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MLP 2018/19: Coursework 2 Due: 23 November 2018
Machine Learning Practical 2018/19: Coursework 2
Released: Monday 5 November 2018
Submission due: 16:00 Friday 23 November 2018
1 Introduction
The aim of this coursework is to further explore the classification of images of handwritten digits using neural
networks. As in the previous coursework, we’ll be using an extended version of the MNIST database, the
EMNIST Balanced dataset, described in the coursework 1 spec. The first part of the coursework will concern the
implementation and experimentation of convolutional networks using the MLP framework. The second part will
involve exploring different convolutional network architectures using PyTorch.
In order to support the experiments you will need to run for the second part of the coursework (which will be
carried out in PyTorch) we have acquired Google Cloud Platform credits which allow the use of the Google
Compute Engine infrastructure. Each student enrolled on the MLP course will receive a $50 Google Cloud credit
coupon which is enough to carry out the experiments required for this coursework. You will receive an email
which will give you the URL you will need to access in order to request your Google Cloud Platform coupon.
As with the previous coursework, you will need to submit your python code and a report. In addition you will
need to submit the outputs of test code that tests your implementation for the first part. The detailed submission
instructions are given in Section 5.2 – please follow these instructions carefully.
2 Github branch mlp2018-9/coursework_2
The provided code and setup information for this coursework is available on the course Github repository on a
branch mlp2018-9/coursework_2. To create a local working copy of this branch in your local repository you
need to do the following.
1. Make sure all modified files on the branch you are currently have been committed (see notes/gettingstarted-in-a-lab.md
if you are unsure how to do this).
2. Fetch changes to the upstream origin repository by running
git fetch origin
3. Checkout a new local branch from the fetched branch using
git checkout -b coursework_2 origin/mlp2018-9/coursework_2
You will now have a new branch in your local repository with everything you need to carry out the coursework.
This branch includes the following additions to your setup:
? For part 1:
– Updates to the mlp python modules including (in the mlp.layers module) skeleton
ConvolutionalLayer and MaxPooling2DLayer classes, and a ReshapeLayer class which
allows the output of the previous layer to be reshaped before being forward propagated to the next
layer in a multilayer model.
– Jupyter notebooks, ConvolutionalLayer_tests.ipynb and MaxPoolingLayer_tests.ipynb
for testing your implementations of convolutional and max pooling layers, and supporting test code.
For part 2:
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MLP 2018/19: Coursework 2 Due: 23 November 2018
– A Jupyter notebook, Coursework_2_Pytorch_experiment_framework.ipynb, which introduces
the PyTorch framework, and provides some sample PyTorch code to get you started implementing
Convolutional Networks in PyTorch and running experiments.
– A directory mlp/pytorch_experiment_scripts, which includes tooling and ready to run scripts
that to enable straightforward experimentation on GPU. Documentation on this is included in
notes/pytorch-experiment-framework.md
– A note on how to use the Google Cloud Platform (using your student credits),
notes/google_cloud_setup.md.
For the report:
– A directory called report which contains the LaTeX template and style files for your report. You
should copy all these files into the directory which will contain your report.
3 Tasks
This coursework comes in two parts. The objective of the first part is to implement convolutional networks in
the MLP framework, and totest your implementation. The objective of the second part is to explore different
approaches to integrating information in convolutional networks: pooling, strided convolutions, and dilated
convolutions.
Carrying out larger convolutional network experiments using the MLP framework is is inefficient because
(1) it runs on CPU and not GPU, and (2) a default implementation of a convolutional layer is unlikely to
be computationally efficient. For this reason the second part of the coursework, which concerns running
convolutional network experiments will use GPU computing (on the Google Compute Engine) and the highly
efficient PyTorch framework.
Please note that part 2 of the coursework does not depend on part 1.
Thus you can do them in either order (or simultaneously).
Part 1: Implementing convolutional networks in the MLP framework
In the first part of the coursework, you should implement convolutional and max-pooling layers in the MLP
framework, and carry out some basic experiments to validate your implementation.
1. Provide a convolutional layer implementations as a class ConvolutionalLayer. This class should
implement the methods fprop, bprop and grads_wrt_params. There are two recommended approaches
you might consider to do the implementation (you only need to do the implementation using one of these
approaches), based on
the methods scipy.signal.convolve2d (and/or scipy.signal.correlate2d);
or using a “serialisation” approach using the method im2col.
Both of these approaches are discussed below.
2. Implement a max-pooling layer. As a default, implement non-overlapping pooling (which was assumed in
the lecture presentation).
3. Verify the correctness of your implementation using the supplied unit tests in the jupyter notebook files
notebooks/ConvolutionalLayer_tests.ipynb and notebooks/MaxPoolingLayer_tests.ipynb
to verify your convolutional layer and max pooling layer implementations respectively. Note: The tests
are not exhaustive and should serve only as an indication of going into the right direction. Ideally you
should write additional tests to validate your code in other scenarios. Take special care to check for edge
cases.
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MLP 2018/19: Coursework 2 Due: 23 November 2018
4. Generate and submit your personalised output files for the unit tests:
test_max_pooling_results_pack.npz and test_convolution_results_pack.npz
These files are automatically generated by activating your conda mlp environment, changing to the
scripts directory in mlpractical, and running the following commands:
python generate_conv_layer_test_file.py --student_id sXXXXXX
python generate_max_pool_layer_test_file.py --student_id sXXXXXXX
Replace the sXXXXXXX with your student ID. Once the commands have run, the two .npz files will be
generated in the scripts folder. You should make sure those files are included as part of your submission
(see Section 5.2.
5. Since the required compute time for your convolutional network implementations will be substantial, it is
not necessary to use this implementation to train and test convolutional networks on the EMNIST data.
For part 1 you should submit your code (in mlp.layers) and your output test files. There should also be a
section of your report describing your implementation and including any analysis of the its efficiency.
Implementing convolutional layers
When you implement a convolutional layer both the fprop and bprop methods can be based on a convolution
operation, as explained in the lectures. If we consider the fprop then the method operates on two 4-dimension
tensors:
The input (previous layer) to the convolution, whose dimensions are (minibatch-size.
num-feature-maps, xin, yin);
The kernels (weight matrices) for the convolutions, whose dimensions are (num-feature-maps-in,
num-feature-maps-out, xkernel, ykernel).
The key to implementing a convolutional layer is how the convolutions are implemented. We recommend that
you consider one of the following approaches:
1. Explicitly compute convolutions using the SciPy convolution function scipy.signal.convolve2d
(or scipy.signal.correlate2d). Note that these functions convolve a 2-dimension image with a
2-dimension kernel, so your code will need to use this in the context of 4-dimension tensors where we
have multiple feature maps and a batch of training examples.
2. “Serialisation” in which the the convolution operation is turned into a single matrix multiplication.
The advantage of this is that implementing the convolutional fprop or bprop as a single large matrix
multiplication is much more computationally efficient than the many small matrix multiplications a naive
implementation would have. The disadvantage of this approach is that the resultant matrix has repeated
elements, and be large (dependent on the number of feature map, batch size, and image size).
This serialisation approach uses a function called im2col (and its reverse col2im). The im2col function
is standard in Matlab and various computer vision packages, but it is not part of NumPy or SciPy; you
may use the external Python implementations im2col_indices and col2im_indices at:
https://github.com/huyouare/CS231n/blob/master/assignment2/cs231n/im2col.py
(35 Marks)
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MLP 2018/19: Coursework 2 Due: 23 November 2018
Dilation 1 Dilation 2 Dilation 3 Dilation 4
Figure 1: Kernels for dilated convolutions with successively increasing receptive field sizes. Figure copied from
Antoniou et al. [2018] with permission.
Part 2: Exploring context in convolutional networks
The second part of the coursework will explore context in convolutional networks using PyTorch and the Google
Compute Engine.
A powerful aspect of using convolutional networks for image classification is their ability to incorporate
information at different scales. One can view a classifier for a task such as EMNIST as a pyramid-shaped
construction of feature maps, in which convolutional layers close to the input are working at local context (in
which each individual unit learns information about a small image patch), whereas units further from the input
have a much broader context (in which each unit learns information about much larger regions of the image).
There are various ways this successive broadening of context can be achieved, while keeping small convolution
filters, for example:
Pooling layers: As discussed in the lectures (and to be implemented in part 1) pooling can be used to
increase the context. In the 1990s, in architectures such as LeNet, average pooling was used. More
recently max pooling has been more commonly used.
Striding: Striding (how much a kernel shifts when scanning the image) to reduce the size of feature maps.
Striding can also be combined with pooling.
Dilation: Dilation is a way to incrementally increase the context of each kernel while keeping the number
of parameters for each kernel the same [Yu and Koltun, 2016]. The basic idea is illustrated in figure 1:
dilating a kernel involves progressively increasing the size of the receptive fields layer-by-layer. As
the receptive field size increases, the number of parameters remains constant by increasing the distance
between the filtered pixels. This is explained in more detail in section 2 of Antoniou et al. [2018] and in
Yu and Koltun [2016]. Dilated convolutions, which were first developed for wavelet signal processing in
the early 1990s, have been extensively used in the past 2-3 years for both computer vision [Yu and Koltun,
2016] and for audio/speech processing [van den Oord et al., 2016]. (Modern speech synthesis, such as
deployed by Google, makes extensive use of dilated convolutions.)
In this task you should investigate different approaches to modelling image context in the classification
of EMNIST. We suggest that you explore pooling, striding, and dilation, as mentioned above. The
material in the coursework_2 GitHub provides examples of convolutional networks, documented in
notes/pytorch-experiment-framework.md, with code in mlp/pytorch_experiment_scripts. The
provided framework supports max and average pooling, strided convolutions and dilated convolutions.
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MLP 2018/19: Coursework 2 Due: 23 November 2018
You should thus construct, carry out, and report a set of experiments, to address one or more research questions.
(An example research question might be “does max pooling lead to more accurate classification than average
pooling?”.)
The expected amount of work for this task is a set of experiments which explore the different ways of modelling
context in image classification using convolutional networks. Your experiments should involve at most 100h of
GPU computation (using a single K80 GPU), which corresponds to the amount of computation covered by your
voucher. Please note that we will not be able to provide extra credits in case the provided ones are spent.
Note that the default hyperparameters provided in the framework (see
pytorch_experiment_scripts/experiment_builder.py) are reasonable – extensive hyperparameter
searches are not required in this coursework. As a starting point you can explore a convolutional
network using 4 convolutional layers with 64 filters, which is easy to run using the script
pytorch_experiment_scripts/train_evaluate_emnist_classification_system.py with the
default num_layers and num_filters options.
Important things to note about experiments:
Your experiments should be designed so you are only varying one component at a time, so you can see the
effect of that component-change.
Designing experiments so that you are in a position to draw conclusions from your experiments is more
important than the doing as many experiments as possible.
When reporting the results of experiments, make sure that the comparison/contrast you are exploring is
clear in the way you present the results.
Your report on this task should include the following:
Introduction. Outline and explain the research questions you are investigating. Provide citations if
appropriate.
Methodology. Explain the methodology used – in this case the approaches to modelling image context
that have you explored. Provide citations if appropriate.
Experiments. Describe carefully the experiments carried out, providing enough information to make them
reproducible. Present your results clearly and concisely. Graphs and tables should be constructed to make
clear the contrasts and comparisons you are interested in based on the research questions. For instance,
some interesting evaluation criteria that can be used to compare different strategies are classification
accuracy and speed of your network.
Discussion and conclusions. Discuss your results, with reference to the research questions, and if
appropriate with reference to the literature. What conclusions can you draw from your experiments?
Using Google Compute Engine
1. You will receive an email containing the URL you will need to access in order to request a Google Cloud
Platform coupon, and information about how to do this.
2. In the coursework_2 branch of the GitHub, notes/google_cloud_setup.md gives the instructions
you should follow to set up a Google Compute Engine instance to carry out this coursework.
3. The PyTorch experimental framework that is used for this coursework is described in
notes/pytorch-experiment-framework.md and in
notebooks/Coursework_2_Pytorch_experiment_framework.ipynb
(65 Marks)
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MLP 2018/19: Coursework 2 Due: 23 November 2018
4 Report
Your coursework will be primarily assessed based on your submitted report.
The directory coursework_2/report contains a template for your report (mlp-cw2-template.tex); the
generated pdf file (mlp-cw2-template.pdf) is also provided, and you should read this file carefully as it
contains some useful information about the required structure and content. The template is written in LaTeX,
and we strongly recommend that you write your own report using LaTeX, using the supplied document style
mlp2018 (as in the template).
You should copy the files in the report directory to the directory containing the LaTeX file of your report, as
pdflatex will need to access these files when building the pdf document from the LaTeX source file. The
coursework 1 spec outlines how to create a pdf file from a LaTeX source file.
Your report should be in a 2-column format, based on the document format used for the ICML conference. The
report should be a maximum of 6 pages long, not including references. We will not read or assess any parts of
the report beyond this limit.
As discussed in the coursework 1 spec, all figures should ideally be included in your report file as vector graphics
files, rather than raster files as this will make sure all detail in the plot is visible.
If you make use of any any books, articles, web pages or other resources you should appropriately cite these in
your report. You do not need to cite material from the course lecture slides or lab notebooks.
5 Mechanics
Marks: This assignment will be assessed out of 100 marks and forms 40% of your final grade for the course.
Academic conduct: Assessed work is subject to University regulations on academic conduct:
http://web.inf.ed.ac.uk/infweb/admin/policies/academic-misconduct
Submission: You can submit more than once up until the submission deadline. All submissions are timestamped
automatically. Identically named files will overwrite earlier submitted versions, so we will mark the latest
submission that comes in before the deadline.
If you submit anything before the deadline, you may not resubmit after the deadline. (This policy allows us to
begin marking submissions immediately after the deadline, without having to worry that some may need to be
re-marked).
If you do not submit anything before the deadline, you may submit exactly once after the deadline, and a late
penalty will be applied to this submission unless you have received an approved extension. Please be aware
that late submissions may receive lower priority for marking, and marks may not be returned within the same
timeframe as for on-time submissions.
Warning: Unfortunately the submit command will technically allow you to submit late even if you submitted
before the deadline (i.e. it does not enforce the above policy). Don’t do this! We will mark the version that we
retrieve just after the deadline.
Extension requests: For additional information about late penalties and extension requests, see the School web
page below. Do not email any course staff directly about extension requests as these are handled by the
ITO; you must follow the instructions on the web page.
http://web.inf.ed.ac.uk/infweb/student-services/ito/admin/coursework-projects/late-coursework-extension-requests
Late submission penalty: Following the University guidelines, late coursework submitted without an authorised
extension will be recorded as late and the following penalties will apply: 5 percentage points will be deducted
for every calendar day or part thereof it is late, up to a maximum of 7 calendar days. After this time a mark of
zero will be recorded.
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MLP 2018/19: Coursework 2 Due: 23 November 2018
5.1 Backing up your work
It is strongly recommended you use some method for backing up your work. Those working in their AFS
homespace on DICE will have their work automatically backed up as part of the routine backup of all user
homespaces. If you are working on a personal computer you should have your own backup method in place
(e.g. saving additional copies to an external drive, syncing to a cloud service or pushing commits to your local
Git repository to a private repository on Github). Loss of work through failure to back up does not constitute
a good reason for late submission.
You may additionally wish to keep your coursework under version control in your local Git repository on the
coursework_2 branch.
If you make regular commits of your work on the coursework this will allow you to better keep track of the
changes you have made and if necessary revert to previous versions of files and/or restore accidentally deleted
work. This is not however required and you should note that keeping your work under version control is a
distinct issue from backing up to guard against hard drive failure. If you are working on a personal computer
you should still keep an additional back up of your work as described above.
5.2 Submission
Your coursework submission should be done electronically using the submit command available on DICE
machines.
Your submission should include
your completed report as a PDF file, using the provided template
your local version of the mlp code including any changes you made to the modules (.py files)
your personalised test_max_pooling_results_pack.npz and test_convolution_results_pack.npz
files. These can be automatically generated by activating your conda mlp environment and pointing your
terminal to the directory scripts in the mlpractical repo and running the following commands:
python generate_conv_layer_test_file.py --student_id sXXXXXX
python generate_max_pool_layer_test_file.py --student_id sXXXXXXX
Replace the sXXXXXXX with your student ID. Once the commands have been ran, the two .npz files
will be generated under the scripts folder. You should make sure those files are included as part of your
submission folder.
your local version of the Pytorch experiment framework (mlp/pytorch_experiment_framework),
including any changes you’ve made to existing files and any newly created files.
a copy of your Pytorch experiment directories, including only the .csv files for your training, validation
and test statistics. Please do not include model weights.
Please do not submit anything else (e.g. log files).
You should copy all of the files to a single directory, coursework2, e.g.
mkdir coursework2
cp reports/coursework2.pdf coursework2
cp scripts/test_max_pooling_results_pack.npz coursework2
cp scripts/test_convolution_results_pack.npz coursework2
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MLP 2018/19: Coursework 2 Due: 23 November 2018
also copy to coursework2 the directories containing your mlp and PyTorch code, as well as the directory
containing the PyTorch csv files.
You should then submit this directory using
submit mlp cw2 coursework2
Please submit the directory, not a zip file, not a tar file.
The submit command will prompt you with the details of the submission including the name of the files /
directories you are submitting and the name of the course and exercise you are submitting for and ask you to
check if these details are correct. You should check these carefully and reply y to submit if you are sure the files
are correct and n otherwise.
You can amend an existing submission by rerunning the submit command any time up to the deadline. It is
therefore a good idea (particularly if this is your first time using the DICE submit mechanism) to do an initial
run of the submit command early on and then rerun the command if you make any further updates to your
submission rather than leaving submission to the last minute.
6 Marking Guidelines
This document (Section 3 in particular) and the template report (mlp-cw1-template.pdf) provide a description
of what you are expected to do in this assignment, and how the report should be written and structured.
Assignments will be marked using the scale defined by the University Common Marking Scheme:
Numeric mark Equivalent letter grade Approximate meaning
< 40 F fail
40-49 D poor
50-59 C acceptable
60-69 B good
70-79 A3 very good/distinction
80-100 A1, A2 excellent/outstanding/high distinction
Please note the University specifications for marks above 70:
A1 90-100 Often faultless. The work is well beyond what is expected for the level of study.
A2 80-89 A truly professional piece of scholarship, often with an absence of errors.
As ‘A3’ but shows (depending upon the item of assessment): significant personal insight / creativity / originality
and / or extra depth and academic maturity in the elements of assessment.
A3 70-79
Knowledge: Comprehensive range of up-to-date material handled in a professional way.
Understanding/handling of key concepts: Shows a command of the subject and current theory.
Focus on the subject: Clear and analytical; fully explores the subject.
Critical analysis and discussion: Shows evidence of serious thought in critically evaluating and integrating the
evidenced and ideas. Deals confidently with the complexities and subtleties of the arguments. Shows elements
of personal insight / creativity / originality.
Structure: Clear and coherent showing logical, ordered thought.
Presentation: Clear and professional with few, relatively minor flaws. Accurate referencing. Figures and tables
well constructed and accurate. Good standard of spelling and grammar.
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MLP 2018/19: Coursework 2 Due: 23 November 2018
Referenceshttp://www.6daixie.com/contents/3/2163.html
Antreas Antoniou, Agnieszka Slowik, Elliot J. Crowley, and Amos Storkey. Dilated DenseNets for relational
reasoning. arXiv:1811.00410, 2018. URL https://arxiv.org/abs/1811.00410.
A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior,
and K. Kavukcuoglu. WaveNet: A generative model for raw audio. arXiv:1609.03499, 2016. URL
https://arxiv.org/abs/1609.03499.
Fisher Yu and Vladlen Koltun. Multi-scale context aggregation by dilated convolutions. In ICLR, 2016. URL
https://arxiv.org/abs/1511.07122.
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