Name: 7.10: TensorFlow.js 色分類器。モデルのトレーニング (7.10: TensorFlow.js Color Classifier: Training the Model)
Uploaded: 2021-01-14T10:25:58.000Z
Duration: 13 min 34 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：

So so far, you know, hopefully you've watched all the previous parts of the serious If you haven't, that's fine, too.

But what I have so far is I've prepared my data set, loaded it from a Jason file.

And then I created a model TF using technology as a T F sequential model, which is designed to receive Archie be inputs and outputs a probability distribution for color labels.

And, you know, again, this is somewhat of a trivial scenario.

But I'm classifying data, simple data with just three guys all between 01 and nine.

So now that I have, this is actually, like, always fears going over in, like, two seconds.

All I need to do is call model dot fit so modeled off it.

Now, what do I need to pass to model dot Fit?

Well, the idea of model dot fit is that I'm saying, Hey, here's the training data here.

All the inputs and their associated target outputs, which I have called exes and wise.

Now, I think I'm gonna get an error right now.

Let me just actually run this, and I'm going to absolutely run this and see if I get the error that I'm expecting.

So a couple of days, welcome to your life doing machine learning shaped mismatching.

You have to think about this one error when checking input expected dense input to have shaped three, but got array with shape 5643 3 So I guess I'm sending in not just three inputs.

The shape of my inputs is many s, so I think if I just do Uh huh.

So I made a mistake, and I used input dimensions where what I really meant was input shape.

All right, let's go look at the documentation and see what it says there.

And I actually, I've got pulled up already.

So you could see what I specified was input dimensions.

Um, if specified defines input shape as bracket input dimensions.

Oh, So actually, I don't even need those that those array brackets there, and that should fix it.

But if I wanted to use those a rape rockets because I'm sending in many data points, I could actually just specify the input shape directly.

And this would then have the array brackets around it.

So too subtle distinction, I think because only input dimensions is documented.

Okay, So now you see, we've got the I wonder why that didn't go because I didn't call fit before.

Modeled outfit returns a promise if you don't know what a promise is gets what?

I have a whole set of videos about what it promises.

I'm also gonna be using eventually awaiting a sink, which also videos about.

But right now, I can just right the dot Then a prom fit returns a promise which I can then call a function called then Thio, where the results will be passed in.

And I'm just going to say, uh, I'm gonna use this arrow syntax this Essex Aargh.

Syntax console dot Log results, and eventually I might want to do more with this, so I'm actually gonna make it a full function.

So this is what I'm saying is, once you fit the model, then log the results.

Look at this history loss, and there's my loss.

What I wanna do is, first of all, I want to train the model for more than just one epoch.

So one thing that I need to do here is passed in some options, so I'm gonna create a variable called options.

And one thing I could specify is like pox.

I'm gonna say do it for 10 and then I'm gonna say, Let's actually let's just say to right now, it's gonna take a while.

So the third argument to modeled outfit is options.

And I look for a model, don't fit because I was right there already.

These are the various options, and I'm gonna be using a bunch of these butt pox.

Is one of them the number of times to generate over the training data.

Um, so let's rub this now and you're gonna do I'm gonna I don't think we need all of this printing stuff.

So I'm gonna get rid of some of the earlier printing things because I don't need to look at all of that s o much.

So let's run, miss, uh, whips options is not defined.

I guess I still have 44 45 uh, console logging stuff, which I don't need.

E didn't get an error that I expect to get, Which is kind of interesting.

One thing that I want to do is I want an update.

You know, at the time of this recording, I think the most recent version of tension Flow Digest is 0.11 point seven.

Um, on when I was previously recording, I was using 0.4, and I think some things have changed.

All right, so let's let this run its running for two box.

Right now, it's finished, and I could look at the history and I can see both lost so we can see the loss went down for the second.

Now let's run this over 10 pox and let's run this.

And let's just console log results Not lost by the way or what was it, is it results dot history dot loss might be that.

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7.10: TensorFlow.js 色分類器。モデルのトレーニング (7.10: TensorFlow.js Color Classifier: Training the Model)

stuff

eventually

expect

split