Docs/notebooks (#557)

* Add notebooks for all examples * Update notebooks.rst * Test colab links * Remove old examples * Fix preview link and clear notebook outputs * Update notebooks
pytorchbearer · May 30, 2019 · 2c065d8 · 2c065d8
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diff --git a/docs/_static/notebooks/amsgrad.ipynb b/docs/_static/notebooks/amsgrad.ipynb
diff --git a/docs/_static/notebooks/basic_opt.ipynb b/docs/_static/notebooks/basic_opt.ipynb
diff --git a/docs/_static/notebooks/cam.ipynb b/docs/_static/notebooks/cam.ipynb
diff --git a/docs/_static/notebooks/gan.ipynb b/docs/_static/notebooks/gan.ipynb
diff --git a/docs/_static/notebooks/quickstart.ipynb b/docs/_static/notebooks/quickstart.ipynb
diff --git a/docs/_static/notebooks/svm_linear.ipynb b/docs/_static/notebooks/svm_linear.ipynb
diff --git a/docs/_static/notebooks/vae.ipynb b/docs/_static/notebooks/vae.ipynb
diff --git a/docs/examples/notebooks.rst b/docs/examples/notebooks.rst
@@ -14,19 +14,27 @@ Deep Learning
 
     This guide will give a quick intro to training PyTorch models with Torchbearer.
 
-    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/tree/master/docs/_static/notebooks/quickstart.ipynb>`__   :download:`Download Notebook </_static/notebooks/quickstart.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/quickstart.ipynb>`__
+    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/quickstart.ipynb>`__   :download:`Download Notebook </_static/notebooks/quickstart.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/quickstart.ipynb>`__
 
 - **Training a VAE**:
 
     This guide covers training a variational auto-encoder (VAE) in Torchbearer, taking advantage of the persistent state.
 
-    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/tree/master/docs/_static/notebooks/vae.ipynb>`__   :download:`Download Notebook </_static/notebooks/vae.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/vae.ipynb>`__
+    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/vae.ipynb>`__   :download:`Download Notebook </_static/notebooks/vae.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/vae.ipynb>`__
 
 - **Training a GAN**:
 
     This guide will cover how to train a Generative Adversarial Network (GAN) in Torchbearer using custom closures to allow for the more complicated training loop.
 
-    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/tree/master/docs/_static/notebooks/gan.ipynb>`__   :download:`Download Notebook </_static/notebooks/gan.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/gan.ipynb>`__
+    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/gan.ipynb>`__   :download:`Download Notebook </_static/notebooks/gan.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/gan.ipynb>`__
+
+- **Class Appearance Model**:
+
+    In this example we will demonstrate the `ClassAppearanceModel <https://torchbearer.readthedocs.io/en/latest/code/callbacks.html#torchbearer.callbacks.imaging.inside_cnns.ClassAppearanceModel>`__ callback included in torchbearer. This implements
+    one of the most simple (and therefore not always the most successful) deep visualisation techniques, discussed in the
+    paper `Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps <https://arxiv.org/abs/1312.6034>`__
+
+    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/cam.ipynb>`__   :download:`Download Notebook </_static/notebooks/cam.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/cam.ipynb>`__
 
 
 Differentiable Programming
@@ -35,16 +43,16 @@ Differentiable Programming
 
     This guide will briefly show how we can do function optimisation using Torchbearer.
 
-    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/tree/master/docs/_static/notebooks/basic_opt.ipynb>`__   :download:`Download Notebook </_static/notebooks/basic_opt.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/basic_opt.ipynb>`__
+    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/basic_opt.ipynb>`__   :download:`Download Notebook </_static/notebooks/basic_opt.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/basic_opt.ipynb>`__
 
 - **Linear SVM**:
 
     This guide will train a linear support vector machine (SVM) using Torchbearer.
 
-    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/tree/master/docs/_static/notebooks/svm_linear.ipynb>`__   :download:`Download Notebook </_static/notebooks/svm_linear.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/svm_linear.ipynb>`__
+    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/svm_linear.ipynb>`__   :download:`Download Notebook </_static/notebooks/svm_linear.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/svm_linear.ipynb>`__
 
 - **Breaking ADAM**:
 
     This guide uses Torchbearer to implement `On the Convergence of Adam and Beyond <https://openreview.net/forum?id=ryQu7f-RZ>`__, one of the top papers at ICLR 2018, which demonstrated a case where ADAM does not converge.
 
-    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/tree/master/docs/_static/notebooks/amsgrad.ipynb>`__   :download:`Download Notebook </_static/notebooks/amsgrad.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/amsgrad.ipynb>`__
+    |nbviewer| `Preview <https://nbviewer.jupyter.org/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/amsgrad.ipynb>`__   :download:`Download Notebook </_static/notebooks/amsgrad.ipynb>`   |colab| `Run on Colab <https://colab.research.google.com/github/ecs-vlc/torchbearer/blob/master/docs/_static/notebooks/amsgrad.ipynb>`__