Spaces:

ARotting
/

flow-pocket-lab

Running

Publish Interactive temperature-controlled RealNVP sampler

a193678 verified 2 months ago

12.5 kB

	<!doctype html>
	<html lang="en">
	<head>
	<meta charset="utf-8">
	<meta name="viewport" content="width=device-width,initial-scale=1">
	<title>Flow Pocket Lab</title>
	<style>
	:root { color-scheme: dark; font-family: Inter, ui-sans-serif, system-ui; }
	* { box-sizing: border-box; }
	body { margin: 0; min-height: 100vh; background: #060817; color: #edf4ff; }
	canvas { position: fixed; inset: 0; width: 100%; height: 100%; opacity: .55; }
	main { position: relative; z-index: 1; width: min(1080px, 92vw); margin: auto;
	padding: 72px 0 96px; }
	.eyebrow { color: #73e6ff; letter-spacing: .18em; text-transform: uppercase;
	font-size: .75rem; font-weight: 800; }
	h1 { font-size: clamp(3rem, 8vw, 7rem); line-height: .9; margin: 14px 0 24px;
	background: linear-gradient(120deg,#fff,#74e7ff 55%,#b48cff);
	-webkit-background-clip: text; color: transparent; }
	.lead { max-width: 760px; color: #b8c7e6; font-size: 1.2rem; line-height: 1.65; }
	.actions { display: flex; flex-wrap: wrap; gap: 12px; margin: 30px 0 48px; }
	a { color: inherit; }
	.button { padding: 12px 18px; border-radius: 999px; text-decoration: none;
	background: #eaf8ff; color: #07101c; font-weight: 800; }
	.button.alt { background: #171d38cc; color: #dce8ff; border: 1px solid #415078; }
	.grid { display: grid; grid-template-columns: 1.1fr .9fr; gap: 20px; }
	.card { border: 1px solid #344269; background: #0c1128dd; border-radius: 24px;
	padding: 24px; backdrop-filter: blur(18px); box-shadow: 0 24px 80px #0008; }
	h2 { margin-top: 0; }
	pre { white-space: pre-wrap; word-break: break-word; color: #a9bddf;
	max-height: 520px; overflow: auto; }
	ul { max-height: 520px; overflow: auto; padding-left: 1.2rem; color: #a9bddf; }
	li { margin: 8px 0; }
	input { width: 100%; padding: 12px; border-radius: 12px; border: 1px solid #344269;
	background: #070b1a; color: white; margin-bottom: 12px; }
	@media (max-width: 780px) { .grid { grid-template-columns: 1fr; } }
	</style>
	</head>
	<body>
	<canvas id="field"></canvas>
	<main>
	<div class="eyebrow">Jacob Garcia · Hugging Face Model Foundry</div>
	<h1>Flow Pocket Lab</h1>
	<p class="lead">Interactive temperature-controlled RealNVP sampler. This showcase backs up the
	trained artifacts, measured evaluation, and complete runnable source.</p>
	<div class="actions">
	<a class="button" href="https://huggingface.co/spaces/ARotting/flow-pocket-lab/tree/main">Explore every file</a>
	<a class="button alt" href="https://huggingface.co/ARotting">View the full foundry</a>
	</div>
	<div class="grid">
	<section class="card">
	<h2>Verified project card</h2>
	<pre># Flow Pocket

	Flow Pocket trains an exactly invertible RealNVP density model on a curved
	five-armed pinwheel distribution. Eight affine coupling layers transform data into
	a standard Gaussian while tracking the exact change-of-variables log determinant.

	The benchmark compares held-out negative log-likelihood and generated-sample MMD
	against a fitted full-covariance Gaussian and a five-component Gaussian mixture.
	It also measures forward/inverse cycle error to verify that the saved neural
	transform is numerically invertible.

	## Verified results

	The flow trained on 40,000 samples and was evaluated on 10,000 independently
	generated samples.

	\| Model \| Held-out NLL \| Sample MMD \|
	\| --- \| ---: \| ---: \|
	\| RealNVP \| 2.2629 \| 0.000236 \|
	\| Five-component GMM \| 2.5887 \| 0.000436 \|
	\| Full-covariance Gaussian \| 3.3527 \| 0.002800 \|

	The eight-coupling-layer RealNVP has 21,536 parameters. Its maximum absolute
	forward/inverse reconstruction error over 2,000 held-out points was `5.78e-6`.
	MMD uses independently randomized 1,000-sample subsets and a shared median
	distance bandwidth.

	## Reproduce

	```powershell
	uv run python projects/flow-pocket/train.py
	```
	</pre>
	<h2>Evaluation snapshot</h2>
	<pre>{
	"benchmark": "Five-arm pinwheel density estimation",
	"training_examples": 40000,
	"heldout_examples": 10000,
	"results": {
	"realnvp": {
	"parameters": 21536,
	"test_nll": 2.2628610134124756,
	"sample_mmd": 0.00023621320724487305,
	"maximum_cycle_error": 5.781650543212891e-06
	},
	"full_covariance_gaussian": {
	"test_nll": 3.352746780780259,
	"sample_mmd": 0.002799742898649704
	},
	"five_component_gmm": {
	"test_nll": 2.5887062549591064,
	"sample_mmd": 0.00043558339810489954
	}
	},
	"training_history": [
	{
	"training_step": 100,
	"training_nll": 2.974477767944336,
	"validation_nll": 3.012097120285034
	},
	{
	"training_step": 200,
	"training_nll": 2.768125295639038,
	"validation_nll": 2.759519100189209
	},
	{
	"training_step": 300,
	"training_nll": 2.7486166954040527,
	"validation_nll": 2.6988091468811035
	},
	{
	"training_step": 400,
	"training_nll": 2.6729800701141357,
	"validation_nll": 2.6116697788238525
	},
	{
	"training_step": 500,
	"training_nll": 2.523287534713745,
	"validation_nll": 2.579714298248291
	},
	{
	"training_step": 600,
	"training_nll": 2.5287487506866455,
	"validation_nll": 2.553705930709839
	},
	{
	"training_step": 700,
	"training_nll": 2.5176467895507812,
	"validation_nll": 2.56897234916687
	},
	{
	"training_step": 800,
	"training_nll": 2.5820188522338867,
	"validation_nll": 2.540039539337158
	},
	{
	"training_step": 900,
	"training_nll": 2.5383265018463135,
	"validation_nll": 2.5277373790740967
	},
	{
	"training_step": 1000,
	"training_nll": 2.48282527923584,
	"validation_nll": 2.535404682159424
	},
	{
	"training_step": 1100,
	"training_nll": 2.428791046142578,
	"validation_nll": 2.5110583305358887
	},
	{
	"training_step": 1200,
	"training_nll": 2.5302720069885254,
	"validation_nll": 2.50561261177063
	},
	{
	"training_step": 1300,
	"training_nll": 2.3884363174438477,
	"validation_nll": 2.4981091022491455
	},
	{
	"training_step": 1400,
	"training_nll": 2.4792046546936035,
	"validation_nll": 2.491697311401367
	},
	{
	"training_step": 1500,
	"training_nll": 2.419118881225586,
	"validation_nll": 2.463804244995117
	},
	{
	"training_step": 1600,
	"training_nll": 2.4760823249816895,
	"validation_nll": 2.4544918537139893
	},
	{
	"training_step": 1700,
	"training_nll": 2.3438639640808105,
	"validation_nll": 2.4196836948394775
	},
	{
	"training_step": 1800,
	"training_nll": 2.284365653991699,
	"validation_nll": 2.4094793796539307
	},
	{
	"training_step": 1900,
	"training_nll": 2.41039776802063,
	"validation_nll": 2.386476516723633
	},
	{
	"training_step": 2000,
	"training_nll": 2.343876361846924,
	"validation_nll": 2.371260166168213
	},
	{
	"training_step": 2100,
	"training_nll": 2.3377065658569336,
	"validation_nll": 2.371122121810913
	},
	{
	"training_step": 2200,
	"training_nll": 2.3578639030456543,
	"validation_nll": 2.3843414783477783
	},
	{
	"training_step": 2300,
	"training_nll": 2.361250400543213,
	"validation_nll": 2.374619960784912
	},
	{
	"training_step": 2400,
	"training_nll": 2.317490339279175,
	"validation_nll": 2.3640923500061035
	},
	{
	"training_step": 2500,
	"training_nll": 2.31643009185791,
	"validation_nll": 2.368194818496704
	},
	{
	"training_step": 2600,
	"training_nll": 2.295240640640259,
	"validation_nll": 2.3607025146484375
	},
	{
	"training_step": 2700,
	"training_nll": 2.38124942779541,
	"validation_nll": 2.3652212619781494
	},
	{
	"training_step": 2800,
	"training_nll": 2.3277204036712646,
	"validation_nll": 2.375239372253418
	},
	{
	"training_step": 2900,
	"training_nll": 2.340425491333008,
	"validation_nll": 2.3583223819732666
	},
	{
	"training_step": 3000,
	"training_nll": 2.306485176086426,
	"validation_nll": 2.3473105430603027
	},
	{
	"training_step": 3100,
	"training_nll": 2.2540557384490967,
	"validation_nll": 2.361114978790283
	},
	{
	"training_step": 3200,
	"training_nll": 2.2472634315490723,
	"validation_nll": 2.3361361026763916
	},
	{
	"training_step": 3300,
	"training_nll": 2.2850630283355713,
	"validation_nll": 2.3237645626068115
	},
	{
	"training_step": 3400,
	"training_nll": 2.372798442840576,
	"validation_nll": 2.346468448638916
	},
	{
	"training_step": 3500,
	"training_nll": 2.311044216156006,
	"validation_nll": 2.32955002784729
	},
	{
	"training_step": 3600,
	"training_nll": 2.2257301807403564,
	"validation_nll": 2.319716453552246
	},
	{
	"training_step": 3700,
	"training_nll": 2.2926011085510254,
	"validation_nll": 2.323981285095215
	},
	{
	"training_step": 3800,
	"training_nll": 2.2168548107147217,
	"validation_nll": 2.305316925048828
	},
	{
	"training_step": 3900,
	"training_nll": 2.2342185974121094,
	"validation_nll": 2.3181967735290527
	},
	{
	"training_step": 4000,
	"training_nll": 2.3150432109832764,
	"validation_nll": 2.279649257659912
	}
	]
	}</pre>
	</section>
	<section class="card">
	<h2>Backed-up artifact tree</h2>
	<input id="filter" placeholder="Filter files…" autocomplete="off">
	<ul id="files"><li><code>README.md</code></li>
	<li><code>__pycache__/app.cpython-311.pyc</code></li>
	<li><code>__pycache__/data.cpython-311.pyc</code></li>
	<li><code>__pycache__/model.cpython-311.pyc</code></li>
	<li><code>app.py</code></li>
	<li><code>artifacts/flow-pocket/classical_controls.joblib</code></li>
	<li><code>artifacts/flow-pocket/evaluation.json</code></li>
	<li><code>artifacts/flow-pocket/generated_samples.npz</code></li>
	<li><code>artifacts/flow-pocket/realnvp.safetensors</code></li>
	<li><code>data.py</code></li>
	<li><code>data/pinwheel_test.parquet</code></li>
	<li><code>model.py</code></li>
	<li><code>requirements.txt</code></li>
	<li><code>train.py</code></li></ul>
	</section>
	</div>
	</main>
	<script>
	const canvas=document.querySelector('#field'),ctx=canvas.getContext('2d');
	let dots=[];
	function resize(){canvas.width=innerWidth;canvas.height=innerHeight;
	dots=Array.from({length:90},()=>({x:Math.random()*innerWidth,
	y:Math.random()innerHeight,vx:(Math.random()-.5).35,vy:(Math.random()-.5)*.35}));}
	function draw(){ctx.clearRect(0,0,canvas.width,canvas.height);
	for(const d of dots){d.x=(d.x+d.vx+innerWidth)%innerWidth;
	d.y=(d.y+d.vy+innerHeight)%innerHeight;ctx.fillStyle='#65dcff99';
	ctx.beginPath();ctx.arc(d.x,d.y,1.4,0,7);ctx.fill();}requestAnimationFrame(draw);}
	addEventListener('resize',resize);resize();draw();
	document.querySelector('#filter').addEventListener('input',e=>{
	const q=e.target.value.toLowerCase();for(const li of document.querySelectorAll('li'))
	li.hidden=!li.textContent.toLowerCase().includes(q);});
	</script>
	</body>
	</html>