解析COMP222 Second CA

Posted 2021-09-12 交社惧恐症

COMP222 - 2018 - Second CA Assignment
Individual coursework
Train Deep Learning Agents
Assessment Information
Assignment Number 2 (of 2)
Weighting 10%
Assignment Circulated 16 November 2018
Deadline Friday December 28, 15:00
Submission Mode Electronic
Learning outcome assessed 3. Ability to explain how deep neural networks are constructed
and trained, and apply deep neural networks
to work with large scale datasets

4. Understand reinforcement learning, and is able to
   develop deep reinforcement learning algorithms for suitable
   applications
   Purpose of assessment To design and implement deep learning agents for either
   image classification task or reinforcement learning task.
   Marking criteria The marking scheme can be found in Section 3.2
   Submission necessary in order No
   to satisfy Module requirements?
   Late Submission Penalty Standard UoL Policy.
   1
5. Objective
   This assignment requires you to implement one of the following tasks:
   two image classifiers with convolutional neural networks,
   a deep reinforcement learning model for a video game from OpenAI Universe.
   Considering their di?erent diculties
   and the fact that the second needs at least an implementation
   of the first, we enable the possibility of getting 140% marks for the completion of
   the second.
   In the following, the Requirement and Description and the Marking Criteria for the two
   tasks are explained separately.
6. CNN-based Image Classification
   2.1 Requirement and Description
   Language and Platform Python (version 3.5 or above) and Tensorflow (newest version).
   You may use any libraries available on Python platform, such as numpy, scikit-learn, panda,
   etc.
   Dataset You can use any dataset which is convenient for you. Unless exceptional circumstance,
   it is recommended that the dataset is not too small (e.g., no less than 10,000
   items) and not too big (e.g., no more than 100,000 items). The images in the dataset are
   not too large, as that will cost you too much time on training a good model. There are a
   few suggested datasets:
   MNIST handwritten dataset;
   CIFAR10 small image dataset;
   but you are encouraged to use your preferred dataset.
   Learning Task You need to train two classifiers on the chosen dataset.
   DNN Architecture There are a few existing DNN architectures for such small scale image
   dataset, including
   LeNet, see tutorial e.g., goo.gl/S6RQiS
   AlexNet, see tutorial e.g., goo.gl/UkjY6u
   But you can design you own architecture.
   2
   Assignment Tasks The implementation task (as suggested in the Objective) is to learn
   two models (of di?erent architecture) from the dataset you select. You need to write a
   proper document explaining the architectures, your training parameters, and the results
   (e.g., accuracy).
   Submission files You submission needs to contain the following two files:
   a package containing your source codes (with the instruction on how to run them) and
   a document explaining your architectures, your training parameters, and the results.
   Detailed requirements on the document are given below.
   2.2 Marking Criteria
   The assignment is split in a number of steps. Every step gives you some marks. At the
   beginning of the submitted document, please include a check list indicating whether the
   below marking points have been implemented successfully. Unless exceptional cases, the
   length of the submitted document needs to be within 4 pages (A4 paper, 11pt or 12pt font
   size).
   Step 1: Loading Data 10%
   Successfully load the dataset and use python commands to display the dataset information,
   e.g., the number of data entries, the number of classes, number of data entries for each
   classes, etc.
   Step 2: Write two CNN models 20%
   Write your CNN models with tensorflow. To get the marks, you need to explain in the
   document what the inputs and outputs are and what the hidden layers are.
   Step 3: Train your CNN models 30%
   To get the marks, you need to explain in the document the training parameters, e.g., learning
   rate, loss function, etc, and the accuracy of your resulting models over testing dataset.
   Step 4: Predict with Trained Model, 20%
   You need to be able to predict the class label by giving an image. For example, predict the
   label for the 100th image in the MNIST test dataset. To get the marks, you need to clearly
   identify in the document which part of the code is for this purpose.
   3
   2.3 Extra 20%
   You can see that marks for the steps described add up to 80%. In order to get 20% extra
   you have to be creative. For example (you are encouraged to not follow this example), you
   may adjust your models by adding or removing some layers and compare the performance
   of the resulting models. You are expecected to have a clear explanation on what you have
   done and what you have observed.
7. Deep Reinforcement Learning
   3.1 Requirement and Description
   Language and Platform Python (version 3.5 or above) and Tensorflow (newest version).
   You may use any libraries available on Python platform, such as numpy, scikit-learn,
   panda, etc. You may also need to install OpenAI universe (https://github.com/openai/
   universe) if you choose the second challenge.
   Dataset You can use any game in OpenAI universe or OpenAI gym.
   Learning Task You need to train a deep reinforcement learning model to play the game
   you selected.
   DNN Architecture There are a few existing deep reinforcement models, including
   Deep Q-Network
   Double Q-Learning
   Actor-Critic algorithm
   You are able to find various tutorials and implemenations from Github, for example goo.
   gl/1q6L31.
   Assignment Tasks The implementation task (as suggested in the Objective) is to train
   a deep reinforcement agent for a simple video game. Here, the main task is not to design a
   new algorithm, but to get yourself familiar with the concept of reinforcement learning and
   understand how the existing methods work.
   Submission files You submission needs to contain the following two files:
   a package containing your source codes (with the instruction on how to run them) and
   a document explaining your connection to openAI universe/gym, your network architectures,
   your training parameters, and the results. Detailed requirements on the
   document are given below.
   4
   3.2 Marking Criteria
   The assignment is split in a number of steps. Every step gives you some marks.
   Step 1: Import an OpenAI universe/gym game 20%
   As the starting point, you need to import a game.
   Step 2: Creating a network 20%
   You need to use a deep neural network.
   Step 3: Connection of the game to the network 10%
   You will need to explicitly associate the observations, actions, and rewards of the game to
   the network’s input and output. Clearly identify this part of the code in your document.
   Step 4: Deep reinforcement learning model 30%
   This part is for di?erent deep reinforcement learning models. You need to clearly state which
   model you are using, the parameters of the model, and how do you train/update the model.
   Step 5: Experimental results 20%
   You may record a video demo to exhibit what your agent can do. Alternatively, you can
   describe in details with texts.

8. Deadlines and How to Submit
   Deadline for submitting the second assignment is Friday, 28 December 2018 at 3pm.
   Submission is via the departmental submission system accessible (from within the department)
   from
   http://intranet.csc.liv.ac.uk...

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