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you've seen how a basic RNN works in.this video you learn about the gated.recurrent unit which is a modification.to the RN and hidden layer that makes it.much better at capturing long range.connections and helps a lot with the.vanishing gradient problems let's take a.look.you've already seen the formula for.computing the activations ad time T of.an RN n is the activation function.applied to the parameter W a times the.activations for previous time set up in.the current input and then plus a bias.so I'm going to draw this as a picture.so the RN end unit is I'm going to draw.as a picture drawn as a box which inputs.a of t minus 1 the activation for the.last time step and also inputs XT and.these two go together and after some.weights and after this type of linear.calculation if G is a teenage activation.function then after the ten age it.computes the output back to Batian a and.the active output activation 80 might.also be passed to you know say a softmax.unit or something that could then be.used to output Y hat team.so this is maybe a visualization of the.RN and unit of the hidden layer of the.RN n in terms of a picture and I want to.show you this picture because we're.gonna use a similar picture to explain.the GI you are the gated recurrent unit.lot of the ideas of GI use were due to.these two papers respectively by June.Yong Chun.Caggiula gasa here Chun Kuen Cho and.Yoshi Benjo and sometimes gonna refer to.this sentence which we'd seen the last.video to motivate that given a sentence.like this you might need to remember the.cat was singular to make sure you.understand why that was rather than were.right so the cat was for the cats were.full so as we read in the sentence from.left to right the GRU unit is going to.have a new variable called.C which stands for cell for memory cell.and what the memory cell do is it will.provide a bit of memory to remember for.example whether cat was singular or.plural so that when it gets much further.into the sentence it can still worth.under consideration whether the subject.of the sentence was singular row to row.and so at time T the memory cell will.have some value C of T and what we'll.see is that the GRE unit will actually.output an activation value a of T that's.equal to C of T and for now I wanted to.use different symbols C and a to denote.the memory cell value and the output.activation value even though they are.the same I'm using this notation because.we talked about LST Ms a little bit.later this will be two different values.but for now for the GRU C of T is equal.to the output activation a of T so these.are the equations that govern the.computations of a GRU unit and every.time stem we're going to consider.overwriting the memory cell with a value.C toll there of T so there's gonna be a.candidate for replacing.of T and we're gonna compute this using.an activation function Chan H of W C and.so that's the parameter matrix W C and.we'll posture this parameter matrix the.previous value of the memory cell the.activation value as well as the current.input value XT and then plus a bias so C.tilde of T is going to be a candidate.for replacing C T and then the key.really the important idea of the gru it.will be that we'll have a gate so the.gate I'm gonna call gamma u this is the.capital Greek alphabet gamma subscript u.and u stands for update gate and this.will be a value between 0 and 1 and for.to develop your intuition about how GI.Jews were think of gamma U this gate.value as being always 0 or 1 although in.practice you know you computed with a.sigmoid function applied to this so.remember that the sigmoid function looks.like this and so as values always.between 0 and 1 and for most of the.possible ranges of the input the sigmoid.function is either very very close to 0.or very very close to 1 so for intuition.think of gamma as being you know either.0 or 1.most of the time and this alphabet U.stands for so you know I chose the.alphabet gamma for this because if you.look at the gate to the fence you know.looks a bit like this I guess then you.know there are a lot of sort of gammas.in this fence so that's why your gamma.you were gonna use to denote the gate.oh also Greek alphabet G right G for.gate so G for gamma and G for gained and.then Nick's the key part of the GI u is.this equation which is that we have come.up with a candidate way we're thinking.of updating see you.using SI tilde and then the gates will.decide whether or not we actually.updated and so the way to think about it.is maybe this memory cell see is going.to be set to either 0 or 1 depending on.whether the word you are considering.really the subject of the sentence is.singular or plural so because a singular.let's say that we said this one and it.was plural maybe ou set it 0 and then.the GRU unit will memorize the value of.the CT all the way until here where you.know this is still equal to 1 and so.that tells it it was singular so use the.choice was and the job of the gate of.gamma you is it decides when do you.update this value so in particular when.you see the phrase VCAT you know that.you're talking about a new concept the.subject of sentences can so that would.be a good time to update this bit and.then maybe when you're done using it you.know the cat blah blah blah was full.then you know okay I don't need to.memorize anymore.I can I can just forget that so the.specific equation were used for the GRU.is the following which is that the.actual value of C T will be equal to.this gate times the candidate value plus.1 minus the gate times the old value C t.minus 1 so notice that if the gate if.this update value of this is equal to 1.then the saying set the new value of C T.equal to this candidate value so that's.like over here set the gate equal to 1.so go ahead and update that bit and then.for all of these values in the middle.you should have the gate equals 0 so do.the same don't update it don't update it.don't update it just hang on to the old.value because if gamma U is equal to 0.then this will be 0 and this would be 1.and so it's just setting C T equal to.the old value.you scan the sentence from left to right.so when the gate is equal to zero is.saying don't help they said don't update.- just hang on to the value and don't.forget what this value was and so that.way even when you get all the way down.here hopefully you've just been setting.CT equal CT minus one all along and it.still memorizes the cat was singular so.let me also draw a picture to denote the.GRU unit and by the way when you look at.num online blog pose and textbooks and.tutorials.these types of pictures are quite.popular for explaining gr use as well as.we'll see later Oh STM units I.personally find the equations easier to.understand in the pictures.so if the picture doesn't make sense.don't worry about it without but I just.draw if in case it helps some of you but.so a gr u-- unit inputs CT - one for the.previous time step and this happens to.be equal to 80 minus 1 so it takes as.input and then it also takes this input.XT then these two things get combined.together and with some appropriate.weighting and some 10h this gives you.see told er T which is a candidate for.replacing CT and then with different set.of parameters and through a sigmoid.activation function this gives you gamma.U which is the update gate and then.finally all of these things combined.together through another operation and I.won't write out the formula but this box.here which are shaded in purple.represents this equation which we had.down there so that's what this purple.operation represents and it takes as.input the gate value they can to the new.value or that's the gate value again and.the o value for CT right so it takes as.input this this and this and together.they generate the new value for the.memory cell and so that's C T equals.eight and if you wish you could also use.this and pass this to softmax or.something to make some prediction for.you know why.why a team so that is the GRU unit or at.least a slightly simplified version of.it and what is remarkably good at is.through the gate deciding that when.you're scanning the sentence from left.to right say that that's a good time to.update once the Chimera cell and then to.not change it not change it until you.get to the point where you really need.it to use this memory cell D has set.even much earlier in the sentence and.because the sigmoid value now because.the gate is quite easy to set to zero.right so so long as this quantity is a.large negative value then up to the.America roundoff the update game will be.essentially zero very very very close to.zero so when that's the case then this.update equation in subsetting C T equals.CT minus one and so this is very good at.maintaining the value for the cell and.because gamma can be so close to zero.zero point zero zero zero zero zero one.or even smaller than that it doesn't.suffer from much of a vanishing gradient.problem because you say gamma so close.to zero that this becomes essentially C.T equals C t minus one and the value of.C T is maintained pretty much exactly.even across many many many many time.steps so this can help significantly.with the vanishing gradient problem and.therefore allow a neural network to.learn even very long range dependencies.such as the captain was are related even.if the separated by a lot of words in.the middle now I just talk over some.some more details of how you implement.this in the equations are written C T.can be a vector so if you have a hundred.dimensional hidden activation value then.CT can be a hundred dimensional say and.so C tilde T would also be the same.dimension.and gammer would also be the same.dimension as the other things on drawing.in boxes and in that case these.asterisks are actually element-wise.multiplication so here if gamma you if.the gates is a hundred dimensional.vector what it is is really a hundred.dimensional a vector of bits values.mostly zero and one that tells you of.this hundred dimensional memory cell.which are the bits you want to update.and of course in practice gamma won't be.exactly zero one sometimes those take.values in the middle as well but it's.convenient for intuition to think of it.as mostly taking on values that you know.exactly zero pretty much exactly zero or.pretty much exactly one and what these.element-wise multiplication do is it.just element wise tells the gru unit.which are the bits in your it just tells.you which are you which are the.dimensions of your memory cell vector to.update and every time step so you can.choose to keep some bits constant while.updating other bits so for example maybe.use one bit to remember the singular or.plural capped and maybe use some other.bits to realize that you're talking.about food and so because you know we.talked about eating and talk about food.then you'd expect to talk about whether.the cat is full later.right so you can use different bits and.change only a subset of the bits at.every point in time you now understand.the most important ideas of the GRU what.are presenting them to slide that Janki.what I presented on this slide is.actually slightly simplified GRU unit.let me describe the full GRU unit so to.do that let me copy the three main.equations this one this one and this one.to the next slide so here they are.and for the full GRU unit I'm just.making one change that is which is for.the first equation which was calculating.the candidate new value for the memory.cell I'm going to add just one term they.push that a little bit to the right and.I'm going to add one.more date so this is a get another gate.camera or you can think of our standing.for relevance so this gate gamma R tells.you how relevant is CT -1 to computing.the next candidate for CT and this gate.gamma R is computed pretty much as you.expect with a new parameter matrix W R.and then the same things as input XT.plus B so as you can imagine there are.multiple ways to design these types of.neural networks and why do we have gamma.R why not just you know always why not.use the simpler version from the.previous lines so it turns out that over.many years researchers have experimented.with many many different possible.versions of how to design these units to.try to have longer range connections to.try to have model longer range effects.and also address vanishing gradient.problems and the GRU is one of the most.commonly used versions that researchers.have converged through and have found.has robust and useful for many different.problems if you wish you could try to.invent new versions of these units if.you want but the GRU is a standard one.does just calling me use although you.can imagine that researchers have tried.other versions that are similar but not.exactly the same as when I'm writing.down here as well and the other common.version is called an LS TM which stands.the long short term memory which we'll.talk about in the next video but gr use.an LS TM on two specific instantiation.of this set of ideas that are most.commonly used just one note on notation.I try to define a consistent notation to.make these ideas easier to understand if.you look at the academic literature you.more call you sometimes see people what.if you look at the academic literature.sometimes you see people use an.alternative notation it would be.till there you are an age to refer to.these quantities as well I tried to use.a more consistent notation between GI.use and our stems as well as using a.more consistent notation gammer to refer.to begins to hopefully make these ideas.easier to understand so that's it for.the gru for the gated recurrent unit.this is one of the ideas in our n ends.that has enabled them to become much.better at capturing very long range.dependencies as made our n ins much more.effective next as I briefly mention the.other most commonly used variation of.this class of ideas is something called.the LS TM unit of the long short-term.memory unit let's take a look at that in.the next video.

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