This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.
What are deep ConvNets learning?
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Convolutional Neural Networks
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Special applications: Face recognition & Neural style transfer
Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces!
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Andrew NgCEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of 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
What are deep ConvNets really learning?
In this video, I want to share with you some visualizations that will help you
hone your intuition about what the deeper layers of a ConvNet really are doing.
And this will help us think through how you can implement
neural style transfer as well.
Let's start with an example.
Lets say you've trained a ConvNet, this is an alex net like network,
and you want to visualize what the hidden units in different layers are computing.
Here's what you can do.
Let's start with a hidden unit in layer 1.
And suppose you scan through your training sets and find out what are the images or
what are the image patches that maximize that unit's activation.
So in other words pause your training set through your neural network, and figure
out what is the image that maximizes that particular unit's activation.
Now, notice that a hidden unit in layer 1,
will see only a relatively small portion of the neural network.
And so if you visualize, if you plot what activated unit's activation,
it makes makes sense to plot just a small image patches,
because all of the image that that particular unit sees.
So if you pick one hidden unit and find the nine input images that maximizes
that unit's activation, you might find nine image patches like this.
So looks like that in the lower region of an image that this particular hidden
unit sees, it's looking for an egde or a line that looks like that.
So those are the nine image patches that maximally activate
one hidden unit's activation.
Now, you can then pick a different hidden unit in layer 1 and do the same thing.
So that's a different hidden unit, and looks like this second one,
represented by these 9 image patches here.
Looks like this hidden unit is looking for a line sort of in that portion of its
input region, we'll also call this receptive field.
And if you do this for other hidden units, you'll find other hidden units,
tend to activate in image patches that look like that.
This one seems to have a preference for a vertical light edge, but
with a preference that the left side of it be green.
This one really prefers orange colors, and this is an interesting image patch.
This red and green together will make a brownish or a brownish-orangish color,
but the neuron is still happy to activate with that, and so on.
So this is nine different representative neurons and for
each of them the nine image patches that they maximally activate on.
So this gives you a sense that, units, train hidden units in layer 1,
they're often looking for
relatively simple features such as edge or a particular shade of color.
And all of the examples I'm using in
this video come from this paper by Mathew Zeiler and
Rob Fergus, titled visualizing and understanding convolutional networks.
And I'm just going to use one of the simpler ways to visualize
what a hidden unit in a neural network is computing.
If you read their paper, they have some other more sophisticated ways of
visualizing when the ConvNet is running as well.
But now you have repeated this procedure several times for
nine hidden units in layer 1.
What if you do this for
some of the hidden units in the deeper layers of the neuron network.
And what does the neural network then learning at a deeper layers.
So in the deeper layers, a hidden unit will see a larger region of the image.
Where at the extreme end each pixel
could hypothetically affect the output of these later layers of the neural network.
So later units are actually seen larger image patches,
I'm still going to plot the image patches as the same size on these slides.
But if we repeat this procedure, this is what you had previously for layer 1,
and this is a visualization of what maximally activates nine different
hidden units in layer 2.
So I want to be clear about what this visualization is.
These are the nine patches that cause one hidden unit to be highly activated.
And then each grouping, this is a different set of nine image patches that
cause one hidden unit to be activated.
So this visualization shows nine hidden units in layer 2, and
for each of them shows nine image patches that causes that hidden unit
to have a very large output, a very large activation.
And you can repeat these for deeper layers as well.
Now, on this slide, I know it's kind of hard to see these tiny little
image patches, so let me zoom in for some of them.
For layer 1, this is what you saw.
So for example, this is that first unit we saw which was highly activated, if
in the region of the input image, you can see there's an edge maybe at that angle.
Now let's zoom in for layer 2 as well, to that visualization.
So this is interesting,
layer 2 looks it's detecting more complex shapes and patterns.
So for example, this hidden unit looks like it's looking for
a vertical texture with lots of vertical lines.
This hidden unit looks like its highly activated when
there's a rounder shape to the left part of the image.
Here's one that is looking for very thin vertical lines and so on.
And so the features the second layer is detecting are getting more complicated.
How about layer 3?
Let's zoom into that, in fact let me zoom in even bigger, so
you can see this better, these are the things that maximally activate layer 3.
But let's zoom in even bigger, and so this is pretty interesting again.
It looks like there is a hidden unit that seems to respond highly
to a rounder shape in the lower left hand portion of the image, maybe.
So that ends up detecting a lot of cars, dogs and
wonders is even starting to detect people.
And this one look like it is detecting certain textures like honeycomb shapes,
or square shapes, this irregular texture.
And some of these it's difficult to look at and manually figure out what is it
detecting, but it is clearly starting to detect more complex patterns.
How about the next layer?
Well, here is layer 4, and you'll see that the features or
the patterns is detecting or even more complex.
It looks like this has learned almost a dog detector, but
all these dogs likewise similar, right?
Is this, I don't know what dog species or dog breed this is.
But now all those are dogs, but they look relatively similar as dogs go.
Looks like this hidden unit and therefore it is detecting water.
This looks like it is actually detecting the legs of a bird and so on.
And then layer 5 is detecting even more sophisticated things.
So you'll notice there's also a neuron that seems to be a dog detector,
but set of dogs detecting here seems to be more varied.
And then this seems to be detecting keyboards and things with a keyboard
like texture, although maybe lots of dots against background.
I think this neuron here may be detecting text, it's always hard to be sure.
And then this one here is detecting flowers.
So we've gone a long way from detecting relatively simple things
such as edges in layer 1 to textures in layer 2,
up to detecting very complex objects in the deeper layers.
So I hope this gives you some better intuition about what the shallow and
deeper layers of a neural network are computing.
Next, let's use this intuition to start building a neural-style transfer
algorithm.
