Diffusion Notebook

docs/optimizers/index.html

@@ -82,12 +82,12 @@ <h2>Generic Adaptive Optimizer Base class and Weight Decay</h2>
 <p>We also define a special class for L2 weight decay, so that we don&#x27;t have to implement it inside each of the optimizers, and can easily extend to other weight decays like L1 without changing the optimizers.</p>
 <p>Here are some concepts on PyTorch optimizers:</p>
 <h3>Parameter groups</h3>
-<p>PyTorch optimizers group parameters into sets called groups. Each group can have it&#x27;s own hyper-parameters like learning rates.</p>
+<p>PyTorch optimizers group parameters into sets called groups. Each group can have its own hyper-parameters like learning rates.</p>
 <p>In most common cases there will be only one group. This is when you initialize your optimizer with,</p>
 <pre class="highlight lang-python"><code><span></span><span class="n">Optimizer</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span></code></pre>
 <p>You can define multiple parameter groups when initializing the optimizer:</p>
 <pre class="highlight lang-python"><code><span></span><span class="n">Optimizer</span><span class="p">([{</span><span class="s1">&#39;params&#39;</span><span class="p">:</span> <span class="n">model1</span><span class="o">.</span><span class="n">parameters</span><span class="p">()},</span> <span class="p">{</span><span class="s1">&#39;params&#39;</span><span class="p">:</span> <span class="n">model2</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="s1">&#39;lr&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">}])</span></code></pre>
-<p>Here we pass a list of groups. Each group is a dictionary with it&#x27;s parameters under the key &#x27;params&#x27;. You specify any hyper-parameters as well. If the hyper parameters are not defined they will default to the optimizer level defaults.</p>
+<p>Here we pass a list of groups. Each group is a dictionary with its parameters under the key &#x27;params&#x27;. You specify any hyper-parameters as well. If the hyper parameters are not defined they will default to the optimizer level defaults.</p>
 <p>You can access (and even change) these groups, and their hyper-parameters with <code class="highlight"><span></span><span class="n">optimizer</span><span class="o">.</span><span class="n">param_groups</span></code>
 . Most learning rate schedule implementations I&#x27;ve come across do access this and change &#x27;lr&#x27;.</p>
 <h3>States</h3>

docs/sitemap.xml

@@ -246,7 +246,7 @@
 
     <url>
       <loc>https://nn.labml.ai/experiments/arithmetic_dataset.html</loc>
-      <lastmod>2022-06-02T16:30:00+00:00</lastmod>
+      <lastmod>2022-06-03T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
 
@@ -379,14 +379,14 @@
 
     <url>
       <loc>https://nn.labml.ai/diffusion/ddpm/index.html</loc>
-      <lastmod>2022-03-21T16:30:00+00:00</lastmod>
+      <lastmod>2022-06-09T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
 
 
     <url>
       <loc>https://nn.labml.ai/diffusion/ddpm/experiment.html</loc>
-      <lastmod>2021-10-21T16:30:00+00:00</lastmod>
+      <lastmod>2022-06-09T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
 
@@ -435,7 +435,7 @@
 
     <url>
       <loc>https://nn.labml.ai/optimizers/index.html</loc>
-      <lastmod>2021-10-19T16:30:00+00:00</lastmod>
+      <lastmod>2022-06-03T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
 
@@ -610,35 +610,35 @@
 
     <url>
       <loc>https://nn.labml.ai/transformers/rope/index.html</loc>
-      <lastmod>2022-05-31T16:30:00+00:00</lastmod>
+      <lastmod>2022-06-03T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
 
 
     <url>
       <loc>https://nn.labml.ai/transformers/rope/value_pe/arithmetic_experiment.html</loc>
-      <lastmod>2022-06-02T16:30:00+00:00</lastmod>
+      <lastmod>2022-06-03T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
 
 
     <url>
       <loc>https://nn.labml.ai/transformers/rope/value_pe/index.html</loc>
-      <lastmod>2022-06-02T16:30:00+00:00</lastmod>
+      <lastmod>2022-06-03T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
 
 
     <url>
       <loc>https://nn.labml.ai/transformers/rope/value_pe/experiment.html</loc>
-      <lastmod>2022-05-31T16:30:00+00:00</lastmod>
+      <lastmod>2022-06-03T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
 
 
     <url>
       <loc>https://nn.labml.ai/transformers/rope/experiment.html</loc>
-      <lastmod>2022-05-31T16:30:00+00:00</lastmod>
+      <lastmod>2022-06-03T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
 

labml_nn/diffusion/ddpm/__init__.py

@@ -157,7 +157,8 @@
 [training code](experiment.html).
 [This file](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)
+[![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