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Batch norm processes your data one mini batch at a time,
but the test time you might need to process the examples one at a time.
Let's see how you can adapt your network to do that.
Recall that during training,
here are the equations you'd use to implement batch norm.
Within a single mini batch,
you'd sum over that mini batch of the ZI values to compute the mean.
So here, you're just summing over the examples in one mini batch.
I'm using M to denote the number of examples
in the mini batch not in the whole training set.
Then, you compute the variance and then you compute Z norm by
scaling by the mean and standard deviation with Epsilon added for numerical stability.
And then Z total is taking Z norm and rescaling by gamma and beta.
So, notice that mu and sigma squared which you
need for this scaling calculation are computed on the entire mini batch.
But the test time you might not have a mini batch of
6428 or 2056 examples to process at the same time.
So, you need some different way of coming up with mu and sigma squared.
And if you have just one example,
taking the mean and variance of that one example, doesn't make sense.
So what's actually done?
In order to apply your neural network and test time is to
come up with some separate estimate of mu and sigma squared.
And in typical implementations of batch norm,
what you do is estimate this using
a exponentially weighted average where the average is
across the mini batches.
So, to be very concrete here's what I mean.
Let's pick some layer L and let's say you're going through mini batches X1,
X2 together with the corresponding values of Y and so on.
So, when training on X1 for that layer L,
you get some mu L. And in fact,
I'm going to write this as mu for the first mini batch and that layer.
And then when you train on the second mini batch for that layer
and that mini batch,you end up with some second value of mu.
And then for the fourth mini batch in this hidden layer,
you end up with some third value for mu.
So just as we saw how to use
a exponentially weighted average to compute the mean of Theta one, Theta two,
Theta three when you were trying to compute
a exponentially weighted average of the current temperature,
you would do that to keep track of what's
the latest average value of this mean vector you've seen.
So that exponentially weighted average becomes
your estimate for what the mean of the Zs is for that hidden layer and similarly,
you use an exponentially weighted average to keep track of
these values of sigma squared that you see on the first mini batch in that layer,
sigma square that you see on second mini batch and so on.
So you keep a running average of the mu and the sigma squared that you're
seeing for each layer as you train the neural network across different mini batches.
Then finally at test time,
what you do is in place of this equation,
you would just compute Z norm using whatever value your Z have,
and using your exponentially weighted average of
the mu and sigma square whatever was the latest value you have to do the scaling here.
And then you would compute Z total
on your one test example using that Z norm that we just computed on
the left and using the beta and
gamma parameters that you have learned during your neural network training process.
So the takeaway from this is that during training time mu and
sigma squared are computed on an entire mini batch of say 64 engine,
28 or some number of examples.
But that test time, you might need to process a single example at a time.
So, the way to do that is to estimate mu and sigma squared
from your training set and there are many ways to do that.
You could in theory run your whole training
set through your final network to get mu and sigma squared.
But in practice, what people usually do is implement and
exponentially weighted average where you just keep
track of the mu and sigma squared values you're seeing
during training and use and exponentially the weighted average,
also sometimes called the running average,
to just get a rough estimate of mu and sigma
squared and then you use those values of mu and sigma squared
that test time to do the scale and you need the head and unit values Z.
In practice, this process is pretty robust
to the exact way you used to estimate mu and sigma squared.
So, I wouldn't worry too much about exactly how you do
this and if you're using a deep learning framework,
they'll usually have some default way to estimate
the mu and sigma squared that should work reasonably well as well.
But in practice, any reasonable way to estimate the mean and
variance of your head and unit values Z should work fine at test.
So, that's it for batch norm and using it.
I think you'll be able to train much deeper networks
and get your learning algorithm to run much more quickly.
Before we wrap up for this week,
I want to share with you some thoughts on deep learning frameworks as well.
Let's start to talk about that in the next video.
Andrew NgCo-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Head Teaching Assistant - Kian KatanforooshLecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant - Younes Bensouda MourriMathematical & Computational Sciences, Stanford University, deeplearning.ai
