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So in this deep learning module,
we talked about two really cool things that you can do with deep learning.
One is classify images, and the other one is retrieve images.
And in both of them, we talked about deep features.
So let's just go ahead and build a notebook for
image classifications using deep features.
So, let's give this notebook a name.
So, this is going to be Image Classification
with Deep Features.
And I'm going to hide the header and
the toolbar to get a little bit more space, as usual.
And we'll fire up.
Actually I just did a little typo here so
we will, Fire up GraphLabCreates.
So let's import GraphLab, So here we go.
So the first thing that we're going to do is load a common image analysis dataset.
So this is a benchmark dataset called a SIFR10.
We select a subset of those where we just have four categories
of objects we're trying to recognize.
So I'm going to load the data set, and
this data set has already been split into train data and test data,
because in computer vision, often that split happens so every researcher uses
the same training set and the same test set, so the papers become more comparable.
So, I'm just gonna load an S frame that contains the data set.
So, graphlab.SFrame and for the training
data, there's a directory here called image_train_data.
And then for the test data, so let's call that image_test.
We have another SFrame.
Code image_test_data.
Here we go, so we are loading this two data sets,
and let's start by exploring the data a little bit.
So, exploring the image data.
So, what we're gonna do is use canvas.
So, I'm gonna point canvas to show it on the notebook,
as we have been doing on several notebooks.
So, it becomes all one thing that we can take home.
So, I'm gonna set graphlab.canvas and
I'm going to set the target to be the ipython notebook.
So it's going to print anything on the iPython notebook that we do.
And let's start by taking a look at the string data.
So image string, and here's a few columns but
the column that we really care about here is the image column.
I am just going to type .show on that and
we are going to take a quick look at that image column.
And these images are not too large, so I'm temporarily going to increase
the font here so we can take a look at the images.
And so if you look at it, there's a bird, there's some cats, there's some dogs and
some cars.
So this data set is all about cats, dogs, cars, and birds.
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