22574 lines
1.8 MiB
22574 lines
1.8 MiB
{
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"cells": [
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||
{
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"cell_type": "markdown",
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"metadata": {
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"id": "CvmiakWDcuyf"
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||
},
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"source": [
|
||
"# Лабораторная работа №4\n",
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"## по предмету \"Системы искусственного интеллекта\"\n",
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"\n",
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"Целью работы является введение и знакомство с нейронными сетями.\n"
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]
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||
},
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||
{
|
||
"cell_type": "markdown",
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||
"metadata": {
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||
"id": "YEZ0T1uwj34v"
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||
},
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"source": [
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||
"### Задание 1\n",
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"\n",
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"Загрузите набор данных MNIST.\n",
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"Проверьте баланс классов, нормализуйте данные."
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]
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},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 1,
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||
"metadata": {
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||
"id": "91NHysjQj26f"
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||
},
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"outputs": [],
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"source": [
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"from PIL import Image\n",
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"import os\n",
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"import numpy as np\n",
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"\n",
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"def load_dataset(path):\n",
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" if not os.path.isdir(path):\n",
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" raise Exception('Invalid path')\n",
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" print(f'Loading data from {path}')\n",
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" \n",
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" x = []\n",
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" y = []\n",
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"\n",
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" for dir in os.listdir(path):\n",
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" for image in os.listdir(os.path.join(path, dir)):\n",
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" image = os.path.join(path, dir, image)\n",
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" print(f'{image}')\n",
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" img = Image.open(image).convert(\"L\")\n",
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" x.append(list(img.getdata()))\n",
|
||
" y.append(dir)\n",
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||
"\n",
|
||
" return [x, np.array(y).astype(int)]"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 2,
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||
"metadata": {},
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"outputs": [
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||
{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loading data from ./MNIST Dataset JPG format/MNIST - JPG - training\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\58076.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\59887.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\54451.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\10529.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\52030.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\14984.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\55159.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\59668.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\10810.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\39927.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\25046.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\18382.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\45625.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\23944.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\40528.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\53154.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\55780.jpg\n",
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"./MNIST Dataset JPG format/MNIST - JPG - training\\9\\3355.jpg\n",
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||
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||
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||
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||
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||
"./MNIST Dataset JPG format/MNIST - JPG - training\\2\\44653.jpg\n",
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||
"./MNIST Dataset JPG format/MNIST - JPG - training\\6\\36504.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - training\\6\\42409.jpg\n"
|
||
]
|
||
},
|
||
{
|
||
"ename": "KeyboardInterrupt",
|
||
"evalue": "",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13644\\248954580.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0mx_train_raw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'./MNIST Dataset JPG format/MNIST - JPG - training'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13644\\1429168712.py\u001b[0m in \u001b[0;36mload_dataset\u001b[1;34m(path)\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{image}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"L\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetdata\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||
"\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(fp, mode, formats)\u001b[0m\n\u001b[0;32m 3090\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3091\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3092\u001b[1;33m \u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3093\u001b[0m \u001b[0mexclusive_fp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3094\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"[x_train_raw, y_train] = load_dataset('./MNIST Dataset JPG format/MNIST - JPG - training')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 85,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Loading data from ./MNIST Dataset JPG format/MNIST - JPG - testing\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\10.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1001.jpg\n",
|
||
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|
||
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|
||
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|
||
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||
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|
||
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||
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"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9544.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9553.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9554.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9562.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9571.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9580.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9587.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9610.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\962.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9620.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9638.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9642.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9659.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\966.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9668.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9676.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9692.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9701.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9713.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9723.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9733.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9740.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9741.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9767.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9769.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9784.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9785.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9808.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9825.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9829.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9843.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9856.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9890.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9891.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\99.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9901.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9918.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\992.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9924.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9932.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9945.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9973.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\999.jpg\n",
|
||
"./MNIST Dataset JPG format/MNIST - JPG - testing\\9\\9992.jpg\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"[x_test_raw, y_test] = load_dataset('./MNIST Dataset JPG format/MNIST - JPG - testing')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 86,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
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"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from collections import Counter\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"train_counts = Counter(y_train)\n",
|
||
"test_counts = Counter(y_test)\n",
|
||
"\n",
|
||
"classes = sorted(set(train_counts.keys()) | set(test_counts.keys()))\n",
|
||
"\n",
|
||
"train_vals = [train_counts.get(c, 0) for c in classes]\n",
|
||
"test_vals = [test_counts.get(c, 0) for c in classes]\n",
|
||
"\n",
|
||
"x = range(len(classes))\n",
|
||
"\n",
|
||
"plt.figure(figsize=(10,5))\n",
|
||
"plt.bar([i - 0.2 for i in x], train_vals, width=0.4, label=\"train\")\n",
|
||
"plt.bar([i + 0.2 for i in x], test_vals, width=0.4, label=\"test\")\n",
|
||
"\n",
|
||
"plt.xticks(x, classes, rotation=45)\n",
|
||
"plt.ylabel(\"Количество образцов\")\n",
|
||
"plt.title(\"Баланс классов в train и test\")\n",
|
||
"plt.legend()\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 87,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"x_train = np.array(x_train_raw, dtype=np.float32) / 255.0\n",
|
||
"x_test = np.array(x_test_raw, dtype=np.float32) / 255.0"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "hgqsngyck7xl"
|
||
},
|
||
"source": [
|
||
"### Задание 2\n",
|
||
"\n",
|
||
"Реализуйте однослойный перцептрон с применением алгоритма градиентного спуска для обучения."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"class LogisticRegression:\n",
|
||
" def __init__(self, input_size=784, output_size=10):\n",
|
||
" self.W = np.random.randn(input_size, output_size) * 0.01\n",
|
||
" self.b = np.zeros((1, output_size))\n",
|
||
" \n",
|
||
" def forward(self, X):\n",
|
||
" Z = np.dot(X, self.W) + self.b\n",
|
||
" return self.softmax(Z)\n",
|
||
" \n",
|
||
" def softmax(self, Z):\n",
|
||
" exp_Z = np.exp(Z - np.max(Z, axis=1, keepdims=True))\n",
|
||
" return exp_Z / np.sum(exp_Z, axis=1, keepdims=True)\n",
|
||
" \n",
|
||
" def compute_loss(self, y_true, y_pred):\n",
|
||
" m = y_true.shape[0]\n",
|
||
" loss = -np.sum(y_true * np.log(y_pred + 1e-8)) / m\n",
|
||
" return loss\n",
|
||
" \n",
|
||
" def train(self, X, Y, lr=0.1, epochs=10, batch_size=32):\n",
|
||
" X = np.array(X)\n",
|
||
" Y = np.array(Y).astype(int)\n",
|
||
" \n",
|
||
" for epoch in range(epochs):\n",
|
||
" total_loss = 0\n",
|
||
" num_batches = 0\n",
|
||
" \n",
|
||
" # Перемешиваем данные каждый эпох\n",
|
||
" indices = np.random.permutation(len(X))\n",
|
||
" X_shuffled = X[indices]\n",
|
||
" Y_shuffled = Y[indices]\n",
|
||
" \n",
|
||
" for i in range(0, len(X), batch_size):\n",
|
||
" end_idx = min(i + batch_size, len(X))\n",
|
||
" x_batch = X_shuffled[i:end_idx]\n",
|
||
" y_batch = Y_shuffled[i:end_idx]\n",
|
||
" \n",
|
||
" y_true = np.zeros((len(x_batch), 10))\n",
|
||
" y_true[np.arange(len(x_batch)), y_batch] = 1\n",
|
||
" \n",
|
||
" y_pred = self.forward(x_batch)\n",
|
||
" \n",
|
||
" loss = self.compute_loss(y_true, y_pred)\n",
|
||
" total_loss += loss\n",
|
||
" num_batches += 1\n",
|
||
" \n",
|
||
" dZ = y_pred - y_true\n",
|
||
" dW = np.dot(x_batch.T, dZ) / x_batch.shape[0]\n",
|
||
" db = np.sum(dZ, axis=0, keepdims=True) / x_batch.shape[0]\n",
|
||
" \n",
|
||
" self.W -= lr * dW\n",
|
||
" self.b -= lr * db\n",
|
||
" \n",
|
||
" avg_loss = total_loss / num_batches\n",
|
||
" print(f\"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}\")\n",
|
||
" \n",
|
||
" def predict(self, X):\n",
|
||
" if X.ndim == 1:\n",
|
||
" X = X.reshape(1, -1)\n",
|
||
" y_pred = self.forward(X)\n",
|
||
" return np.argmax(y_pred, axis=1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 89,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/10, Loss: 0.4154\n",
|
||
"Epoch 2/10, Loss: 0.3138\n",
|
||
"Epoch 3/10, Loss: 0.2968\n",
|
||
"Epoch 4/10, Loss: 0.2870\n",
|
||
"Epoch 5/10, Loss: 0.2811\n",
|
||
"Epoch 6/10, Loss: 0.2769\n",
|
||
"Epoch 7/10, Loss: 0.2732\n",
|
||
"Epoch 8/10, Loss: 0.2706\n",
|
||
"Epoch 9/10, Loss: 0.2680\n",
|
||
"Epoch 10/10, Loss: 0.2659\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"lr = LogisticRegression()\n",
|
||
"\n",
|
||
"lr.train(x_train, y_train)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 90,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0.9259\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"pred = lr.predict(x_test)\n",
|
||
"\n",
|
||
"ctr = 0\n",
|
||
"\n",
|
||
"for i, label in enumerate(y_test):\n",
|
||
" label = int(label)\n",
|
||
" if label == pred[i]:\n",
|
||
" ctr += 1\n",
|
||
"\n",
|
||
"print(ctr/len(y_test))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "og_HzsA2lHyX"
|
||
},
|
||
"source": [
|
||
"### Задание 3\n",
|
||
"\n",
|
||
"Изучите документацию библиотеки PyTorch, посмотрите, как создаются сверточные, полносвязные слои и слои пуллинга. Напишите свою нейронную сеть, поэксперементируйте со слоями. Обучите ее на основе набора данных MNIST."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 102,
|
||
"metadata": {
|
||
"id": "nwDuPoSHlKDP"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch\n",
|
||
"import torch.nn as nn\n",
|
||
"import torch.optim as optim\n",
|
||
"from torch.utils.data import DataLoader, TensorDataset\n",
|
||
"from sklearn.metrics import accuracy_score\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"class SingleLayerPerceptron(nn.Module):\n",
|
||
" def __init__(self, input_size, output_size):\n",
|
||
" super(SingleLayerPerceptron, self).__init__()\n",
|
||
" self.linear = nn.Linear(input_size, output_size)\n",
|
||
" \n",
|
||
" def forward(self, x):\n",
|
||
" return self.linear(x)\n",
|
||
"\n",
|
||
"def train_and_evaluate(model, x_train, y_train, x_test, y_test, epochs=20, lr=0.01):\n",
|
||
" train_dataset = TensorDataset(x_train, y_train)\n",
|
||
" test_dataset = TensorDataset(x_test, y_test)\n",
|
||
" \n",
|
||
" train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
|
||
" test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
|
||
" \n",
|
||
" # Оптимизатор и функция потерь\n",
|
||
" optimizer = optim.SGD(model.parameters(), lr=lr)\n",
|
||
" criterion = nn.CrossEntropyLoss()\n",
|
||
" \n",
|
||
" # Обучение\n",
|
||
" train_losses = []\n",
|
||
" for epoch in range(epochs):\n",
|
||
" model.train()\n",
|
||
" epoch_loss = 0\n",
|
||
" for batch_x, batch_y in train_loader:\n",
|
||
" optimizer.zero_grad()\n",
|
||
" outputs = model(batch_x)\n",
|
||
" loss = criterion(outputs, batch_y)\n",
|
||
" loss.backward()\n",
|
||
" optimizer.step()\n",
|
||
" epoch_loss += loss.item()\n",
|
||
" \n",
|
||
" train_losses.append(epoch_loss / len(train_loader))\n",
|
||
" \n",
|
||
" print(f'Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss/len(train_loader):.4f}')\n",
|
||
" \n",
|
||
" # Предсказания\n",
|
||
" def get_predictions(loader):\n",
|
||
" model.eval()\n",
|
||
" all_predictions = []\n",
|
||
" all_labels = []\n",
|
||
" with torch.no_grad():\n",
|
||
" for batch_x, batch_y in loader:\n",
|
||
" outputs = model(batch_x)\n",
|
||
" _, predicted = torch.max(outputs.data, 1)\n",
|
||
" all_predictions.extend(predicted.cpu().numpy())\n",
|
||
" all_labels.extend(batch_y.cpu().numpy())\n",
|
||
" return all_predictions, all_labels\n",
|
||
" \n",
|
||
" test_predictions, test_labels = get_predictions(test_loader)\n",
|
||
" \n",
|
||
" test_accuracy = accuracy_score(test_labels, test_predictions)\n",
|
||
" \n",
|
||
" print(f'Test Accuracy: {test_accuracy:.4f}')\n",
|
||
" \n",
|
||
" return train_losses, test_accuracy\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 103,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Однослойный персептрон:\n",
|
||
"Epoch [1/10], Loss: 0.9915\n",
|
||
"Epoch [2/10], Loss: 0.5592\n",
|
||
"Epoch [3/10], Loss: 0.4770\n",
|
||
"Epoch [4/10], Loss: 0.4371\n",
|
||
"Epoch [5/10], Loss: 0.4126\n",
|
||
"Epoch [6/10], Loss: 0.3950\n",
|
||
"Epoch [7/10], Loss: 0.3821\n",
|
||
"Epoch [8/10], Loss: 0.3719\n",
|
||
"Epoch [9/10], Loss: 0.3637\n",
|
||
"Epoch [10/10], Loss: 0.3565\n",
|
||
"Test Accuracy: 0.9093\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Loss')"
|
||
]
|
||
},
|
||
"execution_count": 103,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x400 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"x_train_tensor = torch.tensor(x_train, dtype=torch.float32)\n",
|
||
"y_train_tensor = torch.tensor(y_train, dtype=torch.long)\n",
|
||
"x_test_tensor = torch.tensor(x_test, dtype=torch.float32)\n",
|
||
"y_test_tensor = torch.tensor(y_test, dtype=torch.long)\n",
|
||
"\n",
|
||
"model = SingleLayerPerceptron(784, 10)\n",
|
||
"\n",
|
||
"print(\"Однослойный персептрон:\")\n",
|
||
"\n",
|
||
"# Обучение и оценка\n",
|
||
"train_losses, test_acc = train_and_evaluate(\n",
|
||
" model, x_train_tensor, y_train_tensor, x_test_tensor, y_test_tensor,\n",
|
||
" epochs=10, lr=0.01\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.figure(figsize=(10, 4))\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"plt.plot(train_losses)\n",
|
||
"plt.title('Функция потерь во время обучения')\n",
|
||
"plt.xlabel('Эпоха')\n",
|
||
"plt.ylabel('Loss')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 106,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"class SimplePerceptron(nn.Module):\n",
|
||
" def __init__(self, input_size, output_size):\n",
|
||
" super(SimplePerceptron, self).__init__()\n",
|
||
" \n",
|
||
" # Полносвязный слой (классический персептрон)\n",
|
||
" self.fc = nn.Linear(input_size, output_size)\n",
|
||
" \n",
|
||
" def forward(self, x):\n",
|
||
" x = x.view(x.size(0), -1)\n",
|
||
" \n",
|
||
" x = self.fc(x)\n",
|
||
" return x"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Персептрон со скрытым слоем:\n",
|
||
"Epoch [1/10], Loss: 0.9927\n",
|
||
"Epoch [2/10], Loss: 0.5579\n",
|
||
"Epoch [3/10], Loss: 0.4761\n",
|
||
"Epoch [4/10], Loss: 0.4363\n",
|
||
"Epoch [5/10], Loss: 0.4117\n",
|
||
"Epoch [6/10], Loss: 0.3945\n",
|
||
"Epoch [7/10], Loss: 0.3815\n",
|
||
"Epoch [8/10], Loss: 0.3714\n",
|
||
"Epoch [9/10], Loss: 0.3630\n",
|
||
"Epoch [10/10], Loss: 0.3563\n",
|
||
"Test Accuracy: 0.9091\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Loss')"
|
||
]
|
||
},
|
||
"execution_count": 107,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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jcfvtt8PLy6vaZe+44w7ExMRg27ZteOSRRxAQENBgdSkUCqxbtw49e/bEiy++iA8++MDq8QsXLuDYsWNWbUII6HQ6nDx5Eh06dLC0f/vttwgMDLT8lW3PlClTUF5ejhUrVjTY67jeHXfcgWHDhuHFF1+EXq9H//798fvvv+OVV15Bjx49qj1mcSMPPfQQ1q1bhzvvvBPTpk1D7969oVAocO7cOWzbtg0jR47E6NGjLfOHhITgnnvuwauvvorg4GB89tlnSElJwdtvvw1XV1erdZu3sdFoREZGBr744gt07969RufRnDx5Evv27YPJZMKZM2ewePFi+Pj4IDw8HAAwY8YMrF27FnfddRfmz5+P8PBwfP/990hKSsJTTz1lOd/uRvLy8rBv3z4AVd1uH374IWQymc0xNT8/PwwZMgTz5s2Dm5sbkpKScOzYMWzYsMEyz/z585GSkoL4+HhMnToVUVFRKCsrw5kzZ5CcnIxly5ahTZs2NjWMHTsW4eHh6Nixo906s7OzMWnSJDzxxBO45557bvq6rlXT7RQfH49Ro0Zh1qxZ0Ol06Nu3Lw4cOIBXX30Vffr0wQMPPGC1Xi8vL4wfPx5Lly5FeHg4RowYUau6zGr7/quNtWvXomvXrpg8ebLdeUwmE8aNG4e2bdsiMTGxTs/T4CSJxFagpnsKZq+++qrdv5zqe56TEEIsW7ZMyGQykZycbLXOG03X/sVkPh/m+vNarv8LcP369UImk4ktW7ZYzdfQe05CVJ3n9OKLL4rw8HChUChEcHCweOqpp6zOc7rWjfachBCioqJCvPvuu5ZTA9zd3UXnzp3Fk08+KY4fP25T45dffim6dOkilEqlaNeunc1e3PXbWC6Xi+DgYPH3v//d5vdzvevPc3JychIBAQFixIgRllMRzM6ePSsefvhh4evrKxQKhYiKihILFiyo9XlOAISXl5fo16+f+PLLL63mA6rOc0pKShIdOnQQCoVCdO7c2WZPQoiqEZtTp04VERERQqFQCB8fHxEXFyfmzp1rOUfL3p7R9a/f/Lj51IGOHTvanOdVkz2n2mynsrIyMXv2bNG2bVvh7OwsQkNDxbPPPit0Ol216/3ll18EAPGvf/3rpjWYVdfjUNv33/Xs7TnJZDKxZ88eq/br95zeeustoVKpxO+//241n5R7Trx8kYPo1asXZDIZfvvtN6lLAVB1cPfVV1+t8YH71qRdu3aIjY3Fd999J3UpTUYmk2HKlClYvHix1KU4nOeeew5Lly5FVlaWzfFGqjt260lIr9fjzz//xHfffYe0tDR88803UpdkodForLr0iMjavn37kJGRgaSkJDz55JMMpgbGcJLQgQMHMHjwYPj6+uKVV17BqFGjpC7JYvTo0XXu4yZqDfr16wdXV1fcfffdeOONN6Qup8Vhtx4RETkcnoRLREQOh+FEREQOh+FEREQOp9UNiDCZTLhw4QI8PDx4G3UioiYmhEBRURFCQkJsLkB7rVYXThcuXLC6XhgRETW9rKysaq8WYtbqwsl8AcWsrCx4enpKXA0RUeui1+sRFhZmczHb67W6cDJ35Xl6ejKciIgkcrPDKhwQQUREDofhREREDofhREREDofhREREDkfScNqxYwdGjBiBkJAQyGQybNq06abLbN++HXFxcVCr1Wjfvj2WLVvW+IUSEVGTkjScSkpK0K1btxrfI+b06dO48847MXDgQBw8eBAvvfQSpk6diq+++qqRKyUioqYk6VDy4cOHY/jw4TWef9myZWjbti0WLVoEAIiOjkZqaireffdd3HfffY1UJRERNbVmdcxp7969SEhIsGobNmwYUlNTUVFRUe0yBoMBer3eaiIiIsfWrMIpOzsbgYGBVm2BgYGorKxEfn5+tcskJiZCo9FYJl66iIjI8TWrcAJszyo23yvR3tnGc+bMgU6ns0xZWVn1ev6TecXYdPA8zuSX1Gs9RERkX7MKp6CgIGRnZ1u15ebmwtnZGb6+vtUuo1KpLJcqaohLFv3rf8cwfWM6fj6WW6/1EBGRfc0qnPr164eUlBSrtq1bt6JXr15QKBRNUkN0cFW4HdHy2BURUWORNJyKi4uRnp6O9PR0AFVDxdPT05GZmQmgqktu/PjxlvknT56Ms2fPYubMmTh69ChWr16NVatWYdasWU1Wc8yVcDrKcCIiajSSDiVPTU3F4MGDLT/PnDkTAPDoo4/i448/hlartQQVAERERCA5ORkzZszAkiVLEBISgg8//LBJh5Gbw+l4TjEqjCYo5M1q55OIqFmQCfOIglZCr9dDo9FAp9PV6fiTySTQ7bWtKDJUYsv0gegcxNtuEBHVVE2/g/lnfy05OcmuHne6wK49IqLGwHCqg+jgqjs4MpyIiBoHw6kOYkKuDIrIZjgRETUGhlMdXNut18oO2RERNQmGUx10CvSA3EmGwssVyNaXSV0OEVGLw3CqA7VCjg7+bgB4vhMRUWNgONURR+wRETUehlMdXb1SRJHElRARtTwMpzoyj9jjNfaIiBoew6mOzN16ZwpKUGKolLgaIqKWheFUR37uKgR4qCAEcCybXXtERA2J4VQPvH0GEVHjYDjVg+VKEQwnIqIGxXCqhxgOJyciahQMp3owd+sdy9bDaOJljIiIGgrDqR4i/NygVjihrMKEMwUlUpdDRNRiMJzqQe4ks9xskF17REQNh+FUTxyxR0TU8BhO9cQRe0REDY/hVE8xvCsuEVGDYzjVU1SQJ2QyILfIgPxig9TlEBG1CAynenJXOaOdL+/tRETUkBhODSCaXXtERA2K4dQArt7bieFERNQQGE4NgMPJiYgaFsOpAZiHk5/MK0FZhVHiaoiImj+GUwMI8lTD21UBo0ngeE6x1OUQETV7DKcGIJPJruna00lcDRFR88dwaiBXB0XwrrhERPXFcGog5uNOHE5ORFR/DKcGEn3NcHIheG8nIqL6YDg1kA7+7lDKnVBkqMS5wlKpyyEiatYYTg1E6eyEyAB3AMBhdu0REdWL5OGUlJSEiIgIqNVqxMXFYefOnTecf8mSJYiOjoaLiwuioqKwdu3aJqr05nj7DCKihuEs5ZNv3LgR06dPR1JSEvr374+PPvoIw4cPx5EjR9C2bVub+ZcuXYo5c+ZgxYoVuPXWW7F//3488cQT8Pb2xogRIyR4BdZieKUIIqIGIRMSHr3v06cPevbsiaVLl1raoqOjMWrUKCQmJtrMHx8fj/79+2PBggWWtunTpyM1NRW7du2q0XPq9XpoNBrodDp4enrW/0VcY+/JAvx9xT6Eerlg9+whDbpuIqKWoKbfwZJ165WXlyMtLQ0JCQlW7QkJCdizZ0+1yxgMBqjVaqs2FxcX7N+/HxUVFXaX0ev1VlNjMe85nb9UCl1p9fUQEdHNSRZO+fn5MBqNCAwMtGoPDAxEdnZ2tcsMGzYMK1euRFpaGoQQSE1NxerVq1FRUYH8/Pxql0lMTIRGo7FMYWFhDf5azDSuCoR6uQDgcSciovqQfECETCaz+lkIYdNmNm/ePAwfPhx9+/aFQqHAyJEjMWHCBACAXC6vdpk5c+ZAp9NZpqysrAat/3qWyxhxxB4RUZ1JFk5+fn6Qy+U2e0m5ubk2e1NmLi4uWL16NS5fvowzZ84gMzMT7dq1g4eHB/z8/KpdRqVSwdPT02pqTByxR0RUf5KFk1KpRFxcHFJSUqzaU1JSEB8ff8NlFQoF2rRpA7lcjg0bNuDuu++Gk5PkO4EAgBjzXXEZTkREdSbpUPKZM2di3Lhx6NWrF/r164fly5cjMzMTkydPBlDVJXf+/HnLuUwZGRnYv38/+vTpg8LCQixcuBB//vknPvnkEylfhpWYYA0A4HhOMSqMJijkjhGaRETNiaThNGbMGBQUFGD+/PnQarWIjY1FcnIywsPDAQBarRaZmZmW+Y1GI9577z389ddfUCgUGDx4MPbs2YN27dpJ9ApstfF2gYfKGUWGSpzMK0bnoMbtRiQiaokkPc9JCo15npPZA8v24LczhVj4YDfc27NNozwHEVFz5PDnObVkMcEcFEFEVB8Mp0YQzcsYERHVC8OpEVwdTl7EezsREdUBw6kRdAr0gNxJhosl5cjRG6Quh4io2WE4NQK1Qo72fm4AgCNancTVEBE1PwynRnJt1x4REdUOw6mRxPAae0REdcZwaiQcsUdEVHcMp0ZiDqczBSUoMVRKXA0RUfPCcGok/h4q+HuoIARwLJvHnYiIaoPh1Ih4pQgiorphODUi84g9HnciIqodhlMj4l1xiYjqhuHUiMzden9lF8Fo4mWMiIhqiuHUiCL83KBWOKG0wogzBSVSl0NE1GwwnBqR3EmGqCB27RER1RbDqZFxxB4RUe0xnBpZTLAHAI7YIyKqDYZTI7t6AViGExFRTTGcGllUkCdkMiBHb0B+Me/tRERUEwynRuaucka4jysA7j0REdUUw6kJsGuPiKh2GE5NIJrDyYmIaoXh1AR4V1wiotphODUBczidyCtGWYVR4mqIiBwfw6kJBHmq4eWqgNEkcDynWOpyiIgcHsOpCchkMl4pgoioFhhOTcQcTrxSBBHRzTGcmgjv7UREVHMMpyZy7blOQvDeTkREN8JwaiId/N2hkMtQZKjEucJSqcshInJoDKcmonR2QscAXqGciKgmGE5NyNy1x+NOREQ3Jnk4JSUlISIiAmq1GnFxcdi5c+cN51+3bh26desGV1dXBAcH47HHHkNBQUETVVs/0RyxR0RUI5KG08aNGzF9+nTMnTsXBw8exMCBAzF8+HBkZmZWO/+uXbswfvx4TJw4EYcPH8YXX3yB3377DZMmTWriyuuG5zoREdWMpOG0cOFCTJw4EZMmTUJ0dDQWLVqEsLAwLF26tNr59+3bh3bt2mHq1KmIiIjAgAED8OSTTyI1NbWJK68bczidKyyFrrRC4mqIiByXZOFUXl6OtLQ0JCQkWLUnJCRgz5491S4THx+Pc+fOITk5GUII5OTk4Msvv8Rdd91l93kMBgP0er3VJBWNqwKhXi4AuPdERHQjkoVTfn4+jEYjAgMDrdoDAwORnZ1d7TLx8fFYt24dxowZA6VSiaCgIHh5eeHf//633edJTEyERqOxTGFhYQ36Omorml17REQ3JfmACJlMZvWzEMKmzezIkSOYOnUqXn75ZaSlpWHLli04ffo0Jk+ebHf9c+bMgU6ns0xZWVkNWn9txQRfGU7OEXtERHY5S/XEfn5+kMvlNntJubm5NntTZomJiejfvz+ef/55AEDXrl3h5uaGgQMH4o033kBwcLDNMiqVCiqVquFfQB1ZrhSRzXAiIrJHsj0npVKJuLg4pKSkWLWnpKQgPj6+2mUuX74MJyfrkuVyOQA0m0sCxQRrAAAZ2cWoMJokroaIyDFJ2q03c+ZMrFy5EqtXr8bRo0cxY8YMZGZmWrrp5syZg/Hjx1vmHzFiBL7++mssXboUp06dwu7duzF16lT07t0bISEhUr2MWmnj7QJ3lTPKjSaczOO9nYiIqiNZtx4AjBkzBgUFBZg/fz60Wi1iY2ORnJyM8PBwAIBWq7U652nChAkoKirC4sWL8dxzz8HLywtDhgzB22+/LdVLqDUnJxmigz3w25lCHNXq0TnIU+qSiIgcjkw0l/6wBqLX66HRaKDT6eDpKU0wvPztn1i79yyeGBiBuXfFSFIDEZEUavodLPlovdbo6pUiiiSuhIjIMTGcJGC5ACzv7UREVC2GkwQ6BXrASQZcLClHjt4gdTlERA6H4SQBtUKODv7uAHilCCKi6jCcJHJt1x4REVljOEnEcm8nXsaIiMgGw0kivLcTEZF9DCeJmPecTheU4HJ5pcTVEBE5FoaTRPw9VPD3UEEI4Fg2z3ciIroWw0lCMTzuRERULYaThCyDInjciYjICsNJQpZ7OzGciIisMJwkZL4r7jFtEYwmXsaIiMiM4SShCD93qBVOKK0w4kxBidTlEBE5DIaThOROMkQFsWuPiOh6DCeJmbv2OGKPiOgqhpPEeKUIIiJbDCeJ8QKwRES2GE4SMx9zytEbUFDMezsREQEMJ8m5q5zRztcVAG/bTkRkxnByAFevFKGTuBIiIsfAcHIAVwdFcM+JiAhgODkEy6AIDicnIgLAcHII5m69E3nFKKswSlwNEZH0GE4OIFijhperAkaTwIncYqnLISKSHMPJAchkMkQHsWuPiMiM4eQgeDIuEdFVDCcHEcMbDxIRWTCcHET0NdfYE4L3diKi1o3h5CAiA9yhkMtQVFaJc4WlUpdDRCQphpODUDo7oWPAldtnsGuPiFo5hpMDsVzGiCP2iKiVYzg5EPOIPd7biYhaO8nDKSkpCREREVCr1YiLi8POnTvtzjthwgTIZDKbqUuXLk1YceOJDma3HhERIHE4bdy4EdOnT8fcuXNx8OBBDBw4EMOHD0dmZma183/wwQfQarWWKSsrCz4+PnjggQeauPLGYR5Ofq6wFLrSComrISKSjqThtHDhQkycOBGTJk1CdHQ0Fi1ahLCwMCxdurTa+TUaDYKCgixTamoqCgsL8dhjj9l9DoPBAL1ebzU5Ki9XJUK9XAAAx7j3REStmGThVF5ejrS0NCQkJFi1JyQkYM+ePTVax6pVq3D77bcjPDzc7jyJiYnQaDSWKSwsrF51NzZ27RER1TGcsrKycO7cOcvP+/fvx/Tp07F8+fIaryM/Px9GoxGBgYFW7YGBgcjOzr7p8lqtFv/73/8wadKkG843Z84c6HQ6y5SVlVXjGqUQE8xBEUREdQqnhx9+GNu2bQMAZGdn44477sD+/fvx0ksvYf78+bVal0wms/pZCGHTVp2PP/4YXl5eGDVq1A3nU6lU8PT0tJocGa+xR0RUx3D6888/0bt3bwDAf/7zH8TGxmLPnj1Yv349Pv744xqtw8/PD3K53GYvKTc312Zv6npCCKxevRrjxo2DUqmsy0twWOZznTKyi1FhNElcDRGRNOoUThUVFVCpVACAH3/8Effccw8AoHPnztBqtTVah1KpRFxcHFJSUqzaU1JSEB8ff8Nlt2/fjhMnTmDixIl1qN6xhXm7wl3ljHKjCafySqQuh4hIEnUKpy5dumDZsmXYuXMnUlJS8Le//Q0AcOHCBfj6+tZ4PTNnzsTKlSuxevVqHD16FDNmzEBmZiYmT54MoOp40fjx422WW7VqFfr06YPY2Ni6lO/QnJxk6BxkHhShk7gaIiJpONdlobfffhujR4/GggUL8Oijj6Jbt24AgM2bN1u6+2pizJgxKCgowPz586HVahEbG4vk5GTL6DutVmtzzpNOp8NXX32FDz74oC6lNwsxIZ5IPVuIo9oijO4hdTVERE1PJup4fwaj0Qi9Xg9vb29L25kzZ+Dq6oqAgIAGK7Ch6fV6aDQa6HQ6hx0csWF/JmZ//QcGRPrhs0l9pC6HiKjB1PQ7uE7deqWlpTAYDJZgOnv2LBYtWoS//vrLoYOpuYi+5saDvLcTEbVGdQqnkSNHYu3atQCAS5cuoU+fPnjvvfcwatQou1d3oJqLCvKAkwy4WFKO3CKD1OUQETW5OoXTgQMHMHDgQADAl19+icDAQJw9exZr167Fhx9+2KAFtkZqhRzt/d0B8PYZRNQ61SmcLl++DA+PqhFlW7duxb333gsnJyf07dsXZ8+ebdACW6uYYJ6MS0StV53CKTIyEps2bUJWVhZ++OEHy/XxcnNzHXaQQXPDK0UQUWtWp3B6+eWXMWvWLLRr1w69e/dGv379AFTtRfXowbHPDcE8KOIou/WIqBWq03lO999/PwYMGACtVms5xwkAhg4ditGjRzdYca2ZuVvvdEEJLpdXwlVZp18VEVGzVOdvPPM9lc6dOweZTIbQ0NBanYBLN+bvoYK/hwp5RQYcyy5Cz7beN1+IiKiFqFO3nslkwvz586HRaBAeHo62bdvCy8sLr7/+OkwmXqy0oVjOd2LXHhG1MnXac5o7dy5WrVqFf/3rX+jfvz+EENi9ezdeffVVlJWV4c0332zoOlulmGBP7MjI472diKjVqVM4ffLJJ1i5cqXlauQA0K1bN4SGhuLpp59mODUQ3hWXiFqrOnXrXbx4EZ07d7Zp79y5My5evFjvoqhKlyvDyY9pi2A08TJGRNR61CmcunXrhsWLF9u0L168GF27dq13UVQlws8daoUTSiuMOFvAezsRUetRp269d955B3fddRd+/PFH9OvXDzKZDHv27EFWVhaSk5MbusZWS+4kQ1SgBw6d0+GIVm+5pBERUUtXpz2nQYMGISMjA6NHj8alS5dw8eJF3HvvvTh8+DDWrFnT0DW2auYrRXBQBBG1JnU+zykkJMRm4MOhQ4fwySefYPXq1fUujKrEcDg5EbVCddpzoqYTzQvAElErxHBycJ2vhFOO3oCCYt7biYhaB4aTg3NXOSPc1xUAcFRbJHE1RERNo1bHnO69994bPn7p0qX61EJ2xAR74mzBZRzV6jGgo5/U5RARNbpahZNGo7np4+PHj69XQWQrJtgT//szm8ediKjVqFU4cZi4NHgBWCJqbXjMqRkwn+t0Mq8YZRVGiashImp8DKdmIFijhsZFgUqTwIncYqnLISJqdAynZkAmk/FkXCJqVRhOzYS5a4+DIoioNWA4NRO8UgQRtSYMp2bC3K13VKuHELy3ExG1bAynZiIywB0KuQxFZZU4V1gqdTlERI2K4dRMKJ2dEBnA27YTUevAcGpGru3aIyJqyRhOzUh08JU9Jw4nJ6IWjuHUjHA4ORG1FpKHU1JSEiIiIqBWqxEXF4edO3fecH6DwYC5c+ciPDwcKpUKHTp0aDV33jV3650rLIWutELiaoiIGk+db9PeEDZu3Ijp06cjKSkJ/fv3x0cffYThw4fjyJEjaNu2bbXLPPjgg8jJycGqVasQGRmJ3NxcVFZWNnHl0vByVSJEo8YFXRmOafXo095X6pKIiBqFpOG0cOFCTJw4EZMmTQIALFq0CD/88AOWLl2KxMREm/m3bNmC7du349SpU/Dx8QEAtGvX7obPYTAYYDBcvYOsXt+8u8RiQjxxQVeGowwnImrBJOvWKy8vR1paGhISEqzaExISsGfPnmqX2bx5M3r16oV33nkHoaGh6NSpE2bNmoXSUvvn/SQmJkKj0VimsLCwBn0dTS2GV4ogolZAsj2n/Px8GI1GBAYGWrUHBgYiOzu72mVOnTqFXbt2Qa1W45tvvkF+fj6efvppXLx40e5xpzlz5mDmzJmWn/V6fbMOKF7GiIhaA0m79YCqK25fSwhh02ZmMpkgk8mwbt06y115Fy5ciPvvvx9LliyBi4uLzTIqlQoqlarhC5eIecReRk4xKowmKOSSj2khImpwkn2z+fn5QS6X2+wl5ebm2uxNmQUHByM0NNTqdvHR0dEQQuDcuXONWq+jCPN2hZtSjvJKE07llUhdDhFRo5AsnJRKJeLi4pCSkmLVnpKSgvj4+GqX6d+/Py5cuIDi4qs33MvIyICTkxPatGnTqPU6CicnmaVrj1eKIKKWStI+oZkzZ2LlypVYvXo1jh49ihkzZiAzMxOTJ08GUHW8aPz48Zb5H374Yfj6+uKxxx7DkSNHsGPHDjz//PN4/PHHq+3Sa6l4Mi4RtXSSHnMaM2YMCgoKMH/+fGi1WsTGxiI5ORnh4eEAAK1Wi8zMTMv87u7uSElJwbPPPotevXrB19cXDz74IN544w2pXoIkonlXXCJq4WSild0cSK/XQ6PRQKfTwdPTU+py6uRQ1iWMXLIbvm5KpP7zdrsDSIiIHE1Nv4M51KsZigrygJMMKCgpR26R4eYLEBE1MwynZkitkKO9vzsAdu0RUcvEcGqmeKUIImrJGE7NFK8UQUQtGcOpmTIPJ+e5TkTUEjGcmilzt97p/BJcLm8dtwwhotaD4dRM+Xuo4OeughDAsewiqcshImpQDKdmjF17RNRSMZyasehgDwAcTk5ELQ/DqRnjcHIiaqkYTs1Ylyvdese0RcjRl0lcDRFRw2E4NWMRfu5o7++G0goj/r58H3IZUETUQjCcmjG5kwyfPNYboV4uOJVfgr+v2IfcIgYUETV/DKdmLszHFZ8/0RchGjVO5pVg7IpfkV/Mi8ESUfPGcGoB2vq64vN/9EWQpxrHc4vx8Ip9KGBAEVEzxnBqIcJ93fD5P/oi0FOFjJxijF35Ky6WlEtdFhFRnTCcWpAIPzesf6Iv/D1UOJZdhLErf0UhA4qImiGGUwvTwd8dnz/RF37uKhzV6vHIql9x6TIDioiaF4ZTCxQZ4I7Pn+gDP3clDl/QY9yq/dBdrpC6LCKiGmM4tVAdAz2wblJf+Lgp8cd5Hcav/hW6UgYUETUPDKcWLCrIA+uf6ANvVwUOndPh0dX7UVTGgCIix8dwauE6B3li3aS+8HJVID3rEh5dvR/FBt7/iYgcG8OpFYgJ8cRnE/tA46LAgcxLmMCAIiIHx3BqJWJDNfhsYh94qp2RerYQj6/5DSUMKCJyUAynVuSWNhp8OrEPPNTO2H/mIh7/+Dfe4p2IHBLDqZXpFuaFtY/3hofKGb+evoiJH6eitNwodVlERFYYTq1Qj7be+Pjx3nBTyrH3VAGeWJuKsgoGFBE5DoZTKxUX7o1PHu8NV6Ucu07kM6CIyKEwnFqxXu188PFjVQG183g+nvw0jQFFRA6B4dTK9Y7wweoJt8JFIcf2jDw89VkaDJUMKCKSFsOJ0Le9L1ZN6AW1wgnb/srDlHUHUF5pkrosImrFGE4EAIjv4IdVj94KlbMTfjyaiynrGVBEJB2GE1n0j/TDivG9oHR2QsqRHDz7+QFUGBlQRNT0JA+npKQkREREQK1WIy4uDjt37rQ77y+//AKZTGYzHTt2rAkrbtlu6+SP5ePioJQ74YfDOZi24SADioianKThtHHjRkyfPh1z587FwYMHMXDgQAwfPhyZmZk3XO6vv/6CVqu1TB07dmyiiluH/4sKwEdXAir5j2xM35iOSgYUETUhScNp4cKFmDhxIiZNmoTo6GgsWrQIYWFhWLp06Q2XCwgIQFBQkGWSy+V25zUYDNDr9VYT3dzgzgFY+khPKOQyfP+7FjP/c4gBRURNRrJwKi8vR1paGhISEqzaExISsGfPnhsu26NHDwQHB2Po0KHYtm3bDedNTEyERqOxTGFhYfWuvbUYGh2IJQ/3hLOTDJsPXcCsLw7BaBJSl0VErYBk4ZSfnw+j0YjAwECr9sDAQGRnZ1e7THBwMJYvX46vvvoKX3/9NaKiojB06FDs2LHD7vPMmTMHOp3OMmVlZTXo62jpEroEYfGVgNqUfgHPf8mAIqLG5yx1ATKZzOpnIYRNm1lUVBSioqIsP/fr1w9ZWVl49913cdttt1W7jEqlgkqlariCW6G/xQbh33/vgWc+P4ivD5yHk0yGd+7rCien6n9PRET1Jdmek5+fH+Ryuc1eUm5urs3e1I307dsXx48fb+jy6DrDbwnGBw91h9xJhi/TzmHO13/AxD0oImokkoWTUqlEXFwcUlJSrNpTUlIQHx9f4/UcPHgQwcHBDV0eVePuriF4f0x3OMmAjalZmLvpTwYUETUKSbv1Zs6ciXHjxqFXr17o168fli9fjszMTEyePBlA1fGi8+fPY+3atQCARYsWoV27dujSpQvKy8vx2Wef4auvvsJXX30l5ctoVe7pFgKTSWDmf9Lx+f5MyJ2A10fG2u2KJSKqC0nDacyYMSgoKMD8+fOh1WoRGxuL5ORkhIeHAwC0Wq3VOU/l5eWYNWsWzp8/DxcXF3Tp0gXff/897rzzTqleQqs0qkcoTELguS8O4bN9mXCSyfDaPV0YUETUYGRCiFbVL6PX66HRaKDT6eDp6Sl1Oc3aF6lZeOGr3yEEMCG+HV4ZEcOAIqIbqul3sOSXL6Lm64FeYXj73q4AgI/3nMEb3x9FK/tbh4gaCcOJ6uXBW8OQeO8tAIBVu04j8X/HGFBEVG8MJ6q3v/duizdGxQIAlu84hUmfpOL3c5ekLYqImjXJT8KlluGRvuEQQuCVzYfx07Fc/HQsF4Oj/PHs0I7o2dZb6vKIqJnhgAhqUCdyi5G07QQ2pZ+H+RSogR39MHVoR9zazkfa4ohIcjX9DmY4UaM4k1+CJdtO4JuD51F5JaX6tvfB1KEd0a+9L0f1EbVSDCc7GE5NK+viZST9chJfpmWhwlj1Vru1nTemDu2IAZF+DCmiVobhZAfDSRrnL5Xio+0nsWF/Fsqv3Beqe5gXpg3tiP+L8mdIEbUSDCc7GE7SytaV4aMdJ7H+10wYKqtC6pZQDZ4dEok7YgIZUkQtHMPJDoaTY8gtKsPKnafx6d6zKK0wAgCigz0xdUgkhnUJ4u04iFoohpMdDCfHUlBswKpdp/HJnjMoKa8KqU6B7nhmSEfcdUsw5AwpohaF4WQHw8kxXbpcjtW7TmPNnjMoKqsEALT3d8OzQyIxomsInOU8X5yoJWA42cFwcmy60gp8sucMVu06DV1pBQAg3NcVUwZHYnSPUCgYUkTNGsPJDoZT81BUVoFP953Fyp2ncbGkHADQxtsFT/9fJO6PawOlM0OKqDliONnBcGpeSgyVWPfrWSzfcQr5xVUhFaJR46n/64AHeoVBrZBLXCER1QbDyQ6GU/NUWm7E5/szsWz7SeQWGQAAgZ4qPHlbBzzcpy1DiqiZYDjZwXBq3soqjPgiNQtJv5yEVlcGAPBzV+HJ29pjbN+2cFXyWsZEjozhZAfDqWUwVBrxVdp5JP1yAucKSwEAPm5KTBoYgfH92sFdxZAickQMJzsYTi1LhdGEbw6ex5JtJ3C24DIAwMtVgcf7R+DR+HbQuCgkrpCIrsVwsoPh1DJVGk347+8X8O+fT+BUXgkAwEPtjHF9w3HnLcHoEuLJSyMROQCGkx0Mp5bNaBL4/g8tFv98HBk5xZb2YI0at0cH4vaYQPRt7wOVMwdQEEmB4WQHw6l1MJkEth7JxjcHz2NHRr7l+n0A4KaU47ZO/rg9OhCDOwfAx00pYaVErQvDyQ6GU+tTVmHE3pMFSDmagx+P5FiGogOAkwzoFe6D22MCcHt0INr7u0tYKVHLx3Cyg+HUuplMAn9e0OHHIzlIOZqLo1q91ePt/d1wx5Xuv55tvXnhWaIGxnCyg+FE1zpXeBk/Hc3Fj0dzsO9UgeVuvQDg7arAkM6BuCMmAAM7+sONw9OJ6o3hZAfDiezRl1VgR0YefjySg21/5VkuPAsASrkT+nXwxe0xgbg9OgDBGhcJKyVqvhhOdjCcqCYqjSakni280v2XYzmHyiw21LNq9F90IIepE9UCw8kOhhPVlhACJ/OKkXKkqvvvQGYhrv3UcJg6Uc0xnOxgOFF95Rcb8POxXPx4JAc7j9sOUx8U5Y+hnTlMnag6DCc7GE7UkDhMnah2GE52MJyosdRkmHp8B190D/NG9zAN2vu5w4lD1amVYTjZwXCipnKjYepA1bX/urXxQrcwzZXA8oK/h0qiaomaBsPJDoYTSUFfVoHdx/NxILMQ6VmX8Md5HcoqTDbzhXq5oHvY1cC6JVQDFyUHWFDL0WzCKSkpCQsWLIBWq0WXLl2waNEiDBw48KbL7d69G4MGDUJsbCzS09Nr/HwMJ3IElUYT/sopQnrWJRzKuoT0rEs4nluM6z+NcicZOgV6oHuYF7pfCazIAHdeuYKarWYRThs3bsS4ceOQlJSE/v3746OPPsLKlStx5MgRtG3b1u5yOp0OPXv2RGRkJHJychhO1CIUlVXgj/M6pGddQnpmVWBdO8DCzE0pxy1trnYFdg/zQpBGLUHFRLXXLMKpT58+6NmzJ5YuXWppi46OxqhRo5CYmGh3uYceeggdO3aEXC7Hpk2bbhhOBoMBBsPVD7her0dYWBjDiZoFra4Uh7Iu4eCVwPrjvA6Xy4028wV5qq90B1aF1S1tNLwbMDmkmoaTZO/e8vJypKWlYfbs2VbtCQkJ2LNnj93l1qxZg5MnT+Kzzz7DG2+8cdPnSUxMxGuvvVbveomkEKxxQbDGBX+LDQZQdb+q47lFSM+8hEPnLuFg5iVk5BQhW1+GLYezseVwNoCqYewdAzysAqtToDuc5U5SvhyiGpMsnPLz82E0GhEYGGjVHhgYiOzs7GqXOX78OGbPno2dO3fC2blmpc+ZMwczZ860/GzecyJqjuROMnQO8kTnIE881Luq67vEUIk/zd2BV45hXdCV4a+cIvyVU4SNqVkAABeFHLeEatC9rRe6tfFC1zYatPF24aWXyCFJvt9//QdDCFHth8VoNOLhhx/Ga6+9hk6dOtV4/SqVCioVh+dSy+Wmckaf9r7o097X0parL7OEVXrWJfx+TodiQyX2n7mI/WcuWuZzVcrRwd8dkQFVk/n/4b6uUHAviyQkWTj5+flBLpfb7CXl5uba7E0BQFFREVJTU3Hw4EE888wzAACTyQQhBJydnbF161YMGTKkSWoncnQBnmokdAlCQpcgAFUnCJ/MK8bBa0YH/pVdhMvlRvxxXoc/zuusllfIZQj3dUPkNcEVGeCO9v5ucFVK/jcttQKSvcuUSiXi4uKQkpKC0aNHW9pTUlIwcuRIm/k9PT3xxx9/WLUlJSXh559/xpdffomIiIhGr5mouXJykqFjoAc6BnrgwV5V3doVRhPOFpTgRG7x1SmvGCdzS1BaYbS04bD1ukK9XKwCKzLAHZH+7vDmdQSpAUn6J9DMmTMxbtw49OrVC/369cPy5cuRmZmJyZMnA6g6XnT+/HmsXbsWTk5OiI2NtVo+ICAAarXapp2Ibk4hd0JkgAciAzys2k0mgQu6Uks4ncy7Gl6Flytw/lIpzl8qxfaMPKvlfN2U6HBNWJmDK1ij5nEtqjVJw2nMmDEoKCjA/PnzodVqERsbi+TkZISHhwMAtFotMjMzpSyRqNVxcpKhjbcr2ni74v+iAqweKyg2WPawqoKrBCdzi3H+UikKSspRcPoi9p++aLWMm1JeFVr+7lfDK8Ad4T6uHD1Idkl+hYimxpNwiRpeiaESp/JKcCKvyKqb8GzBZVSaqv+KUchlaOfrZgmrdr5uaOPtgjY+rgjyVPMqGC2Uw5/nREQth5vKGbe00eCWNhqr9vJKEzIv2j+udTy3GMdzi23W5+wkQ7CXGm28XKsCy9sVod4uV/7vgiBPNfe6WjiGExE1GqVzzY9rZV68jHOFpbhwqRQVRoGsi6XIulha7XrlTjIEa9RXg8vLxfL/Nt4uCNYwvJo7dusRkUMxmgRyi8pwrrAU5wtLca6wKrTOXfn/+SvhdSNyJxmCPNXV7nWFebsiSKPmeVwSaRbX1pMCw4moeTOZBHKLDDh/yTq0zP8/X1iKcqPt7Uiu5SSrujSUJbS8ru51tfF2RbAXw6uxMJzsYDgRtWwmk0BescEmtM4VXq7aE7tUivLKG4eXTAb4uasQ6KlCkKcagVemIE81AjVqS7vGRcFh8rXEcLKD4UTUuplMAvklhmr3uswBZrhJeJmpnJ0soRVwJbCCNGoEXGkzt6sVvGGkGUfrERFVw8lJhgAPNQI81OjZ1tvmcSEE8ovLkaMvQ46+DNn6MuToypCjN1T9/8pUeLkChkoTMi9eRubFyzd8Ti9XBQI9qva6gjxV1ntinmoEalTwc1PBicPnLRhORETXkMlk8PdQwd9DhdhQjd35yiqMyCuqCqxs3dXQujbEsnVlMFSacOlyBS5drsBfOUV21+fsVPW8VcGlutqF6FG19+XrpoKvuxLerkoonVv+8TCGExFRHagVcoT5uCLMx9XuPEII6Esrr4aVvgy5V/7N1hmQW1QVYPnFBlSaBLS6Mmh1ZTd9bk+1M3zdVfB1U8LHTQlfdyV83VRW/6/6VwlvN2WzHNzBcCIiaiQymQwaVwU0rgpEBXnYna/SaEJ+cblVt2H2la7EHH0Z8ooMKCgpx8USA0wC0JdVQl9WidP5JTWqQ+OigO+V4PJxU8LHTQU/d3Owqawfc1U6xDliDCciIok5y50QpKkaTHEjJpOArrQCBSUGFBSX42JJOfJLynGxuLyqraQcBcUGXCwpR0FxOQovl8MkAF1pBXSlFThVwzDzclXAx00Jvyt7Yz7uSvi5XQ2ztj6u6Bbm1QCv3D6GExFRM+HkJIP3la66yICbz280h1mxObiq9r7yrwTbxZJy5JvDrKQqzISA5RjZqbzqw2xgRz98OrFPA786awwnIqIWSu4ku9KNp0THGsxvNAkUXi637HkVlBiuBFhVqFW1lSMmpPFPw2E4ERERgKow83NXwc9dBdjekLxJSX/Ui4iI6DoMJyIicjgMJyIicjgMJyIicjgMJyIicjgMJyIicjgMJyIicjgMJyIicjgMJyIicjgMJyIicjgMJyIicjit7tp6QggAVfexJyKipmX+7jV/F9vT6sKpqKjqNslhYWESV0JE1HoVFRVBo9HYfVwmbhZfLYzJZMKFCxfg4eEBmUxW6+X1ej3CwsKQlZUFT8/Gv2x8c8PtYx+3jX3cNva1tG0jhEBRURFCQkLg5GT/yFKr23NycnJCmzZt6r0eT0/PFvFGaSzcPvZx29jHbWNfS9o2N9pjMuOACCIicjgMJyIicjgMp1pSqVR45ZVXoFKppC7FIXH72MdtYx+3jX2tddu0ugERRETk+LjnREREDofhREREDofhREREDofhREREDofhVEtJSUmIiIiAWq1GXFwcdu7cKXVJkktMTMStt94KDw8PBAQEYNSoUfjrr7+kLsshJSYmQiaTYfr06VKX4hDOnz+PRx55BL6+vnB1dUX37t2RlpYmdVkOobKyEv/85z8REREBFxcXtG/fHvPnz4fJZJK6tCbBcKqFjRs3Yvr06Zg7dy4OHjyIgQMHYvjw4cjMzJS6NElt374dU6ZMwb59+5CSkoLKykokJCSgpKRE6tIcym+//Ybly5eja9euUpfiEAoLC9G/f38oFAr873//w5EjR/Dee+/By8tL6tIcwttvv41ly5Zh8eLFOHr0KN555x0sWLAA//73v6UurUlwKHkt9OnTBz179sTSpUstbdHR0Rg1ahQSExMlrMyx5OXlISAgANu3b8dtt90mdTkOobi4GD179kRSUhLeeOMNdO/eHYsWLZK6LEnNnj0bu3fvZu+DHXfffTcCAwOxatUqS9t9990HV1dXfPrppxJW1jS451RD5eXlSEtLQ0JCglV7QkIC9uzZI1FVjkmn0wEAfHx8JK7EcUyZMgV33XUXbr/9dqlLcRibN29Gr1698MADDyAgIAA9evTAihUrpC7LYQwYMAA//fQTMjIyAACHDh3Crl27cOedd0pcWdNodRd+rav8/HwYjUYEBgZatQcGBiI7O1uiqhyPEAIzZ87EgAEDEBsbK3U5DmHDhg1IS0tDamqq1KU4lFOnTmHp0qWYOXMmXnrpJezfvx9Tp06FSqXC+PHjpS5Pci+++CJ0Oh06d+4MuVwOo9GIN998E3//+9+lLq1JMJxq6frbbAgh6nTrjZbqmWeewe+//45du3ZJXYpDyMrKwrRp07B161ao1Wqpy3EoJpMJvXr1wltvvQUA6NGjBw4fPoylS5cynFB1jPuzzz7D+vXr0aVLF6Snp2P69OkICQnBo48+KnV5jY7hVEN+fn6Qy+U2e0m5ubk2e1Ot1bPPPovNmzdjx44dDXJbkpYgLS0Nubm5iIuLs7QZjUbs2LEDixcvhsFggFwul7BC6QQHByMmJsaqLTo6Gl999ZVEFTmW559/HrNnz8ZDDz0EALjllltw9uxZJCYmtopw4jGnGlIqlYiLi0NKSopVe0pKCuLj4yWqyjEIIfDMM8/g66+/xs8//4yIiAipS3IYQ4cOxR9//IH09HTL1KtXL4wdOxbp6emtNpgAoH///janHGRkZCA8PFyiihzL5cuXbW7GJ5fLW81Qcu451cLMmTMxbtw49OrVC/369cPy5cuRmZmJyZMnS12apKZMmYL169fj22+/hYeHh2XvUqPRwMXFReLqpOXh4WFz7M3NzQ2+vr6t/pjcjBkzEB8fj7feegsPPvgg9u/fj+XLl2P58uVSl+YQRowYgTfffBNt27ZFly5dcPDgQSxcuBCPP/641KU1DUG1smTJEhEeHi6USqXo2bOn2L59u9QlSQ5AtdOaNWukLs0hDRo0SEybNk3qMhzCf//7XxEbGytUKpXo3LmzWL58udQlOQy9Xi+mTZsm2rZtK9RqtWjfvr2YO3euMBgMUpfWJHieExERORwecyIiIofDcCIiIofDcCIiIofDcCIiIofDcCIiIofDcCIiIofDcCIiIofDcCIiIofDcCIiIofDcCJqRBUVFfj4448xYMAA+Pv7w8XFBV27dsXbb7+N8vJyqcsjcli8fBFRI0pPT8dzzz2Hp59+Gj169EBZWRn++OMPvPrqqwgKCsLWrVuhUCikLpPI4XDPiagRxcbG4qeffsJ9992H9u3bIyYmBmPGjMGOHTtw+PBhLFq0CEDVTSyrm6ZPn25ZV2FhIcaPHw9vb2+4urpi+PDhOH78uOXxxx9/HF27doXBYABQtdcWFxeHsWPHWuZ58cUX0alTJ7i6uqJ9+/aYN28eKioqmmRbENUGw4moETk7V39XGn9/f9x7771Yt26dpW3NmjXQarWWqV+/flbLTJgwAampqdi8eTP27t0LIQTuvPNOS7h8+OGHKCkpwezZswEA8+bNQ35+PpKSkizr8PDwwMcff4wjR47ggw8+wIoVK/D+++839Msmqjfez4moCXTp0gVnz561aquoqLC62aCXlxeCgoIsPyuVSsv/jx8/js2bN2P37t2Wm1uuW7cOYWFh2LRpEx544AG4u7vjs88+w6BBg+Dh4YH33nsPP/30EzQajWU9//znPy3/b9euHZ577jls3LgRL7zwQoO/ZqL6YDgRNYHk5GSb7rN33nnHas/pRo4ePQpnZ2f06dPH0ubr64uoqCgcPXrU0tavXz/MmjULr7/+Ol588UXcdtttVuv58ssvsWjRIpw4cQLFxcWorKyEp6dnPV4ZUeNgOBE1gepuPX7y5El07NixRsvbG7ckhIBMJrP8bDKZsHv3bsjlcqvjUQCwb98+PPTQQ3jttdcwbNgwaDQabNiwAe+9914tXglR0+AxJ6JGdPHiRRQVFdm0p6amYtu2bXj44YdrtJ6YmBhUVlbi119/tbQVFBQgIyMD0dHRlrYFCxbg6NGj2L59O3744QesWbPG8tju3bsRHh6OuXPnolevXujYsaNNVyORo2A4ETWizMxMdO/eHatWrcKJEydw6tQpfPrppxg5ciQGDhxoNRrvRjp27IiRI0fiiSeewK5du3Do0CE88sgjCA0NxciRIwFUDVt/+eWXsWrVKvTv3x8ffPABpk2bhlOnTgEAIiMjkZmZiQ0bNuDkyZP48MMP8c033zTWSyeqHwlvEU/U4lVUVIhPPvlEDBgwQPj6+gq1Wi1iY2PFW2+9JcrKyizzARDffPON1bKDBg0S06ZNs/x88eJFMW7cOKHRaISLi4sYNmyYyMjIEEIIUVpaKmJiYsQ//vEPq3WMHj1axMfHi8rKSiGEEM8//7zw9fUV7u7uYsyYMeL9998XGo2mUV47UX3wJFwiInI47NYjIiKHw3AiIiKHw3AiIiKHw3AiIiKHw3AiIiKHw3AiIiKHw3AiIiKHw3AiIiKHw3AiIiKHw3AiIiKHw3AiIiKH8//H5ARrb1L93gAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 1000x400 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"model = SimplePerceptron(784, 10)\n",
|
||
"\n",
|
||
"print(\"Персептрон со скрытым слоем:\")\n",
|
||
"\n",
|
||
"# Обучение и оценка\n",
|
||
"train_losses, test_acc = train_and_evaluate(\n",
|
||
" model, x_train_tensor, y_train_tensor, x_test_tensor, y_test_tensor,\n",
|
||
" epochs=10, lr=0.01\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.figure(figsize=(10, 4))\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"plt.plot(train_losses)\n",
|
||
"plt.title('Функция потерь во время обучения')\n",
|
||
"plt.xlabel('Эпоха')\n",
|
||
"plt.ylabel('Loss')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch\n",
|
||
"import torch.nn as nn\n",
|
||
"import torch.optim as optim\n",
|
||
"from torch.utils.data import DataLoader, TensorDataset\n",
|
||
"from sklearn.metrics import accuracy_score\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"class CNNPerceptron(nn.Module):\n",
|
||
" def __init__(self, input_size, output_size):\n",
|
||
" super(CNNPerceptron, self).__init__()\n",
|
||
" \n",
|
||
" # Сверточный слой\n",
|
||
" self.conv1 = nn.Conv2d(\n",
|
||
" in_channels=1,\n",
|
||
" out_channels=16,\n",
|
||
" kernel_size=3,\n",
|
||
" padding=1\n",
|
||
" )\n",
|
||
" \n",
|
||
" # Пулинг слой (уменьшает размерность)\n",
|
||
" self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
|
||
" \n",
|
||
" conv_output_size = 16 * 14 * 14\n",
|
||
" \n",
|
||
" # Полносвязный слой (классический персептрон)\n",
|
||
" self.fc = nn.Linear(conv_output_size, output_size)\n",
|
||
" \n",
|
||
" def forward(self, x):\n",
|
||
" x = x.view(-1, 1, 28, 28)\n",
|
||
" \n",
|
||
" # Применяем сверточный слой + ReLU + пулинг\n",
|
||
" x = self.pool(torch.relu(self.conv1(x)))\n",
|
||
" \n",
|
||
" x = x.view(x.size(0), -1)\n",
|
||
" \n",
|
||
" x = self.fc(x)\n",
|
||
" return x"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 105,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Сверточный персептрон:\n",
|
||
"Epoch [1/10], Loss: 2.0494\n",
|
||
"Epoch [2/10], Loss: 1.2180\n",
|
||
"Epoch [3/10], Loss: 0.6953\n",
|
||
"Epoch [4/10], Loss: 0.5274\n",
|
||
"Epoch [5/10], Loss: 0.4553\n",
|
||
"Epoch [6/10], Loss: 0.4159\n",
|
||
"Epoch [7/10], Loss: 0.3909\n",
|
||
"Epoch [8/10], Loss: 0.3731\n",
|
||
"Epoch [9/10], Loss: 0.3600\n",
|
||
"Epoch [10/10], Loss: 0.3496\n",
|
||
"Test Accuracy: 0.9112\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x400 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"model = CNNPerceptron(784, 10)\n",
|
||
"\n",
|
||
"print(\"Сверточный персептрон:\")\n",
|
||
"\n",
|
||
"# Обучение и оценка\n",
|
||
"train_losses, train_acc = train_and_evaluate(\n",
|
||
" model, x_train_tensor, y_train_tensor, x_test_tensor, y_test_tensor,\n",
|
||
" epochs=10, lr=0.001\n",
|
||
")\n",
|
||
"\n",
|
||
"# Визуализация процесса обучения\n",
|
||
"plt.figure(figsize=(12, 4))\n",
|
||
"\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"plt.plot(train_losses)\n",
|
||
"plt.title('Функция потерь во время обучения')\n",
|
||
"plt.xlabel('Эпоха')\n",
|
||
"plt.ylabel('Loss')\n",
|
||
"plt.grid(True)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"provenance": []
|
||
},
|
||
"kernelspec": {
|
||
"display_name": "base",
|
||
"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": "3.9.13"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0
|
||
}
|