---
title: "MNIST"
author: "Prof. Eric A. Suess"
format: 
  revealjs:
    embed-resources: true
---

## MNIST

In Chapter 1 the MNIST dataset is discussed and the associated classification problem.  The dataset contain 60,000 images of handwritten digits 0 - 9.  The data is originally in a tensor of 28x28*60,000.  The data is converted to a matrix that 28x28 columns and 60,000 rows.  Each column is a pixel in the 28x28 image.  The values are on a grey scale from 0 - 255.  The data is normalized to values between 0 and 1.

## MNIST

What does the Neural Network look like?

## model keras3
    
    > model <- keras_model_sequential() |>
    >   layer_dense(units = 512, activation = "relu") |>
    >   layer_dense(units = 10, activation = "softmax")

## MNIST

Try a deeper Neural Network and add dropout layers.  Does the model fit better?

## model keras3

    > model <- naive_sequential_model(list(
    >   layer_naive_dense(input_size = 28 * 28, output_size = 512,
                    activation = "op_relu"),
    >   layer_naive_dense(input_size = 512, output_size = 10,
                    activation = "op_softmax")
    >  ))