data with epsilon greedy
1415495: (ie the policy used to sample versus the policy to act)
reCurse: Than if it's a model completely unrelated
1415495: yes
reCurse: Trying to find that paper now...
reCurse: Got it I think
reCurse: https://arxiv.org/abs/1812.02900
1415495: I think they are at least partially compatible
1415495: in the other paper they average multiple model while learning, which will decrease a lot the overestimation (as the models have uncorrelated errors)
1415495: is it enough, no idea
reCurse: Puzzling
1415495: they also use 50*10^6 data points per game
1415495: they also seems to think in the same terms than you: "Offline RL is considered challenging due to the distribu-
tion mismatch between the current policy and the offline policy'
1415495: mmh truncated, the remaining: "data collection policy, i.e., when the policy being learned
takes a different action than the data collection policy, we
don’t know the reward it would have gotten."
reCurse: Well most likely me thinking like them from subconscious reading ;)
1415495: :)
Lucky30_: :thinking:
aCat: Is there a problem with server?
aCat: I got nondeterministic results of a submit of puzzle
aCat: and often "Failure
Process has timed out. "
aCat: for a simple bunch of ifs
aCat: Ah, that's a grrovy problem
BlaiseEbuth: Yeah groovy is a problem...
aCat: found fix to use java within
aCat: made my day
aCat: as the entire goal was to make groovy language achieements
aCat: and now I got it free
aCat: :D
BlaiseEbuth: Same for me. How do you use java in it ?
Joe211: sometimes I feel like the compiler is the problem
Joe211: but im just retarded
aCat: BlaiseEbuth https://www.codingame.com/forum/t/groovy-gets-timeouts-in-every-clash-now/190579/5
BlaiseEbuth: Oh cool. Thx aCat :thumbsup:
aCat: :-)
jacek: nice rank robo, im happy for you :angry:
jacek: :upside_down:
RoboStac: thanks :)
jacek: the nn word?
RoboStac: yeah, realised I was doing something stupid
jacek: like wasting time for this game
parthi_v: can anybody explain how to solve
jacek: solve what?
parthi_v: i m new to this can u tell me how to do
ddreams: how to do what?
ddreams: the art and science of asking questions
parthi_v: chess game
ddreams: It's impossible to help you if you don't provide more information
ddreams: Are you able to do any puzzles?
parthi_v: am learning python
parthi_v: i came here to solve some puzzle
ddreams: which chess game?
parthi_v: https://www.codingame.com/ide/puzzle/chess-board-analyzer
ddreams: how far have you come?
parthi_v: just now i have entered
ddreams: well, then the next step is to think about how to solve it
ddreams: any ideas?
parthi_v: no idea sorry
ddreams: then I suggest you pick another puzzle and come back to this one with more experience
ddreams: it says it's a hard puzzle
aCat: start with some easy ones
aCat: and maybe ithut user names attached - they are original CG puzzles
aCat: better for beginners
aCat: without user names
ddreams: https://www.codingame.com/training/easy
parthi_v: ok fine let me try another one
CodeLoverboy: ooooooooooooofffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff
CodeLoverboy: oof
CodeLoverboy: oof
Greg_3141: In Rust, how can I make a bot return the best move it's found so far instead of timing out?
jacek: how do you do it in other languages
Greg_3141: idk i've never done it before
struct: you need to time it
struct: and return it before times runs out
jacek: so the question is more general than "in rust"
jacek: what kind of search you do? minimax?
Greg_3141: yes
jacek: then use iterative deepening
Greg_3141: ok
Gumarkamole: kotlin compiler is soooo slow
Gumarkamole: i am waiting for tests longer than writing my code
ZarthaxX: maybe your code is slow tho
nuggetbucket54: i'm so trash
ZarthaxX: :(
ZarthaxX: nice self steem
ZarthaxX: esteeem*
itzluku: coc rank update in 5min? or when
ZarthaxX: for your global rank?
ZarthaxX: it's in 3 hours
itzluku: ah ok ty
NomNick: hello, can we use random method in codingame environments ?
NomNick: like golang rand pkg ? https://golang.org/pkg/math/rand/
Greg_3141: Why would you not be able to?
NomNick: because I dont succed at seeding it :(
NomNick: so the better question is how to seedrand on codingame
ZarthaxX: like in any other lang NomNick
ZarthaxX: codingame doesnt work differently
BruteForceLoop: Some of these easy algorithm problems are pretty hard, I tried to do the 1D spreadsheet problem for 3 days and failed, or maybe I'm really bad at algorithms.
ZarthaxX: you will get better with time prob and eventually solve them faster
ZarthaxX: also sometimes there exists that one puzzle that is hard for us
BruteForceLoop: ZarthaxX Thank you for the advice. It's demotivating being stuck at a problem for 3 days, I was having nightmares about the problem.
struct: NomNick I think it works
struct: I just copied snippet from the web and it worked
struct: https://tech.io/snippet/gC7Vrvo
NomNick: hello i tried a default code with some rand testing and it seems to work too
NomNick: so no surprise here, the issue is between the chair and the laptop
NomNick: what's tech.io and how is it related to codingame ?
struct: tech io is from codingame
NomNick: was reading the about us
NomNick: they spin off their tech stack ?
struct: what does that mean?
NomNick: is it a business venture ? trying to sell codingame tech to university for example ?
NomNick: to provide teachers with tools to enhance their materials & classes ?
struct: I dont think so
struct: its free
NomNick: nice of CG
NomNick: ah. I see my mistake at least
NomNick: of course, fucking Golang
NomNick: for i := range rand.Perm(N) will give you the index. I still don't get how the golang designers though it was clever to put the index before the value in range outputs
ddreams: key/value perhaps
Decco: yooo
Decco: my ppls
Decco: i love you guys so muuuuuuch
HapppyDe: yo technoblade
Decco: gaymer
SuperArtyK: gg
KiwiTae: scary doll following me aaaah ~~
KiwiTae: good morning o/
7-4: hi mates
KiwiTae: sup~
7-4: ay
Stormalix: ayyyy
7-4: how is it going
Stormalix: good lol wbu
7-4: bad
Stormalix: aw whyy
Smelty: sad 7-4 nosie
7-4: wtf
SickNerd: g
Alemhan: never gonna let you down
