Add files via upload

Lectures/Lecture_3.ipynb

@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "753d746a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a0449fde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def softmax(s, y):\n",
+    "    l_i = -np.log(np.exp(s[y])/sum(np.exp(s)))\n",
+    "    return l_i\n",
+    "\n",
+    "\n",
+    "\n",
+    "def svm(s, y):\n",
+    "    m = np.maximum(np.zeros_like(s), s - s[y] + 1)\n",
+    "    m[y] = 0   \n",
+    "    l_i = np.sum(m)\n",
+    "    return l_i\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "7531b4de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = np.array([[10, -2, 3],\n",
+    "              [10, 9, 9],\n",
+    "              [10, -100, -100]])\n",
+    "\n",
+    "y = 0\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "c1bf4f3b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "softmax:\n",
+      "\n",
+      "0.0009176050495943237\n",
+      "0.5514447139320511\n",
+      "-0.0\n",
+      "\n",
+      "SVM:\n",
+      "\n",
+      "0\n",
+      "0\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"softmax:\\n\")\n",
+    "for i in range(s.shape[0]):\n",
+    "    print(softmax(s[i,:], y))\n",
+    "\n",
+    "print(\"\\nSVM:\\n\")\n",
+    "for i in range(s.shape[0]):\n",
+    "    print(svm(s[i,:], y))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "49662904",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  20,   -2,    3],\n",
+       "       [  10,    9,    9],\n",
+       "       [  10, -100, -100]])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[0,0] += 10\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "3c85dfef",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "softmax:\n",
+      "\n",
+      "4.167832299541146e-08\n",
+      "0.5514447139320511\n",
+      "-0.0\n",
+      "\n",
+      "SVM:\n",
+      "\n",
+      "0\n",
+      "0\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"softmax:\\n\")\n",
+    "for i in range(s.shape[0]):\n",
+    "    print(softmax(s[i,:], y))\n",
+    "\n",
+    "print(\"\\nSVM:\\n\")\n",
+    "for i in range(s.shape[0]):\n",
+    "    print(svm(s[i,:], y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bac1fcb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}