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.. _machine-learning:
TensorFlow\* machine learning on |CL-ATTR|
##########################################
This tutorial will demonstrate the installation and execusion of a TensorFlow\*
machine learning example on |CL-ATTR|. It uses a Jupyter\* Notebook and MNIST
data for handwriting recognition. 
The initial steps will have you set up a Jupyter kernel and run a Notebook
on a bare-metal |CL| system.
Prerequisites
*************
This tutorial assumes you have installed |CL| on your host system. For
detailed instructions on installing |CL| on a bare metal system, follow the
:ref:`bare metal installation tutorial<bare-metal-install-desktop>`.
Before you install any new packages, update |CL| with the following command:
.. code-block:: bash
sudo swupd update
Once your system is updated, add the following bundles to your system:
* `machine-learning-web-ui`: This bundle contains the Jupyter application.
* `machine-learning-basic`: This bundle contains TensorFlow and other useful
tools.
To install the bundles, run the following commands in your :file:`$HOME`
directory:
.. code-block:: bash
sudo swupd bundle-add machine-learning-web-ui
sudo swupd bundle-add machine-learning-basic
Set up a Jupyter notebook
*************************
With all required packages and libraries installed, set up the file structure
for the Jupyter Notebook.
#. In the :file:`$HOME` directory, create a directory for the Jupyter
Notebooks named :file:`Notebooks`.
.. code-block:: bash
mkdir Notebooks
#. Within :file:`Notebooks`, create a directory named :file:`Handwriting`.
.. code-block:: bash
mkdir Notebooks/Handwriting
#. Change to the new directory.
.. code-block:: bash
cd Notebooks/Handwriting
#. Copy the :file:`MNIST_example.ipynb` file into the :file:`Handwriting`
directory.
.. note::
After installing the `machine-learning basic` bundle, you can find the
example code under
:file:`/usr/share/doc/tensorflow/MNIST_example.ipynb`.
The example code downloads and decompresses the MNIST data directly into the
:file:`./mnist` directory. Alternatively, download the four files directly
from the Yann LeCun’s `MNIST Database website`_ and save them into a
:file:`mnist` directory within the :file:`Handwriting` directory.
The files needed are:
* `train-images-idx3-ubyte.gz`_: Training set images (9912422 bytes)
* `train-labels-idx1-ubyte.gz`_: Training set labels (28881 bytes)
* `t10k-images-idx3-ubyte.gz`_: Test set images (1648877 bytes)
* `t10k-labels-idx1-ubyte.gz`_: Test set labels (4542 bytes)
Run the Jupyter machine learning example code
*********************************************
With |CL|, Jupyter, and TensorFlow installed and configured, we can
run the example code.
#. Go to the :file:`($HOME)/Notebooks` directory and start Jupyter with the
following commands:
.. code-block:: bash
cd ~/Notebooks
jupyter notebook
The Jupyter server starts and opens a web browser showing the Jupyter file
manager with a list of files in the current directory, see figure 1.
.. figure:: figures/machine-learning-1.png
:alt: Jupyter file manager
Figure 1: The Jupyter file manager shows the list of available files.
#. Click on the :file:`Handwriting` directory. The :file:`MNIST_example.ipynb`
file created earlier should be listed there, see figure 2.
.. figure:: figures/machine-learning-2.png
:alt: Example file within the Jupyter file manager
Figure 2: The example file within the Jupyter file manager.
#. To run the hand writing example, click on the :file:`MNIST_example.ipynb`
file to load the notebook, see figure 3.
.. figure:: figures/machine-learning-3.png
:alt: The loaded MNIST_example notebook
Figure 3: The loaded MNIST_example notebook within the Jupyter file
manager.
#. Click the |run-cell| button to execute the code in the current cell and
move to the next.
#. Select the :guilabel:`In [2]` cell and click the |run-cell| button to load
the MNIST data. The successful output is shown on figure 4.
.. figure:: figures/machine-learning-4.png
:alt: Successful import of MNIST data
Figure 4: Output after successfully importing the MNIST data.
After the MNIST data was successfully downloaded and extracted into the
:file:`mnist` directory within the :file:`($HOME)/Notebooks/Handwriting`
directory, four .gz files are present and the four data sets were created:
`trainX`, `trainY`, `testX` and `testY`.
#. To inspect the imported data, the function in :guilabel:`In [3]` first
instructs Jupyter to reshape the data into an array of 28 x 28 images and to
plot the area in a 28 x 28 grid. Click the |run-cell| button twice to show
the first two digits in the `trainX` dataset, see figure 5.
.. figure:: figures/machine-learning-5.png
:alt: Function to reshape data.
Figure 5: A function reshapes the data and displays the first two
digits in the `trainX` dataset.
#. The :guilabel:`In [4]` cell defines the neural network. It provides the
inputs, defines the hidden layers, runs the training model, and sets up
the output layer, see figure 6. Click the |run-cell| button four times to
perform these operations.
.. figure:: figures/machine-learning-6.png
:alt: Defining, building and training the neural network model
Figure 6: Defining, building and training the neural network model.
#. To test the accuracy of the prediction the system makes, select the
:guilabel:`In [8]` cell and click the |run-cell| button. In this example,
the number 6 was predicted with a 99% accuracy, see figure 7.
.. figure:: figures/machine-learning-7.png
:alt: Prediction example
Figure 7: The system predicts a number providing the accuracy of the
prediction.
.. note::
To retest the accuracy of a random data point's prediction, run the
cell :guilabel:`In [8]` again. It will take another random data point
and predict its value.
#. To check the accuracy for the whole dataset, select the :guilabel:`In [10]`
cell and click the |run-cell| button. Our example's accuracy is
calculated as 97.17%, see figure 8.
.. figure:: figures/machine-learning-8.png
:alt: System's accuracy
Figure 8: The system's accuracy for the entire data set.
For more in-depth information on the model used and the mathematics it entails,
visit the TensorFlow tutorials
`TensorFlow MNIST beginners demo`_ and `TensorFlow MNIST pros demo`_.
**Congratulations!**
You have successfully installed a Jupyter kernel on |CL|. In addition, you
trained a neural network to successfully predict the values contained in a
data set of hand-written number images.
Related topics
**************
* `MNIST Database website`_
* `TensorFlow MNIST beginners demo`_
* `TensorFlow MNIST pros demo`_
* `Jupyter main website`_
* `Jupyter documentation`_
* `MNIST at Wikipedia`_
.. _MNIST Database website:
http://yann.lecun.com/exdb/mnist/
.. _train-images-idx3-ubyte.gz:
http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
.. _train-labels-idx1-ubyte.gz:
http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
.. _t10k-images-idx3-ubyte.gz:
http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
.. _t10k-labels-idx1-ubyte.gz:
http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
.. _Jupyter main website: http://jupyter.org/
.. _Jupyter documentation:  https://jupyter.readthedocs.io/en/latest/index.html
.. _TensorFlow MNIST beginners demo:
https://www.tensorflow.org/get_started/mnist/beginners
.. _TensorFlow MNIST pros demo:
https://www.tensorflow.org/get_started/mnist/pros
.. _MNIST at Wikipedia:
https://en.wikipedia.org/wiki/MNIST_database
.. |run-cell| image:: ./figures/run-cell-button.png