Name: モデルのトレーニング - ディープラーニング、Python、TensorFlowでチャットボットを作る p.7 (Training a Model - Creating a Chatbot with Deep Learning, Python, and TensorFlow p.7)
Uploaded: 2021-01-14T12:27:57.000Z
Duration: 17 min 21 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

様態の副詞

And welcome to part seven of our chapel with python tensorflow and Deep Learning tutorial Siris in this video over me doing is actually one deploying a model but to talking about a real high level sense, at least two major kind of model frameworks that I've personally used for chatbots.

Then we'll go ahead and deploy model, and then we'll talk about some more of the intricacies of how those models work and what you can kind of do to tweak them.

So first of all, when I have, like, first started looking into doing chatbots, it was actually kind of challenging to figure out like Okay, well, okay, let's say you wanted to deep learning and you don't want to do a rule Bates chat by like, what do you even used to do that right?

There was so much information out there for doing like a rule based chap, most likely because the most popular and the most successful chatbots, really to date have been rule based, at least to some degree, arguably the most successful ones.

Right now, we're kind of like mixtures of the to like have rules and have a I.

And as we'll see here, and at least all the models I've had almost always needed to upend some rules to the output if not to stop the A I just repeating itself, but also just to stop responses that just never make sense or whatever.

So chances are you're almost always gonna need a combo of the two.

But maybe in a few years we'll be able to make it advanced enough chat bots that don't.

Anyway, as I was looking, I stumbled into sequences, sequence stuff with tensorflow.

And, um, I got kinda hooked into their translation tutorials at the time.

See, this is for specifically version 1.1.

Um, actually, I don't even think this works in 1.1 because they're using a different version of sequence to sequence like, I don't think this actually matches the code that's under get hub.

But regardless, if you want her in this code, you really should be using tensorflow one point like you actually can't even use the latest version of tensorflow, which is unfortunate because, um, this is much slower.

If anybody's been following the stream or the chap on on Twitter for the first while it was actually based completely on this model.

So I was like, you know, reading through this model and seeing you know, this one was English to French translation.

Um, but then I was like, Well, if you can translate from English friend, surely you could translate, um, uh, English to English.

Now, as I'm gonna talk a little bit as we get into the intricacies, a champion is not English to English translation.

It's actually, I would argue a chap out is more complex than translating English to French.

But we'll talk about that a little more later but basically boils down to there's no 100% translation.

There's not even like three translations that it could be.

There's like millions of translations we could have.

So any input in chat input could have just infinite chat outputs that would be acceptable.

So anyway, that's why that's it's much harder, in my opinion.

But regardless, this was the first model I used.

Um, I did a I'm trying to remember back now, I think it was a 10 24 by three model.

If I recall, right, that was the size of the network.

Um, and that was pretty much all that was the premise.

The only thing you really need to switch around.

Um, anyway, this produced a decent chap pot, but I just wasn't exciting.

So then, as I'm following the updates coming out of tensorflow, you've got dynamic, recurrent neural networks.

Then we've got the attention mechanisms and bidirectional neural networks.

Bi directional current neural networks, That is, um and so that leads us here to the neural machine.

That's a much more recent is actually still being updated by tensorflow.

If you do follow the sequence of sequence, just will say it's because it's using an outdated form of secrets and sequence specifically because it doesn't support the, um I don't know if it's officially the dynamic Orin, and that's intense air flow, but that's basically what it is.

The sequence didn't support a dynamic variable input.

Basically, so brings us to this the neural machine translation you could go through the editorial.

There's probably a lot of good information here that you could continue to learn.

Um, but basically, it's It's fairly similar to even this tutorial, at least in explaining sequence to sequence and what's going on because you basically you've got the input string.

It needs to be fed through some sort of encoder, and then it gets fed through the neural network and then that output, because again it has to be converted to numbers.

Somehow then that gets fed through a decoder, which translates into whatever you want.

So anyway, um, this is more along the lines of what we're going to be using.

And like I said, if you want, you can follow through here.

Um, and you can download it from here, but we've actually made a few changes.

Which brings me to the following project I've been working on with Daniel here.

It's basically a set of utilities sitting on top of tensor flows and empty coat, but we didn't need to make one change to an empty.

But at least at the time, um, nnt requires tensorflow 1.4 point out.

But anyway, one of the utility scripts that basically checks to make sure you have the right version it check for, like, a really specific version, which pretty much always failed.

So anyway, we made that change, and then we might have to make changes later on s.

Oh, yeah, you can just grab this recursive lee and get that.

So you come down here and see this lovely read me that we've been building, but basically get the package.

So get clone recursive and then we'll settle this up, so I'm actually gonna go ahead and run through that myself.

You can actually do this locally on your CPU.

You could, in theory, trained it on your CPU.

So I'm gonna go ahead and I'll put a link in the description for referral code.

If anybody wants it, it's $10 which will be more than enough to get you through training a decent chap out, which again in production, Like the chap on on Twitter That responds to you within seconds.

In response to multiple people in seconds, I made him with, um, or he is in production on a CPU VPs on digital ocean.

So, uh, so you can definitely run in production on a CPU.

So anyways, uh, on a box, But I want 16 0 for, uh, if you are falling and you're gonna go to paper space, make sure you choose 16 or force you got Python three tens of low 1.4.

All that good stuff, this'll is probably gonna be fine.

Uh, now we'll just call this an empty tutorial.

I do want a public i p if I want to transfer some data and good creates space.

Okay, so I'm gonna create that, and then I'm gonna posit while I'm waiting for my password and all that, and for let's get split up and then our, uh, restart as we, uh as I can log in and actually grab to get having all that all right.

Something came up, but I guess to you, it doesn't seem like it took any time.

So anyways, once you've got your machine up, I'm gonna go ahead.

And Well, first of all, let me pull up that get home page these fire Fox.

So lash Daniel, I'll put a link in the description.

By the way, if I forget someone remind me, uh, and I'll put one in there cook, yella and an empty dash chat.

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モデルのトレーニング - ディープラーニング、Python、TensorFlowでチャットボットを作る p.7 (Training a Model - Creating a Chatbot with Deep Learning, Python, and TensorFlow p.7)

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