SPEAKER: Comma separated
values, or CSV,
is a commonly-used data format.
In this video, you'll see how
to use CSV and Keras with Eager
Execution turned on
so you can load data
for training a neural network
and view it imperatively.
First of all, here's the URL
where the data is stored.
Keras has a Get
File utility, which
can download a file
from a URL, so this
uses that to get the data
and store it locally.
There are five columns
in the data-- four
for features, one for the label.
So this code simply creates
lists of each of these.
You'll need them later.
We need to specify how large the
batches of data that we'll load
are.
This is how many records
it will process at a time.
And the Make CSV
Data Set utility
is where the magic happens.
You simply tell it the data,
batch size, column, and labels,
and the number of epochs
that you wanted to run for,
and it will load and
slice the data for you.
We can inspect the
data in the debugger
to see that at least
something was loaded.
But if we want to
see more, we can just
look at the next iter
of the training data
set that was created, then print
out the features or labels.
And as you can
see in the output,
we can see that the
features are loaded nicely
into lists for us.
So that's it for loading
CSV into TensorFlow
using Keras Utilities.
To learn more about TensorFlow,
visit tensorflow.org.
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questions about this video,
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the comments below.
Thank you.
