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If one came along looking like Miss Parker, check it didn't bring an anodized pink 9mm.
And change your name if it's Jarod.
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r/technology
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comment
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r/technology
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2024-18-06
|
If you can get your hands a box spring from mattress firm I think it would offer the greatest amount of un-occluded space. Then you could put in a drawer or three if you want to get fancy. At that point you just have them retract from the end of the bed...get a remote...yeah, you could make it a very workable solution. \*\*\* opens up Figma \*\*\* now...let's see...will need a fairly strong motor....
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r/technology
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comment
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r/technology
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2024-18-06
|
>"China's next-gen sexbots"
There was a 1st Gen?
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r/technology
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comment
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r/technology
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2024-18-06
|
I don't think any of us want the robots to have kids.
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r/technology
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comment
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r/technology
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2024-18-06
|
It’s actually a solid point. I remember when I learned that beta max failed in large part because the porn industry went more for VHS.
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r/technology
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comment
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r/technology
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2024-18-06
|
Do they take Apple Pay later?
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r/technology
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comment
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r/technology
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2024-18-06
|
Wait till AI realizes it’s not satisfying and starts acting indifferent.
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r/technology
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comment
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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Verge how to build a pc was actually how to destroy your pc.
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r/technology
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comment
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r/technology
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2024-18-06
|
They're playing nice, with an implication that if T-mobile doesn't comply, the next step is legal action, the kind with expensive teams of lawyers and a hit to their public image. A sort of iron fist in a velvet glove thing.
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r/technology
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comment
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r/technology
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2024-18-06
|
US Mobile. They have SIMs (or eSIMs) available for Verizon or T-Mobile towers (and I read they're adding AT&T's network soon...?).
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r/technology
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comment
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r/technology
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2024-18-06
|
Yep, you can even finance with direct purchase from Moto, Google, Apple, etc anyway, if you need the payments instead paying in full.
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r/technology
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comment
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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r/technology
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2024-18-06
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I am looking for a part time job to assist in paying bills besides videography work and I applied at Best Buy. During the interview I talked about how everyone was doing so poor yet Best Buy was doing great knowing damn well I had just read a headline dooming the company and talking about mass layoffs. I ain’t looking for nothing long term anyways but I thought it was funny.
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r/technology
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comment
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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> People dont see problems with ideas such as this https://kyivindependent.com/ukraine-pins-hopes-on-home-made-drones-to-counter-russia/
Not while this shit is going on. GTFO of Ukraine, tovarich. https://www.reddit.com/r/pics/comments/1diql63/head_of_ukranian_soldier_cutted_of_by_rusians/
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r/technology
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comment
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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r/technology
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2024-18-06
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r/technology
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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Good AI would just talk to your digital finger print and have your meal ready before you even knew you wanted it.
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r/technology
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comment
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r/technology
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2024-18-06
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Maybe try improving the quality of the food?
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r/technology
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comment
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r/technology
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2024-18-06
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Shill someplace else or pay me to listen to your advertisement.
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r/technology
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comment
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r/technology
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2024-18-06
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At some point the government has to protect its people from capitalists using shit chatbots to block any sort of customer service, cancellations, or error resolution. It took me 6 months to get a refund from cancelling Nationwide because the automated voice system was bugged. The more people who don’t speak out about this the more these companies will try to fuck over working class Americans.
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r/technology
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comment
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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It definitely happens to young people too, but there is a lot of social stigma associated with it.
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r/technology
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comment
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r/technology
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2024-18-06
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>AI technology is helping to create false stories about World War II atrocities including Holocaust denial, risking an “explosive spread of anti-Semitism”, the UN warned Tuesday.
Why is it always anti semitism and never anti-slam or anti Hinduism? Kind of peculiar this religion also gets singled out as a race…
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r/technology
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comment
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r/technology
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2024-18-06
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lol, he did an oops.
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r/technology
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comment
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r/technology
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2024-18-06
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Truly a bastion of independent and unbiased reporting you have there.
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r/technology
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comment
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r/technology
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2024-18-06
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Because they’re killing innocent civilians. Lmao. Did you even think about your comment?
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r/technology
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comment
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r/technology
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2024-18-06
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I'm not trying to say schooling is fundamentally wrong. As you said most people aren't going to good schools so let's focus on the ones they are going to - they're bad, and I don't see any real energy in our government to change that.. I mean ultimately the most important thing in America is short term profit, teaching people how to be smart about their finances, their health, their beliefs, how to make sound judgements - there's no short term profit there that can be easily quantified.
You have an uphill battle trying to actually teach people through school. You're better off being a fucking celebrity or youtuber or something, someone with reach. Maybe making some kind of independent institution.
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r/technology
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comment
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r/technology
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2024-18-06
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lol. You went from being somewhat reasonably critical of something to being an outright Putin clown in a few comments. I never agreed with your point, but you were... sort of sensible in your talking at least. Now it just went over the cliff.
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r/technology
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comment
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r/technology
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2024-18-06
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Is it untrue?
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r/technology
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comment
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r/technology
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2024-18-06
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r/technology
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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A friend works for Karma, split from Fisker Karma yrs ago, and they aren’t doing well either. Could have this same headline any day.
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r/technology
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comment
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r/technology
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2024-18-06
|
There are two of them in my neighborhood. They do have a unique sound and you're right, it's not good.
They actually sound like failing electronics. Like a buzzy sort of capacitor whine.
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r/technology
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comment
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r/technology
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2024-18-06
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Have you invested in NIO too?
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r/technology
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comment
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r/technology
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2024-18-06
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I see an SUV looking one in the neighborhood. And the 2 seater one that first came out. I just looked at their site after reading your comment and it looks like they have several new models.
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r/technology
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comment
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r/technology
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2024-18-06
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Back in March they were sitting on roughly 4700 Oceans in their inventory. They went into panic mode because nobody was buying them and slashed prices by up to $24,000 and still couldn't sell them. It's a shame though. I do really like the looks and features and hoped they'd survive.
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r/technology
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comment
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r/technology
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2024-18-06
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It's the 2nd bankruptcy in less than 10 years for a Fisker car company at the very least.
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r/technology
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comment
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r/technology
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2024-18-06
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No, but I do own... Mullen! 🤣🤣😬😬. At least they're not bankrupt!
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r/technology
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comment
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r/technology
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2024-18-06
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EV version of “speed holes”
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r/technology
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comment
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r/technology
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2024-18-06
|
There is a fine line between incompetence and corporate malfeasance when you're the CEO of a publicly traded company. The company committed securities fraud last year when they delayed announcing their Q3 earnings. He took a $700k salary and lets find out how he traded his own personal equity in the company.
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r/technology
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comment
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r/technology
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2024-18-06
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i guess Childish Gambino will need to update the lyrics to Sweatpants now that Fisker’s now a broke ho
(that’s the only time i had heard of Fisker, just so you know)
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r/technology
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comment
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r/technology
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2024-18-06
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They were also sitting on dozens of lemon lawsuits while they filed for chapter 11...
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r/technology
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comment
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r/technology
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2024-18-06
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They *used to* make good gardening tools.
Recently, their quality has dive-bombed hard.
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r/technology
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comment
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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Basically what china is producing today we can do in 6 years.
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r/technology
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comment
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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>Storage isn't a thing.
<gruntsInConfusedEV>
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r/technology
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comment
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r/technology
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2024-18-06
|
<checksExpiryDate>
“Best I can do is $3.50”
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r/technology
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comment
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r/technology
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2024-18-06
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Yes. Worrying about food prices (directly affected by droughts and wildfires) is a very privileged situation /s
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r/technology
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comment
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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Very true. In Australia the people in government want to be able to break all encryption..while at the same time having encryption for themselves.
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r/technology
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comment
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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r/technology
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r/technology
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2024-18-06
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Good. Let’s see that play out.
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r/technology
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comment
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r/technology
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2024-18-06
|
*In a lawsuit filed on Monday, the U.S. state alleges Pfizer made false statements about the safety of its COVID-19 vaccine and then worked to “conceal and suppress” information linking it to various adverse events including myocarditis and failed pregnancies.*
*“Pfizer made multiple misleading statements to deceive the public about its vaccine at a time when Americans needed the truth,” Kansas’ Republican attorney general Kris Kobach said.*
*The lawsuit says Pfizer failed to publicly disclose records of adverse events while also repeatedly stating that its vaccine was safe, even as its own data showed links between the COVID-19 vaccine and various health problems.*
*Pfizer then put pressure on social media platforms including what was then called Twitter to censor statements critical of its vaccine and of the wider response to the COVID-19 pandemic, the lawsuit, which was filed by the state of Kansas and its Republican attorney general Kobach, says.*
*This saw Pfizer contribute towards multiple campaigns aimed at tackling what it said was misinformation. Pfizer board member Scott Gottlieb, the former FDA commissioner, also repeatedly contacted Twitter staff, calling on them to take action against certain COVID-19 related posts, including one by former New York Times reporter Alex Berenson before his account was banned the following day.*
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r/technology
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comment
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r/technology
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2024-18-06
|
I know nothing about vaccines, and I'm not even remotely qualified to say who's right, but I'll laugh my ass off if that conspiracy turned out to be true.
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r/technology
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comment
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r/technology
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2024-18-06
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r/technology
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post
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r/technology
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2024-18-06
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u/dark16sider what was the solution?
|
r/tensorflow
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comment
|
r/tensorflow
|
2023-24-08
|
I am totally new to tf, and I get the following error when trying to import tensorflow as tf" in a Jupyter Notebook.
ModuleNotFoundError: No module named 'tensorflow'
I have pip installed the 2.12 version copy-pasting the code suggested on tensorflow.org after I created an alternative environment I called 'keras' in Anaconda navigator.
I have:
Windows 10
Conda 23.5
Python 3.9.16
Everything looks fine in Anaconda navigator but it does not work when I try to import it. I know it's a common error, I don't seem to find the problem and I am clearly missing something.
I tried opening up the Jupyter Notebook from the keras environment and from the base. I am clearly missing something.
Any help would be appreciated.
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-25-06
|
I have a problem where I'm trying to create an AI model that would recognize different car models, currently I have 8 different car models each with about 160 images of cars in their data folders , but every time I try to run the code
hist=model.fit(train,epochs=20,validation_data=val,callbacks=[tensorboard_callback])
I get a loss that is just exponentially rising into a minus
Epoch 1/20
18/18 [==============================] - 16s 790ms/step - loss: -1795.6414 - accuracy: 0.1319 - val_loss: -8472.8076 - val_accuracy: 0.1625
Epoch 2/20
18/18 [==============================] - 14s 718ms/step - loss: -79825.2422 - accuracy: 0.1493 - val_loss: -311502.5625 - val_accuracy: 0.1250
Epoch 3/20
18/18 [==============================] - 14s 720ms/step - loss: -1431768.2500 - accuracy: 0.1337 - val_loss: -3777775.2500 - val_accuracy: 0.1375
Epoch 4/20
18/18 [==============================] - 14s 716ms/step - loss: -11493728.0000 - accuracy: 0.1354 - val_loss: -28981542.0000 - val_accuracy: 0.1312
Epoch 5/20
18/18 [==============================] - 14s 747ms/step - loss: -61516224.0000 - accuracy: 0.1372 - val_loss: -127766784.0000 - val_accuracy: 0.1250
Epoch 6/20
18/18 [==============================] - 14s 719ms/step - loss: -251817104.0000 - accuracy: 0.1302 - val_loss: -401455168.0000 - val_accuracy: 0.1813
Epoch 7/20
18/18 [==============================] - 14s 755ms/step - loss: -731479360.0000 - accuracy: 0.1476 - val_loss: -1354252672.0000 - val_accuracy: 0.1375
Epoch 8/20
18/18 [==============================] - 14s 753ms/step - loss: -2031392128.0000 - accuracy: 0.1354 - val_loss: -3004264448.0000 - val_accuracy: 0.1625
Epoch 9/20
18/18 [==============================] - 14s 711ms/step - loss: -4619375104.0000 - accuracy: 0.1302 - val_loss: -7603259904.0000 - val_accuracy: 0.1125
Epoch 10/20
2/18 [==>...........................] - ETA: 10s - loss: -7608679424.0000 - accuracy: 0.1094
This is the loss function that I am using
model.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=['accuracy'])
this is my model
model.add(Conv2D(16,(3,3),1,activation='relu',input_shape=(256,256,3)))
model.add(MaxPooling2D())
model.add(Conv2D(32,(3,3),1,activation='relu'))
model.add(MaxPooling2D())
model.add(Conv2D(16,(3,3),1,activation='relu'))
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dense(256,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
I've normalized the data by doing
data=data.map(lambda x,y: (x/255, y))
so the values are from 0 to 1
I've read something online about GPU's so I'm not sure if it's that , I can't find a fix , but I'm using this to speed it up
​
gpus =tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu,True)
Any help is welcome!
I'm trying to train a model and get the loss closer to a zero, and accuracy closer to 1, but it's just exponentially driving into minus infinity.
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-25-06
|
Hello I am trying to run a python file on my schools GPU cluster server.
This server has many GPUs and CPUs to use and I am trying to run a machine learning application.
For some reason even when I request the GPU and it gets allocated my code cannot find the GPU.
I run my code with a .sh file with the following code in it :
\#! /bin/bash -l
\#$ -cwd
\#SBATCH -p Quick -w GPU3
\#SBATCH -p Contributors
\#SBATCH --gpus=1
srun python [myfile.py](https://myfile.py/)
and I have attached the output.
https://preview.redd.it/leo9hlysra8b1.png?width=1330&format=png&auto=webp&s=9aa44d26739775251b0d355b5f684f5d6110eb6f
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-26-06
|
We are currently conducting a beta test for our compute platform and we value external input. Our platform allows you to effortlessly run templates for tensorflow, pytorch, and more. Powered by Nvidia Rtx a4000s, it offers additional advantages such as on-premises persistent storage. If you're interested in participating, please feel free to message!
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-26-06
|
Hi i have a amd 5500xt msi 8gb. i want to use it in machine learning assignment which involves tensorflow and keras ocr how can i do that???
tensor flow isnt picking up my gpu and uses my cpu instead.
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-26-06
|
Hello!
I am new with using TF and just set up everything. I use one of the universal-sentence-encoder and have a bunch of different texts (\~2000) as input. The model then creates the specific embeddings.Now my plan is to calculate the three metrics of the model and visualize it then for this specific amount of input data.
my_model = hub.load("path-to-universal-sentence-encoder")
my_texts = [...]
my_embeddings = [my_model(text) for text in my_texts]
As I have the embeddings for each of my texts, what would be the next proper steps for determining and visualizing these metrics?
Thank you for any specific suggestions and for sharing your experience!
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-27-06
|
I am building a XO (tic tac toe) AI to grasp the basics of tensorflow keras on python. So far I have made the xo environment, and created the model like this:
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(9, activation="relu"))
model.add(tf.keras.layers.Dense(50, activation="relu"))
model.add(tf.keras.layers.Dense(9))
model.compile(optimizer="adam", loss="mse")
I have this (incomplete) function
def ai_move(board):
pass
that makes a move based on this board input:
board = [0, 0, 0, 0, 0, 0, 0, 0, 0]
The question is: How do I train this AI by having 2 instances(?) of it play against each other? What's a smart way to set the rewards?
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-28-06
|
Whenever I try to import tensor flow or spacy I get this error that I have tried everything to solve.
​
For context these are my current versions when I check pkg\_resources.get\_distribution(package).version :
Python version: 3.9.12, pandas: 1.4.2, numpy: 1.21.6, spacy: 3.5.4, tensorflow: 2.12.0, conda: 23.1.0, pip: 23.1.2
​
I have tried the following:
!pip install numpy==1.21.6
​
conda install -c conda-forge spacy
pip install -U spacy python -m spacy [validate](https://spacy.io/api/cli#validate)
​
python -m venv .env
source .env/bin/activate
pip install -U
pip setuptools wheel
pip install -U spacy
​
This is the error:
​
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [7], in <cell line: 4>()
2 import re
3 import nltk
----> 4 import spacy
6 from nltk.corpus import stopwords
7 from nltk.tokenize import word_tokenize
File ~\anaconda3\lib\site-packages\spacy\__init__.py:6, in <module>
3 import sys
5 # set library-specific custom warning handling before doing anything else
----> 6 from .errors import setup_default_warnings
8 setup_default_warnings() # noqa: E402
10 # These are imported as part of the API
File ~\anaconda3\lib\site-packages\spacy\errors.py:2, in <module>
1 import warnings
----> 2 from .compat import Literal
5 class ErrorsWithCodes(type):
6 def __getattribute__(self, code):
File ~\anaconda3\lib\site-packages\spacy\compat.py:3, in <module>
1 """Helpers for Python and platform compatibility."""
2 import sys
----> 3 from thinc.util import copy_array
5 try:
6 import cPickle as pickle
File ~\anaconda3\lib\site-packages\thinc\__init__.py:5, in <module>
2 import numpy
4 from .about import __version__
----> 5 from .config import registry
8 # fmt: off
9 __all__ = [
10 "registry",
11 "__version__",
12 ]
File ~\anaconda3\lib\site-packages\thinc\config.py:4, in <module>
2 import confection
3 from confection import Config, ConfigValidationError, Promise, VARIABLE_RE
----> 4 from .types import Decorator
7 class registry(confection.registry):
8 # fmt: off
9 optimizers: Decorator = catalogue.create("thinc", "optimizers", entry_points=True)
File ~\anaconda3\lib\site-packages\thinc\types.py:8, in <module>
6 import numpy
7 import sys
----> 8 from .compat import has_cupy, cupy
10 if has_cupy:
11 get_array_module = cupy.get_array_module
File ~\anaconda3\lib\site-packages\thinc\compat.py:54, in <module>
51 torch_version = Version("0.0.0")
53 try: # pragma: no cover
---> 54 import tensorflow.experimental.dlpack
55 import tensorflow
57 has_tensorflow = True
File ~\anaconda3\lib\site-packages\tensorflow\__init__.py:37, in <module>
34 import sys as _sys
35 import typing as _typing
---> 37 from tensorflow.python.tools import module_util as _module_util
38 from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
40 # Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
File ~\anaconda3\lib\site-packages\tensorflow\python\__init__.py:42, in <module>
37 from tensorflow.python.eager import context
39 # pylint: enable=wildcard-import
40
41 # Bring in subpackages.
---> 42 from tensorflow.python import data
43 from tensorflow.python import distribute
44 # from tensorflow.python import keras
File ~\anaconda3\lib\site-packages\tensorflow\python\data\__init__.py:21, in <module>
15 """`tf.data.Dataset` API for input pipelines.
16
17 See [Importing Data](https://tensorflow.org/guide/data) for an overview.
18 """
20 # pylint: disable=unused-import
---> 21 from tensorflow.python.data import experimental
22 from tensorflow.python.data.ops.dataset_ops import AUTOTUNE
23 from tensorflow.python.data.ops.dataset_ops import Dataset
File ~\anaconda3\lib\site-packages\tensorflow\python\data\experimental\__init__.py:97, in <module>
15 """Experimental API for building input pipelines.
16
17 This module contains experimental `Dataset` sources and transformations that can
(...)
93 @@UNKNOWN_CARDINALITY
94 """
96 # pylint: disable=unused-import
---> 97 from tensorflow.python.data.experimental import service
98 from tensorflow.python.data.experimental.ops.batching import dense_to_ragged_batch
99 from tensorflow.python.data.experimental.ops.batching import dense_to_sparse_batch
File ~\anaconda3\lib\site-packages\tensorflow\python\data\experimental\service\__init__.py:419, in <module>
1 # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
(...)
13 # limitations under the License.
14 # ==============================================================================
15 """API for using the tf.data service.
16
17 This module contains:
(...)
416 job of ParameterServerStrategy).
417 """
--> 419 from tensorflow.python.data.experimental.ops.data_service_ops import distribute
420 from tensorflow.python.data.experimental.ops.data_service_ops import from_dataset_id
421 from tensorflow.python.data.experimental.ops.data_service_ops import register_dataset
File ~\anaconda3\lib\site-packages\tensorflow\python\data\experimental\ops\data_service_ops.py:22, in <module>
20 from tensorflow.core.protobuf import data_service_pb2
21 from tensorflow.python import tf2
---> 22 from tensorflow.python.data.experimental.ops import compression_ops
23 from tensorflow.python.data.experimental.service import _pywrap_server_lib
24 from tensorflow.python.data.experimental.service import _pywrap_utils
File ~\anaconda3\lib\site-packages\tensorflow\python\data\experimental\ops\compression_ops.py:16, in <module>
1 # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
(...)
13 # limitations under the License.
14 # ==============================================================================
15 """Ops for compressing and uncompressing dataset elements."""
---> 16 from tensorflow.python.data.util import structure
17 from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
20 def compress(element):
File ~\anaconda3\lib\site-packages\tensorflow\python\data\util\structure.py:22, in <module>
18 import itertools
20 import wrapt
---> 22 from tensorflow.python.data.util import nest
23 from tensorflow.python.framework import composite_tensor
24 from tensorflow.python.framework import ops
File ~\anaconda3\lib\site-packages\tensorflow\python\data\util\nest.py:34, in <module>
1 # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
(...)
13 # limitations under the License.
14 # ==============================================================================
16 """## Functions for working with arbitrarily nested sequences of elements.
17
18 NOTE(mrry): This fork of the `tensorflow.python.util.nest` module
(...)
31 arrays.
32 """
---> 34 from tensorflow.python.framework import sparse_tensor as _sparse_tensor
35 from tensorflow.python.util import _pywrap_utils
36 from tensorflow.python.util import nest
File ~\anaconda3\lib\site-packages\tensorflow\python\framework\sparse_tensor.py:25, in <module>
23 from tensorflow.python import tf2
24 from tensorflow.python.framework import composite_tensor
---> 25 from tensorflow.python.framework import constant_op
26 from tensorflow.python.framework import dtypes
27 from tensorflow.python.framework import ops
File ~\anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py:25, in <module>
23 from tensorflow.core.framework import types_pb2
24 from tensorflow.python.eager import context
---> 25 from tensorflow.python.eager import execute
26 from tensorflow.python.framework import dtypes
27 from tensorflow.python.framework import op_callbacks
File ~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py:21, in <module>
19 from tensorflow.python import pywrap_tfe
20 from tensorflow.python.eager import core
---> 21 from tensorflow.python.framework import dtypes
22 from tensorflow.python.framework import ops
23 from tensorflow.python.framework import tensor_shape
File ~\anaconda3\lib\site-packages\tensorflow\python\framework\dtypes.py:37, in <module>
34 from tensorflow.core.function import trace_type
35 from tensorflow.tools.docs import doc_controls
---> 37 _np_bfloat16 = _pywrap_bfloat16.TF_bfloat16_type()
38 _np_float8_e4m3fn = _pywrap_float8.TF_float8_e4m3fn_type()
39 _np_float8_e5m2 = _pywrap_float8.TF_float8_e5m2_type()
TypeError: Unable to convert function return value to a Python type! The signature was
() -> handle
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-28-06
|
Hello everyone,
probably a very noob question, I'm just started in this new magic worl of AI and ML. I've run every tutorial project I could find, i develop my own Dog or Cat model by transfering from MobileNet.
I'm now struggling with the classification of documents.
I have 50 companies that sends us invoices and I want to train a model in order to recognize which company sent us the invoice automatically. The document structure is basically the same (some minor differences in the structure of a table) the main difference lies in the logo of the company of course.
The images are very large, so what I'm trying right now is this:
(using Tensorflow.js if it metters)
https://preview.redd.it/sx7ipq7zqt8b1.png?width=816&format=png&auto=webp&s=5e39fc3e252f4b46ce0b80cee1741d6623a6c27a
This the network i thought it could work.
I process every image in this way:
https://preview.redd.it/2nbdif88rt8b1.png?width=681&format=png&auto=webp&s=7fe5ec1221f9227c5892cc2af99bd34df6938654
Then i try to train the model with this code:
https://preview.redd.it/ysiakhynrt8b1.png?width=711&format=png&auto=webp&s=4212fcfbff972a5ab1a2c652b95970f10916a367
But at this point the log tells me that it will not reach 0.4 as accuracy.
Can you point me in the right direction?
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-28-06
|
I used to write my own models for this one project I'm doing but the results werent great so I want to switch to some premade model but I dont know how to train it on my own images.
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-28-06
|
I'm currently trying to follow a tutorial on tensorflow as I am quite new to the library, but after installing tensorflow, I can't seem to import the tensorflow\_datasets library.
[Imports](https://preview.redd.it/iqcsmqm6kv8b1.png?width=390&format=png&auto=webp&s=aad59c4b07bea3a88e17753afa487da0f9de8805)
the error message reads as
[error message \\"Import \\"tensorflow\_datasets\\" could not be resolved](https://preview.redd.it/4jbk9zp9kv8b1.png?width=851&format=png&auto=webp&s=12d74d92938bf656acaaf17dfbfcbd0c3ffaa71e)
Am I missing something here?
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-29-06
|
I’m new to AI, and I wanted to grasp the basics by making simple projects. I made a sequential model using Keras with python, had 4 layers: input layer 81, 2 hidden layers 128, output layer 81. I loaded the data (csv) using numpy on init, and it went through the whole 800k data set in less than 2 minutes. I thought this was too fast to have actually went through the whole dataset. Am I right to think this?
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-29-06
|
I've been working on building a Sudoku Solver AI. The goal is to take an unsolved Sudoku board (represented as a 1D array of length 81) as input and return a solved board (also a 1D array of length 81) as output. However, I'm encountering some issues. Here's my code:
import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(81, activation="relu"))
model.add(tf.keras.layers.Dense(128, activation="relu"))
model.add(tf.keras.layers.Dense(128, activation="relu"))
model.add(tf.keras.layers.Dense(128, activation="relu"))
model.add(tf.keras.layers.Dense(81))
model.compile(optimizer="adam", loss="mse", metrics="accuracy")
model = tf.keras.models.load_model("sodoku_1m_10e_adam_mse.h5")
"""
Soduko training data
"""
quizzes = np.zeros((1000000, 81), np.int32)
solutions = np.zeros((1000000, 81), np.int32)
for i, line in enumerate(open('sudoku.csv', 'r').read().splitlines()[1:]):
quiz, solution = line.split(",")
for j, q_s in enumerate(zip(quiz, solution)):
q, s = q_s
quizzes[i, j] = q
solutions[i, j] = s
quizzes = quizzes.reshape((-1, 81))
solutions = solutions.reshape((-1, 81))
x_train, x_test, y_train, y_test = train_test_split(quizzes, solutions, test_size=0.2, random_state=42)
def train(model):
model.fit(x_train, y_train, batch_size=32, epochs=10)
def test(model):
loss, accuracy = model.evaluate(x_test, y_test)
print("LOSS: ", loss)
print("ACCURACY: ", accuracy)
def make_move(input_board):
input_data = np.array(input_board).reshape(1, -1)
output_data = model.predict(input_data)
output_board = output_data[0]
output_board = output_data[0]
output_board = np.round(output_board).clip(1, 9)
output_board = output_board.astype(int)
return output_board
​
I trained the model using the train() function, then tested it with the test() function. I thought the make\_move() function would output a solved board, but instead, I'm getting random floats. I then modified the function to output integers between 1 and 9, but the output still seems random. I realized that I haven't explicitly implemented the rules of Sudoku in any way, so even if the output was in the correct format, it might not be a valid solution. I'm not sure how to implement these rules besides repeatedly rejecting invalid boards until a valid one is generated, which doesn't seem efficient.
So the question is: What is wrong with this code? What do I need to do to fix it and make it properly solve sodoku puzzles?
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-29-06
|
Hello everyone,
For the last couple of hours I've been trying to solve a problem of which I'm unsure if it can be fixed, or if I'm trying something that just can't work.
I have collected data from test participants for an emotional analysis, this includes heart rate, galvanic skin response and their facial expression. I have data of 11 participants, with 1Hz sampling, so 480 datapoints per participant. I also have labels that I want to use for training for every datapoint, for every participant, these are unique values (We are calculating their emotional change, so I have a slope value that indicates a positive/negative shift).
We want to train a neural network to be able to determine this slope. My problem is that I have data from 11 participants, in separate csv files. I want the neural network to take each of these 11 files, train on that and update the values, since the relation needs to be assessed within each test participant. Currently I have made 2 networks using LSTM layers, and a CNN for the facial recognition. I use a fusion layer at the end to combine everything.
My question is: Is this a good approach and is this doable, and secondly how do I correctly set this up, especially in regards to reading the data from the different csv files and how to handle the labels (which are also in individual csv files for each participant). Also considering that the end result of the network should be a slope value again.
Thank you very much!
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-29-06
|
Introduction to Tensors in TensorFlow
[https://debuggercafe.com/introduction-to-tensors-in-tensorflow/](https://debuggercafe.com/introduction-to-tensors-in-tensorflow/)
​
https://preview.redd.it/ux54e5ksv19b1.png?width=1000&format=png&auto=webp&s=4ab183e55df9876702a07fd2cb614c6f44da1e5f
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-30-06
|
Hi, I am currently attempting to fit my training datasets into a model but I keep getting a Graph Execution error with my fit. Does anyone have any tips to fix this? Thanks
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-30-06
|
I'm trying to train "manually" a tensorflow network, but the dependence of the loss on the parameters is the following (I will talk about two networks, the one I want to train is NET1):
* Given some input, NET1 gives me an output
* The output from NET1 are imposed as weights of NET2 that, let's say, gives an output "u"
* The loss is computed as some function of "u"
* Now, I want to compute the gradient of the loss with respect to the weights of NET1.
However, the gradients I compute are always zeros.
I tried with the following approach:
def train_step(self, input_weights):
with tf.GradientTape(persistent=True) as tape:
pred_weights = self.NET1(input_weights)
weights = self.transform_weights_from_array(pred_weights)
for j in range(len(weights)):
self.NET2.weights[j].assign(weights[j])
u = self.NET2(SOME_INPUT)
loss = tf.reduce_sum(tf.math.abs(u))
gradients = tape.gradient(loss, self.NET1.trainable_variables,
unconnected_gradients=tf.UnconnectedGradients.ZERO)
where "transform\_weights\_from\_array" is the following:
​
def transform_weights_from_array(self, w_arr):
W = self.NET2.weights
w_shaped = []
k = 0
for i, arr in enumerate(W):
n = 1
for dim in arr.shape:
n *= dim
w_shaped.append(tf.reshape(w_arr[k:k + n], arr.shape))
k += n
return w_shaped
it simply transforms the weights from the vector shape to the list shape.
However, the gradients are not computed as I would have expected.
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-30-06
|
I have a tflite model that I trained on customvision azure to recognize a basketball.
​
When I check the meta data it tells me a lot of stuff that as a beginner i am not sure about what it is supposed to be. For example, my tflite yolo model expects as input a tensor of \[1,13,13,35\]. I get that I am supposed to have one image batch of dimension 13\*13, but why 35? Does that have something to do with the yolo model and the grids?
​
Thanks a lot in advance for any help. This is in flutter how i so far code the screen:
import 'dart:ffi';
import 'dart:math';
import 'package:camera/camera.dart';
import 'dart:io';
import 'package:flutter/material.dart';
import 'package:get/get.dart';
import 'package:hoopster/PermanentStorage.dart';
import 'package:hoopster/statsObjects.dart';
import 'package:tflite\_flutter/tflite\_flutter.dart' as tfl;
import 'dart:typed\_data';
import 'package:image/image.dart' as img;
import 'package:image\_gallery\_saver/image\_gallery\_saver.dart';
import 'package:path\_provider/path\_provider.dart';
import '../main.dart';
import 'home\_screen.dart';
int i = 0;
late CameraImage \_cameraImage;
int counter = 0;
String lastSaved = "";
int Hit = 0;
int Miss = 0;
var height;
var width;
class CameraApp extends StatefulWidget {
const CameraApp({Key? key}) : super(key: key);
u/override
State<CameraApp> createState() => \_CameraAppState();
}
class \_CameraAppState extends State<CameraApp> {
late CameraController controller;
late Future<void> \_initializeControllerFuture;
String \_videoPath = '';
u/override
void initState() {
super.initState();
controller = CameraController(
cameras.last,
ResolutionPreset.medium,
);
// Initiate the loading of the model
loadModel().then((interpreter) {
// Model has been loaded at this point
\_initializeControllerFuture = controller.initialize().then((\_) {
controller.startImageStream((image) {
\_cameraFrameProcessing(image, interpreter);
});
if (!mounted) {
return;
}
setState(() {});
}).catchError((Object e) {
if (e is CameraException) {
switch (e.code) {
case 'CameraAccessDenied':
// Handle access errors here.
break;
default:
// Handle other errors here.
break;
}
}
});
});
}
void \_cameraFrameProcessing(CameraImage image, tfl.Interpreter interpreter) {
\_cameraImage = image;
processCameraFrame(image, interpreter); // Process each camera frame
}
Future<tfl.Interpreter> loadModel() async {
return tfl.Interpreter.fromAsset('Assets\\\\model.tflite');
}
Future<void> processCameraFrame(
CameraImage image, tfl.Interpreter interpreter) async {
try {
print('processing camera frame');
// Convert the CameraImage to a byte buffer
Float32List convertedImage = convertCameraImage(image);
// Create output tensor. Assuming model has a single output
var output = interpreter.getOutputTensor(0).shape;
print(output);
// Create input tensor with the desired shape
var inputShape = interpreter.getInputTensor(0).shape;
//print(inputShape);
print("eo");
//var inputShape = \[1, 13, 13, 35\];
var inputTensor = <List<List<List<dynamic>>>>\[
List.generate(inputShape\[1\], (\_) {
return List.generate(inputShape\[2\], (\_) {
return List.generate(inputShape\[3\], (\_) {
return \[
0.0
\]; // Placeholder value, modify this according to your needs
});
});
})
\];
print("mamaaaaaa");
print(inputTensor);
print(convertedImage.length);
// Copy the convertedImage data into the inputTensor
for (int i = 0; i < convertedImage.length; i++) {
print("see");
int x = i % inputShape\[2\];
int y = (i \~/ inputShape\[2\]) % inputShape\[1\];
int c = (i \~/ (inputShape\[1\] \* inputShape\[2\])) % inputShape\[3\];
//print("see2");
inputTensor\[y\]\[x\]\[c\]\[0\] = convertedImage\[i\];
print("$x,$y,$c,$i");
}
// Run inference on the frame
print("here, line 116");
interpreter.runForMultipleInputs(inputTensor, {0: output});
print(output);
// Process the inference results
//print("here2, line 120");
//processInferenceResults(output);
} catch (e) {
print('Failed to run model on frame: $e');
}
print('done executing');
}
Float32List convertCameraImage(CameraImage image) {
print('converting image');
final width = image.width;
final height = image.height;
final int uvRowStride = image.planes\[1\].bytesPerRow;
final int? uvPixelStride = image.planes\[1\].bytesPerPixel;
// Create an Image buffer
img.Image imago = img.Image(width, height);
for (int x = 0; x < width; x++) {
for (int y = 0; y < height; y++) {
final int uvIndex =
uvPixelStride! \* (x / 2).floor() + uvRowStride \* (y / 2).floor();
final int index = y \* width + x;
final int yValue = image.planes\[0\].bytes\[index\];
final int uValue = image.planes\[1\].bytes\[uvIndex\];
final int vValue = image.planes\[2\].bytes\[uvIndex\];
List rgbColor = yuv2rgb(yValue, uValue, vValue);
// Set the pixel color
imago.setPixelRgba(x, y, rgbColor\[0\], rgbColor\[1\], rgbColor\[2\]);
}
}
// Resize the image to 13x13
img.Image resizedImage = img.copyResize(imago, width: 13, height: 13);
// Create a new Float32List with the correct shape: \[1, 13, 13, 35\]
Float32List modelInput = Float32List(1 \* 13 \* 13 \* 35);
// Copy the resized RGB image data into the first three channels of the model input
for (int i = 0; i < 13 \* 13; i++) {
int x = i % 13;
int y = i \~/ 13;
int pixel = resizedImage.getPixel(x, y) \~/ 255;
;
modelInput\[i \* 35 + 0\] = img.getRed(pixel).toDouble();
modelInput\[i \* 35 + 1\] = img.getGreen(pixel).toDouble();
modelInput\[i \* 35 + 2\] = img.getBlue(pixel).toDouble();
}
// Fill in the remaining 32 channels with zeros (or whatever is appropriate for your model)
for (int i = 0; i < 13 \* 13; i++) {
for (int j = 3; j < 35; j++) {
modelInput\[i \* 35 + j\] = 0.0;
}
}
print('finished converting image');
// Now you can use modelInput as the input to your model
return modelInput;
}
void processInferenceResults(List<dynamic> output) {
print('test');
print(output.toString());
// Process the inference output to get the labels and their coordinates
List<Map<String, dynamic>> labels = \[\];
for (dynamic label in output) {
String text = label\['label'\];
double confidence = label\['confidence'\];
Map<String, dynamic> coordinates = label\['rect'\];
// Check if the label is "ball" or "hoop"
if (text == "ball" || text == "hoop") {
labels.add({
'text': text,
'confidence': confidence,
'coordinates': coordinates,
});
}
}
if (labels.isEmpty) {
// No recognitions found, do nothing
return;
}
// Do something with the filtered labels
// ...
}
u/override
void dispose() {
controller.dispose();
super.dispose();
}
Future<void> \_onRecordButtonPressed() async {
try {
if (controller.value.isRecordingVideo) {
final path = await controller.stopVideoRecording();
setState(() {
\_videoPath = path as String;
});
//processVideo(
// \_videoPath); // Pass the video path to the processing function
} else {
await \_initializeControllerFuture;
final now = DateTime.now();
final formattedDate =
'${now.year}-${now.month}-${now.day} ${now.hour}-${now.minute}-${now.second}';
final fileName = 'hoopster\_${formattedDate}.mp4';
final path = '${Directory.systemTemp.path}/$fileName';
print(path);
//await controller.startVideoRecording();
}
} catch (e) {
print(e);
}
}
Future<void> stopVideoRecording() async {
if (!controller.value.isInitialized) {
return;
}
if (!controller.value.isRecordingVideo) {
return;
}
try {
await controller.stopVideoRecording();
} on CameraException catch (e) {
print('Error: ${e.code}\\n${e.description}');
return;
}
}
Future<void> \_saveImage(List<int> \_imageBytes) async {
counter++;
final directory = await getApplicationDocumentsDirectory();
final imagePath = '${directory.path}/frame${counter}.png';
lastSaved = imagePath;
final imageFile = File(imagePath);
await imageFile.writeAsBytes(\_imageBytes);
print('Image saved to: $imagePath');
}
void capture() async {
int \_1 = Random().nextInt(20);
int \_2 = Random().nextInt(20);
DateTime n = DateTime.now();
setState(() {
// allSessions.add(Session(n, \_1, \_2));
// lView = globalUpdate();
});
if (\_cameraImage != null) {
Uint8List colored = Uint8List(\_cameraImage.planes\[0\].bytes.length \* 3);
int b = 0;
img.Image image = \_cameraImage as img.Image;
var input = \[1, 13, 13, 3\];
//img.Image image = convertCameraImage(\_cameraImage);
img.Image Rimage = img.copyRotate(image, 90);
\_saveImage(Rimage.data);
// Convert the image to RGB format using image package
// img.Image image = img.Image.fromBytes(
// \_cameraImage.width,
// \_cameraImage.height,
// \_cameraImage.planes\[0\].bytes,
// format: img.Format.yuv420,
// );
// img.Image Rimage = img.copyRotate(image, 90);
// \_saveImage(Rimage.getBytes(format: img.Format.rgb));
// Run inference on the converted image
// Process the inference results
}
}
@override
Widget build(BuildContext context) {
if (!controller.value.isInitialized) {
return Container(
color: Color.fromARGB(255, 255, 0, 0),
);
}
return Scaffold(
body: Container(
child: Column(
children: \[
SizedBox(child: CameraPreview(controller)),
Expanded(
child: Container(
color: Color.fromARGB(255, 93, 70, 94),
child: Row(
mainAxisAlignment: MainAxisAlignment.center,
children: \[
Text(
Hit.toString(),
style: TextStyle(
fontFamily: "Dogica",
fontSize: 60,
color: Color.fromARGB(255, 0, 255, 0),
),
),
Padding(
padding:
EdgeInsets.fromLTRB((w / 3) - 65, 0, (w / 3) - 65, 0),
child: GestureDetector(
child: Container(
height: 80,
width: 80,
decoration: BoxDecoration(
image: DecorationImage(
image: AssetImage(basketButton),
fit: BoxFit.fill,
),
boxShadow: \[
BoxShadow(
color: Color.fromARGB(80, 0, 0, 0),
spreadRadius: 1,
blurRadius: 5,
)
\],
color: Color.fromARGB(0, 255, 255, 255),
borderRadius: BorderRadius.all(
Radius.circular(30),
),
),
),
onTap: () => {
//capture(),
setState(() {
Miss++;
Hit++;
})
},
onDoubleTap: () => {
//Session s= Session(DateTime.now(), 10, 7);
},
),
),
Text(
Miss.toString(),
style: TextStyle(
fontFamily: "Dogica",
fontSize: 60,
color: Color.fromARGB(255, 255, 0, 0),
),
),
\],
),
),
),
\],
),
),
);
}
}
Uint8List yuv2rgb(int y, int u, int v) {
double yd = y.toDouble();
double ud = u.toDouble() - 128.0;
double vd = v.toDouble() - 128.0;
double r = yd + 1.402 \* vd;
double g = yd - 0.344136 \* ud - 0.714136 \* vd;
double b = yd + 1.772 \* ud;
r = r.clamp(0, 255).roundToDouble();
g = g.clamp(0, 255).roundToDouble();
b = b.clamp(0, 255).roundToDouble();
return Uint8List.fromList(\[r.toInt(), g.toInt(), b.toInt()\]);
}
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-30-06
|
ERROR: type should be string, got " \n\nhttps://preview.redd.it/f2rv55r7269b1.jpg?width=1280&format=pjpg&auto=webp&s=d28dcd2e176582c1ad60d94f1aceb3c82e9b2a6f\n\nDiscover how to classify audio chords with our latest YouTube tutorial!\n\nIn our latest video tutorial, we will show you how to use a convolutional neural network (CNN) to classify audio chords. 🎧🌈 \n\nWe will start by examining a few audio files and playing them back. Then, we will code a transform process to convert the audio files to spectrogram images. Spectrogram images are visual representations of sound waves. They can be used to identify different frequencies and amplitudes, which can be used to classify chords.\n\nNext, we will write a CNN model to generate a binary classification between major and minor chords. We will train the model on a dataset of spectrogram images that have been labeled with the correct chord. The model will learn to identify the features of each chord and to classify them accordingly.\n\nFinally, we will test the model on a new set of spectrogram images that have not been labeled. The model will predict the chord for each image and you can compare its predictions to the ground truth labels.\n\nThis video is for anyone who is interested in learning how to use deep learning to classify audio chords. It is also a good resource for music producers who want to use machine learning to improve their music.\n\nI hope you enjoy the video!\n\nIf you are interested in learning modern Computer Vision course with deep dive with TensorFlow , Keras and Pytorch , you can find it here : [http://bit.ly/3HeDy1V](http://bit.ly/3HeDy1V)\n\nPerfect course for every computer vision enthusiastic\n\nactually recommend this book for deep learning based on Tensorflow and Keras : [https://amzn.to/3STWZ2N](https://amzn.to/3STWZ2N) I \n\nCheck out our tutorial here : [https://youtu.be/DOOA\\_kaiHSo](https://youtu.be/DOOA_kaiHSo)\n\nYou can find the code for this video here : [https://ko-fi.com/s/585fb97174](https://ko-fi.com/s/585fb97174)\n\nEnjoy\n\nEran\n\n​\n\n\\#DeepLearning #AudioClassification #SpectrogramAnalysis #MusicAI #audioclassification **#computervision** **#tensorflow**" |
r/tensorflow
|
post
|
r/tensorflow
|
2023-30-06
|
I am trying to work on fermi-net a deeplearning model. Unfortunately for me, It is written in tensorflow all the while the language I know is pytorch. So I am transitioning to tensorflow. Is there anything I should know? Perhaps a resource that I can use? Any help would be appreciated.
|
r/tensorflow
|
post
|
r/tensorflow
|
2023-01-07
|
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