Train your First Neural Network on the MNIST dataset

You can train your first neural network on the MNIST dataset (used for image recognition models). The MNIST example is tantamount to being a "hello world" of machine learning programs.

Key features:

• Use Keras API as a "portal" into TensorFlow library to build the neural network
• Use "Sequential" model - allows you to add "layers" sequentially
• "Feed" the model the training data (creating the model takes a bit of study/effort)
• Model gives back a vector of "Logits" or "Logs-odd" scores, one per class
• Run softmax to convert these scores to probabilities
• Compile the model - with an optimizer and a loss function, configure for 'accuracy'
• model.fit
• model.evaluate to see how the model performed (was it a good fit to the data)

Doing this example immediately raises a billion questions! Answering these questions will help you in future machine learning projects with TensorFlow. So get your answers now!

Some numbers to remember in this "post game analysis" are 0 to 255 and 28x28.

All About the Data - the MNSIT Dataset & (Numpy-friendly) Data Format 

The MNIST dataset consists of 60,000 training images of handwritten digits and 10,000 test images, each a size of 28x28 pixels. Images are grayscale and numbers are 0-9. The data set is vectorized and in numpy format. Each pixel has an encoding of 0 to 255 (typical for grayscale images) where the number represents brightness, 0 is black and 255 is white.

The Data Set Loading Process (Involves Normalization)

So MNIST is one of the built-in datasets in Keras. 

The first step is to normalize the data by dividing each pixel value (in the training and testing data set) by PIXEL_MAX=255 which creates a value between 0 and 1 (inclusive) and converts an integral value into a decimal value.

Model.fit - In depth

How does this from a function-calling perspective.

How do I see how good this model is visually?

This requires some additional programming.