-: Hello, and welcome back to the course on deep learning.
I hope you're tracking along
with these intuition tutorials just fine
and that you had a chance to play around
with everything we've learned so far.
And today, we're talking about flattening.
And the good news is that this is a very simple step,
and this tutorial's going to be very quick.
And then, we'll be able to move
onto the next interesting things.
All right, so we, so far,
we've got the pooled layer, pooled feature map,
and that is after we apply the convolution operation
to our image, and then we apply pooling to the result
of the convolution, which is the convolved image.
And so, what are we going to do
with this pooled feature map?
Well, we're going to take it,
and we're going to flatten it into a column.
So basically, just take the numbers row by row
and put them into this one long column.
And the reason for that is
because we want to later input this
into an artificial neural network for further processing.
So, this is what it looks like
when you have many pooling layers,
or you have the pooling layers
with many pooled feature maps, and then you flatten them.
So, you put them into this one long column,
sequentially one off to the other,
and you get one huge vector
of inputs for an artificial neural network.
And so, to sum all of this up, we've got an input image.
We apply a convolution layer,
and let's not forget the ReLU,
or rectified linear units function,
that we apply after the convolution layer as well.
And then, we apply pooling, and then we flatten everything
into a long vector, which will be our input layer
for an artificial neural network.
And exactly how that works,
we'll find out in the next tutorial.
Hope you enjoy today's session,
and I look forward to you next time.
Until then, enjoy deep learning.