erasure

Author: jxbz

.gitignore

@@ -6,6 +6,8 @@ modula/__pycache__/
 docs/build/
 docs/jupyter_execute/
 
+docs/source/examples/assets/
+
 examples/.ipynb_checkpoints/
 examples/data/*
 !examples/data/*.py

docs/Makefile

@@ -21,6 +21,6 @@ livehtml:
 	sphinx-autobuild "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
 
 livedirhtml:
-	sphinx-autobuild -b dirhtml "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	sphinx-autobuild -b dirhtml --watch ../examples --ignore source/examples/assets "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
 
 .PHONY: help Makefile livehtml livedirhtml

docs/source/examples/weight-erasure.nblink

@@ -0,0 +1,6 @@
+{
+    "path": "../../../examples/weight-erasure.ipynb",
+    "extra-media": [
+        "../../../examples/assets/erasure.png"
+    ]
+}

docs/source/index.rst

@@ -61,6 +61,7 @@ The docs currently contain some original research contributions not published an
 
    examples/hello-world
    examples/hello-mnist
+   examples/weight-erasure
 
 .. toctree::
    :hidden:

examples/assets/erasure.png

@@ -0,0 +1,51 @@
+import numpy as np
+import os
+import pickle
+import urllib.request
+import tarfile
+
+def load_cifar10(normalize=True):
+   """
+   Downloads (if needed) and loads the CIFAR-10 dataset.
+   Returns: (train_images, train_labels, test_images, test_labels)
+   """
+   # Create data directory
+   data_dir = os.path.join(os.path.dirname(__file__), "cifar10_files")
+   if not os.path.exists(data_dir):
+       os.makedirs(data_dir)
+
+   # Download files if needed
+   url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+   filepath = os.path.join(data_dir, "cifar-10-python.tar.gz")
+   extracted_dir = os.path.join(data_dir, "cifar-10-batches-py")
+   
+   if not os.path.exists(extracted_dir):
+       if not os.path.isfile(filepath):
+           print(f"Downloading {url}")
+           urllib.request.urlretrieve(url, filepath)
+       with tarfile.open(filepath, 'r:gz') as tar:
+           tar.extractall(data_dir)
+
+   def load_batch(filename):
+       with open(os.path.join(extracted_dir, filename), 'rb') as f:
+           batch = pickle.load(f, encoding='bytes')
+       return batch[b'data'].reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1), np.array(batch[b'labels'])
+
+   # Load training data
+   train_images, train_labels = [], []
+   for i in range(1, 6):
+       images, labels = load_batch(f'data_batch_{i}')
+       train_images.append(images)
+       train_labels.append(labels)
+   
+   train_images = np.concatenate(train_images)
+   train_labels = np.concatenate(train_labels)
+   
+   # Load test data
+   test_images, test_labels = load_batch('test_batch')
+
+   if normalize:
+       train_images = train_images.astype(np.float32) / 255.0
+       test_images = test_images.astype(np.float32) / 255.0
+
+   return train_images, train_labels, test_images, test_labels