Sale!

# CSCE 636: Deep Learning Assignment #3

\$30.00

Category:

CSCE 636: Deep Learning
Assignment #3

1. You need to submit (1) a report in PDF and (2) your code files, both to eCampus.
2. Your PDF report should include (1) answers to the non-programming part, and (2) results
and analysis of the programming part. For the programming part, your PDF report should at
least include the results you obtained, for example the accuracy, training curves, parameters,
etc. You should also analyze your results as needed.
3. Please put all your files (PDF report and code files) into a compressed file named
“HW# FirstName LastName.zip”
4. Unlimited number of submissions are allowed on eCampus and the latest one will be timed
incorrectly submitted files/reports.
6. All students are highly encouraged to typeset their reports using Word or LATEX. In case you
7. Only write your code between the following lines. Do not modify other parts.
1. (10 points) Given a symmetric matrix A ∈ R
3×3
, suppose its eigen-decomposition can be
written as
A =

u11 u12 u13
u21 u22 u23
u31 u32 u33

3 0 0
0 1 0
0 0 −2

u11 u21 u31
u12 u22 u32
u13 u23 u33

 . (1)
What is the singular value decomposition of this matrix?
2. (20 points) Let us consider a binary class classification problem. In view of the kernel methods
we described in class, many methods (logistic regression, support vector machines) can be
written in either the primal form or the dual form. For example, in the case of logistic
regression, the primal formulation is the one that we described at the very beginning of
this class. That is, we use the original data and class label {xi
, yi}
n
i=1 for training, and the
test data {xi}
n+m
i=n+1 for prediction. The dual formulation is the one that uses kernel values
(instead of the original data) described in the context of kernel methods in class. Specifically,
the dual formulation uses the kernel values k(xi
, xj ), i, j = 1, 2, · · · , n and class labels {yi}
n
i=1
for training, and the kernel values k(xi
, xj ), i = 1, 2, · · · , n, j = n + 1, n + 2, · · · , n + m for
prediction.
Suppose that we only have a solver that can only solve logistic regression in the primal
form with `2 norm regularization. On the other hand, we only have kernel values k(xi
, xj ),
1
i = 1, 2, · · · , n, j = 1, 2, · · · , n, n + 1, · · · , n + m and class labels {yi}
n
i=1. Note the original
training and test data {xi}
n+m
i=1 are NOT available. Please describe how we can train a logistic
regression classifier and perform testing using only the solver and data given above.
3. (20 points) Provide a complete proof of the Ky Fan Theorem given on page 4 of the notes
“Principal Component Analysis and Autoencoders” that is available on eCampus.
4. (30 points) (Coding Task) Kernel vs Neural Network: In this assignment, you will apply
two kernel models and a neural network model to the binary classification task on a partial
dataset from MNIST. In this classification task, the model will take a 16 × 16 image of
handwritten digits as inputs and classify the image into two classes. Please check the starting
code in folder “kernel/code” and follow the instructions. The “data” folder contains the
dataset, which has already been split into a training set and a test set. All data examples
are saved in dictionary-like objects using “npz” file. For each data sample, the dictionary
key “x” indicates its raw features, which are represented by a 256-dimensional vector where
the values between [−1, 1] indicate grayscale pixel values for a 16 × 16 image. In addition,
the key “y” is the label for a data example, which can be 0, 1 or 2. In this homework, we
only use the samples with the label 1 or 2. Note that you will need to use PyTorch in this
expected to implement the solutions based on the starting code. The files you need to modify
are “network.py” and “main.py”. You will test your solution by modifying and running the
“main.py” file.
(a) (10 points) Implement and test kernel logistic regression model — complete the forward() function of the class Kernel Layer() and the init () function of the class
Kernel LR(), test your implementation in “main.py”.
(b) (10 points) Implement and test radial basis function network model — complete the
k means() function of the class Kernel Layer() and the init () function of the
class RBF(), test your implementation in “main.py”.
(c) (10 points) Implement and test feed forward neural network model — complete the
init () function of the class FFN(), test your implementation in “main.py”.
Tune the hyper-parameters to achieve best classification performance for three models in
“main.py”. Report your hyper-parameters and test accuracy of three models, then compare
their performance. Note that for the comparison, you need to use the same hidden size for
radial basis function network model and feed forward network model.
5. (70 points) (Coding Task) PCA vs Autoencoder: In this assignment, you will apply the
PCA and the autoencoder (AE) to a collection of handwritten digit images from the USPS
dataset. The data file is stored in the “PCA/data” folder as “USPS.mat”. Please check
the starting code in folder “PCA/code” and follow the instructions. The whole dataset is
already loaded and stored in the matrix A with shape 3000 × 256. Each row of matrix A
represents a 16 × 16 handwritten digit image (between 0 and 9), which is flattened to a 256-
dimensional vector. Note that you will need to use PyTorch in this assignment. Please read
the “Readme” file carefully before getting started. You are expected to implement
the solutions based on the starting code. The files you need to modify are “solution.py” and
“main.py”. You will test your solution by modifying and running the “main.py” file.
(a) (10 points) In the class PCA(), complete the do pca() function.
2
(b) (5 points) In the class PCA(), complete the reconstruction() function to perform
data reconstruction. Please evaluate your code by testing different numbers of the principal component that p = 32, 64, 128.
(c) (10 points) In the class AE(), complete the network() and forward() function.
Please follow the note (http://people.tamu.edu/~sji/classes/PCA.pdf) to implement your network. Note that for problems (c), (e), and (f), the weights need to be
shared between the encoder and the decoder with weight matrices transposed to each
other.
(d) (5 points) In the class AE(), complete the reconstruction() function to perform data
reconstruction. Please test your function using three different dimensions for the hidden
representation d that d = 32, 64, 128.
(e) (10 points) Compare the reconstruction errors from PCA and AE. Note that you need to
set p = d for comparisons. Please evaluate the errors using p = d = 32, 64, 128. Report
the reconstruction errors and provide a brief analysis.
(f) (10 points) Experimentally justify the relations between the projection matrix G in
PCA and the optimized weight matrix W in AE. Note that you need to set p = d
for valid comparisons. Please explore three different cases that p = d = 32, 64, 128.
We recommend to first use frobeniu norm error() to verify if W and G are the
pdf) to implement necessary transformations for two matrices G and W and explore
the relations. You need to modify the code in “main.py”.
(g) (10 points) Please modify the network() and forward() function so that the weights
are not shared between the encoder and the decoder. Report the reconstructions errors
for d = 32, 64, 128. Please compare with the sharing weights case and briefly analyze
you results.
(h) (10 points) Please modify the network() and forward() function to include more
network layers and nonlinear functions. Please set d = 64 and explore different hyperparameters. Report the hyperparameters of the best model and its reconstruction error.