ece, Selected Topics in Machine Learning – Assignment 3
Submit by Sept. , pm
tldr: Classify mnist digits with a convoultional neural network. Get at least 95.5%
accuracy on the test test.
Problem Statement Consider the mnist dataset consisting of 50,000 training
images, and 10,000 test images. Each instance is a 28 × 28 pixel handwritten digit
zero through nine. Train a convolutional neural network for classification using
the training set that achieves at least 95.5% accuracy on the test set. Do not
explicitly tune hyperparameters based on the test set performance, use a validation
set taken from the training set as discussed in class. Use dropout and an L
2 penalty
for regularization. Note: if you write a sufficiently general program the next
assignment will be very easy.
Do not use the built in mnist data class from tensorflow.
Extra challenge (optional) In addition to the above, the student with the fewest
number of parameters for a network that gets at least 80% accuracy on the test set
will receive a prize. There will be an extra prize if any one can achieve 80% on the
test set with a single digit number of parameters. For this extra challenge you can
make your network have any crazy kind of topology you’d like, it just needs to be
optimized by a gradient based algorithm.
