5. Autoencoders

Activity 5.01: The MNIST Neural Network

Solution:

In this activity, you will train a neural network to identify images in the MNIST dataset and reinforce your skills in training neural networks:

1. Import pickle, numpy, matplotlib, and the Sequential and Dense classes from Keras:

   import pickle
   import numpy as np
   import matplotlib.pyplot as plt
   from keras.models import Sequential
   from keras.layers import Dense
   import tensorflow.python.util.deprecation as deprecation
   deprecation._PRINT_DEPRECATION_WARNINGS = False

2. Load the mnist.pkl file, which contains the first 10,000 images and corresponding labels from the MNIST dataset that are available in the accompanying source code. The MNIST dataset is a series of 28 x 28 grayscale images of handwritten digits 0 through 9. Extract the images and labels:

   with open('mnist.pkl', 'rb') as f:
       data = pickle.load(f)
   images = data['images']
   labels = data['labels...