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docs/index.html

@@ -99,6 +99,7 @@ implementations.</p>
 <h4>✨ <a href="recurrent_highway_networks/index.html">Recurrent Highway Networks</a></h4>
 <h4>✨ <a href="lstm/index.html">LSTM</a></h4>
 <h4>✨ <a href="hypernetworks/hyper_lstm.html">HyperNetworks - HyperLSTM</a></h4>
+<h4>✨ <a href="resnet/index.html">ResNet</a></h4>
 <h4>✨ <a href="capsule_networks/index.html">Capsule Networks</a></h4>
 <h4>✨ <a href="gan/index.html">Generative Adversarial Networks</a></h4>
 <ul>

docs/resnet/readme.html

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+    <meta name="twitter:image:src" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/>
+    <meta name="twitter:title" content="Deep Residual Learning for Image Recognition (ResNet)"/>
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+    <meta name="twitter:site" content="@labmlai"/>
+    <meta name="twitter:creator" content="@labmlai"/>
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+    <meta property="og:url" content="https://nn.labml.ai/resnet/readme.html"/>
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+        <div class='docs'>
+            <p>
+                <a class="parent" href="/">home</a>
+                <a class="parent" href="index.html">resnet</a>
+            </p>
+            <p>
+
+                <a href="https://github.com/lab-ml/labml_nn/tree/master/labml_nn/resnet/readme.md">
+                    <img alt="Github"
+                         src="https://img.shields.io/github/stars/lab-ml/nn?style=social"
+                         style="max-width:100%;"/></a>
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+    </div>
+    <div class='section' id='section-0'>
+            <div class='docs'>
+                <div class='section-link'>
+                    <a href='#section-0'>#</a>
+                </div>
+                <h1><a href="https://nn.labml.ai/resnet/index.html">Deep Residual Learning for Image Recognition (ResNet)</a></h1>
+<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper
+<a href="https://arxiv.org/abs/1512.03385">Deep Residual Learning for Image Recognition</a>.</p>
+<p>ResNets train layers as residual functions to overcome the
+<em>degradation problem</em>.
+The degradation problem is the accuracy of deep neural networks degrading when
+the number of layers becomes very high.
+The accuracy increases as the number of layers increase, then saturates,
+and then starts to degrade.</p>
+            </div>
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+                
+            </div>
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@@ -568,14 +568,14 @@
 
     <url>
       <loc>https://nn.labml.ai/transformers/vit/index.html</loc>
-      <lastmod>2021-07-14T16:30:00+00:00</lastmod>
+      <lastmod>2021-07-16T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
     
 
     <url>
       <loc>https://nn.labml.ai/transformers/vit/experiment.html</loc>
-      <lastmod>2021-07-13T16:30:00+00:00</lastmod>
+      <lastmod>2021-07-16T16:30:00+00:00</lastmod>
       <priority>1.00</priority>
     </url>
     

labml_nn/__init__.py

@@ -38,6 +38,8 @@ implementations.
 
 #### ✨ [HyperNetworks - HyperLSTM](hypernetworks/hyper_lstm.html)
 
+#### ✨ [ResNet](resnet/index.html)
+
 #### ✨ [Capsule Networks](capsule_networks/index.html)
 
 #### ✨ [Generative Adversarial Networks](gan/index.html)

labml_nn/resnet/readme.md

@@ -0,0 +1,11 @@
+# [Deep Residual Learning for Image Recognition (ResNet)](https://nn.labml.ai/resnet/index.html)
+
+This is a [PyTorch](https://pytorch.org) implementation of the paper
+[Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385).
+
+ResNets train layers as residual functions to overcome the
+*degradation problem*.
+The degradation problem is the accuracy of deep neural networks degrading when
+the number of layers becomes very high.
+The accuracy increases as the number of layers increase, then saturates,
+and then starts to degrade.

readme.md

@@ -44,6 +44,8 @@ implementations almost weekly.
 
 #### ✨ [HyperNetworks - HyperLSTM](https://nn.labml.ai/hypernetworks/hyper_lstm.html)
 
+#### ✨ [ResNet](https://nn.labml.ai/resnet/index.html)
+
 #### ✨ [Capsule Networks](https://nn.labml.ai/capsule_networks/index.html)
 
 #### ✨ [Generative Adversarial Networks](https://nn.labml.ai/gan/index.html)

utils/diagrams.py

@@ -254,7 +254,8 @@ def main():
 
         with monit.section(str(p)):
             if source_path.suffix == '.svg':
-                parse(source_path, source_path)
+                parse(source_path, dest_path)
+            else:
                 shutil.copy(str(source_path), str(dest_path))