Description
1. [4pts] Feature Maps. Suppose we have the following 1-D dataset for binary classification:
x t
-1 1
1 0
3 1
(a) [2pts] Argue briefly (at most a few sentences) that this dataset is not linearly separable.
(Your argument should resemble the one we used in lecture to prove XOR is not linearly
separable.)
(b) [2pts] Now suppose we apply the feature map
ψ(x) =
ψ1(x)
ψ2(x)
=
x
x
2
.
Assume we have no bias term, so that the parameters are w1 and w2. Write down the
constraint on w1 and w2 corresponding to each training example, and then find a pair
of values (w1, w2) that correctly classify all the examples. Remember that there is no
bias term.
2. [22pts] kNN vs. Logistic Regression. In this problem, you will compare the performance
and characteristics of different classifiers, namely k-Nearest Neighbors and Logistic Regression. You will complete the provided code in q2/ and experiment with the completed code.
You should understand the code instead of using it as a black box.
1
https://markus.teach.cs.toronto.edu/csc311-2020-09
2
http://www.cs.toronto.edu/~rgrosse/courses/csc311_f20/syllabus.pdf
1
CSC311 Homework 2
The data you will be working with is a subset of MNIST hand-written digits, 4s and 9s, represented as 28×28 pixel arrays. We show the example digits in figure 1. There are two training
sets: mnist_train, which contains 80 examples of each class, and mnist_train_small, which
contains 5 examples of each class. There is also a validation set mnist_valid that you should
use for model selection, and a test set mnist_test that you should use for reporting the final
performance. Optionally, the code for visualizing the datasets is located at plot_digits.py.
Figure 1: Example digits. Top and bottom show digits of 4s and 9s, respectively.
2.1. k-Nearest Neighbors. Use the supplied kNN implementation to predict labels for
mnist_valid, using the training set mnist_train.
(a) [2pts] Implement a function run_knn in run_knn.py that runs kNN for different
values of k ∈ {1, 3, 5, 7, 9} and plots the classification rate on the validation set
(number of correctly predicted cases, divided by total number of data points) as a
function of k. Report the plot in the write-up.
(b) [2pts] Comment on the performance of the classifier and argue which value of k you
would choose. What is the classification rate for k
∗
, your chosen value of k? Also
report the classification rate for k
∗ + 2 and k
∗ − 2. How does the test performance
of these values of k correspond to the validation performance3
?
2.2. Logistic Regression. Read the provided code in run_logistic_regression.py and
logistic.py. You need to implement the logistic regression model, where the cost is
defined as:
J =
1
N
X
N
i=1
LCE(y
(i)
, t(i)
) = 1
N
X
N
i=1
−t
(i)
log y
(i) − (1 − t
(i)
) log(1 − y
(i)
)
,
where N is the total number of data points.
(a) [4pts] Implement functions logistic_predict, evaluate, and logistic located
at logistic.py.
3
In general, you shouldn’t peek at the test set multiple times, but we do this for this question as an illustrative
exercise.
2
CSC311 Homework 2
(b) [5pts] Complete the missing parts in a function run_logistic_regression located
at run_logistic_regression.py. You may use the implemented functions from
part (a). Run the code on both mnist_train and mnist_train_small. Check
whether the value returned by run_check_grad is small to make sure your implementation in part (a) is correct. Experiment with the hyperparameters for the
learning rate, the number of iterations (if you have a smaller learning rate, your
model will take longer to converge), and the way in which you initialize the weights.
If you get NaN/Inf errors, you may try to reduce your learning rate or initialize with
smaller weights. For each dataset, report which hyperparameter settings you found
worked the best and the final cross entropy and classification error on the training,
validation, and test sets. Note that you should only compute the test error once you
have selected your best hyperparameter settings using the validation set.
(c) [2pts] Examine how the cross entropy changes as training progresses. Generate and
report 2 plots, one for each of mnist_train and mnist_train_small. In each plot,
you need show two curves: one for the training set and one for the validation set.
Run your code several times and observe if the results change. If they do, how would
you choose the best parameter settings?
2.3. Penalized logistic regression. Next, you need to implement the penalized logistic
regression model, where the cost is defined as:
J =
1
N
X
N
i=1
LCE(y
(i)
, t(i)
) + λ
2
kwk
2
.
Note that you should only penalize the weights and not the bias term.
(a) [2pts] Implement a function logistic_pen in logistic.py that computes the penalized logistic regression.
(b) [3pts] Complete the missing parts in a function run_pen_logistic_regression
located at run_logistic_regression.py. Choose a hyperparameter setting which
seems to work well (for learning rate, number of iterations, and weight initialization). With these hyperparameters, the function evaluates different values of
λ ∈ {0, 0.001, 0.01, 0.1, 1.0} automatically and re-runs (penalized) logistic regression
5 times for each value of λ. So you will have two nested loops: the outer loop is over
values of λ and the inner loops is over multiple re-runs. Your code should average
the training metrics (cross entropy and classification error) over the different re-runs.
Train on both mnist_train and mnist_train_small, and report averaged cross entropy and classification error on the training and validation sets for each λ. Also, for
each λ, select one run, and report 2 plots that shows how the cross entropy changes
as training progresses, one for each of mnist_train and mnist_train_small. In
each plot, you need to show two curves: one for the training set and one for the
validation set. In total, you will have to generate 10 plots.
(c) [2pts] For each dataset, how does the cross entropy change when you increase λ?
Do they go up, down, first up and then down, or down and then up? Explain
why you think they behave this way. Which is the best value of λ, based on your
experiments? Report the test cross entropy and classification rate for the best value
of λ.
3. [15pts] Neural Networks. In this problem, you will experiment on a subset of the Toronto
3
CSC311 Homework 2
Faces Dataset (TFD). You will complete the provided code in q3/ and experiment with the
completed code. You should understand the code instead of using it as a black box.
We subsample 3374, 419 and 385 grayscale images from TFD as the training, validation and
testing set, respectively. Each image is of size 48 × 48 and contains a face that has been
extracted from a variety of sources. The faces have been rotated, scaled and aligned to make
the task easier. The faces have been labeled by experts and research assistants based on their
expression. These expressions fall into one of seven categories: 1-Anger, 2-Disgust, 3-Fear,
4-Happy, 5-Sad, 6-Surprise, 7-Neutral. We show one example face per class in Figure 2.
Figure 2: Example faces. From left to right, the the corresponding class is from 1 to 7.
The code for training a neural network (Multilayer Perceptrons) is partially provided in nn.py.
(a) [4 pts] Follow the instructions in nn.py to implement the missing functions that perform
the backward pass of the network.
(b) [2 pts] Train the neural network with the default set of hyperparameters. Report training, validation, and testing errors and a plot of error curves (training and validation).
Examine the statistics and plots of training error and validation error (generalization).
How does the network’s performance differ on the training set vs. the validation set
during learning?
(c) [3 pts] Try different values of the learning rate α. Try 5 different settings from 0.001
to 1.0. What happens to the convergence properties of the algorithm (looking at both
cross-entropy and percent-correct)? Try 5 different mini-batch sizes, from 10 to 1000.
How does mini-batch size affect convergence? How would you choose the best value of
these parameters? In each of these hold the other parameters constant while you vary
the one you are studying.
(d) [3 pts] Try 3 different values of the number of hidden units for each layer of the Multilayer Perceptron (range from 2 to 100). You might need to adjust the learning rate and
the number of epochs. Comment on the effect of this modification on the convergence
properties, and the generalization of the network.
(e) [3 pts] Plot some examples where the neural network is not confident of the classification
output (the top score is below some threshold), and comment on them. Will the classifier
be correct if it outputs the top scoring class anyways?
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