CMSC 25400/STAT 27725 Homework 5


Category: You will Instantly receive a download link for .zip solution file upon Payment


5/5 - (3 votes)

1. We recommend that you use the following architecture:
(a) The input layer (ℓ = 0) will consist of 785 = 28 × 28 + 1 neurons, of which the ouput of
neuron 0 ≤ i ≤ 784 is just the value of pixel i in the image, while the last neuron is a
bias neuron whose output is always 1.
(b) The hidden layers (ℓ = 1, 2, . . . L − 1) will each have Nℓ neurons.
(c) The output layer (ℓ = L) will have 10 neurons corresponding to the 10 classes.
2. Use a fully connected NN with the sigmoid transfer function, i.e. the vector of activations in
layer ℓ = 1, 2, . . . should be aℓ = σ(Wℓaℓ−1) where σ(x) = (1 + e
3. For the outputs use the softmax loss ℓ(y, aL) = e
[aL]y /(
i=1 e
[aL]i ).
4. Train your neural network example-by-example with stochastic gradient descent, as discussed
in class, or with minibatches of size b, with a fixed learning rate η. You will need to cycle
through the training data multiple times (multiple epochs) and stop when error on the holdout
set starts increasing substantially. The network should be started with random weights.
5. Training data is provided in the csv files TrainDigitX.csv.gz and TrainDigitY.csv.gz (if
you use Python, the loadtxt function can automatically decompress these files). The test
files are similarly named.
6. Experiment with varying the learning rate η, the minibarch size, the number of hidden layers,
and the number of neurons in each layer, say Nℓ ∈ {32, 64, 128, 256}. For extra credit, you
can also try coding up a convolutional neural net. For a comparison for how well different
algorithms work for this data see
7. For this assignment you may use matrix libraries, but please do not use a neural network
library or somebody else’s implementation: the goal of the assigment is to give you the
experience of coding up a neural network “from scratch”.
Your writeup for this assignment should include the following:
1. A short description of your code and what choices you made during the implementation.
2. A study of how performance varies as a function of η, b, L, Nℓ and the number of epochs.
Try and optimize these parameters for the best performance on a hold-out set. Include plots
for the error rate vs. each of these three parameters (with the other two set to reasonable
values) on the test set.
In addition to your writeup please hand in the following:
1. Your full code in a form that the TAs can easily run it on the data if they want to verify it.
2. Your predictions on the two test sets TestDigitX.csv and TestDigitX2.csv (for the second
one we do not publish reference labels).