Reference no: EM133417447

1. Read through the MNIST handwritten digits example on pages 16-24 of Deep Learning with Keras. Copy the code from pages 22-23 into your Jupyter Notebook.
   
   Note: More information about the training and test data sets can be found in The MNIST Database of Handwritten Digits.
2. Run the code in your Jupyter Notebook. Follow the examples in the book to establish an accuracy rate for the training, validation, and test data sets with two hidden layers.
3. The remainder of the chapter provides examples of how to modify different parameters within the code (number of hidden layers, hidden neurons, BATCH_SIZE, number of epochs, and so on). Pick one parameter and run two or three different experiments, modifying the parameter values to establish accuracy scores with different parameter values. Make sure that the experiments result in significant changes in accuracy rates. Be sure to place each experiment in a different code block so that your instructor can view all of your changes.
   
   Note: You may have to do some research beyond the information provided in the book to implement these changes.
4. Create a Markdown cell in your Jupyter Notebook after your code and its outputs. In this cell, explain the changes in accuracy rates by comparing and contrasting your results from Steps 3 and 4. What happens to the accuracy rates for the training, validation, and test data sets as you change the parameters? Why?

Specifically, you must address the following rubric criteria:

• Configure the notebook correctly and use the proper naming convention.
• Establish an accuracy rate for the training, validation, and test data sets with two hidden layers with the code working correctly.
• Establish accuracy scores with different parameter values with the code working correctly.
• Explain the changes in accuracy rates when the parameters are modified.