Discussion on macroeconomy exam

macroeconomy exam
macroeconomy exam (limited time 45mins) 19 th Nov 6.30 Sydney time3 multiple choice questions, 7 short answersi will provide the Exam scope

Installation and setup
Installing python 3
Go to https://www.python.org/downloads/ to download python 3. Alternatively, you can install a python
distribution such as Anaconda. Please select python 3 (not python 2).
Installing the dependencies
Install all dependencies using
python3 -m pip install -r requirements.txt
Note: On some systems, you might be required to use pip3 instead of pip for python 3.
If you’re using conda use
conda env create environment.yml
The test grader will not have any dependencies installed, other than native python3 libraries and libraries
mentioned in requirements.txt. This includes packages like pandas. If you use additional dependencies ask on
piazza first, or risk the test grader failing.
Manual installation of pytorch
Go to https://pytorch.org/get-started/locally/ then select the stable Pytorch build, your OS, package (pip if you
installed python 3 directly, conda if you installed Anaconda), python version, cuda version. Run the provided
command. Note that cuda is not required, you can select cuda = None if you don’t have a GPU or don’t want to do
GPU training locally. We will provide instruction for doing remote GPU training on Google Colab for free.
Manual installation of the Python Imaging Library (PIL)
The easiest way to install the PIL is through pip or conda.
python3 -m pip install -U Pillow
There are a few important considerations when using PIL. First, make sure that your OS uses libjpeg-turbo and not
the slower libjpeg (all modern Ubuntu versions do by default). Second, if you’re frustrated with slow image
transformations in PIL use Pillow-SIMD instead:
CC=”cc -mavx2″ python3 -m pip install -U –force-reinstall Pillow-SIMD
The CC=”cc -mavx2″ is only needed if your CPU supports AVX2 instructions. pip will most likely complain a bit
about missing dependencies. Install them, either through conda, or your favorite package manager (apt, brew, …).
Running your assignment on google colab
You might need a GPU to train your models. You can get a free one on google colab. We provide you with a
ipython notebook that can get you started on colab for each homework.
If you’ve never used colab before, go through colab notebook (tutorial)
When you’re comfortable with the workflow, feel free to use colab notebook (shortened)
Follow the instructions below to use it.
• Go to http://colab.research.google.com/.
• Sign in to your Google account.
• Select the upload tab then select the .ipynb file.
• Follow the instructions on the homework notebook to upload code and data.

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starter code In this homework, we will train a CNN to do vision-based driving in SuperTuxKart. We will design a simple low-level controller that acts as an auto-pilot to drive in supertuxkart. We then use this auto-pilot to train a vision-based driving system. To get started, first download and install SuperTuxKart on your machine.
python3 -m pip install -U PySuperTuxKart When running the simulator, if you encountered errors about irrlicht devices, make sure you have EGL installed, also try using the following environment variables: IRR_DEVICE_TYPE=x11 or IRR_DEVICE_TYPE=sdl. You can install EGL using conda install -c anaconda mesa-libegl-cos6-x86_64 if you are using conda.
Controller In the first part of this homework, you’ll write a low-level controller in controller.py. The controller function takes as input an aim-point and the current velocity of the car. The aim-point is a point on the center of the track 15 meters away from the kart, as shown below. We use screen coordinates for the aim-point: [-1..1] in both x and y directions. The point (-1,-1) is on the top left, (1, 1) the bottom right. In the first part of this assignment, we will use a ground truth aim-point from the simulator itself. In the second part, we remove this restriction and predict the aim-point directly from the image. The goal of the low-level controller is to steer towards this point. The output of the low-level controller is a pystk.Action. You can specify:
• pystk.Action.steer the steering angle of the kart normalized to -1 … 1
• pystk.Action.acceleration the acceleration of the kart normalized to 0 … 1
• pystk.Action.brake boolean indicator for braking
• pystk.Action.drift a special action that makes the kart drift, useful for tight turns
• pystk.Action.nitro burns nitro for fast acceleration Implement your controller in the control function in controller.py. You won’t need any deep learning to design this low-level controller. You may use numpy instead of pytorch for this part. Once you finish, you could test your controller using python3 -m homework.controller [TRACK_NAME] -v You should tune the hyper-parameters of your controller. You might want to look into gradient-free optimization or exhaustive search. The reference controller completes each level relatively efficiently: zengarden and lighthouse in under 50 sec, hacienda and snowtuxpeak in under 60 sec, cornfield_crossing and scotland in under 70 sec. Grade your controller using python3 -m grader homework
Hint: Skid if the steering angle is too large
Hint: Target a constant velocity
Hint: Use the aim-point to compute the absolute steering angle, learn or tune a scaling factor between absolute and normalized steering.
Planner In the second part, you’ll train a planner to predict the aim-points. The planner takes as input an image and outputs the aim-point in the image coordinate. Your controller then maps those aim-points to actions.
Data Use your low-level controller to collect a training set for the planner. python3 -m homework.utils zengarden lighthouse hacienda snowtuxpeak cornfield_crossing scotland We highly recommend you limit yourself to the above training levels, adding additional training levels may create an unbalanced training set and lead to issues with the final test_grader. This function creates a dataset of images and corresponding aim-points in drive_data. You can visualize the data using python3 -m homework.visualize_data drive_data
Model Implement your planner model in Planner class of planner.py. Your planner model is a torch.nn.Module that takes as input an image tensor and outputs the aiming point in image coordinates (x:0..127, y:0..95). We recommend using an encoder-decoder structure to predict a heatmap and extract the peak using a spatial argmax layer in utils.py. Complete the training code in train.py and train your model using python3 -m
homework.train.
Vision-Based Driving Once you completed everything, use
python3 -m homework.planner [TRACK_NAME] -v to drive with your CNN planner and controller.
Grading We will grade both your controller and planner in the following 6 tracks
• hacienda
• lighthouse
• cornfield_crossing
• scotland
• zengarden
• snowtuxpeak Your controller/planner should complete each track within a certain amount of time. You receive 5% of your grade by completing each track with your low-level controller. You receive 10% of your grade by completing each track with your image-based agent. You may train on all the above testing track. For the last 10%, you’ll need to complete an unseen test track. We chose a relatively easy test track. You can test your solution against the grader by
python3 -m grader homework
Extra credit (up to 10pt) We will run a little tournament with all submissions, the top 9 submissions will receive 10, 9, 8, … extra credit respectively. The tournament uses several unreleased test tracks.
Submission (ID is 3849) Once you finished the assignment, create a submission bundle using
python3 bundle.py [YOUR ID] and submit the zip file online. If you want to double-check that your zip file was properly created, you can grade it again
python3 -m grader [YOUR ID].zip
Grading The test grader we provide
python3 -m grader homework -v will run a subset of test cases we use during the actual testing. The point distributions will be the same, but we will use additional test cases. More importantly, we evaluate your model on the test set. The performance on the test grader may vary. Try not to overfit to the validation set too much.
Online grader We will use an automated grader to grade all your submissions. There is a limit of 5 submissions per assignment. The online grading system will use a slightly modified version of python and the grader:
• Please do not use the exit or sys.exit command, it will likely lead to a crash in the grader
• Please do not try to access, read, or write files outside the ones specified in the assignment. This again will lead to a crash. File writing is disabled.
• Network access is disabled. Please do not try to communicate with the outside world.
• Forking is not allowed!
• print or sys.stdout.write statements from your code are ignored and not returned.
Running your assignment on google colab You might need a GPU to train your models. You can get a free one on google colab. We provide you with an ipython notebook that can get you started on colab for each homework. If you’ve never used colab before, go through colab notebook (tutorial) When you’re comfortable with the workflow, feel free to use colab notebook (shortened) Follow the instructions below to use it.
• Go to http://colab.research.google.com/.
• Sign in to your Google account.
• Select the upload tab then select the .ipynb file.
• Follow the instructions on the homework notebook to upload code and data.

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