Importing the relevant packages and load the data

دوره: یادگیری عمیق با TensorFlow / فصل: The MNIST example / درس 3

Importing the relevant packages and load the data

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All right.

Let’s start by importing the packages we’ll need for this exercise as usual will import none pi as MP

and tensor flow as T F we’ll use tensor flows data provider for amnesty in order to use it properly.

We must also import the tensor flow data sets module as TFT s for short.


Let’s proceed to acquiring our data and storing it in the variable M.A.

data set to load the data.

We should write TAF D.S.

load and the name of the data set.

We want to load but also happens to be the only required argument which in our case is amnesty.

Fortunately tensor flow data sets has a large number of datasets ready for modeling with this operation.

We can download the data set in the default path directory for instance on a Windows system with the

usual default path.

You will find it in C users your user name tensor flow datasets or if you’re a Linux user you’ll find

it in your home directory till the slash tensor flow data sets.

Anyhow the first time you execute TFT Yes load a data set will be downloaded on your computer.

Therefore each consecutive time you run the code it will automatically load this local copy on your

computer OK.

There are two tweaks we’d rather make.

First we can set the argument as supervised to true.

This will load the data set in a two tuple structure input and target.

In addition let’s include one final argument with info equal to true and stored in the variable amnesty


This will provide us with a tuple containing information about the version features a number of samples

of the dataset.

OK great.

We have successfully loaded the dataset.

Now we can either execute this line of code or continue our pre processing in the same cell.

It’s up to you.

You should know that the first time it will take a bit longer to execute since you’ll be actually downloading

the dataset.

That’s why I suggest you run the cell in our next lecture.

We’ll do a bit of data pre processing.

Thanks for watching.

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