OASIS brain data set using VQVAE - Daniel Miller 45810536 - #458

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OASIS brain data set using VQVAE - Daniel Miller 45810536#458
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A VQVAE in tensorflow has been implemented to generate reconstructed images from OASIS brain dataset. When reconstructing images from VQVAE, a SSIM of 0.734 was achieved. A readme file explains the process and gives a brief rundown of how it works.

Files included:
modules.py: VQVAE model
dataset.py: data loader
train.py: training functions
predict.py: predicts results using model
readme_images: summary

Daniel Millerand others added 30 commits October 11, 2022 16:16
…ataset and the other loads the training images into a numpy array
….py file for normalising and preprocessing training data
…th one hot encoding. Train.py now calls this for training data.
…owever, quantised layer function still needs to be implemented and is currently preventing the code to run.
…tion from dataset.py. Reformatted the functions in modules.py -> encoder and decoder are now in terms of modules instead of sequenitial and the vq layer and overall model builder are in classes to allow other atrributes to be assessed of the model functionality. train.py has reduced in code whereas data is no longer being batched before training (now batched in model.fit). Read.me file has notes being written
… currently does not work however. Training for data is also working, however there is a high loss.
… decrease realistically. The bug occurred due to the value of the variance variable being feed into model.fit.
… calculated incorrectly. Wrote draft read.me file
@SiyuLiu0329

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This is an initial inspection, no action is required at this point

  • Reconstruction: OK
  • Generative part: pixel-cnn part seems to be missing
  • Readme seems incomplete with placeholder(s)

@shakes76

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Good Practice (Design/Commenting, TF/Torch Usage)

Adequate use and implementation (missing pixel CNN) -2
Good spacing and comments
Header blocks

Recognition Problem

Solves problem (no generations) -2
Driver Script present
File structure present
Shows Usage & Demo & Visualisation & Data usage (no plots) -1
Module present
Commenting
No Data leakage
Difficulty: Hard

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe OK, model info and background missing -2
Good Description and Comments
Markdown used PDF submitted

Pull Request

Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch)
Feedback required, remove duplicate Readmes -2
Request Description OK, more info would be good -1

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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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OASIS brain data set using VQVAE - Daniel Miller 45810536 - #458

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OASIS brain data set using VQVAE - Daniel Miller 45810536#458
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A VQVAE in tensorflow has been implemented to generate reconstructed images from OASIS brain dataset. When reconstructing images from VQVAE, a SSIM of 0.734 was achieved. A readme file explains the process and gives a brief rundown of how it works.

Files included:
modules.py: VQVAE model
dataset.py: data loader
train.py: training functions
predict.py: predicts results using model
readme_images: summary

Daniel Millerand others added 30 commits October 11, 2022 16:16
…ataset and the other loads the training images into a numpy array
….py file for normalising and preprocessing training data
…th one hot encoding. Train.py now calls this for training data.
…owever, quantised layer function still needs to be implemented and is currently preventing the code to run.
…tion from dataset.py. Reformatted the functions in modules.py -> encoder and decoder are now in terms of modules instead of sequenitial and the vq layer and overall model builder are in classes to allow other atrributes to be assessed of the model functionality. train.py has reduced in code whereas data is no longer being batched before training (now batched in model.fit). Read.me file has notes being written
… currently does not work however. Training for data is also working, however there is a high loss.
… decrease realistically. The bug occurred due to the value of the variance variable being feed into model.fit.
… calculated incorrectly. Wrote draft read.me file
@SiyuLiu0329

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This is an initial inspection, no action is required at this point

  • Reconstruction: OK
  • Generative part: pixel-cnn part seems to be missing
  • Readme seems incomplete with placeholder(s)

@shakes76

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Owner

Good Practice (Design/Commenting, TF/Torch Usage)

Adequate use and implementation (missing pixel CNN) -2
Good spacing and comments
Header blocks

Recognition Problem

Solves problem (no generations) -2
Driver Script present
File structure present
Shows Usage & Demo & Visualisation & Data usage (no plots) -1
Module present
Commenting
No Data leakage
Difficulty: Hard

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe OK, model info and background missing -2
Good Description and Comments
Markdown used PDF submitted

Pull Request

Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch)
Feedback required, remove duplicate Readmes -2
Request Description OK, more info would be good -1

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@dapmiller@SiyuLiu0329@shakes76@LinfengLiu98
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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OASIS brain data set using VQVAE - Daniel Miller 45810536 - #458

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OASIS brain data set using VQVAE - Daniel Miller 45810536#458
dapmiller wants to merge 30 commits into
shakes76:topic-recognitionfrom
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A VQVAE in tensorflow has been implemented to generate reconstructed images from OASIS brain dataset. When reconstructing images from VQVAE, a SSIM of 0.734 was achieved. A readme file explains the process and gives a brief rundown of how it works.

Files included:
modules.py: VQVAE model
dataset.py: data loader
train.py: training functions
predict.py: predicts results using model
readme_images: summary

Daniel Millerand others added 30 commits October 11, 2022 16:16
…ataset and the other loads the training images into a numpy array
….py file for normalising and preprocessing training data
…th one hot encoding. Train.py now calls this for training data.
…owever, quantised layer function still needs to be implemented and is currently preventing the code to run.
…tion from dataset.py. Reformatted the functions in modules.py -> encoder and decoder are now in terms of modules instead of sequenitial and the vq layer and overall model builder are in classes to allow other atrributes to be assessed of the model functionality. train.py has reduced in code whereas data is no longer being batched before training (now batched in model.fit). Read.me file has notes being written
… currently does not work however. Training for data is also working, however there is a high loss.
… decrease realistically. The bug occurred due to the value of the variance variable being feed into model.fit.
… calculated incorrectly. Wrote draft read.me file
@SiyuLiu0329

Copy link
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Collaborator

This is an initial inspection, no action is required at this point

  • Reconstruction: OK
  • Generative part: pixel-cnn part seems to be missing
  • Readme seems incomplete with placeholder(s)

@shakes76

Copy link
Copy Markdown
Owner

Good Practice (Design/Commenting, TF/Torch Usage)

Adequate use and implementation (missing pixel CNN) -2
Good spacing and comments
Header blocks

Recognition Problem

Solves problem (no generations) -2
Driver Script present
File structure present
Shows Usage & Demo & Visualisation & Data usage (no plots) -1
Module present
Commenting
No Data leakage
Difficulty: Hard

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe OK, model info and background missing -2
Good Description and Comments
Markdown used PDF submitted

Pull Request

Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch)
Feedback required, remove duplicate Readmes -2
Request Description OK, more info would be good -1

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@dapmiller@SiyuLiu0329@shakes76@LinfengLiu98
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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OASIS brain data set using VQVAE - Daniel Miller 45810536 - #458

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OASIS brain data set using VQVAE - Daniel Miller 45810536#458
dapmiller wants to merge 30 commits into
shakes76:topic-recognitionfrom
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A VQVAE in tensorflow has been implemented to generate reconstructed images from OASIS brain dataset. When reconstructing images from VQVAE, a SSIM of 0.734 was achieved. A readme file explains the process and gives a brief rundown of how it works.

Files included:
modules.py: VQVAE model
dataset.py: data loader
train.py: training functions
predict.py: predicts results using model
readme_images: summary

Daniel Millerand others added 30 commits October 11, 2022 16:16
…ataset and the other loads the training images into a numpy array
….py file for normalising and preprocessing training data
…th one hot encoding. Train.py now calls this for training data.
…owever, quantised layer function still needs to be implemented and is currently preventing the code to run.
…tion from dataset.py. Reformatted the functions in modules.py -> encoder and decoder are now in terms of modules instead of sequenitial and the vq layer and overall model builder are in classes to allow other atrributes to be assessed of the model functionality. train.py has reduced in code whereas data is no longer being batched before training (now batched in model.fit). Read.me file has notes being written
… currently does not work however. Training for data is also working, however there is a high loss.
… decrease realistically. The bug occurred due to the value of the variance variable being feed into model.fit.
… calculated incorrectly. Wrote draft read.me file
@SiyuLiu0329

Copy link
Copy Markdown
Collaborator

This is an initial inspection, no action is required at this point

  • Reconstruction: OK
  • Generative part: pixel-cnn part seems to be missing
  • Readme seems incomplete with placeholder(s)

@shakes76

Copy link
Copy Markdown
Owner

Good Practice (Design/Commenting, TF/Torch Usage)

Adequate use and implementation (missing pixel CNN) -2
Good spacing and comments
Header blocks

Recognition Problem

Solves problem (no generations) -2
Driver Script present
File structure present
Shows Usage & Demo & Visualisation & Data usage (no plots) -1
Module present
Commenting
No Data leakage
Difficulty: Hard

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe OK, model info and background missing -2
Good Description and Comments
Markdown used PDF submitted

Pull Request

Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch)
Feedback required, remove duplicate Readmes -2
Request Description OK, more info would be good -1

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@dapmiller@SiyuLiu0329@shakes76@LinfengLiu98
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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OASIS brain data set using VQVAE - Daniel Miller 45810536 - #458

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OASIS brain data set using VQVAE - Daniel Miller 45810536#458
dapmiller wants to merge 30 commits into
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A VQVAE in tensorflow has been implemented to generate reconstructed images from OASIS brain dataset. When reconstructing images from VQVAE, a SSIM of 0.734 was achieved. A readme file explains the process and gives a brief rundown of how it works.

Files included:
modules.py: VQVAE model
dataset.py: data loader
train.py: training functions
predict.py: predicts results using model
readme_images: summary

Daniel Millerand others added 30 commits October 11, 2022 16:16
…ataset and the other loads the training images into a numpy array
….py file for normalising and preprocessing training data
…th one hot encoding. Train.py now calls this for training data.
…owever, quantised layer function still needs to be implemented and is currently preventing the code to run.
…tion from dataset.py. Reformatted the functions in modules.py -> encoder and decoder are now in terms of modules instead of sequenitial and the vq layer and overall model builder are in classes to allow other atrributes to be assessed of the model functionality. train.py has reduced in code whereas data is no longer being batched before training (now batched in model.fit). Read.me file has notes being written
… currently does not work however. Training for data is also working, however there is a high loss.
… decrease realistically. The bug occurred due to the value of the variance variable being feed into model.fit.
… calculated incorrectly. Wrote draft read.me file
@SiyuLiu0329

Copy link
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Collaborator

This is an initial inspection, no action is required at this point

  • Reconstruction: OK
  • Generative part: pixel-cnn part seems to be missing
  • Readme seems incomplete with placeholder(s)

@shakes76

Copy link
Copy Markdown
Owner

Good Practice (Design/Commenting, TF/Torch Usage)

Adequate use and implementation (missing pixel CNN) -2
Good spacing and comments
Header blocks

Recognition Problem

Solves problem (no generations) -2
Driver Script present
File structure present
Shows Usage & Demo & Visualisation & Data usage (no plots) -1
Module present
Commenting
No Data leakage
Difficulty: Hard

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe OK, model info and background missing -2
Good Description and Comments
Markdown used PDF submitted

Pull Request

Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch)
Feedback required, remove duplicate Readmes -2
Request Description OK, more info would be good -1

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4 participants

@dapmiller@SiyuLiu0329@shakes76@LinfengLiu98
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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OASIS brain data set using VQVAE - Daniel Miller 45810536 - #458

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shakes76:topic-recognitionfrom
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OASIS brain data set using VQVAE - Daniel Miller 45810536#458
dapmiller wants to merge 30 commits into
shakes76:topic-recognitionfrom
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A VQVAE in tensorflow has been implemented to generate reconstructed images from OASIS brain dataset. When reconstructing images from VQVAE, a SSIM of 0.734 was achieved. A readme file explains the process and gives a brief rundown of how it works.

Files included:
modules.py: VQVAE model
dataset.py: data loader
train.py: training functions
predict.py: predicts results using model
readme_images: summary

Daniel Millerand others added 30 commits October 11, 2022 16:16
…ataset and the other loads the training images into a numpy array
….py file for normalising and preprocessing training data
…th one hot encoding. Train.py now calls this for training data.
…owever, quantised layer function still needs to be implemented and is currently preventing the code to run.
…tion from dataset.py. Reformatted the functions in modules.py -> encoder and decoder are now in terms of modules instead of sequenitial and the vq layer and overall model builder are in classes to allow other atrributes to be assessed of the model functionality. train.py has reduced in code whereas data is no longer being batched before training (now batched in model.fit). Read.me file has notes being written
… currently does not work however. Training for data is also working, however there is a high loss.
… decrease realistically. The bug occurred due to the value of the variance variable being feed into model.fit.
… calculated incorrectly. Wrote draft read.me file
@SiyuLiu0329

Copy link
Copy Markdown
Collaborator

This is an initial inspection, no action is required at this point

  • Reconstruction: OK
  • Generative part: pixel-cnn part seems to be missing
  • Readme seems incomplete with placeholder(s)

@shakes76

Copy link
Copy Markdown
Owner

Good Practice (Design/Commenting, TF/Torch Usage)

Adequate use and implementation (missing pixel CNN) -2
Good spacing and comments
Header blocks

Recognition Problem

Solves problem (no generations) -2
Driver Script present
File structure present
Shows Usage & Demo & Visualisation & Data usage (no plots) -1
Module present
Commenting
No Data leakage
Difficulty: Hard

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe OK, model info and background missing -2
Good Description and Comments
Markdown used PDF submitted

Pull Request

Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch)
Feedback required, remove duplicate Readmes -2
Request Description OK, more info would be good -1

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4 participants

@dapmiller@SiyuLiu0329@shakes76@LinfengLiu98
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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OASIS brain data set using VQVAE - Daniel Miller 45810536 - #458

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shakes76:topic-recognitionfrom
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OASIS brain data set using VQVAE - Daniel Miller 45810536#458
dapmiller wants to merge 30 commits into
shakes76:topic-recognitionfrom
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A VQVAE in tensorflow has been implemented to generate reconstructed images from OASIS brain dataset. When reconstructing images from VQVAE, a SSIM of 0.734 was achieved. A readme file explains the process and gives a brief rundown of how it works.

Files included:
modules.py: VQVAE model
dataset.py: data loader
train.py: training functions
predict.py: predicts results using model
readme_images: summary

Daniel Millerand others added 30 commits October 11, 2022 16:16
…ataset and the other loads the training images into a numpy array
….py file for normalising and preprocessing training data
…th one hot encoding. Train.py now calls this for training data.
…owever, quantised layer function still needs to be implemented and is currently preventing the code to run.
…tion from dataset.py. Reformatted the functions in modules.py -> encoder and decoder are now in terms of modules instead of sequenitial and the vq layer and overall model builder are in classes to allow other atrributes to be assessed of the model functionality. train.py has reduced in code whereas data is no longer being batched before training (now batched in model.fit). Read.me file has notes being written
… currently does not work however. Training for data is also working, however there is a high loss.
… decrease realistically. The bug occurred due to the value of the variance variable being feed into model.fit.
… calculated incorrectly. Wrote draft read.me file
@SiyuLiu0329

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This is an initial inspection, no action is required at this point

  • Reconstruction: OK
  • Generative part: pixel-cnn part seems to be missing
  • Readme seems incomplete with placeholder(s)

@shakes76

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Good Practice (Design/Commenting, TF/Torch Usage)

Adequate use and implementation (missing pixel CNN) -2
Good spacing and comments
Header blocks

Recognition Problem

Solves problem (no generations) -2
Driver Script present
File structure present
Shows Usage & Demo & Visualisation & Data usage (no plots) -1
Module present
Commenting
No Data leakage
Difficulty: Hard

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe OK, model info and background missing -2
Good Description and Comments
Markdown used PDF submitted

Pull Request

Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch)
Feedback required, remove duplicate Readmes -2
Request Description OK, more info would be good -1

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@dapmiller@SiyuLiu0329@shakes76@LinfengLiu98
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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OASIS brain data set using VQVAE - Daniel Miller 45810536 - #458

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dapmiller wants to merge 30 commits into
shakes76:topic-recognitionfrom
dapmiller:topic-recognition
Open

OASIS brain data set using VQVAE - Daniel Miller 45810536#458
dapmiller wants to merge 30 commits into
shakes76:topic-recognitionfrom
dapmiller:topic-recognition

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@dapmiller

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A VQVAE in tensorflow has been implemented to generate reconstructed images from OASIS brain dataset. When reconstructing images from VQVAE, a SSIM of 0.734 was achieved. A readme file explains the process and gives a brief rundown of how it works.

Files included:
modules.py: VQVAE model
dataset.py: data loader
train.py: training functions
predict.py: predicts results using model
readme_images: summary

Daniel Millerand others added 30 commits October 11, 2022 16:16
…ataset and the other loads the training images into a numpy array
….py file for normalising and preprocessing training data
…th one hot encoding. Train.py now calls this for training data.
…owever, quantised layer function still needs to be implemented and is currently preventing the code to run.
…tion from dataset.py. Reformatted the functions in modules.py -> encoder and decoder are now in terms of modules instead of sequenitial and the vq layer and overall model builder are in classes to allow other atrributes to be assessed of the model functionality. train.py has reduced in code whereas data is no longer being batched before training (now batched in model.fit). Read.me file has notes being written
… currently does not work however. Training for data is also working, however there is a high loss.
… decrease realistically. The bug occurred due to the value of the variance variable being feed into model.fit.
… calculated incorrectly. Wrote draft read.me file
@SiyuLiu0329

Copy link
Copy Markdown
Collaborator

This is an initial inspection, no action is required at this point

  • Reconstruction: OK
  • Generative part: pixel-cnn part seems to be missing
  • Readme seems incomplete with placeholder(s)

@shakes76

Copy link
Copy Markdown
Owner

Good Practice (Design/Commenting, TF/Torch Usage)

Adequate use and implementation (missing pixel CNN) -2
Good spacing and comments
Header blocks

Recognition Problem

Solves problem (no generations) -2
Driver Script present
File structure present
Shows Usage & Demo & Visualisation & Data usage (no plots) -1
Module present
Commenting
No Data leakage
Difficulty: Hard

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe OK, model info and background missing -2
Good Description and Comments
Markdown used PDF submitted

Pull Request

Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch)
Feedback required, remove duplicate Readmes -2
Request Description OK, more info would be good -1

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Projects

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Development

Successfully merging this pull request may close these issues.

4 participants

@dapmiller@SiyuLiu0329@shakes76@LinfengLiu98