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.

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