---
title: "MNIST"
author: "Prof. Eric A. Suess"
format: 
  revealjs:
    self-contained: 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

    > network <- keras_model_sequential() %>% 
    >   layer_dense(units = 512, activation = "relu", input_shape = 
          c(28 * 28)) %>% 
    >   layer_dense(units = 10, activation = "softmax")

## MNIST

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

## model

    > network <- keras_model_sequential() %>% 
    >   layer_dense(units = 512, activation = "relu", 
            input_shape = c(28 * 28)) %>% 
    >     layer_dropout(rate = 0.4) %>% 
    >   layer_dense(units = 128, activation = "relu") %>%
    >     layer_dropout(rate = 0.3) %>%
    >   layer_dense(units = 10, activation = "softmax")



