cs501r_f2017:lab7
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| - | ====WARNING THIS LAB SPEC IS UNDER DEVELOPMENT:==== | ||
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| - | ====Objective:==== | ||
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| - | To learn about deconvolutions, variable sharing, trainable variables, | ||
| - | and generative adversarial models. | ||
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| - | ---- | ||
| - | ====Deliverable:==== | ||
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| - | For this lab, you will need to implement a generative adversarial | ||
| - | network (GAN). | ||
| - | Specifically, we will be using the technique outlined in the paper [[https://arxiv.org/pdf/1704.00028|Improved Training of Wasserstein GANs]]. | ||
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| - | You should turn in an iPython notebook that shows a two plots. The first plot should be random samples from the final generator. The second should show interpolation between two faces by interpolating in ''z'' space. | ||
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| - | You must also turn in your code, but your code does not need to be in a notebook, if it's easier to turn it in separately (but please zip your code and notebook together in a single zip file). | ||
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| - | **NOTE:** this lab is complex. Please read through **the entire | ||
| - | spec** before diving in. | ||
| - | |||
| - | ---- | ||
| - | ====Grading standards:==== | ||
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| - | Your code/image will be graded on the following: | ||
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| - | * 20% Correct implementation of discriminator | ||
| - | * 20% Correct implementation of generator | ||
| - | * 20% Correct implementation of loss functions | ||
| - | * 20% Correct sharing of variables | ||
| - | * 10% Correct training of subsets of variables | ||
| - | * 10% Tidy and legible final image | ||
| - | |||
| - | ---- | ||
| - | ====Dataset:==== | ||
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| - | The dataset you will be using is the [[http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html|"celebA" dataset]], a set of 202,599 face images of celebrities. Each image is 178x218. You should download the "aligned and cropped" version of the dataset. [[https://www.dropbox.com/sh/8oqt9vytwxb3s4r/AADSNUu0bseoCKuxuI5ZeTl1a/Img?dl=0&preview=img_align_celeba.zip|Here is a direct download link (1.4G)]], and | ||
| - | [[https://www.dropbox.com/sh/8oqt9vytwxb3s4r/AAB06FXaQRUNtjW9ntaoPGvCa?dl=0&preview=README.txt|here is additional information about the dataset]]. | ||
| - | |||
| - | ---- | ||
| - | ====Description:==== | ||
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| - | This lab will help you develop several new tensorflow skills, as well | ||
| - | as understand some best practices needed for building large models. | ||
| - | In addition, we'll be able to create networks that generate neat images! | ||
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| - | ==Part 0: Implement a generator network== | ||
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| - | ==Part 1: Implement a discriminator network== | ||
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| - | ==Part 2: Implement the Improved Wasserstein GAN training algorithm== | ||
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| - | tf.gradients | ||
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cs501r_f2017/lab7.txt ยท Last modified: 2021/06/30 23:42 (external edit)
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