Neural Networks and Deep Learning
Assigned Thu Oct 12, 2017
Due: Thu Oct 26, 2017
The goal of this assignment is to use
tensorflow to explore convolutional nets and to optimize a
network for a challenging visual classification task.
You will experiment with CIFAR-10
a data set of tiny (32x32) color images in 10 classes: airplane,
automobile, bird, cat, deer, dog, frog, horse, ship, truck. This
task is nontrivial: an error rate of 15-25% is likely.
The data set is divided into 50,000 training images and 10,000
test images. For part 1 of the assignment, do not peek at
the test images.
Instead, split your 50k training
examples into a training set and a validation set. Decide how
many examples to use for each.
has a very nice tutorial using
the CIFAR-10 data with a
sample architecture and code
for reading the data
and annealing learning rates,
etc. It is fair to start with
this demo and modify it to
optimize your architecture.
(1a) Split your 50k training examples into a
training set and a validation set. Decide how many examples to
use for each. Describe your split into training and validation.
(You may wish to do k
-fold cross validation instead of
just a single fold of validation, or you may want to do
many-fold validation where you resample the training and
validation sets each time. Simply describe the strategy
you have selected.)
(1b) Use tensorflow to build a convolutional neural net for this
task. You can use any of the tricks we've discussed in the
course, including pooling, max-pooling, batch normalization,
data augmentation, residual networks, drop out, etc. Of course
there is a huge literature on CIFAR-10 on the web (including
results from various approaches
), but I ask you not to pay
much attention to this literature and try to invent your own
architecture from scratch. You will want to experiment with
different variations of your architecture and the combination of
tricks you use. I want you to report the history of experiments
you've performed by presenting the following information for
- Describe the architecture in enough detail that another
member of the class could replicate the work. For
example, you may vary the number of hidden layers or whether
you are using dropout. But report details such as the
receptive field size of each convolutional layer and the
stride, and if you're using dropout, what dropout % you
- Report training classification accuracy (regardless of the
loss function you use for training) and the validation
You will undoubtably play with some minor variations (e.g.,
changing learning rates, loss function, etc.). You needn't
report every such tweek. My goal is for you to convey what you
believe are the most important architectural manipulations
needed to get good performance on this task.
(2a) For the variation that obtained the best
performance in Part 1, compute error on the test set (the 10k
examples you had set aside until now).
Part 3 (Extra Credit)
It might be interesting to pull some examples
of images from the 80
million tiny images
data set from classes other than the
10 classes you trained on, e.g., buildings or goats or
mailboxes. Run these images through your trained network and see
whether you can use the output entropy (or some similar measure)
to determine whether the test image can be rejected as not
belonging to any object category. If you pick an entropy
threshold, you will not only reject some of the out-of-class
examples, but you will also falsely reject some in-class
(3a) Describe the rejection criterion you use, and for that
criterion, report on the % of out-of-class examples you've
correctly rejected, but also the % of in-class examples (from
the test set) that you erroneously rejected.
Present objects from an untrained class.