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EECE5644 Matlab function
创建日期：2024-07-30 16:34:42
标签： EECE5644
Matlab function
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EECE5644 
Please submit your solutions on Blackboard in a single PDF file that includes all math and
numerical results. Only the contents of this PDF will be graded.
For control purposes, please also provide any code you write to solve the questions in one of
the following ways:
1. Include all of your code in the PDF as an appendix.
2. In the PDF provide a link to your online code repo where the code resides.
This is a graded assignment and the entirety of your submission must contain only your own
work. Do not make your code repo public until after the submission deadline. If people find your
code and use it to solve questions, we will consider it cheating for all parties involved. You may
benefit from publicly available literature including software (that is not from classmates), as long
as these sources are properly acknowledged in your submission.
Copying math or code from each other is clearly not allowed and will be considered as academic
dishonesty. Discussing with the instructor and the teaching assistant to get clarification, or
to eliminate doubts is acceptable.
By submitting a PDF file in response to this take home assignment you are declaring that the
contents of your submission, and the associated code is your own work, except as noted in your
acknowledgement citations to resources.
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that your mathematical design, code implementations, and generated results are legitimate and
correct/accurate.
1
Question 1 (50%)
In this exercise, you will train many multilayer perceptrons (MLP) to approximate the class
label posterior, using maximum likelihood parameter estimation (equivalently, with minimum average
cross-entropy loss).
Pick your own C value (number of classes) from this set: {3,4,5}. Pick two activation functions
for the perceptrons; one sigmoid, one soft-ReLu type differentiable function. Your MLP
structure will consist of 2 fully-connected layers of adaptive weights, followed by a softmax layer
to ensure your model output conforms to the requirements of a probability distribution over discrete
class labels. The dataset you will classify consists of 2-dimensional real vectors. The number
of perceptrons in the first layer of the MLP will be determined using cross-validation procedures
(e.g., from the set {1,...,6}).
Using the Matlab function generateMultiringDataset.m generate two sets of data. Dtrain will
have 100 iid samples and their class labels. Dtest will have 10000 iid samples and their class labels.
Do NOT use Dtest in making any training or model selection choices in the process. You will use
it only for final performance evaluation.
Using 10-fold cross-validation with Dtrain, and minimum classification error probability as the
objective, select the best combination of number of perceptrons in the first layer and the activation
function.
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