Preprocessing the dataset

The greyscale assigned to each pixel within an image has a value range of 0-255. We will want to flatten (smoosh… scale…) this range to 0-1. To achieve this flattening, we will exploit the data structure that our images are stored in, arrays. You see, each image is stored as a 2-dimensional array where each numerical value in the array is the greyscale code of particular pixel. Conveniently, if we divide an entire array by a scalar we generate a new array whose elements are the original elements divided by the scalar.

>>> train_images = train_images / 255.0
>>> test_images = test_images / 255.0
>>>

Two vital notes about the above.

1. Use the value "255.0". This value is a floating point number (float), and will always return a float during algebraic operations. In Python, the division operator always returns a float to avoid rounding; but, that is not true for all programming languages, so it's a good habit to include that decimal because it automatically sets that number to be a float.
2. Do not rescale the train_labels or test_labels arrays, these values are already in the range 0-9, as they should be!

Enter a comment (TRUE or FALSE) about the following statement:

We need to rescale both the images and labels, so they are on the same scale.

Basics of TensorFlow Tutorial Using Fashion MNIST

Figure 1 Fashion-MNIST samples (by Zalando, MIT License).

Introduction

Machine learning (ML)/Neural Network (NN) tools have recently made a huge splash with applications in data analysis, image classification, and data generation. Although ML methods have existed for decades, recent advancements in hardware have generated systems powerful enough to run these algorithms.

The typical "hello world" example for ML is a classifier trained over the MNIST dataset; a dataset of the handwritten digits 0-9. This dataset is getting a little stale and is no longer impressive with employers as a proof of capability due to both its seeming simplicity and to the plethora of existing tutorials on the topic. Here we will use a newer dataset to perform our ML "hello world", the Fashion MNIST dataset!

The Zeroth Step of ML (that should be completed before ever putting a hand to mouse, or finger to key) is understanding the format and sizes of your data. This step is often referred to as feature engineering. Feature engineering, typically, includes selecting and preprocessing the particular aspects of training data to give to your algorithm. You and I will start a good habit of examining the data and its format to make decisions concerning the appropriate size and format for our NN.

The Fashion MNIST dataset is comprised of 70,000 grayscale images of articles of clothing. The greyscale values for a pixel range from 0-255 (black to white). Each low-resolution image is 28x28 pixels and is of exactly one clothing item. Alongside each image is a label that places the article within a category; these categories are shown in Figure 2 with an example image belonging to the class.

Figure 2 class numbers are shown next to image labels

This is an interactive tutorial where you will be prompted to do something at the end of each step. Sometimes you need to refresh the browser before the bot will respond.

Enter a comment (TRUE or FALSE) about the following statement:

"Before starting a machine learning project, you often need to select and process some of the data"

Pip Installing Packages

To complete this project we will need a few packages; these are add-ons available to Python but not included in the base install. Very luckily, we installed the latest Python 3, which includes the Pip Installation module of Python. Pip is a fast and easy way to install packages and their dependencies. As we are doing an NN-based project, we will need to use TensorFlow. For those who only recognize TensorFlow by its association with NNs, it may be shocking to learn that TensorFlow is a more general tool for the manipulation of mathematical entities called tensors. NNs are just a single use of tenor mechanics, and therefore TensorFlow.

As tensors and TensorFlow can be fairly complex to manage, there exists another package named Keras that acts as a high-level API (Application Programming Interface), allowing users to easily generation, define and manipulate the structures within TensorFlow.

Finally, as we wish to visualize aspects of our dataset and generate some informational plots, we will want Python's Mathematical Plotting Library, MatPlotLib. Additionally, MatPlotLib facilitates the generation of graphical plots in new windows, even when executed from the Command Line. And finally, to handle numerical computations and array-based operations we will import Numpy.

Starting from the c-prompt (where we left off above), you will need to tell the easy Python package manager (a program that helps you install, uninstall, update and upgrade ancillary features to a larger application) that we want to install Numpy, MatPlotLib, and Tensorflow.

pip install Numpy
pip install MatPlotLib
pip install Tensorflow

If you encounter a permissions error, you may need to add --user to the end of each install. For example pip install Numpy --user.

After each of these actions, pip will display that it is downloading and installing the package. If you have an existing (but not up-to-date) version, pip will first uninstall the old version before installing the new. If you have previously installed the current version of any package, pip will inform you that the 'requirement already exists'.

Now we can test if Python has installed our Tensorflow correctly (this being the testier of the three packages), by returning to the Python Environment and printing the version of our Tensorflow:

python
Python 3.6.7 (v3.6.7:6ec5cf24b7, Oct 20 2018, 13:35:33) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> print(tf.__version__)
1.14.0
>>> exit()

We can see that I have Tensorflow version 1.14.0 installed! Unless you have specified an earlier version (and dialing in the version that you need for any particular existing project can be a hassle with Tensorflow) you will have automatically installed the newest stable release, which at the time of this writing was version 2.0.0.
And yes, I have intentionally had to enter and exit the Python environment from the CMD several times. This is called Reinforcement Learning; you've been subjected to the human equivalent of another machine learning technique you will learn about later.

Leave a comment with the letter (A, B, C, or D) that is false about TensorFlow

A. TensorFlow can be installed using pip
B. TensorFlow is only used for Neural Networks
C. TensorFlow is used to manipulate tensors
D. The Keras package can make using TensorFlow more easy