Name: TensorFlowと深層強化学習、博士号なしで（Google I/O '18 (TensorFlow and deep reinforcement learning, without a PhD (Google I/O '18))
Uploaded: 2021-01-14T10:33:31.000Z
Duration: 40 min 47 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：

Welcome to this Tensorflow session about rain enforcement.

And today we would like to build a neural network with you.

If I say a serious question, if I say soft max or across entropy, raise your hand if you know what that is.

Those neurons, they always do the same thing they do awaited some off all of their inputs, and then you can layer them in layers.

The neurons in the first layer they do awaited some off.

If we are analyzing images, then you don't see in the second layer will be doing weighted.

Sum some off outputs from the first layer.

And if you're building a classifier, let's say here probably you want to classify those little square images into airplanes and no airplanes.

You will end up on the last layer, which has as many neurons as you have classes.

Yeah, if those weights or configured correctly, we'll get to that that one of those neurons will have a strong output on certain images and tell you this is an airplane or the other one we have.

We'll have a strong output that you will tell.

So a little bit more details about what a neuron does for those who like it.

You have the full transfer function here.

That's just an additional degree of freedom.

And then you feed this through activation function.

And in your own networks, that is always a non linear function.

And to simply five for us here on Lee to activation functions that counts.

So for all the intermediate layers, the white laters.

That's the simplest function you can imagine.

Let's not go any further on the last layer, though.

If we are building a classifier, typically what you use is the soft max activation function, and that is a new exponential, followed by a normalization.

So here I have a different classifier that classifies in 10 classes, and you have your waited sums coming out of your 10 final neurons.

And what you do in Soft Max is that you elevate all of them to the exponential, and then you compute the norm off that vector of 10 elements, and you divide everything by north.

The effect of that, since exponentially it's very steeply increasing function is that it will pull the winner apart.

I made a little animation to show you like this.

So yours, you see much clearer which off those neurons is indicating the winning class.

Okay, that's why it's called soft plaques.

It pulls the winner and parks like Max, but it doesn't completely destroy the rest of the information.

Okay, so just those two activation functions with that, we can build the stuff we want to build.

So now, coming out over a neural network, we have our let's say, here, 10 final neurons producing values which have been normalized between zero and one.

We can say those values are probabilities.

Okay, The probability off this image being in this class, How are we going to determine the weights in those weighted sums?

Initially, it's all just, you know, random.

So initially, our network doesn't do anything actually so useful.

So you provided images which you have labeled beforehand.

And on those images, your network is going to output this set of probabilities.

But you know what the correct answer is because you are doing supervised learning, so you will include your correct answer.

In a format that looks like what the network is producing, it's the simplest encoding you can think of.

It's called one harting coating, and basically it's a bunch of zeros was just 11 in the middle at the index off the class you want so here to represent the six, I have a vector of zeros with the one in the sixth position, and now those vectors were very similar.

I can compute the distance between them and the good of the people who studied this in a classifier, they tell us, don't choose any distance used across entropy distance.

I just follow in the interest of the cross entropy distances computed like this.

So you multiply element by element wth e elements of the vector, the known answer from the top by the logarithms off the probabilities you got from your neural network and you then sum that up across the fact this is the distance between what the network has predicted and the correct answer.

If you want to trade a neural network, you get that it's called a natural function or lost function.

From there on, Tensorflow can take over and do the training for you.

So in a nutshell, those are the ingredients in our part that I want you to be aware off.

You have only to activation functions that you can use either the Railly activation function on intermediate layers or if you build a classifier on the last layer.

And the other function that we are going to use is the cross entropy error function that I had on the previous line.

Okay, all good on this bit of code, this is how you would write it in Tensorflow.

There is a high level A P I in Tensorflow called layers where you can in Stan.

You see, I mean, Stan, she the first layer here has 200 neurons and is activated by the radio activation function.

It's this layer here and this also in Stan.

She hates the weights and biases for this layer in the background.

You don't see that it's in the background.

I have a second later here, which is this one is just 20 neurons, again ready activation function on this.

Do this little final layer, so it's a dense layer again with two neurons, and even if you do don't see it on the screen, it is activated by soft max, but you don't see it because I use its output in the cross entropy function, which has so slacks built in.

It's just that you don't see Ridge itself backs in the coat.

Finally, this here is my better function, which is the distance between the correct answer here.

One hot encoded and the output from my neural net.

Once I have that, I can give it to tensorflow taken optimizer.

Ask it to optimize this loss and the magic will happen.

Magic Tensorflow will take this at our function.

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TensorFlowと深層強化学習、博士号なしで（Google I/O '18 (TensorFlow and deep reinforcement learning, without a PhD (Google I/O '18))

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