From a24e341a1c421d0b3ae3b70b594e111dc87d0c1d Mon Sep 17 00:00:00 2001
From: Eryn Wells <eryn@erynwells.me>
Date: Sat, 27 Jan 2018 07:56:52 -0800
Subject: [PATCH] Add NN IPython notebook from CS231n at Stanford

---
 CS231n-1 Nearest Neighbor.ipynb | 154 ++++++++++++++++++++++++++++++++
 1 file changed, 154 insertions(+)
 create mode 100644 CS231n-1 Nearest Neighbor.ipynb

diff --git a/CS231n-1 Nearest Neighbor.ipynb b/CS231n-1 Nearest Neighbor.ipynb
new file mode 100644
index 0000000..62da387
--- /dev/null
+++ b/CS231n-1 Nearest Neighbor.ipynb	
@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# http://cs231n.github.io/classification/#nn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Archive exists, proceeding: /Users/eryn/Code/learnmeamachine/scratch/cifar10.tar.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "cifar10 = datasets.cifar10()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create two arrays: one containing images, one containing labels.\n",
+    "# Partition those arrays into a training set, *tr, and a validation set, *val\n",
+    "\n",
+    "# Training set is the first 4 batches. (40k rows)\n",
+    "Xtr = np.concatenate([b[b'data'] for b in cifar10.all_data_batches])\n",
+    "Ytr = np.concatenate([b[b'labels'] for b in cifar10.all_data_batches])\n",
+    "\n",
+    "# Validation set is the last 1000 rows.\n",
+    "Xte = cifar10.test_batch[b'data']\n",
+    "Yte = cifar10.test_batch[b'labels']\n",
+    "\n",
+    "# Reshape the images into 1D arrays\n",
+    "Xtr = Xtr.reshape(Xtr.shape[0], 32*32*3)\n",
+    "Xte = Xval.reshape(Xval.shape[0], 32*32*3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a NearestNeighbor classifier. Copied from the lecture material above.\n",
+    "\n",
+    "class NearestNeighbor(object):\n",
+    "  def __init__(self):\n",
+    "    pass\n",
+    "\n",
+    "  def train(self, X, y):\n",
+    "    \"\"\" X is N x D where each row is an example. Y is 1-dimension of size N \"\"\"\n",
+    "    # the nearest neighbor classifier simply remembers all the training data\n",
+    "    self.Xtr = X\n",
+    "    self.ytr = y\n",
+    "\n",
+    "  def predict(self, X):\n",
+    "    \"\"\" X is N x D where each row is an example we wish to predict label for \"\"\"\n",
+    "    num_test = X.shape[0]\n",
+    "    # lets make sure that the output type matches the input type\n",
+    "    Ypred = np.zeros(num_test, dtype = self.ytr.dtype)\n",
+    "\n",
+    "    # loop over all test rows\n",
+    "    for i in range(num_test):\n",
+    "      # find the nearest training image to the i'th test image\n",
+    "      # using the L1 distance (sum of absolute value differences)\n",
+    "      distances = np.sum(np.abs(self.Xtr - X[i,:]), axis = 1)\n",
+    "      min_index = np.argmin(distances) # get the index with smallest distance\n",
+    "      Ypred[i] = self.ytr[min_index] # predict the label of the nearest example\n",
+    "\n",
+    "    return Ypred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train the NN classifier on the data.\n",
+    "nn = NearestNeighbor()\n",
+    "nn.train(Xtr, Ytr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy: 0.2492\n"
+     ]
+    }
+   ],
+   "source": [
+    "Yte_predict = nn.predict(Xte)\n",
+    "print('accuracy: {}'.format(np.mean(Yte_predict == Yte)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}