{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 01wk-2: 회귀분석 (1) – 단순회귀의 학습전략, 경사하강법\n", "\n", "최규빈 \n", "2024-03-06\n", "\n", "\n", "\n", "# 1. 강의영상\n", "\n", "\n", "\n", "# 2. Imports" ], "id": "a339c0e7-793d-496b-b76b-72dded9d91f9" }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import matplotlib.pyplot as plt " ], "id": "903d2bef-18cf-436f-aba0-bbf57ea3bbcb" }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. 로드맵\n", "\n", "`-` 회귀분석 $\\to$ 로지스틱 $\\to$ 심층신경망(DNN) $\\to$\n", "합성곱신경망(CNN)\n", "\n", "# 4. 파이썬 문법 복습을 위한 참고자료\n", "\n", "`-` 넘파이 문법이 약하다면? (reshape, concatenate, stack, row/col\n", "vector)\n", "\n", "1. [reshape](https://guebin.github.io/IP2022/2022/04/06/(6%EC%A3%BC%EC%B0%A8)-4%EC%9B%946%EC%9D%BC.html):\n", " 넘파이공부 2단계 reshape 참고\n", "2. [concatenate,stack](https://guebin.github.io/IP2022/2022/04/11/(6%EC%A3%BC%EC%B0%A8)-4%EC%9B%9411%EC%9D%BC.html):\n", " 아래 링크의 넘파이공부 4단계 참고\n", "3. [col-vec,row-vec](https://guebin.github.io/STBDA2022/2022/03/14/(2주차)-3월14일.html):\n", " 3x1 col-vec 선언방법, 1x3 row-vec 선언방법에서 `[[1],[2],[3]]` 혹은\n", " `[[1,2,3]]` 와 같은 표현이 이해안되면 링크의 첫번째 동영상 12:15 -\n", " 22:45 에 해당하는 분량을 학습할 것.\n", "\n", "# 5. 회귀모형\n", "\n", "## A. 모형소개\n", "\n", "`-` model:\n", "$y_i= w_0+w_1 x_i +\\epsilon_i = 2.5 + 4x_i +\\epsilon_i, \\quad i=1,2,\\dots,n$\n", "\n", "`-` model: ${\\bf y}={\\bf X}{\\bf W} +\\boldsymbol{\\epsilon}$\n", "\n", "- ${\\bf y}=\\begin{bmatrix} y_1 \\\\ y_2 \\\\ \\dots \\\\ y_n\\end{bmatrix}, \\quad {\\bf X}=\\begin{bmatrix} 1 & x_1 \\\\ 1 & x_2 \\\\ \\dots \\\\ 1 & x_n\\end{bmatrix}, \\quad {\\bf W}=\\begin{bmatrix} 2.5 \\\\ 4 \\end{bmatrix}, \\quad \\boldsymbol{\\epsilon}= \\begin{bmatrix} \\epsilon_1 \\\\ \\dots \\\\ \\epsilon_n\\end{bmatrix}$\n", "\n", "## B. 회귀모형에서 데이터 생성" ], "id": "0a759cc1-ffea-45fe-9e7d-d36dba8721e1" }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "torch.manual_seed(43052)\n", "ones= torch.ones(100).reshape(-1,1)\n", "x,_ = torch.randn(100).sort()\n", "x = x.reshape(-1,1)\n", "X = torch.concat([ones,x],axis=-1)\n", "W = torch.tensor([[2.5],[4]])\n", "ϵ = torch.randn(100).reshape(-1,1)*0.5\n", "y = X@W + ϵ" ], "id": "069b91c5-6a7a-432f-8916-c7c334c646c5" }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "output_type": "display_data", "metadata": {}, "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGdCAYAAAAvwBgXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90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\nREQkahREREREJGoURERERCRq/j8CNDBpXX4aDwAAAABJRU5ErkJggg==\n" } } ], "source": [ "plt.plot(x,y,'o')\n", "plt.plot(x,2.5+4*x,'--')" ], "id": "fb9fe438-f094-4e77-9ca5-39e8fd2c6fd3" }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6. 회귀모형에서 학습이란?\n", "\n", "`-` 파란점만 주어졌을때, 주황색 점선을 추정하는것. 좀 더 정확하게 말하면\n", "given data로 $\\begin{bmatrix} \\hat{w}_0 \\\\ \\hat{w}_1 \\end{bmatrix}$를\n", "최대한 $\\begin{bmatrix} 2.5 \\\\ 4 \\end{bmatrix}$와 비슷하게 찾는것.\n", "\n", "- given data : $\\big\\{(x_i,y_i) \\big\\}_{i=1}^{n}$\n", "\n", "- parameter: ${\\bf W}=\\begin{bmatrix} w_0 \\\\ w_1 \\end{bmatrix}$\n", "\n", "- estimated parameter:\n", " ${\\bf \\hat{W}}=\\begin{bmatrix} \\hat{w}_0 \\\\ \\hat{w}_1 \\end{bmatrix}$\n", "\n", "`-` 더 쉽게 말하면 아래의 그림을 보고 `적당한` 추세선을 찾는것이다." ], "id": "887957a7-855a-418e-9756-87e2a24a5186" }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "output_type": "display_data", "metadata": {}, "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGdCAYAAAAvwBgXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90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\n3pwNAAAAAElFTkSuQmCC\n" } } ], "source": [ "plt.plot(x,y,'o')" ], "id": "c19493ee-5e09-40b6-b15a-c6f6b5761d62" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`-` 시도: $(\\hat{w}_0,\\hat{w}_1)=(-5,10)$을 선택하여 선을 그려보고\n", "적당한지 판단. –\\> “인간”지능을 활용하여 더 나은 선을 찾아보자." ], "id": "7c526e28-0f24-4a00-9054-a0f24d8ec01d" }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "output_type": "display_data", "metadata": {}, "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAGdCAYAAADT1TPdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90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\nYII=\n" } } ], "source": [ "plt.plot(x,y,'o')\n", "plt.plot(x,-5+10*x,'--')" ], "id": "31dd1fd0-7601-4466-b733-38935f3f2664" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`-` 벡터표현으로 주황색점선을 계산" ], "id": "1a0419fd-e2c7-4e82-accf-500792fda06b" }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "What = torch.tensor([[-5.0],[10.0]])" ], "id": "5b8a5089-d9e0-400c-b3fb-c615131e9422" }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "X.shape" ], "id": "41a2a793-e41c-43c2-af1a-317975286f7b" }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "output_type": "display_data", "metadata": {}, "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAGdCAYAAADT1TPdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90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\nYII=\n" } } ], "source": [ "plt.plot(x,y,'o')\n", "plt.plot(x,X@What,'--')" ], "id": "003c7fcb-2862-45ae-8785-7bd440bacdbb" }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 이건 일단 망한 학습같음..\n", "\n", "# 7. 학습전략\n", "\n", "`-` 이론적으로 추론 \\<- 회귀분석시간에 배운것\n", "\n", "`-` **컴퓨터의 반복계산을 이용하여 추론 (손실함수도입 + 경사하강법)**\n", "\\<- 우리가 오늘 파이토치로 실습해볼 내용.\n", "\n", "`-` 전략: 아래와 같은 3단계 전략을 취한다.\n", "\n", "- 1단계: 아무 점선이나 그어본다..\n", "- 2단계: 1단계에서 그은 점선보다 더 좋은 점선으로 바꾼다.\n", "- 3단계: 1-2단계를 반복한다.\n", "\n", "# 8. `#1단계`실습 – 최초의 직선\n", "\n", "> 1단계 = 아무 점선이나 그어보자..\n", "\n", "`-` $\\hat{w}_0=-5, \\hat{w}_1 = 10$ 으로 설정하고 (왜? 그냥) 임의의 선을\n", "그어보자." ], "id": "ee6f0608-14c6-465b-9b11-5d5b901957b4" }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "What = torch.tensor([[-5.0],[10.0]],requires_grad=True)\n", "What" ], "id": "02fbce78-fedf-487e-8649-7a629a4f9e5b" }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 처음에는\n", " ${\\bf \\hat{W}}=\\begin{bmatrix} \\hat{w}_0 \\\\ \\hat{w}_1 \\end{bmatrix}=\\begin{bmatrix} -5 \\\\ 10 \\end{bmatrix}$\n", " 를 대입해서 주황색 점선을 적당히 그려보자는 의미\n", "\n", "- 끝에 requires_grad=True는 나중에 미분을 위한 것" ], "id": "33b3555c-f286-42fa-a204-0a95d40f06af" }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "yhat = X@What " ], "id": "c0c5555c-c893-4f7f-bf37-608a3f0a64e5" }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "output_type": "display_data", "metadata": {}, "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAGdCAYAAADT1TPdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\nbGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9h\nAAAPYQGoP6dpAABCO0lEQVR4nO3de3xTZbr28V9a2rRAGyiFtkCRCnjAqgiCFlAOKoiAIsqM48iA\nBxQBHXQcFX1HcY/KCIw6iiKKgjMOnlFUlAEHBREQAREBjwgWpZVDIYUCPSTr/WNNC6Vpm6RJVg7X\n9/PJ3l0rK8lN94ZcPut57sdmGIaBiIiISJiKs7oAERERkboorIiIiEhYU1gRERGRsKawIiIiImFN\nYUVERETCmsKKiIiIhDWFFREREQlrCisiIiIS1hpZXUBDud1udu7cSUpKCjabzepyRERExAuGYXDg\nwAFat25NXFzdYycRH1Z27txJdna21WWIiIiIH3bs2EHbtm3rvCbiw0pKSgpg/mFTU1MtrkZERES8\nUVxcTHZ2dtX3eF0iPqxU3vpJTU1VWBEREYkw3kzh0ARbERERCWsKKyIiIhLWFFZEREQkrCmsiIiI\nSFhTWBEREZGwprAiIiIiYU1hRURERMKawoqIiIiENYUVERERCWsKKyIiIhLWFFZEREQkrCmsiIiI\nSFhTWBEREZGwprAiIiIiYU1hRURERI6qKIPFf4HCr6yupIrCioiIiJj2bYc5F8PKJ+D1a83gEgYa\nWV2AiIiIhIGv34W3x0OpE5IccNED0CjR6qoAhRUREZHYVlFq3vZZM8s8btsdrnwBmrWztq5jKKyI\niIjEqpK98NJwKNhgHve8BS64H+ITLC3reAorIiIisSq5mXnLJzkNLn8GThpodUUeBXWC7ZQpU+je\nvTspKSm0atWKYcOG8e2331a7xjAMJk+eTOvWrUlOTqZv375s3rw5mGWJiIjErvIj5gMgLh6umA1j\nV4RtUIEgh5Vly5Yxfvx4Vq9ezZIlS6ioqGDAgAGUlJRUXTN16lQeffRRZsyYweeff05mZiYXXXQR\nBw4cCGZpIiIisWfP9zD7Alh099FzTVuBo411NXnBZhiGEaoP2717N61atWLZsmWcf/75GIZB69at\nmThxInfddRcApaWlZGRk8Mgjj3DTTTfV+57FxcU4HA6cTiepqanB/iOIiIhEpo2vwbsTobwEGqfD\nuNXQtKVl5fjy/R3SPitOpxOAtLQ0ALZt20ZhYSEDBgyousZut9OnTx9Wrlzp8T1KS0spLi6u9hAR\nEZFalB2CBRNg/hgzqLQ/D27+1NKg4quQhRXDMLj99tvp3bs3ubm5ABQWFgKQkZFR7dqMjIyq5443\nZcoUHA5H1SM7Ozu4hYuIiESqXd/Ac/3hi38BNuhzN/xhAaRkWl2ZT0IWViZMmMDGjRt5+eWXazxn\ns9mqHRuGUeNcpUmTJuF0OqseO3bsCEq9IiIiEa2iDF66AnZ/DU0zzJDSb5I5qTbChGTp8i233MI7\n77zD8uXLadu2bdX5zEwz2RUWFpKVlVV1fteuXTVGWyrZ7XbsdntwCxYREYl0jRJh8HT4bBYMf9ac\nSBuhgjqyYhgGEyZMYP78+SxdupScnJxqz+fk5JCZmcmSJUuqzpWVlbFs2TJ69uwZzNJERESiz6+b\n4cePjx6fPAhGvhXRQQWCPLIyfvx45s2bx4IFC0hJSamah+JwOEhOTsZmszFx4kQefvhhOnXqRKdO\nnXj44Ydp3LgxV199dTBLExERiR6GAev/CR/cCQnJZt8Ux//uZNQyrSKSBDWszJw5E4C+fftWOz9n\nzhxGjx4NwJ133snhw4cZN24c+/bt45xzzmHx4sWkpKQEszQREZHoUHrAXJK86Q3zuP150CjZ0pIC\nLaR9VoJBfVZERCRmFWyE10dD0VawxcMF90HPWyEupJ1J/OLL97f2BhIREYlEn8+GRfeAqxRS25o7\nJbc7x+qqgkJhRUREJBIVfmUGlZMGwbCnoXGa1RUFjcKKiIhIpDCMoxNmL/4btO0OXX4fFZNo6xL+\nN7VERERinWHA6pkw7zfgdpnnEpLhrGuiPqiARlZERETC2+F95t4+37xnHm95G3KvsLSkUFNYERER\nCVc/r4XXrwVnPsQnwoCH4LThVlcVcgorIiIi4cbthtVPwYeTwV0BzXNgxBxofZbVlVlCYUVERCTc\nLLob1swyfz5tOAz9ByTFbi8xTbAVEREJN11HQpIDhjxu9k+J4aACGlkRERGxntsNBV9Am27mcebp\nMHFTzIeUShpZERERsdLB3fDvK+H5gfDzuqPnFVSqKKyIiIhYZfsKeKY3bP0vxDWC/T9ZXVFY0m0g\nERGRUHO74JO/w8dTwHBD+snwmxeh1alWVxaWFFZERERC6cCvMH8MbFtmHne5Bi6ZColNrK0rjCms\niIiIhNKWBWZQSWgMQx6DM6+yuqKwp7AiIiISSj3GmHNTuo6ClidZXU1E0ARbERGRYCougAXjofSg\neWyzwcCHFFR8oJEVERGRYPn+Q3jrRji0F2zxcOkTVlcUkRRWREREAs1VAR89CCseM48zT4def7S2\npgimsCIiIhJIzp/hjethx2rzuPsN5m7JCUnW1hXBFFZEREQCJX81vHwVHN4H9lS49Ek4bZjVVUU8\nhRUREZFAaZ5jdqLN6gIj5kDaiVZXFBUUVkRERBriUBE0TjN/TsmAUe9BWg40sltbVxTR0mURERF/\nff0ePNEFNs0/eq7VKQoqAaawIiIi4quKUvjgLnj193DECV+8BIZhdVVRS7eBREREfFH0I7x+LRRs\nMI973gIX3G82e5OgUFgRERHx1ua34J1bobQYktPg8mfgpIFWVxX1FFZERES88etmeH20+XP2uXDl\nC+BoY2lJsUJhRURExBsZp0HeBIhPhH73Qnz0f4W63AZrthWx68ARWqUk0SMnjfi40N/uiv7ftIiI\niL82vQnZ54CjrXk84MGYmZuyaFMBD7y7hQLnkapzWY4k7h/amYtzs0Jai1YDiYiIHK/sELxzC7xx\nndk631Vhno+hoHLzS+urBRWAQucRbn5pPYs2FYS0HoUVERGRY+3+FmZfAOv/CdjgxD4xE1LAvPXz\nwLtb8LQQu/LcA+9uweUO3VJthRUREZFKG+bBs31h1xZo0gr+sAD63QNx8VZXFjJrthXVGFE5lgEU\nOI+wZltRyGrSnBUREZGyQ7DwT/DlPPP4xL4w/Dlo2srSskKpcjLtB17e4tl1oPZAE2gKKyIiImA2\nebPFQd974LzbY2o0xdNk2vq0SkkKYkXVKayIiEhsqmyPb7NBYmMY8SKU7IL2va2tK8QqJ9N6OwPF\nBmQ6zGXMoaI5KyIiEntKD8D8MfDJ9KPnWp4Uc0Glrsm0nlROM75/aOeQ9lvRyIqIiMSWgo3wxrWw\n9weIt0OXayA1tH1DwkV9k2mPl2lRnxWFFRERiQ2GAWufh0X3gKsUUtuYLfNjNKiA95Nk/5B3AoNy\ns9TBVkREJGiOOOHdP5obEQKcdDEMmwmNQzfvIhga2g7f20myg3KzyOvQwt8yG0xhRUREopurAp4f\nCLu/hrhGcOEDkDc+4hu9BaIdfrcTmhNng7r6u8XZzOuspAm2IiIS3eIbQY8bwNEOrvsP9JwQFUEl\nEO3w1/20r86gAmaQWffTPn9LDQiFFRERiT6H95tt8yudfT2MWwltz7aspEAJZDt8b+eshLIBnCcK\nKyIiEl1+XguzzoN/jzBDC5gjKfYUS8sKlEC2w/d2zkooG8B5orAiIiLRwTBg5Qx4YSDszzcDysFd\nVlcVcIEcDemRk0aWI4naborZMOfBhLIBnCcKKyIiEvkOFcHLV8Hie8FdAaddDjctNxu9RZlAjobE\nx9m4f2hngBqBxaoGcJ4orIiISGTLXw3PnAffLTKbvA1+FK6cA0kOS8pxuQ1Wbd3Lgg2/sGrrXq/m\njvgi0KMhF+dmMfOarmQ6qoebTEcSM6/pGvIGcJ5o6bKIiES2T5+A4p8hrQOMmAtZZ1hWSiCWE9en\ncjTk5pfWY4NqE239HQ25ODeLizpnNqhnSzDZDMMIbOQLseLiYhwOB06nk9TUVKvLERGRUCvZC8un\nQv//Z+kk2to2BKz8ug/0KEUoglEw+fL9rbAiIiKRZfsK85bPgAetrqSKy23Q+5Glta7SqdypeMVd\n/QM6WtHQDrZW8uX7W7eBREQkMrhd8Mnf4eMpYLihdVfIHW51VYBvy4kD2bY+Ps5maRv8UFFYERGR\n8HfgV5g/BrYtM4/PvBpOGmhtTccIxHLiSB4lCTaFFRERCW8/fgxvjoGSXZDQGAb/HbpcbXVV1TR0\nOXGkzz8JNi1dFhGR8LVyBvxzmBlUWnWGGz8Ou6ACDVtOHKh9fqJZUMPK8uXLGTp0KK1bt8Zms/H2\n229Xe94wDCZPnkzr1q1JTk6mb9++bN68OZgliYhIJMkwG5bRdRSMWQotT7a2nlr421wtkPv8RLOg\nhpWSkhLOPPNMZsyY4fH5qVOn8uijjzJjxgw+//xzMjMzueiiizhw4EAwyxIRkXBWsvfozx36w82f\nwqVPQEKydTV5wZ/maoHc5yeaBXXOyqBBgxg0aJDH5wzD4PHHH+fee+9l+HBzNveLL75IRkYG8+bN\n46abbgpmaSIiEm5cFfDRg7D2BfN2T9qJ5vmM0ywtyxe+NleLlF2PrWbZnJVt27ZRWFjIgAEDqs7Z\n7Xb69OnDypUra31daWkpxcXF1R4iIhLZXPt2UPzMAFjxGBxx4v56odUl+a1yOfFlXdqQ16FFnSt6\nImXXY6tZFlYKCwsByMjIqHY+IyOj6jlPpkyZgsPhqHpkZ2cHtU4REQmudYvncfAf55K6ex3FRjLj\nym6l1/LOMTGxNFJ2Pbaa5auBbLbq/ycyDKPGuWNNmjQJp9NZ9dixY0ewSxQRkWBwlbPt3xPptvJm\nHBxkozuHIWUP8777XJ9WwgR748BgipRdj61mWZ+VzMxMwBxhyco6Oulo165dNUZbjmW327Hb7UGv\nT0REgsv92Sxyvp8DwJyKgUypuJoyEgBzYqkNcyXMRZ0za/2yjob+JJUTc4//c2RG2J8jmCwLKzk5\nOWRmZrJkyRLOOussAMrKyli2bBmPPPKIVWWJiEiIrEm/gjLXK7zkupDF7u41nq+vRX1tGwdWjsoE\neuPAYAr3XY+tFtSwcvDgQX744Yeq423btrFhwwbS0tJo164dEydO5OGHH6ZTp0506tSJhx9+mMaN\nG3P11eHX8EdERBqootRc6dN9DMQ34tdDbv5Yfjc1b4BU52klTH39SbwZlQk3sbLPjz+CGlbWrl1L\nv379qo5vv/12AEaNGsXcuXO58847OXz4MOPGjWPfvn2cc845LF68mJQU67b4FhGRICj6EV6/Fgo2\nQMluuOA+0pvYqS+oAP+7rjqrNg4UawQ1rPTt2xfDqH2ik81mY/LkyUyePDmYZYiIiJU2vwXv3Aql\nxZDcHNr2MM97O+Dh4Tr1J4kt2shQRESCo/wI/OceWPu8eZx9Llz5PDjaArDnYKlXb+PpOvUniS0K\nKyIiMczlNoIzqXPvVnh9FBR+ZR73vh363QvxR792GhI4KvuTFDqPeJy3YsNcTRPr/UmihcKKiEiM\nCuqyX1e5GVgap8PwWdDxwhqXNCRwVPYnufml9dig2uvVnyT6WN4UTkREQq9y2e/xk1R9acZWg9t9\n9OdWp8Bv/gVjV3gMKtDwhmj+bBwokclm1DUDNgIUFxfjcDhwOp2kpqZaXY6ISNhzuQ16P7K01tU0\nlSMaK+7q7/3IxO5v4c3rYdBUOKGnT/U0dIQnaLeyJKh8+f7WbSARkRgT8GW/G+bBwj9B+SGMRZNY\nfcGb7DpY6nVwOL4hWnoTO9jMibWrtu6t9z3UnyT6KayIiMSYgC37LSuBhXfAl/MA2NMqjz/suZ4t\nsz+ruiQz1c7kS0+rd4SkMnAs2lTAHW98GdHt8yXwNGdFRCTGBGTZ769b4Nm+ZlCxxfF951vpkT+e\nLQcaV7ussLiUsf+bA1PfhoNBmUcjUUEjKyIiMWZfSf39TdKaJNDthOaen9z9HTzXHyoOQ9NMXMNn\nM+Klw7gpr/X9bn/tS1KTtlBY7HnEJBrb50vgaGRFRCSGuNwGf134db3XFZWU02faR1WjGdVGRZxp\nGJ0GQIcLYOwKVrtOZf+h2oMKwKEyV7WgAtVHTHyZRyOxRyMrIiIxZPWPe+sMBceqDBM3np/D5i9W\n8eWBVA5g3uZpn3oVdw89k4ubtmTVj9/4VcuxIyZ3XnyKV69R+/zYpLAiIhJljl/K2+2E5qz7aR9L\nthTy6todXr+P8b//efDT53i+0b/4MOEsxpf/EbDxU7HBzf/ewMxr4vB+kx/Pn1HgPEKRl6331T4/\nNimsiIhEEU89S+Js4Pajo1YKh5iSMJsh8asBsFOOnXJKSaw2KjL1ijOY8VHD6k5rkqj2+VIrzVkR\nEYkSta2m8Seo5Np+5L3EexgSv5pyI54Hy3/PDeV3UEpi1TWVoyJxNhvNGic0qPZMR3KDutlKdFNY\nEREJofqW7/r7urpW0/jGYFT8f3gzcTInxO3iZyOd35Tdx2zXYGq73bOnpJS/DT/dr0+zYa4K6pGT\npvb5UivdBhIRCRF/28p787r6VtN4K5VDjG30LnZbBf9xnc2fy2+kmKZ1vqZVShJ5HVrwzDVdmfxO\nzeXJl56ZxbPLtwH1bzh4fDdbtc8X0N5AIiIhUXmL5vh/cCu/gmsbOajvdU9dfRbNm9h5/6ud/Gt1\nfkBq7W77htPitjPXNZC6Js962kOotn16grrDs0QkX76/FVZERILM340D63sd+D959iiD6+M/4Ehi\nM/6b0N/nURFfbs9ow0E5ljYyFBEJA5Vfziu+3+3zxoEut8HcT7fVe2unIUHFwUGmJzzDRfHrqYhL\n5v9uvok1RU1qhImz2jWvMSqS6ceoiDYcFH8prIiIBMGiTQU15m/Up7LhmadbJoHW1fYdTyY+SRvb\nXlxxiTS6+EFolk1e85ojHZpHIlZTWBERCbBFmwoY+9J6n1/XKiWp1jkqgWLDzU3x73FHo9doZHNj\npHUgfsRcyDqjztdpVESspLAiIhJALrfB3fO/8uu1u51HmLwwEMuPPYvDzeyE6fSP32CeOH0EtiGP\ngT0lSJ8oEhgKKyIiAbT6x731bupXmz++tiFoQQVgXL+TOOlAT4xvvsE2aCp0/QPYdCtHwp/CiohI\nAK3autfv1/oSVLxdBRSHGwcHSXK04raLTiLe+Cvsuw7SO/ldp0ioKayIiPihtmW4RlDHRkx/GXwq\nWY5kxs8z58XU9onpOHk8YQYptsMUDn77fxNiGymoSMRRWBER8ZGn1TppTRK4omtb3ljn/a7Gvqrs\nxzK6Vw7xcTZmxnWtddPCvLjNPJEwg5Y2JxXxyZyZths4IWi1iQSTmsKJSExpaGOyYK/WqYuNmk3Y\njv/zdMtO5df3/o+2G5/EhoHR8lRsI+ZCq1MsqFikdmoKJyJyHJfbYMbSH5jz6Tb2Hz46AdaXlu+B\n2yzQdy2aJPLQ5bk16qy2pLi4AOZdTvb2T8zjs0aaE2kTG4e4WpHAUlgRkahz/GjDvpIy7nn7K4+r\ndAqdR7j5pfVetY0P1GaBx6tvsmxakwRWTbqAxEZxdb/RO7fA9k8gsSkMeRzOGBHQOkWsorAiIlHF\n1+6vBubtlQfe3cJFnTPrvCVU2WE2ECb060CnjJSqMOVpsmxlJQ9ffnr9QQXgkqmwYAIMfQLSOwas\nVhGrKayISMSob76Jv/NJPO3N40mrlCT/CvegV8eW1T7L02TZevffcf4CW5dC15HmcdqJcO37AatR\nJFworIhIRPC0105mahKTLzW/zAMxn6S+kZMeOWmkNUmgqMS/pm9wdEVPj5y0aud93n/nu//AW2Ph\n8D5IbQ0dL/C7JpFwp7AiImHF0+jJki2FHvfaKSw+wtiX1vPMNV1xJCc2eD7J978eZNXWvbWGhPg4\nGw9elsu4eV/49f6V73j/0M61vn+9+++4yuG/D8DKJ83jrDMhLcevekQihZYui0jY8DTfJDPVzoEj\nFZSUuWp9XfPGCdw39DRue3VDQOpolpzAtb3aM6F/J4+hYsr7W5i1fJvP7+vLyiOP9ufDG9fBz5+b\nxz1uggF/hUZ2/95PxEJauiwiEae2+SaFxaX1vnbfoXL2HKj/Om/tP1zOYx9+z5yV2/nb8NNrhItJ\nl3TmzLbN+X8LNlFUUlbne028oCM5LZv61dOlmm/eh7fHwhEnJDngsqfg1KH+vZdIhFFYEZGA8LbZ\nmqfrgAbPN9l3qIwsRxKFziMB64Oy/1B5rcuaLzkji4G55hyTJVsKeXvDzmrBpcGjKMc7vM8MKm26\nwZVzoLm60Urs0G0gEWkwT7dvPH1Z13bdVd2zeezD7xtUw4R+Hcht4/A4t6WhshxJrLirf52jIg3t\njOuR2w1xxyxZ/uoNOPVSaJTYsPcVCQO+fH97sXBfRKR2lbdvjp/cWtlsbdGmAlxug398+D1ja7mu\noUEFIO/E9Aa/R20qlzXXpXJy7GVd2pDXoUXDg8qWBfBMLzh0zOeefqWCisQk3QYSEb/VtVy4stna\n3fO/4v4Fm/m1ljklgRjabZwYT/ecNPpM+ygA7+ZZIBvC1an8CCy+Fz6fbR6vfAIunByazxYJUwor\nIuK3+trPG+CxxX2gHSpzMfPjH3xaupyZaudIhdvr+gLZEK5We7fC66OhcKN53Gsi9Ls3+J8rEuYU\nVkTEI2/mYIRstKEeNmDOp9u9uvYPeScwKDerzv4tx7+3pyZuAffVG/DuH6HsIDRuAZfPgk4XBfcz\nRSKEwoqIANXDyfY9h3h5TX61brGeJswGerRhyBlZvP9VQZ2b+nliQLWdlOsyKDerqvHaxblZPHNN\nV+6e73mTw/qauAXMurlmUAFo1xOufN7sSisigFYDiQjebf5nwwwFt13YifbpTUhvYuezbXt5YukP\nDf58G+BonIDzUHmD5rA0S07Aedjze1SOkHha1eNyG8xY+gNzPt1WLfQEfPlxbQ7vh2f7mhNo+9wN\n8frvSIl+vnx/K6yIxDh/N/8LtGaNExo8v+W2C0/i8Q+/AzzvXuypX8qxgrL8uDbbPoH2vcH2v/cv\nOwSJjYPzWSJhSEuXRcQrgdj8r6GyHEncdmGnBgUV2//eZ0L/jsy8piuZjuq3pzIdSfUGFQjC8mNP\nykrg7XHw4hBY+8LR8woqIrXSWKNIFPJ2hKC+1TzBdtuFnZjQvxPvbdzp93scP6/E592LQ2nX1+Zq\nn93fgC3O7EgrIvVSWBGJMt52ky2rcPPa5/lWlFjl5MwU4uNsXk/Uve3Ck3jl8/zqGx16+LN5tXtx\nKBkGfPESvP9nqDgMTTPhitmQc57VlYlEBM1ZEYkitc0/OX7OxpT3t/DcJ9t8XnUTSMdOeAXo/cjS\nWvf1Of7asBw1qU3pQXjvNvjqNfO4Q3+4/Flo2tLaukQspl2XRWKQN91kH3h3C59v38fzK7aFuLqa\nDI62sc/r0IL7h3bm5pfWV606quRp+XBYjZrU59dNsOkNsMVD/3uh123V9/sRkXrpb4xIlPCmm2yB\n80hYBJVjVTaWuzg3q0GTY8NWu3Nh0FQYvRDO+5OCiogfNLIiEiU+3FJodQnVXNG1DW+u/6Xe646d\nrxLWk2O9daQYFt1ttspveZJ5rscYS0sSiXQKKyIRoHJ1T6HzMEUlZaQ1tZOZevSL3OU2mO9FMAil\n4We1ZeXWvfXOQzm+jX3YTY71xc4N5mqffdvM2z9jPtZIikgAKKyIhLm6usumNUng8i5tcB6uYJ+X\n7eZDZU9JqU/zUCKaYcCaZ2Hx/wNXGTiy4ZLpCioiAaKwIhLG6usuW1RSzvNebuAXaq1Sksjr0IKZ\n13StEbY8LTeOWIf3wzsT4Ot3zeOTB8NlM6BxkDc+FIkhCisiIVBfkzZPzwOWdJe1N4qjtMJd6/OO\n5EYkJzTi12Lvbu9ExTyU2uz7yexEuz8f4hJgwF/hnLFHW+iLSECERVh5+umnmTZtGgUFBZx22mk8\n/vjjnHeemiVJdKivSVttz1/VPTvk3WXHnJdDtxOaM/al9bVe88gVZwD4dHsnoueh1CW1DaS0Bmww\nYg606WZ1RSJRyfKmcK+++iojR47k6aefplevXsyaNYvZs2ezZcsW2rVrV+/r1RROwll9t3GGnJHF\nwo0FHpu4hfov5oyrujCkSxvArHvyO5spLC6tej4z1c7kS0+runXjbafcqHOoCBKbQqNE8/hAISQk\nQ5LD2rpEIkxE7bp8zjnn0LVrV2bOnFl17tRTT2XYsGFMmTKl3tcrrEgo+bIrr8tt0PuRpZbuvXO8\nZo0TSGoUT2Fx/QHDmz9rSHcpDgf5n8Eb18Fpw2DgQ1ZXIxLRIqaDbVlZGevWrePuu++udn7AgAGs\nXLnS42tKS0spLT36X3vFxcVBrVGkkq8jCVZvEnisyvjwt+Gnez1/xJtbN1F7e+d4bjesfAL++39g\nuODbD6DvJLA3tboykZhg6bq6PXv24HK5yMjIqHY+IyODwkLPDa6mTJmCw+GoemRnZ4eiVIlxlbdz\njg8fhc4j3PzSehZtKqjxmsrOrOHg2C6wlQHjsi5tyOvQIrpHQgKhZA/M+w18eL8ZVHKvhJuWKaiI\nhFBYNAGwHTdz3jCMGucqTZo0CafTWfXYsWNHKEqUGFbfnjtgrtpxHbcroLc7CdfntgtPIsvh+3ul\nNUng+l7teXnMuay4q390zyMJlu2fwjO94Ycl0CgJhv7D3C3ZnmJ1ZSIxxdLbQOnp6cTHx9cYRdm1\na1eN0ZZKdrsdu90eivJEAO/33KnckK9Sj5w0shxJtXZwrU/lEuAJ/TsyoX9HVm/dy7h563HW0fyt\niT2eB4edXq27rfip9AC8cjUc2Q8tOsGIuZCZa3VVIjHJ0pGVxMREunXrxpIlS6qdX7JkCT179rSo\nKokFLrfBqq17WbDhF1Zt3VtjVORY3t7OOf66+Dgb9w/t7Fd9xy8Bjo+z0atTOo9ccTq2Y54/3t9H\nnMnlZ+n2TkDYU2DIY3DGVXDjxwoqIhayvM/K7bffzsiRIzn77LPJy8vj2WefJT8/n7Fjx1pdmkQp\nXyfKens7x9N1lTsJH78MuD61dXitfL+YXDIcCj8uMxu65ZxvHucONx8iYinLly6D2RRu6tSpFBQU\nkJuby2OPPcb555/v1Wu1dFl8UVvfk8oxiMpJqMeqXIJc1+2cZo0TWHPPhaz7aZ/HVTYut8Fdb2zk\njfU/11vjXwafyuheOXWOjMTckuFgc7tg2SOwbCo0aQljV0CK51vRIhIYEdVnpaEUVsRb9fU9qZwj\nsuKu/jW++BdtKqizqyuYgWX/oaPzSY4f7XC5Dbo9uKTaNd5+vgRRcQHMHwPbPzGPzxoJg6ZCYmNr\n6xKJcr58f4fFaiCRUPBlouzxLuqcSbPGCXW+//Eh5PhlzfFxNv42/HSP802ibhfiSPHDf83VPts/\ngYQmcPmz5iaECioiYUVhRaLW8ZNoj+3aWhdPE2rXbCuqdUSkNp6WNVfOOTl+KfKxfVAkBNxus8Hb\nS1fAoT2QcTrctBzO/K3VlYmIB5ZPsBXxlTfzNTxNok1rUvfISKXtew7VOOdvgzdPy5qjehfiSGGz\ngfMXwICzr4OBD5v7+4hIWFJYkYjizUqe2ibRFpV4NzLy+IffcXJm02qjHA1t8OZpWXNMtKkPN24X\nxMWbYWXw36HzpXDKYKurEpF66DaQRAxvWt7X1W3WF8d3pK1s8Obv2EegutmKn1zlsPgv8Oo1ULmm\nwN5UQUUkQiisSETwtuX96h/3NnjzQE8TbY9t8OZLYLFhjvz0yElrUE3SAPt3wJxLzI0Iv30fti23\nuiIR8ZHCikQEb1fyrNq6N2Cfefytm8rJsZnHTY6tXCV0fIjRCp8w8M375mqfn9eA3QG/+Rec2Mfq\nqkTER5qzIhHB+wmugWsbVFtHWk+TY5dsKawxl6a2LrQSAhVl8OFkWP2Uedy6K4yYA83bW1mViPhJ\nYUUs40sXVm/nfOSdmM6b63/xe/PASnXduvE0OVYrfMLM/DGw5W3z53PHw4WToVGilRWJSAMorIgl\nfN2fp74djCu7v57boQX3D+3MzS+tx4b/4yz+3LrRCp8w0vMW+GklDP0HnHKJ1dWISANpzoqEnDer\neqB6U7c124r4y2DPE1yPnxtS29wSb/us3HbhSbp1E2nKj8BPq44etz0bJm5UUBGJEhpZkZCqb1WP\nDXNVj9sNf11Yc+TlxvNzeOfLgnrnhni6LdPthOb0mfZRnbeIMlPtTOjfMSB/VgmRvVvh9dGw+1sY\nsxQyc83zavImEjUUViSkvF3VM25ezU0DC51HeHb5Np66+iyaN7HXOzfE022Z2m4RVb568qWnaZ5J\nJPnqDXh3IpQdgMYt4HDNfZ1EJPLpNpCElL9t6+FouPjrwq/pkZPGZV3akNehhU/horZbRNqbJ8KU\nH4Z3/whvXm8GlXY9YewKyDnf6spEJAg0siIh1dBOrp722vGVVu5EuD3fm7d9ft0E2OD8O6DP3RCv\nf85EopX+dktI1beqx1sNGaEBrdyJaFveNoNKk5Yw/Fno0N/qikQkyHQbSEKqsm19Q1u3aa+dGNb7\nduh9m3nbR0FFJCYorEiDHbvEeNXWvdU2APTE7Taw+XnHRXvtxKBdX8Pr15rzVMDcNfnCyZCSaWlZ\nIhI6ug0kDeJrc7dFmwoYN+8Lvz5Le+3EGMOADf+GhXdAxWFwtIUBf7W6KhGxgEZWxG/eNnerVNlj\nxV9asRNDSg/CWzfBgvFmUOnQH3reanVVImIRjayIX7xt7nZR58yqUZD6eqx4MqFfBzplpGjFTiwp\n3GSu9tn7Pdjiof+90Os2iNN/W4nEKoUV8Yu3zd2OXWLszwqeXh1batVOLPlmIbxxHVQcgZTWcOUL\ncEKe1VWJiMUUVsQv3gaPY6/zdQWPJtLGoMwzoFGS2dxt2DPQREFVRBRWxE/eBo9jr/Olx4oNTaSN\nGQcKj67saZZt7u/TPEe3fUSkiv41EL9UBo/aooSnJcaVPVYqn69NlibSxgbDgDXPweNnwLeLjp5v\n0UFBRUSq0b8I4pe6gkddS4xr25snrUkC1/dqz8tjzmXFXf0VVKLd4f3w2h/g/TvAVQpfv2N1RSIS\nxmyGYTS0mailiouLcTgcOJ1OUlNTrS4n5vjaZ6WSy21ob55Y9cs6s8nb/p8gLsHsnXLOWPzuFCgi\nEcmX72+FlQgXDl/64VCDRADDgNUzYcl94C6HZifAiDnQppvVlYmIBXz5/tYE2wjm76hGoGlTQPHK\nT5/CfyaZP596KVz6JCQ3s7QkEYkMmrMSoXztHitiufa9ocdNcMl0+M0/FVRExGsKKxGovu6xYHaP\nrW9DQZGgcrth9TNwcNfRc5dMhR5jND9FRHyisNJAvu44HAi+dI8VsUTJXnj5t7DoLpg/xgwuIiJ+\n0pyVBrBqzog/3WNFQuanlfDG9XBgp9mNtvMwjaSISINoZMVPVs4Z8ad7rEjQud2wfDrMHWwGlRad\n4Ib/wtnXKqyISIMorPjB6jkj/nSPFQmqkr3w0nBY+lcw3HDGb+HGjyEz1+rKRCQKKKz4weo5I/52\njxUJmkaJ4NwBjZLh0hlw+SywN7W6KhGJEgorfgiHOSO1ta3P1L46Eipul9noDcCeYi5HvvEj6DpS\nt31EJKA0wdYP4TJn5OLcLC7qnKnusRJ6BwrNVT4nDYK8cea5jNOsrUlEopbCih8q54wUOo94nLdi\nwxzhCMWcEXWPlZDbuhTm3wglu6FgI5z1e0hyWF2ViEQx3Qbyg+aMSExyVcB//wr/Gm4GlVanwfVL\nFFREJOgUVvykOSMSU5y/wItD4ZPpgAHdRsOY/0LLk6yuTERigG4DNYDmjEhMKCuB5/rDwUJITIGh\nj8PpV1pdlYjEEIWVBtKcEYl6iU2g5wTY+BqMmAstOlhdkYjEGIUVEalp/w4oPwQtTzaPzx0PPW6E\nRnZr6xKRmKSwUguX29DtHYlN37wPb98MTVqaXWjtTSEuDuIUVETEGgorHli1QaGIpSrK4MPJsPop\n8zjtRCgtVidaEbGcVgMdx8oNCkUss287vDDwaFA5dzxc9x9IbW1pWSIioLBSjdUbFIpYYss78Mz5\nsHM9JDWDq16Gix829/sREQkDCivHsHqDQpGQMwz4fDaUOqFtDxj7CZxyidVViYhUozkrxwiHDQpF\nQspmg+HPwbo5cN6fID7B6opERGrQyMoxwmWDQpGg2vQmLP7L0eOUDOh7t4KKiIQtjawcI5w2KBQJ\nuPLDsGiSOYoCcGJf6HiBpSWJiHhDIyvH0AaFErX2fA+zL/xfULGZt3xy+lhdlYiIVxRWjqMNCiXq\nfPkqzOoDv26CxulwzZtwwX0Qr4FVEYkMQf3X6qGHHmLhwoVs2LCBxMRE9u/fX+Oa/Px8xo8fz9Kl\nS0lOTubqq69m+vTpJCZat2xSGxRK1PjPvbBqhvlz+/PgitmQkmltTSIiPgpqWCkrK2PEiBHk5eXx\n/PPP13je5XIxePBgWrZsyYoVK9i7dy+jRo3CMAyefPLJYJZWL21QKFHhhJ6w+mk4/07ocyfExVtd\nkYiIz2yGYQS9w9ncuXOZOHFijZGVDz74gCFDhrBjxw5atzY7Zb7yyiuMHj2aXbt2kZqaWu97FxcX\n43A4cDqdXl0vEvWKd1bvPLt3q3ZKFpGw48v3t6VzVlatWkVubm5VUAEYOHAgpaWlrFu3zuNrSktL\nKS4urvYQEaD0ILw1Fmb2BOcvR88rqIhIhLM0rBQWFpKRkVHtXPPmzUlMTKSwsNDja6ZMmYLD4ah6\nZGdnh6JUkfBWuAme6wdfvgxHnLB9hdUViYgEjM9hZfLkydhstjofa9eu9fr9bLaak1YNw/B4HmDS\npEk4nc6qx44dO3z9I4hED8OAtXNg9gWw5ztIaQ2j3oMzf2t1ZSIiAePzBNsJEyZw1VVX1XlN+/bt\nvXqvzMxMPvvss2rn9u3bR3l5eY0Rl0p2ux273e7V+4tEtSPF8N5tsOkN87jjRXD5LGiiieEiEl18\nDivp6emkp6cH5MPz8vJ46KGHKCgoICvL7F+yePFi7HY73bp1C8hniEStT/9hBhVbPFx4P+TdAnFq\nnSQi0SeoS5fz8/MpKioiPz8fl8vFhg0bAOjYsSNNmzZlwIABdO7cmZEjRzJt2jSKioq44447GDNm\njFb2iNTn/Dug8Cvzf2f3sLoaEZGgCerS5dGjR/Piiy/WOP/RRx/Rt29fwAw048aNq9EUzttbPVq6\nLDHjiBPWPAe9b9cIiohEPF++v0PSZyWYFFYkJvyyDt64DvZth/5/MUdTREQimC/f39ocRCScGQZ8\n9gws/gu4y6FZOzixn9VViYiElMKKSLg6VAQLJsC3C83jU4fCpTMguZmlZYmIhJrCikg4+mUdvDYK\nnDsgPhEGPgzdb4Ba+g+JiEQzhRWRcBSXAAd3QfMcGDEXWnexuiIREcsorIiEC1cFxP/vr2TWGfC7\nl6Ftd0jSxHERiW1a/ygSDn5aBTO6wS/rj57reIGCiogICisi1nK74ZO/w9zB5rLkjx62uiIRkbCj\n20AiVjm4G966EbYuNY/P+C0MftTamkREwpDCiogVtn0Cb94ABwuhUTIMng5dfq/VPiIiHiisiITa\njjXwz0vBcEPLU8zVPq1OtboqEZGwpbAiEmptzoaOF0KTVnDJVEhsYnVFIiJhTWFFJBS2r4CsLmBv\nam5C+NuXoJF3m3WKiMQ6rQYSCSZXBSx9EOYOgfeP2XxQQUVExGsaWREJluKd5iTanz41jxvZqzd+\nExERr+hfTZFg+P5Dc1nyob2Q2BSG/gNOv9LqqkREIpLCikggucrho4dgxWPmceYZ5mqfFh0sLUtE\nJJIprIgE0uF9sP5f5s/dx8CAByEhydqaREQinMKKSCA1bQVXPAdHiuG0YVZXIyISFRRWRBqiogz+\n+4C5O3JlOOnQ39KSRESijcKKiL/2bYc3roNf1oHdATnnQ+M0q6sSEYk6Cisi/vj6XXh7PJQ6IakZ\nDJupoCIiEiQKKyK+qCiFxX+BNbPM47bd4coXoFk7a+sSEYliCisi3io/Ai8MhIIN5nHPW+GC+yA+\nwdKyRESincKKiLcSkqBdHuzPh8ufgZMGWl2RiEhMUFgRqUv5ESg9AE1bmscXPQC9boXU1tbWJSIS\nQ7SRoUht9vwAsy+E10aae/qAub+PgoqISEgprIh4svE1mHU+/PoV7Pke9m2zuiIRkZil20Aixyo7\nBB/cCV/8r2V++/PgitmQkmltXSIiMUxhRaTSrm/g9dGw+2vABn3ugj53Qly81ZWJiMQ0hRURAMOA\nBePMoNI0A4Y/Byf2sboqERFBc1ZETDYbXPY0nHwJjF2hoCIiEkYUViR2/boZ1v/z6HGrU+B3L5s7\nJ4uISNjQbSCJPYYB61+ED+4CVzm06AQn5FldlYiI1EJhRWJL6QF4dyJsesM87nABpHeytCQREamb\nworEjoKN5mqfoq1gizf39el5K8TpbqiISDhTWJHYsG4uvH8nuEohta25U3K7c6yuSkREvKCwIrHB\nVW4GlZMvgcuegsZpVlckIiJeUliR6OUqh/gE8+fuN0BqGzh5kLlMWUREIoZu1kv0MQxY/QzM7AVH\nnOY5mw1OuURBRUQkAimsSHQ5vA9evQYW3QV7voUvXrK6IhERaSDdBpLo8fNaeP1acOZDfCIMeAh6\njLG6KhERaSCFFYl8bjesfgo+nAzuCmieAyPmQusuFhcmIiKBoLAike+Tv8NHD5o/nzYchv4DklKt\nrUlERAJGc1Yk8p19rTmaMuQxs3+KgoqISFTRyIpEHrcbvl8MJ19sHjdJh/FroFGitXWJiEhQaGRF\nIsvB3fDvK+Dl38KGl4+eV1AREYlaGlmRyLHtE3jzBjhYCI2SAcPqikREJAQUViT8uV2wfDos+xsY\nbkg/2Vztk9HZ6spERCQEFFYkvB34FeaPgW3LzOMu18AlUyGxibV1iYhIyCisSHjbtdkMKglNYMij\ncOZVVlckIiIhprAi4a1Df7hkOuT0gZYnWV2NiIhYQKuBJLwU74SXfwdF246e6zFGQUVEJIZpZEXC\nx/cfwls3wqG9UHYQRr1rdUUiIhIGFFbEeq5yWPogfPq4eZx5Ogx53MqKREQkjATtNtD27du5/vrr\nycnJITk5mQ4dOnD//fdTVlZW7br8/HyGDh1KkyZNSE9P59Zbb61xjUQx588wd8jRoNL9Brj+Q2jR\nwdKyREQkfARtZOWbb77B7XYza9YsOnbsyKZNmxgzZgwlJSVMnz4dAJfLxeDBg2nZsiUrVqxg7969\njBo1CsMwePLJJ4NVmoSLwk3w4hA4vA/sqXDpE3Da5VZXJSIiYcZmGEbI2oBOmzaNmTNn8uOPPwLw\nwQcfMGTIEHbs2EHr1q0BeOWVVxg9ejS7du0iNbX+DemKi4txOBw4nU6vrpcwUlEKzw8Amw2unANp\nOVZXJCIiIeLL93dI56w4nU7S0tKqjletWkVubm5VUAEYOHAgpaWlrFu3jn79+tV4j9LSUkpLS6uO\ni4uLg1u0BJbzF2iaAfGNoJEdrn4VkpubP4uIiHgQsqXLW7du5cknn2Ts2LFV5woLC8nIyKh2XfPm\nzUlMTKSwsNDj+0yZMgWHw1H1yM7ODmrdEkBfvwcz88y2+ZVSMhVURESkTj6HlcmTJ2Oz2ep8rF27\nttprdu7cycUXX8yIESO44YYbqj1ns9lqfIZhGB7PA0yaNAmn01n12LFjh69/BAm1ilL44C549fdw\nxAnblpsrgERERLzg822gCRMmcNVVdbc8b9++fdXPO3fupF+/fuTl5fHss89Wuy4zM5PPPvus2rl9\n+/ZRXl5eY8Slkt1ux27Xf4lHjKIf4fVroWCDedzzVrjgPohPsLQsERGJHD6HlfT0dNLT07269pdf\nfqFfv35069aNOXPmEBdXfSAnLy+Phx56iIKCArKysgBYvHgxdrudbt26+VqahJvNb8E7t0JpMSSn\nweXPwEkDra5KREQiTNBWA+3cuZM+ffrQrl07/vnPfxIfH1/1XGZmJmAuXe7SpQsZGRlMmzaNoqIi\nRo8ezbBhw7xeuqzVQGHqwK/wRBcoPwTt8uCK58HRxuqqREQkTITFaqDFixfzww8/8MMPP9C2bdtq\nz1Xmo/j4eBYuXMi4cePo1asXycnJXH311VV9WCSCpWSYGxDu/QH63Wuu/hEREfFDSPusBINGVsLI\nxtehWTa0O9fqSkREJMyFxciKxJCyQ7DoLlj/T0htA2NXQOO0+l8nIiLiBYUVaZjd38Lro2HXFsAG\nZ10DSQ6rqxIRkSiisCL+2zAPFv7JnETbpBUMfxY61Ow6LCIi0hAKK+K7ijJ494/w5TzzOKcPDH/O\nnFQrIiISYAor4rv4BCg7ALY46HsPnHc7xMXX/zoRERE/KKyIdwzDbJHfKNHcJfnSGXDueDghz+rK\nREQkyoVsI0OJYKUHYP6N8NaNZmgBSG6moCIiIiGhkRWpW8FGeONas7mbLR4Kv4KsM6yuSkREYojC\ninhmGLD2eVh0D7hKIbUtXPmCgoqIiIScworUdMRprvbZ/JZ5fNIgGPa0Gr2JiIglFFakOsOAl38H\nP30KcY3gwgcgb7w5qVZERMQCmmAr1dls5saDzXPguv9AzwkKKiIiYimNrAgc3geFmyDnPPO4fS+Y\n8LnZT0VERMRiGlmJdT+vhVnnw7zfwu7vjp5XUBERkTChsBKrDANWzoAXBsL+fGjaEiqOWF2ViIhI\nDboNFIsOFcHbN8N3i8zj0y6Hof/QbskiIhKWFFZiTf5n8MZ1UPwzxNvh4ilw9nWaRCsiImFLYSXW\nfPeBGVRadIQRcyHzdKsrEhERqZPCSqzpdy8kNIZzbwZ7itXViIiI1EsTbKPd9hXwyu+hosw8jk+A\nPncqqIiISMRQWIlWbhcsmwovDoVv3oNVM6yuSERExC+6DRSNDvwK88fAtmXmcZffwzk3WVuTiIiI\nnxRWos2PH8ObY6Bklzk3ZfCj0OV3VlclIiLiN4WVaLLuRXO3ZAxo1dlc7dPyZKurEhERaRCFlWjS\nvjckNoXc4TDoEUhItroiERGRBlNYiXRF2yAtx/y5RQcYvxocba2tSUREJIC0GihSuSrgw8nwZDfY\n+tHR8woqIiISZRRWIpHzZ5g7GFY8BobL7KUiIiISpXQbKNJ89x946yY4vA/sqeYGhLnDra5KREQk\naBRWIoWrHP77AKx80jzO6gIj5kDaiZaWJSIiEmwKK5Hiu/8cDSrn3AwXPQCN7NbWJCIiEgIKK5Hi\nlMHQ40bI6QOnDrG6GhERkZDRBNtwVVEKSx+CQ0Xmsc0Gl0xTUBERkZijkZVwVPQjvH4tFGyAXzfB\nVfPMsCIiIhKDFFbCzea34J1bobQYkptD11EKKiIiEtMUVsJF+RFYfC98Pts8zj4XrnxeTd5ERCTm\nKayEg/358MrVUPiVedz7duh3D8QnWFuXiIhIGFBYCQf2FDjihMYtYPiz0PFCqysSEREJGworVqko\nhfhEcz5KcnO46mVonAapra2uTEREJKxo6bIVdn8Lz/aFdXOPnsvMVVARERHxQGEl1Da8bAaVXVtg\nxaPmCIuIiIjUSreBQqWsBN7/M2z4t3mc0weGP6eW+SIiIvVQWAmFX7fA66Nhz7dgi4O+k+C8P0Fc\nvNWViYiIhD2FlWAr2QPPXwRlByElC66YDe17W12ViIhIxFBYCbYm6dDzVtjxmbksuUm61RWJiIhE\nFIWVYCjYCAmNIb2jeXz+HYAN4jSfWURExFf69gwkwzDb5c++EF4fBeWHzfNx8QoqIiIiftLISqAc\nccK7fzQ3IgRIbWMuS05ItrYuERGRCKewEgg7N5irffZtg7hGcOFkOHe8RlNEREQCQGGlIQwD1jxn\n7pbsKgNHO7jyBcjubnVlIiIiUUNhpSHcLvjqdTOonDwYhj1l7vMjIiIiAaOw0hDxjcyRlO8WQfcb\nzE0JRUREJKA0qcIXhgErZ8CHk4+ea5YNPcYoqIiIiASJRla8dagI3r7ZHEUBOHUotOlmbU0iIiIx\nIKgjK5deeint2rUjKSmJrKwsRo4cyc6dO6tdk5+fz9ChQ2nSpAnp6enceuutlJWVBbMs3+WvhmfO\nM4NKvB0G/x1ad7W6KhERkZgQ1LDSr18/XnvtNb799lvefPNNtm7dypVXXln1vMvlYvDgwZSUlLBi\nxQpeeeUV3nzzTf70pz8Fsyzvud3wyaMw5xIo/hnSOsANH2p+ioiISAjZDMMwQvVh77zzDsOGDaO0\ntJSEhAQ++OADhgwZwo4dO2jdujUAr7zyCqNHj2bXrl2kpqbW+57FxcU4HA6cTqdX1/vk9dFHm7yd\nPgKGPAb2lMB+hoiISAzy5fs7ZBNsi4qK+Pe//03Pnj1JSEgAYNWqVeTm5lYFFYCBAwdSWlrKunXr\nQlVa7U4ZAo2SYOgTMPw5BRURERELBD2s3HXXXTRp0oQWLVqQn5/PggULqp4rLCwkIyOj2vXNmzcn\nMTGRwsJCj+9XWlpKcXFxtUfQnH4l3PoFdBul2z4iIiIW8TmsTJ48GZvNVudj7dq1Vdf/+c9/5osv\nvmDx4sXEx8fzhz/8gWPvPNk8hADDMDyeB5gyZQoOh6PqkZ2d7esfwTepreu/RkRERILG5zkre/bs\nYc+ePXVe0759e5KSkmqc//nnn8nOzmblypXk5eVx3333sWDBAr788suqa/bt20daWhpLly6lX79+\nNd6jtLSU0tLSquPi4mKys7ODM2dFREREgsKXOSs+91lJT08nPT3dr8Iqc1Fl2MjLy+Ohhx6ioKCA\nrKwsABYvXozdbqdbN889TOx2O3a73a/PFxERkcgTtKZwa9asYc2aNfTu3ZvmzZvz448/ct9999Gh\nQwfy8vIAGDBgAJ07d2bkyJFMmzaNoqIi7rjjDsaMGaNREhEREQGCOME2OTmZ+fPnc8EFF3DyySdz\n3XXXkZuby7Jly6pGRuLj41m4cCFJSUn06tWL3/zmNwwbNozp06cHqywRERGJMCHtsxIMQe2zIiIi\nIkERln1WRERERPyhsCIiIiJhTWFFREREwprCioiIiIQ1hRUREREJaworIiIiEtYUVkRERCSsKayI\niIhIWAtau/1QqexpV1xcbHElIiIi4q3K721vetNGfFg5cOAAANnZ2RZXIiIiIr46cOAADoejzmsi\nvt2+2+1m586dpKSkYLPZrC7HEsXFxWRnZ7Njxw5tORAE+v0Gj363waXfb/Dod9twhmFw4MABWrdu\nTVxc3bNSIn5kJS4ujrZt21pdRlhITU3VX5og0u83ePS7DS79foNHv9uGqW9EpZIm2IqIiEhYU1gR\nERGRsKawEgXsdjv3338/drvd6lKikn6/waPfbXDp9xs8+t2GVsRPsBUREZHoppEVERERCWsKKyIi\nIhLWFFZEREQkrCmsiIiISFhTWIki27dv5/rrrycnJ4fk5GQ6dOjA/fffT1lZmdWlRY2HHnqInj17\n0rhxY5o1a2Z1ORHv6aefJicnh6SkJLp168Ynn3xidUlRYfny5QwdOpTWrVtjs9l4++23rS4pakyZ\nMoXu3buTkpJCq1atGDZsGN9++63VZUU9hZUo8s033+B2u5k1axabN2/mscce45lnnuGee+6xurSo\nUVZWxogRI7j55putLiXivfrqq0ycOJF7772XL774gvPOO49BgwaRn59vdWkRr6SkhDPPPJMZM2ZY\nXUrUWbZsGePHj2f16tUsWbKEiooKBgwYQElJidWlRTUtXY5y06ZNY+bMmfz4449WlxJV5s6dy8SJ\nE9m/f7/VpUSsc845h65duzJz5syqc6eeeirDhg1jypQpFlYWXWw2G2+99RbDhg2zupSotHv3blq1\nasWyZcs4//zzrS4namlkJco5nU7S0tKsLkOkmrKyMtatW8eAAQOqnR8wYAArV660qCoR3zmdTgD9\nOxtkCitRbOvWrTz55JOMHTvW6lJEqtmzZw8ul4uMjIxq5zMyMigsLLSoKhHfGIbB7bffTu/evcnN\nzbW6nKimsBIBJk+ejM1mq/Oxdu3aaq/ZuXMnF198MSNGjOCGG26wqPLI4M/vVwLDZrNVOzYMo8Y5\nkXA1YcIENm7cyMsvv2x1KVGvkdUFSP0mTJjAVVddVec17du3r/p5586d9OvXj7y8PJ599tkgVxf5\nfP39SsOlp6cTHx9fYxRl165dNUZbRMLRLbfcwjvvvMPy5ctp27at1eVEPYWVCJCenk56erpX1/7y\nyy/069ePbt26MWfOHOLiNHhWH19+vxIYiYmJdOvWjSVLlnD55ZdXnV+yZAmXXXaZhZWJ1M0wDG65\n5RbeeustPv74Y3JycqwuKSYorESRnTt30rdvX9q1a8f06dPZvXt31XOZmZkWVhY98vPzKSoqIj8/\nH5fLxYYNGwDo2LEjTZs2tba4CHP77bczcuRIzj777KpRwPz8fM2xCoCDBw/yww8/VB1v27aNDRs2\nkJaWRrt27SysLPKNHz+eefPmsWDBAlJSUqpGBx0OB8nJyRZXF8UMiRpz5swxAI8PCYxRo0Z5/P1+\n9NFHVpcWkZ566injhBNOMBITE42uXbsay5Yts7qkqPDRRx95/P/TUaNGWV1axKvt39g5c+ZYXVpU\nU58VERERCWua0CAiIiJhTWFFREREwprCioiIiIQ1hRUREREJaworIiIiEtYUVkRERCSsKayIiIhI\nWFNYERERkbCmsCIiIiJhTWFFREREwprCioiIiIQ1hRUREREJa/8fUYcY72NbhHMAAAAASUVORK5C\nYII=\n" } } ], "source": [ "plt.plot(x,y,'o')\n", "plt.plot(x,yhat.data,'--') # 그림을 그리기 위해서 yhat의 미분꼬리표를 제거" ], "id": "76e517a4-2268-49e9-aa9e-a805984ddd7f" }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 9. `#2단계`실습 – 업데이트 (다음시간이어서..)\n", "\n", "> 2단계 = 업데이트 = 최초의 점선에 대한 ‘적당한 정도’를 판단하고 더\n", "> ’적당한’ 점선으로 업데이트 한다.\n", "\n", "## A. 손실함수\n", "\n", "`-` ’적당한 정도’를 판단하기 위한 장치: loss function 도입!\n", "\n", "$loss=\\sum_{i=1}^{n}(y_i-\\hat{y}_i)^2=\\sum_{i=1}^{n}(y_i-(\\hat{w}_0+\\hat{w}_1x_i))^2$\n", "\n", "$=({\\bf y}-{\\bf\\hat{y}})^\\top({\\bf y}-{\\bf\\hat{y}})=({\\bf y}-{\\bf X}{\\bf \\hat{W}})^\\top({\\bf y}-{\\bf X}{\\bf \\hat{W}})$\n", "\n", "`-` loss 함수의 특징\n", "\n", "- $y_i \\approx \\hat{y}_i$ 일수록 loss값이 작다.\n", "- $y_i \\approx \\hat{y}_i$ 이 되도록 $(\\hat{w}_0,\\hat{w}_1)$을 잘\n", " 찍으면 loss값이 작다.\n", "- (중요) 주황색 점선이 ‘적당할 수록’ loss값이 작다." ], "id": "f3491c06-1db4-4d0e-bdf9-ee2ecb4107c9" }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "loss = torch.sum((y-yhat)**2)\n", "loss" ], "id": "cf438ac1-92d4-4a97-a950-329e6556a91f" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`-` 우리의 목표: 이 loss(=8587.6875)을 더 줄이자.\n", "\n", "- 궁극적으로는 아예 모든 조합 $(\\hat{w}_0,\\hat{w}_1)$에 대하여 가장\n", " 작은 loss를 찾으면 좋겠다. (단계2에서 할일은 아님)\n", "\n", "`-` 문제의 치환: 생각해보니까 우리의 문제는 아래와 같이 수학적으로\n", "단순화 되었다.\n", "\n", "- 적당해보이는 주황색 선을 찾자 $\\to$ $loss(w_0,w_1)$를 최소로하는\n", " $(w_0,w_1)$의 값을 찾자.\n", "\n", "`-` 수정된 목표: $loss(w_0,w_1)$를 최소로 하는 $(w_0,w_1)$을 구하라.\n", "\n", "- 단순한 수학문제가 되었다. 이것은 마치 $f(x,y)$를 최소화하는\n", " $(x,y)$를 찾으라는 것임.\n", "- 함수의 최대값 혹은 최소값을 컴퓨터를 이용하여 찾는것을 “최적화”라고\n", " 하며 이는 산공교수님들이 가장 잘하는 분야임. (산공교수님들에게\n", " 부탁하면 잘해줌, 산공교수님들은 보통 최적화해서 어디에 쓸지보다\n", " 최적화 자체에 더 관심을 가지고 연구하심)\n", "- 최적화를 하는 방법? 경사하강법\n", "\n", "## B. 경사하강법\n", "\n", "`-` 경사하강법 아이디어 (1차원)\n", "\n", "1. 임의의 점을 찍는다.\n", "2. 그 점에서 순간기울기를 구한다. (접선) \\<– 미분\n", "3. 순간기울기(=미분계수)의 부호를 살펴보고 부호와 반대방향으로\n", " 움직인다.\n", "\n", "> 팁: 기울기의 절대값 크기와 비례하여 보폭(=움직이는 정도)을 조절한다.\n", "> $\\to$ $\\alpha$를 도입\n", "\n", "> 최종수식:\n", "> $w \\leftarrow w - \\alpha \\times \\frac{\\partial}{\\partial w}loss(w)$\n", "\n", "`-` 경사하강법 아이디어 (2차원)\n", "\n", "1. 임의의 점을 찍는다.\n", "2. 그 점에서 순간기울기를 구한다. (접평면) \\<– 편미분\n", "3. 순간기울기(=미분계수)의 부호를 살펴보고 부호와 반대방향으로 각각\n", " 움직인다.\n", "\n", "> 팁: 여기서도 기울기의 절대값 크기와 비례하여 보폭(=움직이는 정도)을\n", "> 각각 조절한다. $\\to$ $\\alpha$를 도입.\n", "\n", "`-` 경사하강법 = **loss를 줄이도록 ${\\bf W}$를 개선하는 방법**\n", "\n", "- 업데이트 공식: 수정값 = 원래값 - $\\alpha$ $\\times$\n", " 기울어진크기(=미분계수)\n", "- 여기에서 $\\alpha$는 전체적인 보폭의 크기를 결정한다. 즉 $\\alpha$값이\n", " 클수록 한번의 update에 움직이는 양이 크다.\n", "\n", "# 10. HW\n", "\n", "없음" ], "id": "4994e4b8-4aa9-40c9-b948-077e92932106" } ], "nbformat": 4, "nbformat_minor": 5, "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3 (ipykernel)", "language": "python" }, "language_info": { "name": "python", "codemirror_mode": { "name": "ipython", "version": "3" }, "file_extension": ".py", "mimetype": "text/x-python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.8" } } }