experiment links

docs/diffusion/ddpm/experiment.html

@@ -70,10 +70,10 @@
                 <a href='#section-0'>#</a>
             </div>
             <h1><a href="index.html">Denoising Diffusion Probabilistic Models (DDPM)</a> training</h1>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
 <p>This trains a DDPM based model on CelebA HQ dataset. You can find the download instruction in this <a href="https://forums.fast.ai/t/download-celeba-hq-dataset/45873/3">discussion on fast.ai</a>. Save the images inside <a href="#dataset_path"><code class="highlight"><span></span><span class="n">data</span><span class="o">/</span><span class="n">celebA</span></code>
  folder</a>.</p>
 <p>The paper had used a exponential moving average of the model with a decay of <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">0.9999</span></span></span></span>. We have skipped this for simplicity.</p>
-<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://www.comet.ml/labml/diffuse/1260757bcd6148e084ad3a46c38ac5c4?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
 
         </div>
         <div class='code'>

docs/diffusion/ddpm/index.html

@@ -70,6 +70,7 @@
                 <a href='#section-0'>#</a>
             </div>
             <h1>Denoising Diffusion Probabilistic Models (DDPM)</h1>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
 <p>This is a <a href="https://pytorch.org">PyTorch</a> implementation/tutorial of the paper <a href="https://papers.labml.ai/paper/2006.11239">Denoising Diffusion Probabilistic Models</a>.</p>
 <p>In simple terms, we get an image from data and add noise step by step. Then We train a model to predict that noise at each step and use the model to generate images.</p>
 <p>The following definitions and derivations show how this works. For details please refer to <a href="https://papers.labml.ai/paper/2006.11239">the paper</a>.</p>
@@ -297,7 +298,6 @@ M834 80h400000v40h-400000z"></path></svg></span></span></span><span class="vlist
 <p>This minimizes <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord" style="color:cssclasscoloredeqcssclassequ;">−</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mop coloredeq equ" style=""><span style="">l</span><span style="">o</span><span style="margin-right:0.01389em">g</span></span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord" style="color:lightgreen;"><span class="mord mathnormal" style="color:lightgreen;">p</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.33610799999999996em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight" style="color:lightgreen"><span class="mord mathnormal mtight" style="margin-right:0.02778em">θ</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mopen" style="color:cssclasscoloredeqcssclassequ;">(</span><span class="mord coloredeq eqbu" style=""><span class="mord" style=""><span class="mord mathnormal" style="">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight" style=""><span class="mord mtight" style="">0</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span><span class="mord" style="color:cssclasscoloredeqcssclassequ;">∣</span><span class="mord" style="color:cssclasscoloredeqcssclassequ;"><span class="mord mathnormal" style="color:cssclasscoloredeqcssclassequ;">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight coloredeq equ" style=""><span class="mord mtight" style="">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose" style="color:cssclasscoloredeqcssclassequ;">)</span></span></span></span></span> when <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord coloredeq eqbt" style=""><span class="mord coloredeq eqbx" style=""><span class="mord mathnormal" style="">t</span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel" style="">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mord" style="">1</span></span></span></span></span> and <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.891661em;vertical-align:-0.208331em;"></span><span class="mord coloredeq eqbq" style=""><span class="mord" style=""><span class="mord mathnormal" style="">L</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.301108em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight" style=""><span class="mord mtight" style=""><span class="mord mtight coloredeq eqbx" style=""><span class="mord mathnormal mtight" style="">t</span></span><span class="mbin mtight" style="">−</span><span class="mord mtight" style="">1</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.208331em;"><span></span></span></span></span></span></span></span></span></span></span> for <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.65418em;vertical-align:-0.0391em;"></span><span class="mord coloredeq eqbx" style=""><span class="mord mathnormal" style="">t</span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">&gt;</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">1</span></span></span></span> discarding the weighting in <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.891661em;vertical-align:-0.208331em;"></span><span class="mord coloredeq eqbq" style=""><span class="mord" style=""><span class="mord mathnormal" style="">L</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.301108em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight" style=""><span class="mord mtight" style=""><span class="mord mtight coloredeq eqbx" style=""><span class="mord mathnormal mtight" style="">t</span></span><span class="mbin mtight" style="">−</span><span class="mord mtight" style="">1</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.208331em;"><span></span></span></span></span></span></span></span></span></span></span>. Discarding the weights <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:1.646988em;vertical-align:-0.5769599999999999em;"></span><span class="mord coloredeq eqr" style=""><span class="mord" style=""><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.070028em;"><span style="top:-2.6264200000000004em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight" style=""><span class="mord mtight" style=""><span class="mord mtight" style="">2</span><span class="mord mtight coloredeq eqbk" style=""><span class="mord mtight" style=""><span class="mord mathnormal mtight" style="margin-right:0.03588em">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8051142857142858em;"><span style="top:-2.209457142857143em;margin-left:-0.03588em;margin-right:0.07142857142857144em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight" style=""><span class="mord mtight coloredeq eqbx" style=""><span class="mord mathnormal mtight" style="">t</span></span></span></span><span style="top:-2.8448em;margin-right:0.07142857142857144em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight" style=""><span class="mord mtight" style="">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.29054285714285716em;"><span></span></span></span></span></span></span></span><span class="mord mtight coloredeq eqbn" style=""><span class="mord mtight" style=""><span class="mord mathnormal mtight" style="margin-right:0.0037em">α</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.29634285714285713em;"><span style="top:-2.357em;margin-left:-0.0037em;margin-right:0.07142857142857144em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight" style=""><span class="mord mtight coloredeq eqbx" style=""><span class="mord mathnormal mtight" style="">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.143em;"><span></span></span></span></span></span></span></span><span class="mopen mtight" style="">(</span><span class="mord mtight" style="">1</span><span class="mbin mtight" style="">−</span><span class="mord mtight coloredeq eqbj" style=""><span class="mord accent mtight" style=""><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.56778em;"><span style="top:-2.7em;"><span class="pstrut" style="height:2.7em;"></span><span class="mord mtight" style="color:cssclasscoloredeqcssclasseqbn;"><span class="mord mathnormal mtight" style="margin-right:0.0037em;color:cssclasscoloredeqcssclasseqbn;">α</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.29634285714285713em;"><span style="top:-2.357em;margin-left:-0.0037em;margin-right:0.07142857142857144em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight coloredeq eqbn" style=""><span class="mord mtight coloredeq eqbx" style=""><span class="mord mathnormal mtight" style="">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.143em;"><span></span></span></span></span></span></span></span><span style="top:-2.7em;"><span class="pstrut" style="height:2.7em;"></span><span class="accent-body" style="left:-0.25em;"><span class="mord mtight" style="">ˉ</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.143em;"><span></span></span></span></span></span></span><span class="mclose mtight" style="">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em"></span></span><span style="top:-3.446108em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight" style=""><span class="mord mtight" style=""><span class="mord mtight" style=""><span class="mord mtight coloredeq eqbr" style=""><span class="mord mtight" style=""><span class="mord mathnormal mtight" style="margin-right:0.05278em">β</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.29634285714285713em;"><span style="top:-2.357em;margin-left:-0.05278em;margin-right:0.07142857142857144em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight" style=""><span class="mord mtight coloredeq eqbx" style=""><span class="mord mathnormal mtight" style="">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.143em;"><span></span></span></span></span></span></span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913142857142857em;"><span style="top:-2.931em;margin-right:0.07142857142857144em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight" style=""><span class="mord mtight" style="">2</span></span></span></span></span></span></span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.5769599999999999em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span> increase the weight given to higher <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.61508em;vertical-align:0em;"></span><span class="mord coloredeq eqbx" style=""><span class="mord mathnormal" style="">t</span></span></span></span></span> (which have higher noise levels), therefore increasing the sample quality.</p>
 <p>This file implements the loss calculation and a basic sampling method that we use to generate images during training.</p>
 <p>Here is the <a href="unet.html">UNet model</a> that gives <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord coloredeq eqbg" style=""><span class="mord" style=""><span class="mord coloredeq eqbo" style=""><span class="mord mathnormal" style="">ϵ</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.33610799999999996em;"><span style="top:-2.5500000000000003em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight" style=""><span class="mord mathnormal mtight" style="margin-right:0.02778em">θ</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span><span class="mord coloredeq eqbm" style=""><span class="mopen" style="">(</span><span class="mord coloredeq eqbv" style=""><span class="mord" style=""><span class="mord mathnormal" style="">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2805559999999999em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight" style=""><span class="mord mtight coloredeq eqbx" style=""><span class="mord mathnormal mtight" style="">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span><span class="mpunct" style="">,</span><span class="mspace" style="margin-right:0.16666666666666666em"></span><span class="mord coloredeq eqbx" style=""><span class="mord mathnormal" style="">t</span></span><span class="mclose" style="">)</span></span></span></span></span></span> and <a href="experiment.html">training code</a>. <a href="evaluate.html">This file</a> can generate samples and interpolations from a trained model.</p>
-<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://www.comet.ml/labml/diffuse/1260757bcd6148e084ad3a46c38ac5c4?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
 
         </div>
         <div class='code'>

docs/diffusion/ddpm/readme.html

@@ -70,10 +70,10 @@
                 <a href='#section-0'>#</a>
             </div>
             <h1><a href="https://nn.labml.ai/diffusion/ddpm/index.html">Denoising Diffusion Probabilistic Models (DDPM)</a></h1>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
 <p>This is a <a href="https://pytorch.org">PyTorch</a> implementation/tutorial of the paper <a href="https://papers.labml.ai/paper/2006.11239">Denoising Diffusion Probabilistic Models</a>.</p>
 <p>In simple terms, we get an image from data and add noise step by step. Then We train a model to predict that noise at each step and use the model to generate images.</p>
-<p>Here is the <a href="https://nn.labml.ai/diffusion/ddpm/unet.html">UNet model</a> that predicts the noise and <a href="https://nn.labml.ai/diffusion/ddpm/experiment.html">training code</a>. <a href="https://nn.labml.ai/diffusion/ddpm/evaluate.html">This file</a> can generate samples and interpolations from a trained model.</p>
-<p><a href="https://app.labml.ai/run/a44333ea251411ec8007d1a1762ed686"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen"></a> </p>
+<p>Here is the <a href="https://nn.labml.ai/diffusion/ddpm/unet.html">UNet model</a> that predicts the noise and <a href="https://nn.labml.ai/diffusion/ddpm/experiment.html">training code</a>. <a href="https://nn.labml.ai/diffusion/ddpm/evaluate.html">This file</a> can generate samples and interpolations from a trained model. </p>
 
         </div>
         <div class='code'>

docs/sitemap.xml

@@ -372,7 +372,7 @@
 
     <url>
       <loc>https://nn.labml.ai/diffusion/ddpm/unet.html</loc>
-      <lastmod>2021-10-24T16:30:00+00:00</lastmod>
+      <lastmod>2022-06-09T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
     
@@ -400,7 +400,7 @@
 
     <url>
       <loc>https://nn.labml.ai/diffusion/ddpm/evaluate.html</loc>
-      <lastmod>2021-10-24T16:30:00+00:00</lastmod>
+      <lastmod>2022-06-09T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
     

labml_nn/activations/fta/__init__.py

@@ -8,6 +8,9 @@ summary: >
 
 # Fuzzy Tiling Activations (FTA)
 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/activations/fta/experiment.ipynb)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/fta/69be11f83693407f82a86dcbb232bcfe?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&viewId=rlJOpXDGtL8zbkcX66R77P5me&xAxis=step)
+
 This is a [PyTorch](https://pytorch.org) implementation/tutorial of
 [Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online](https://papers.labml.ai/paper/aca66d8edc8911eba3db37f65e372566).
 
@@ -54,9 +57,6 @@ FTA uses this to create soft boundaries between bins.
 $$\phi_\eta(z) = 1 - I_{\eta,+} \big( \max(\mathbf{c} - z, 0) + \max(z - \delta - \mathbf{c}, 0) \big)$$
 
 [Here's a simple experiment](experiment.html) that uses FTA in a transformer.
-
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/activations/fta/experiment.ipynb)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/fta/69be11f83693407f82a86dcbb232bcfe?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&viewId=rlJOpXDGtL8zbkcX66R77P5me&xAxis=step)
 """
 
 import torch

labml_nn/activations/fta/experiment.py

@@ -7,6 +7,9 @@ summary: >
 
 # [Fuzzy Tiling Activation](index.html) Experiment
 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/activations/fta/experiment.ipynb)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/fta/69be11f83693407f82a86dcbb232bcfe?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&viewId=rlJOpXDGtL8zbkcX66R77P5me&xAxis=step)
+
 Here we train a transformer that uses [Fuzzy Tiling Activation](index.html) in the
 [Feed-Forward Network](../../transformers/feed_forward.html).
 We use it for a language model and train it on Tiny Shakespeare dataset
@@ -14,9 +17,6 @@ for demonstration.
 
 However, this is probably not the ideal task for FTA, and we
 believe FTA is more suitable for modeling data with continuous variables.
-
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/activations/fta/experiment.ipynb)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/fta/69be11f83693407f82a86dcbb232bcfe?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&viewId=rlJOpXDGtL8zbkcX66R77P5me&xAxis=step)
 """
 
 import copy

labml_nn/capsule_networks/__init__.py

@@ -28,7 +28,6 @@ Here's a notebook for training a Capsule Network on MNIST dataset.
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/capsule_networks/mnist.ipynb)
 [![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/e7c08e08586711ebb3e30242ac1c0002)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/capsule-networks/reports/capsule-networks)
 """
 
 import torch.nn as nn

labml_nn/capsule_networks/mnist.py

@@ -10,8 +10,6 @@ This is an annotated PyTorch code to classify MNIST digits with PyTorch.
 
 This paper implements the experiment described in paper
 [Dynamic Routing Between Capsules](https://papers.labml.ai/paper/1710.09829).
-
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=mnist)](https://www.comet.ml/labml/capsule-networks/reports/capsule-networks)
 """
 from typing import Any
 

labml_nn/diffusion/ddpm/__init__.py

@@ -8,6 +8,9 @@ summary: >
 
 # Denoising Diffusion Probabilistic Models (DDPM)
 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels)
+
 This is a [PyTorch](https://pytorch.org) implementation/tutorial of the paper
 [Denoising Diffusion Probabilistic Models](https://papers.labml.ai/paper/2006.11239).
 
@@ -156,9 +159,6 @@ training.
 Here is the [UNet model](unet.html) that gives $\textcolor{lightgreen}{\epsilon_\theta}(x_t, t)$ and
 [training code](experiment.html).
 [This file](evaluate.html) can generate samples and interpolations from a trained model.
-
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/1260757bcd6148e084ad3a46c38ac5c4?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step)
 """
 from typing import Tuple, Optional
 

labml_nn/diffusion/ddpm/experiment.ipynb

@@ -11,7 +11,7 @@
    "source": [
     "[![Github](https://img.shields.io/github/stars/labmlai/annotated_deep_learning_paper_implementations?style=social)](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb)\n",
-    "[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/1260757bcd6148e084ad3a46c38ac5c4?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step)\n",
+    "[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels)\n",
     "\n",
     "## [Denoising Diffusion Probabilistic Models (DDPM)](https://nn.labml.ai/diffusion/ddpm/index.html)\n",
     "\n",
@@ -201,7 +201,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Initializ"
    ]
@@ -209,7 +213,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "configs.init()"
@@ -282,7 +290,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": []
   }
@@ -314,4 +326,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file

labml_nn/diffusion/ddpm/experiment.py

@@ -8,15 +8,15 @@ summary: >
 
 # [Denoising Diffusion Probabilistic Models (DDPM)](index.html) training
 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels)
+
 This trains a DDPM based model on CelebA HQ dataset. You can find the download instruction in this
 [discussion on fast.ai](https://forums.fast.ai/t/download-celeba-hq-dataset/45873/3).
 Save the images inside [`data/celebA` folder](#dataset_path).
 
 The paper had used a exponential moving average of the model with a decay of $0.9999$. We have skipped this for
 simplicity.
-
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/1260757bcd6148e084ad3a46c38ac5c4?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step)
 """
 from typing import List
 

labml_nn/diffusion/ddpm/readme.md

 # [Denoising Diffusion Probabilistic Models (DDPM)](https://nn.labml.ai/diffusion/ddpm/index.html)
 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/diffusion/ddpm/experiment.ipynb)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://www.comet.ml/labml/diffuse/view/FknjSiKWotr8fgZerpC1sV1cy/panels)
+
 This is a [PyTorch](https://pytorch.org) implementation/tutorial of the paper
 [Denoising Diffusion Probabilistic Models](https://papers.labml.ai/paper/2006.11239).
 
@@ -11,5 +14,3 @@ Here is the [UNet model](https://nn.labml.ai/diffusion/ddpm/unet.html) that pred
 [training code](https://nn.labml.ai/diffusion/ddpm/experiment.html).
 [This file](https://nn.labml.ai/diffusion/ddpm/evaluate.html) can generate samples and interpolations
 from a trained model.
-
-[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/a44333ea251411ec8007d1a1762ed686)

labml_nn/normalization/deep_norm/__init__.py

@@ -7,6 +7,9 @@ summary: >
 
 # DeepNorm
 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/normalization/deep_norm/experiment.ipynb)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=deep_norm&file=model)](https://www.comet.ml/labml/deep-norm/61d817f80ff143c8825fba4aacd431d4?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step)
+
 This is a [PyTorch](https://pytorch.org) implementation of
 the DeepNorm from the paper
 [DeepNet: Scaling Transformers to 1,000 Layers](https://papers.labml.ai/paper/2203.00555).
@@ -66,10 +69,6 @@ Where $N$ is the number of layers in the encoder and $M$ is the number of layers
 Refer to [the paper](https://papers.labml.ai/paper/2203.00555) for derivation.
 
 [Here is an experiment implementation](experiment.html) that uses DeepNorm.
-
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/normalization/deep_norm/experiment.ipynb)
-[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/ec8e4dacb7f311ec8d1cd37d50b05c3d)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=deep_norm&file=model)](https://www.comet.ml/labml/deep-norm/61d817f80ff143c8825fba4aacd431d4?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step)
 """
 
 from typing import Union, List

labml_nn/normalization/deep_norm/experiment.ipynb

@@ -11,7 +11,6 @@
    "source": [
     "[![Github](https://img.shields.io/github/stars/labmlai/annotated_deep_learning_paper_implementations?style=social)](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/normalization/deep_norm/experiment.ipynb)\n",
-    "[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/ec8e4dacb7f311ec8d1cd37d50b05c3d)\n",
     "[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=deep_norm&file=colab)](https://www.comet.ml/labml/deep-norm/61d817f80ff143c8825fba4aacd431d4?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step)\n",
     "\n",
     "## DeepNorm\n",

labml_nn/normalization/deep_norm/experiment.py

@@ -8,7 +8,6 @@ summary: >
 # [DeepNorm](index.html) Experiment
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/normalization/deep_norm/experiment.ipynb)
-[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/ec8e4dacb7f311ec8d1cd37d50b05c3d)
 [![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=deep_norm&file=experiment)](https://www.comet.ml/labml/deep-norm/61d817f80ff143c8825fba4aacd431d4?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step)
 """