experiment links

Author: vpj

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

@@ -70,10 +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/diffusion/ddpm/readme.html

@@ -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>
 

docs/sitemap.xml

@@ -8,6 +8,9 @@
 
 # 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 @@
 $$\phi_\eta(z) = 1 - I_{\eta,+} \big( \max(\mathbf{c} - z, 0) + \max(z - \delta - \mathbf{c}, 0) \big)$$
 
 [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/__init__.py

@@ -7,16 +7,16 @@
 
 # [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
 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/activations/fta/experiment.py

@@ -28,7 +28,6 @@
 
 [![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/__init__.py

@@ -10,8 +10,6 @@
 
 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/capsule_networks/mnist.py

@@ -8,6 +8,9 @@
 
 # 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 @@
 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/__init__.py

@@ -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",
@@ -201,15 +201,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Initializ"
    ]
   },
   {
    "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
-}
+}

labml_nn/diffusion/ddpm/experiment.ipynb

@@ -8,15 +8,15 @@
 
 # [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/experiment.py

@@ -1,5 +1,8 @@
 # [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/diffusion/ddpm/readme.md

@@ -7,6 +7,9 @@
 
 # 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 @@
 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/__init__.py

@@ -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.ipynb

@@ -8,7 +8,6 @@
 # [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)
 """