{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "feature_crosses.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"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": "3.6.8"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "RLLI6XbdkOB0"
},
"source": [
"## ডিপ লার্নিং কেন? নন-লিনিয়ার সমস্যা, ফিচার ক্রস\n",
"\n",
"এই প্রশ্নটা প্রায় অনেকেই করেন, মেশিন লার্নিং থাকতে ডিপ লার্নিং কেন দরকার পড়লো? এর উত্তর সবার জানা, তবে যে জন্য আমি ডিপ লার্নিংয়ে এসেছি সেটা আলাপ করি বরং। আমার একটা সমস্যা হচ্ছে, যে কোন ডাটা পেলেই সেটাকে আগে কাগজে প্লট করে ফেলি। তাহলে সেটা বুঝতে সুবিধা হয়। \n",
"\n",
"মনে আছে আমাদের আইরিশ ডাটা সেটের কথা? সেখানে সবগুলো প্রজাতির ডাটাকে প্লট করলে কিছুটা এরকম দেখা যেত। তিন প্রজাতিকে ছবির মধ্যে আলাদা করা খুব একটা সমস্যা ছিল না। কারণ তিনটা ছবির মধ্যে দুটো লাইন বা সরলরেখা টানলেই কিন্তু তিনটা প্রজাতিকে আলাদা করে ফেলা যেত। এক পিকচার থেকে আরেক পিকচারের ডিসিশন সারফেস এবং ডিসিশন বাউন্ডারি সরলরেখার। \n",
"\n",
" চিত্রঃ আইরিশ ডেটাসেটের ডিসিশন বাউন্ডারি\n",
"\n",
"---\n",
"এখানে ইচ্ছেমতো সাইকিট-লার্ন এবং টেন্সর-ফ্লো ব্যবহার করছি কাজের সুবিধার্থে। কখন কোনটা কাজে লাগে সেটা জানবেন নিজে নিজে।\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "JzBIxKCynCim"
},
"source": [
"## দুটো ফিচার নিয়ে আইরিশ ডেটাসেটকে প্লট করি\n",
"\n",
"সরল রেখায় ডিসিশন সারফেস/বাউন্ডারি বানানো যায় সহজে। কোড কমানোর জন্য 'plot_decision_regions' নামের একটা হেলপার ফাংশন ব্যবহার করি।"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "4BjZLitwXUI6",
"outputId": "2c172ea1-f05f-4743-ddf5-a0bd03110075",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
}
},
"source": [
"from sklearn import datasets\n",
"from sklearn.svm import SVC\n",
"from mlxtend.plotting import plot_decision_regions\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# দুটো ফিচার নিচ্ছি\n",
"iris = datasets.load_iris()\n",
"X = iris.data[:, [0, 2]]\n",
"y = iris.target\n",
"\n",
"# ক্লাসিফায়ার ট্রেনিং করছি\n",
"svm = SVC(C=0.5, kernel='linear')\n",
"svm.fit(X, y)\n",
"\n",
"\n",
"# প্লট করছি নতুন লাইব্রেরি দিয়ে\n",
"plot_decision_regions(X, y, clf=svm, legend=2)\n",
"\n",
"# দু পাশের লেখাগুলো সেট করছি\n",
"plt.xlabel('sepal length [cm]')\n",
"plt.ylabel('petal length [cm]')\n",
"plt.title('SVM on Iris')\n",
"plt.show()"
],
"execution_count": 1,
"outputs": [
{
"output_type": "display_data",
"data": {
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5L82nen812bnZjJ10GudcPTHkdgj/Im9dTR15vXKo2lGDehTJEPJ65VBXU+e4f7Sqd6we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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "j6QY3PjWXJs4"
},
"source": [
"বয়সের সাথে ওজন বাড়বে, বাড়ি স্কয়ার ফিট এর সাথে দাম বাড়বে, এধরনের লিনিয়ার সম্পর্কগুলোতে ডাটাকে প্লট করলে সেগুলোকে সোজা লাইন দিয়ে আলাদা করে দেখা যায় সহজে। কিন্তু ডাটা যদি এমন হয়? কিভাবে একটা লাইন দিয়ে দুটো ফিচারকে ভাগ করবেন?\n",
"\n",
" \n",
"\n",
"চিত্রঃ নন-লিনিয়ার ক্লাসিফিকেশন সমস্যা \n",
"\n",
"এটা নন-লিনিয়ার ক্লাসিফিকেশন সমস্যা। নন-লিনিয়ার এর সমস্যা হচ্ছে তাদের 'ডিসিশন সারফেস' সরলরেখা নয়। এই ছবিতে যদি দুটো ফিচার থাকে তাহলে সেটা দিয়ে এই সমস্যার সমাধান করা সম্ভব নয়। সেটার জন্য প্রয়োজন ফিচার ক্রস। মানে এই নতুন ফিচার কয়েকটা ফিচার স্পেসের গুণফলের আউটকাম নন-লিনিয়ারিটিকে এনকোড করে মানে আরেকটা সিনথেটিক ফিচার তৈরি করে সেটার মাধ্যমে এই নন-লিনিয়ারিটিকে কিছুটা ডিল করা যায়। ক্রস এসেছে ক্রস প্রোডাক্ট থেকে। এখানে x1 = x2x3 (সাবস্ক্রিপ্ট হবে)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "N9rRARqdXJs5"
},
"source": [
"## একটা নন-লিনিয়ারিটির উদাহরণ দেখি আসল ডেটা থেকে"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "rQN_GvIhkOB1",
"colab": {}
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import sklearn\n",
"\n",
"from sklearn.model_selection import train_test_split"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "OCwPfWSfXJs8"
},
"source": [
"## ভার্সন চেক করি"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "dBXgoSoNXJs9",
"outputId": "045b7697-d340-4c7a-836b-3501f2adbcf8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
"sklearn.__version__"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'0.21.3'"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "-peF4S1Noc39",
"outputId": "b7489cfb-3df7-49b8-b08f-a877c11af81d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" # শুধুমাত্র জুপিটার নোটবুক/কোলাবে চেষ্টা করবো টেন্সর-ফ্লো ২.০ এর জন্য\n",
" %tensorflow_version 2.x\n",
"except Exception:\n",
" pass"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"TensorFlow 2.x selected.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BAGl_DL6kOB-"
},
"source": [
"## ১. ডেটাকে আমরা লোড করে সেটাকে ট্রেইন এবং টেস্টসেটে ভাগ করি "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "268qDbW0XJtF"
},
"source": [
"ধরুন আপনি ডাটা প্লট করে দেখলেন এই অবস্থা। কি করবেন? নিচের ছবি দেখুন। এই ডাটাসেটে অক্ষাংশ, দ্রাঘিমাংশ, ভূমির উচ্চতা ইত্যাদি আছে। আমরা সবগুলোর মধ্যে শুধুমাত্র অক্ষাংশ, দ্রাঘিমাংশ প্লট করছি। দেখুন কি অবস্থা। একটা ফিচার ভেতরে আরেকটা ঘিরে রয়েছে সেটাকে চারপাশ দিয়ে। এখন কিভাবে এদুটোকে আলাদা করবেন? সোজা লাইন টেনে সম্ভব না। শুরুতে চেষ্টা করি সাপোর্ট ভেক্টর মেশিন, এরপর নিউরাল নেটওয়ার্ক দিয়ে। "
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "1F4403n3kOB_",
"colab": {}
},
"source": [
"# df = pd.read_csv('geoloc_elev.csv')\n",
"df = pd.read_csv('https://raw.githubusercontent.com/raqueeb/TensorFlow2/master/datasets/geoloc_elev.csv')\n",
"\n",
"# আমাদের দুটো ফিচার হলেই যথেষ্ট \n",
"X = df[['lat', 'lon']].values\n",
"y = df['target'].values"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "nhaD099jkOCD",
"outputId": "421cfe0d-eac5-462b-e0a5-f2374b3ebffb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 258
}
},
"source": [
"df.plot(kind='scatter',\n",
" x='lat',\n",
" y='lon',\n",
" c='target',\n",
" cmap='bwr');"
],
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
"data": {
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Og0FjLQ0zvPoq0YgRSEXy7LOYDyvn2SgCyqmKqeyN+DTDG28gdcXevdBD33MPjLTFheHD\nFY+dWG6vs2fDA6dmTUQOb96s7DtyBEFzTZti3IcOwcYR6RuZPx95lMaORURyjRq4rsxKumwZDNNV\nqkAVtWyZkXAHg0huR4TxxOOanp2Nkqpr14LhSTgcILJmqqtQCM9lzBi4u/7vfxi/3o02EkIhY9I/\nCTPjeFoa7DexMHascm5eHuZ+zhxrY7JRCNg2CBvJwvTpRsPx9OlwITU79qWX8L4+8IC5yiRR2LoV\nOnOpV9+7F/aA3buxr0cPEGxmSD4rVoDQu1xaSeDbb6Fbnz3bSBAPHoSaZe9eqGOuuw7Ed8AABJK9\n/z4MvFJHHwzCLsGsuMTGg5Ej0U8wSPThh/CeIoLk06YNVDU7dij2EIcDq/WTJ9HX/PnmrrIOB7ar\nJQqnEwzz4YehDqpcGQxGairee894nfbtza+/Ywfeh82bMS969VooFN0Tqyhghgrw6FFEolthYOUS\nZZD4W4HNIEo59PptIUDE9Jg9GytvSQjGjIGx04yRJAJr1mh14czwINq7F0FxarXT1q0gsLt3Y9X/\nzDMgurNmgShHI165uUigd9lluGYoBGPs998jqV67dti/ejXsD9KbKRpzkGkszCBVMVddBfVPhQr4\n3a8fpItbbkGqj8qVwbymTtX2pb+ux6PQDmb0Xbs2vKOmTQNxz86GSqlfP8xr8+ZI2KeHmaorLw8G\n/v37MY5Nm4w2iry84knex4w0IPPnY2xCICakXbvE91WqYQfK2UgWnnlGUW8IAWPr2LHG4yZPNkoa\nU6YU37jq1jUS4WAQbqTbt2sJZW4umEM4jP8ffxwptdWqkEjIz4ex++BBxV5w6hSisYkQOX3ppUhS\nZwaz79bthottNDidSLOtRiAAd9mMDGW1bmZn8Hoxro4dwUjUNo9QCOlD1qyBi61aHZWXp6QeGTVK\nq9YKBMBM9di2DQkX5bNgNj4XZjgbJBpz58IFNysLYzh2rPgWJKUe5VTFVPZGfJqhWzeoZ+6/H1HI\na9eau1VK18hY2xKFXr1gr9AbgzMyEBSmX+2qiVZWFlaa0SKFZSzCf/8LyUGfN0pv3O3cWXuM3w+J\nygwOBzydoiEYjF0JbsiQyJllly+HVBOPasflUoLjRo6Egb9xY9hxJk2CE4EeFSsa58ZMepJxIInE\n9u1GdZaeqZ4WKMcFg2wGUQbQti1sC88/D2JhhsceM644x46FgfL996FTP368cP0zI5DruutgMM/M\nxDfx3nvIY6RHfj5W4G43vgmpppHweqESi+TB43ZDRfbDD7Cl9OplPEamKScCEb74YuW3EHDr7NvX\nnFiGw5A6XnjBqNOX9RymT4+tTz/jDBjz9RVuw2FFRdSnj7XcSHKeRoxQ7uH220GEpb3HDHXqIIZC\nRqGnpCip1dWIVKd6+XIwoZQUjHX//thjlWjbVqlDQQTG26KF9fPLFcqpBFH2WJoNU3TvDoL65psg\nFGPGgMC1aIFVnrRdrF2rBGeFw9Cn33svVECNGsEuoK97cPvt0JfLgK85c7A69nrh0vnll0ZpwOmE\nlNCqFQi1OixF1j6IhPx86OY7FXiL16kDaUjdRyiEGIuTJ6GzP3FCq2Y5ehRGZTPMng3p4MEHca/P\nPQeJ4YYbkL+pcePYEdQS7dpBolOvnPPzwdT278fKvWtXqIL08Hphj+jTBx5b990X2aspGl5/HfaQ\nP/7AMzSTjswq2/3zD9GFFyrMbMUK2EHWr7fG1AYMgE1m0iQw9fT009RbqhzbIOxAuXKMQYMQLCUJ\np8sF1cXkydB9n3ceonHVr0ClSiDectW/fz9WyOpI5rQ0ENn+/XHtwYONabt9Pujply+H0Vod8GUF\nPp+in//1V0Q+66/hdIJReL24B3VmWK8X96s/x+9PvEdPgwbmqpW8PBBOZqT2eP110JK6dcHsOnSA\nZGjmdGAVW7bAEyw1FYb8zz9XpBA1Klc2phSfMwfSh1q68HiQCj6ewLqMDDDkRo0iq9xKKxISKFel\nCq8eMMBafzNn2oFyNkoH9u7VqliCQYWQPf00Ygz064NwGCtIIgTlNWxoTHMhk+YRYeG0YAFWwVLd\nEAhAsmjQAKks9DpyKwiFlLF16oSVvdkxRJBI8vMVm0sggLxEZjmSZOyB1wtD9dq18Y9Nj2bNjNvU\nKmfpdrxrF5jvTz+B6U2ZojCHAwcgdXi9kJjUGWgjYelSMJkHHoDU0K4djPlmMEuZUrGi8fnLIkXx\noHp1SHHRmMO2bVDbLVoUO4CwzCHBcRBCiP5CiC1CiO1CCEMCFSFEfSHEEiHEOiHEeiGENe5UCNgq\npnKEpUtB9M84AwS6Xz+sMOVKPBBQIqN/+81oYCQCoa1YEe6Sd9+tXZVLZGZqV5hCgKBNngzPnI4d\nFY+bbt1AKOV1nE6lsFAk4dXngwFYqjmEgCvnqlXR7/+yyzC27t3BsBYvRg3pY8cUJpWXp4xlxw4E\n5+3dW/hUFJHUZRMmaNU0R49iBd+ggbm76oABUBEFg1D9DB4MRh3JO4uI6D//0UpDf/8Nic8sBsRM\nqjj3XDyr1avxjvh8sFup7QqJwKJFeDaSPnbtCi+2cpV5NkEGaCGEk4heJ6ILiGgvEf0qhFjAzJtU\nh40lotnM/KYQohURLSKihgkZgA62BFFOMG4cDLUPPQTf9KuuwrbBg/EhulwwdEr9dIcORi8nlwuM\npXVrEPpoifr0XjFuN0qWTplCNHq08vH36gVjsAySa9mS6JtvUHOiZ0+k5h43DgygWjWoX2680Rgo\n1qRJdK8sZhjjDx4Egzv3XEhJmZnIehpJpy4DDwuLX34x1mDweJQgOyJEYtesiRV+vXpgWmpkZ4MZ\nqOfb4Yhdj0OvMsrNxep88WKta/SwYWDeejidOHbSJDgfzJuHwD0iSFZdu8JGMmhQZIcCK7j+eszz\nqVNoq1Yhy225QWIliC5EtJ2Z/2LmPCKaRUSDdccwEUnFZAUiKsLTiQ5bgigHOHYM8RJyZZyXh+jc\nDRsQpSwrnqlXbLffDr24hBAoIPTII0rVtmiqIbN9O3aAGOzZA8nh7behQrn9dhjNs7KUwL++fbXn\nPvaY8v+UKQisE4Lo//4P0sgNN2D7mjXR52L5chAgNbHdvz+ytEIEyevuu6NfNxKiBd0RISWIfDZ5\neWBYAwYgzffKlWBo7duDeepVL1WqRO/bbKWfnm5ur4kElwtzq8aPP+IaUgr5/HMwiy1b4lc/SYcB\nNfLz4/OWKhOwbqSuKoRQG0jfYmZ1hrU6RPS36vdeIuqqu8aTRPSNEOIOIkohovPjG6x1JJVBCCHe\nJaKBRJTBzAYtsxBCENFrRDSAiLKI6AZmToDWuHzh6FGtGocIv3/8EavHgwexem3XDkFnHg/SIqgJ\nEjO8kXbvxmrWLJJXwu+H4VmNv/+Gx5QkzH//DT371q2QLpxO7Bs8GCvjmjUhJXTurL3OjBnwqpKq\nkzvvhAvmiBHwuKpfP3quI2aj5BPLBlK7dvT90dC5MyQb9Xzl58OmUK8e0e+/G8ezaxdUW7/9BgaT\nmwsCvGYNjvV4wDSiJUqUket6fP65eSBlLGRkQJrbvBkqM72KKiMDz/Pss+O7rhCQVteuVd43pzNy\navcyifi8mA4nwEh9JRFNY+bxQojuRPS+EKI1M8eZXCY2kq1imkZE0fKFXkRETQvaaCJ6swTGVOZQ\nrx4Mr2o1SiiEVfnu3TAor1wJNcORIyAs335rJJy//AJ31lWrUI3NDKmpUEX07q3d/vTTRkK4Zw/0\n28ePE115JTK6LliAMWzcCCli3z7tOW+/bYwIf/tt/C/PT0tLnFdhSgoIo8TatVB/nX8+XH5jwes1\nrtaZEbOyezcYoZmq7uefcd6pU3gOy5aBcN53H57T999H19FHs+FEQ2YmorSbNYONavt2MNyuXeHV\n9Ntv5vEy6jTp8YAZQZ61a+OZ+XzIOaVfGJR5JE7FtI+I1CGadQu2qTGSiGYTETHzCiLyEVGxlIpK\nKoNg5h+JKEp1YRpMRDMYWElEFYUQtUpmdGUHLpcScyDrFDz8cHQCEwpp93s8+Jhjrba7dAFh0cMs\nEEumfejfH5KLflWal4fYDSIQ0cmTjZXTiECEly+H7r5vX9RQ+PJLqDus+OvrIQRsGv/5DxhY8+bY\nvmEDjNvz5hF99x1UW1OnYt8//2BfWhrsKGpVl1lE+MKFeB5XXmnd9fPUKRjMr7kmNjF2OIxMmghp\nTKJh8GCimTPhVfTdd1AFfvcd5jSazal+fTz7eBAOw9lg5EioQWU52meewZy0aWMeH1ImkTgG8SsR\nNRVCNBJCeIhoBBEt0B2zh4j6EhEJIVoSGMQhKgYkW4KIBTN9XB2zA4UQo4UQq4UQqw8dKpa5SjgO\nH4bqpGVL5LApyrCbNFEMy9IGEM2d0OlEgJfLhf87dLDmvfL99/Dn12PkSOP7X6kSvHbWrTMfSzAI\niYQZhOS++4zxBH4/VtsXXQR312uuwXj79cNqd9w4GFhj5VZSo3ZtqNFatADR8/mQFfbNN7XSQFYW\nanGEw2BMy5eDiG/ejBiSjAwwh9RUYx/5+Tg/Pz8+N99PPjGvS6HG88/jWUmpQ8Lni15y9ORJMGTp\nvSbdldevN0ojkp65XAjCW7/e3PsqGhYtwvty6hT6zs5Guvq//sKcbNxIdM455p5yZQoJTLXBzEEi\nup2IviaiPwneShuFEOOEEAX1BOk+IrpZCPE7EX1EUL0XT0AbMye1EdyzNkTY9zkR9VL9/o6IOsW6\nZseOHbm0Iz+fuWVLZo8HygK3m7lpU+bcXO1x06czV6nC7PMxDx3KfPKkteuHQsxduqgdSpXmcDBX\nqsS8ezdzXh5zVhb+tm6tjCda8/mYf/rJ2Oe77zKnpjILwexyMffqxfzrr7g3s+tUroz73bCBORDQ\n7nM6ma+9lrlxY+32lBTmjz829r14Mc6JNXYi5rQ05kcf1fbp9zO3aWM8tnFj5n/+wT3rrzFoEO7T\n4Yjen9/PXLu2tbE5HMwXXqi9t0OHmCdOZH7xRebXXoveX6NGzFdeiXmqVo15xgzlOllZGK/+nAsv\nxLP3epXxXnQRczhs7V2LhDffxLWi3W9qKvPmzUXrpyggotVcRBrWsUYN5nvvtdQS0V9JttLuxWRF\nH1cmsXkzDLly9SQNmxs2YDVPhIAq6f1DhBXZqFHWdOMOh9F7RKJNG6X28ogR0EP37Anf9GefhS3i\nwAGkrzh1yrj6z8lBbiaZI2n+fHgvyVTZXGAoXrYMK+3hw1FpTa2K8flgUGXGql2/uPL7EQDWVee/\nkZ0NwzcRdOW33Qb7SuPGGPsjj8QOxMrJgXun2taRnQ2JLhBQtsvI89RU4zVzc2HHsVKzOxw2usJG\nO3b7dnhWXX89nmFOjuItFQ5HT2W+Zw9sTDk5mNdbboEU17s3nm+7dtq0J0R4TlOnYvumTTAgP/RQ\n4dR3anTpor2Gw2G0neTnl5NyqOU01UZpv6sFRHSdALoR0XFmLhcOcm638UMPh7X66m+/1Xrs5Obi\nI7eCUAiExgzbtsFTpWtXEPf16+FRNGwYCOwvvyAeIj8/MrGV6aM3b4ae/fhxc8NpVhZcJl99Fcbf\niy6C62qlStheoQIMw2lpirrE6YSqpHlzqIHURMbvR5wGM9RMc+bAvfa774heeQVzVq1a9LnJzzcP\nuqtVC66+6voNEybgHh54QJsQT6bxjgWXC/1FYiQej/aZe70wIPftC2J/8iTOz8uL/jyIlPlTjys7\nGwuLO+9EfIxZBprsbMzhk09C3fnTT1DbWbm/aOjQAQZprxfve+PGeK9SUjAv0kHAat6rUg07WV/i\nIYT4iIjOIfgG7yWiJ4jITUTEzJMJEYIDiGg7wc01Qk7LsodmzbBSW74cH6jfjw+qVSvlmCpV8HGp\nP1R9ZtRIcDrxAZq5qzqdIAaHDysER3o6NWoEo/IXX0QnRnJMK1dGf++ZQQCvvx5GYSJEeh84oDCT\n224DQwgEMBeBADxfPB7kfOrTR/H2uf56GFoPHABjk/r0UAjn5ufDHbZPH+yzssKXczJ0KNKLSMYt\n621PmQLDavfuILANG0KP/uKL5tHoEpKxRVvxu1xg1EuXYj5yc2FzibcanseDhHwZGdrn5vHgHZow\nIbKu3++HDad3bzB8OYYVK8B45X1wgRNDPPmWbroJz+zUKby7zHAC2L4diRIvvDC++yyVsJP1lS2U\nlWR9ubkIVluzBqL/gw9qo4WYo8UeAAAgAElEQVRPnoQ//P79+DDdbqyYL7rI2vXr14caywzRArz0\nmVP1cLnA4IYNA9F5/fXIcRMpKRiDVCNkZ0NaiKUGCgSg9rjqKszT9u2IkK5T4KJw5AiMzWqil5oK\n76FzzsHxzz9P9MEH1lfCcgWuH9u99xKNH6/dtnMnpBvZvyxCtGUL5iccxhxHMlCnpGAuzKSuWKhZ\nE4z8r78w1sxMjP3UKW2aDZkYcO5cqBD1Y5Fup0OHQsLo21f7HP1+GJJllt+RIzGXrVtjAVG3bnzj\nLo1ISLK+WrV49U03Wevv2WfLVLK+0m6DKNfweokefTTy/rQ0eOp89BFUOP36GVNxx7q+HpIxRCNK\nZgRVqleyskCod+1CMZ9AQKmbEAqBYIZCOLZRI6yM1Tpmnw/nRKpPIJGVhVXvVVfhPvRFkqpUQX6f\nzz7DsT4fCLQsrdmkCaSQDz+M3o8aZkzL7wcB1eOOO7THu1yIGJ80CXMTCplLL9IzSFaYKwyqVoXk\nOX06pC+1GlJ9TYcD81+vHhiYnkG4XJAAW7eGyk1vc5B1tNevhyQg+9mwAdHgMqnjaQ/pxVQOUT7l\nolKE33+HHvajjwqX1TQ1FVHL998fH3Mggn5XnRrB7zf3nbeCLl1QrMjhgGpKGnKzsrCaf+kl2ABW\nr1bcGnfsMFZlEwJEOxAwdw9VI1Yyt+nTkefpiitgVP35Z60rZosWkMoCAaSgcLvBWKRrrxUMGmRe\nsEjvupudjbHIGtNmzEbSEclEC4NAAMWOiCBBREup4XDgft1uqBT1CIWgjpPRzrVqKfMnmfIZZ0DV\npEY4jMSC339fuHsolyinNoiyN+IyhDlziHr0AJG6+WYQmsIwicLg0CHozJ95BvEDvXqBGNx6q1LW\nksiYo8nlMn+PlywBUTRbFefnQw101lnQz5tVNQuFQMTr1oXKZsIEGMbbtzdPwhcIRJauQiHYCm6/\nHeOdORPSSteuYKLqRIJPPonV8cyZ0K8fPoygrQkTEFMRy1Nn/nzz7U2aGM9Ve6WZgbloPv9uNxIZ\nymC4jh2N8yzHFAjAQ03arO6/32i/CoXgkEAEu8Ly5XA46NgR/Xz0ESSUt982lyoHDYqekuW0QYLT\nfZcmlE+5qBRg61YkQVO7Um7cCKZx5ZXF2/e77+LD9njwYQeDeDfvuguEVQ2HAwFqqanQbdeqBcJ5\n4oR13Xg4jGR3KSm43pdfKqoeiYcfhq1CzsfddyOr68qV0O+/9BL6lBHeQ4aAAOnBDNXS11/jWtJW\nsXmzcu0xY8B0Lr8cv1u31taTSEmBiqhTJ4w32mo+khro3XdBSNVpKYrTnJeSAoKtnpNBg3CvEyeC\nUdaoAfvUgQOQFO+4Qzn2yBFjoJvPB5dniSpVlMy2x4/DYSIjI7KhPxSCOs2sVsdphzJI/K3AZhDF\ngK++ggFXX7ksGCxatLQZmJGC4sQJfOxHjmBlnZOjXfVJtcDAgVqddSgEI6baJfbnn5E63CztRTRI\ndceQISAs6hX2hx8a4w5mz4Zkc9ZZWhfOUIjo448hYeg9ZrZvx/zKe8jKgp1GTcizsuB5JBmEGXJz\nIRHFsgNESpjn98cfWaw+1+EAI5SxIxJCEL32GhwXvvgCcxoKQfLTM0whwFgfeQQqvTp1IqvOBg40\n5liqUwcMxgxffIGxRfMCC4eLluiw3KAcezGVz7tKMvSSg4TDAffLRCEchhqhc2e4CzZqBFVQJDfE\nUMg8YGvHDiX47uhR6LjNSmhKRKvLQAQmNXYs1Ft792KbPk2006mk/pYurPqxmum4s7KM9kAzNVGs\nMXo8RgKfkqJ4SRHBKSBS3YIxY8wDEWOprBo2hHH3l1/ASPXw+7HynzYNksDXX0OdNWECAt5WrjSe\nU6kSPNYiMYecHNiG9HO8bx9W/zt2GM8JBqNLRELA5lSYGtrlEglKtVHaYDOIYsC/JukHfT589O3a\nJa6fjz7CSi8rC6u9w4exoixMgJPMaDpmDNRjeuIgBIjXyJHIF9S+feRrMcPF9IknQNwefBBN2j6c\nTujDb70Vv3v3NrfNfPSRcVvLliBK6qC6KlW0dpVAIHrK6yVLEJylj2FwOCBVZGaCQH79deRvetMm\nc9VULKLaogXmpFUrSGnqcbtcUJ+pr3X11VCfyRxb/frFL4V6PObMIycH78z11xv39e+P8+TC2O9H\nYOPo0Yjm/+03pfjUaQ/bBmEjHnTpoi1a4/djNdytW2L72bxZ68XCDJ1wrVqxE77pId1OpdFSDZcL\n0bgXXKBs698f8QbLlyvbUlPBrEIhbbDZyy9DWnjnHcxLWhpSQMjVeq1aiLLVZ/Y0kwI8HqSGuOEG\n2HRatgTjPXgQyfaEAJPrFMHTfP9+RPOaGVdzc0H8Ihmm1ejYESo4ydj8fhDcSAzC7cYxr7yibLvs\nMqgHn30W89S/P9Ebbyj79+6FNKZ3XV27Nr4AM4cDHlaPPmqUbGVqDz2qV8ezuusuSBr9+ilZWG2Y\noAwSfyuwGUQxYO5c6HzXrsUH9b//WWMOR45AhZCaCt18LFfM1q2hFlEzicxM69XE1JAFXFq1MtZY\nZgYRV8PlAqHOyVEqpR08iLgFfUlNZhDkL76AN5EZXnoJxvvsbBD5QADBW2aoWxe5lNSoX99ajYF1\n6yLPa16eluFFwxtvgEHJ4jpt20KNo5Yq0tPhFHDwIBjEJZdoVVhCQMp6/HGcp5dWKlY0L36kztjK\nDK+tn36CG/Kdd2qlEom778YYJ06EE4G6RnizZriOXj3WtCkWBjZiwLZB2IgHNWqg+ta6dTAc33EH\nVmTTpkU+548/oHq46iowFytpkC+/HK2oSdXq1VO8m6ZONSZPY46cA8rnAyGsVQvqs6uuMl/5h8PG\nGspE0LNPmwYGM2sWmMQNN2D1qg+OSwRq1IjualyzprXrVK4MNcu6dWCI3bsbVU5eL2I07rwTUo2a\nOagRKc6qYkV4f6WkgMGkpODd6NABRvwBA0DEx4yBdPbkk9Fdqc87DzaVK67AwkUIPJdff4U7dqtW\nsGM99ljh4zROW9gqJhvxYONGqDnkx5qdjQ+5Zk1zz5jrroN/vsSaNfA/l3p6iexsrACdTuju330X\nNoFo/uhCQK1z6pSirpARzdnZ6LdPH6yAa9WCZNCunTL2cBjqieuui50Ir39/1GjQIxAAYVJDEla5\nSq5UCQQ3Wk2DoqJDB6h25s5Fv7m5IL4y6vzdd61fy+kEgSbCc/V6tXaNRNzHk0/i2axdCzXckCFg\n5uqyrBI5OYhyvu46LEp69DBez+GAK+umTbgms5KHS+KVV7D96aeLPv7TBmWQ+FtB+byrUoCnnjKu\n5HJyEAdhBn3OJJlhU4233sIqctgwpH844wzk/om16q1fHyt0tS47JwfG9Oxs2B8yMpBllQgrfb3X\nkRBwO33rLaxeN20y7+uZZ4z3LSN59cbQO+6AcV2qxQ4cQM3js88Gk7GaaC8eyPuYMweeQT/+iP+n\nTAHDKmwpzDFjoPpKSVGY7+TJ5sd+/DEYrc8HiUDv6qrHeech0G3oUIz/uefMveSIIHXOmgV70Sef\nKNuZ0c/x42D+a9ZEtpdkZSnxEDYsIIEFg0obyt6IywjM6voSRc5936kTMmeqDds1auC3ywXXyNtu\nUz5qZvQxahR00P36KXmWAgG0ypXhI3/RRVBXRFMbBIOK7aFVK/MaEJKBCIHV8nPPQbethpkk07Ur\n0mXr8c8/WiIVDIJIM0Oa+fNPc0+mokKIxGcRTU+HymnOHNzXhg2wq2zYAOYh1YC//IK8RpLAL16M\nanaRDOPMYN4yBTqRNS+1rCxIGZddhpQc/frBqM5sTX2kXyDYiALbBmFD4uBBpIwYORIFbyLhmmuM\nyfICASNBlZgxAx45Xq+SBfSpp5Ro3UjJaXfuhJpm0yasgj/+GF4nf/6JFfuUKbALjBiB/qOpQvv2\nxV9mRD2bGTvl/pwc6McPHNDuGzVKS1wCAcyVGfr1M/YhGUZWFohtpJVyaURqKlRAkyZh9f7552CM\n992nHPPdd1o1VG6u0eAukZkJ6aFBA9gvhg3DczaLvzAj6NJZYcAAvCfBoHXm8NxzsY+zgldeAWOr\nWBFSULm1bZRTG0TSS9oVRyuukqOHDjHXqKGUbQwEmF9/PfLxEycyV6+O8pQXXsi8bx+25+Qwv/ce\nSkiuWqUcHw4zDxumLZ3p9TLffDPzN9+YlwO97DJjv7m5zO3bo/SnPM7tZr7iCub//te8DGSFCig7\nOnYs+vH5YpfSTE9nXrMGfZ46xXz11Zif2rWZ69ZFuc5o85OTg/E7nehLX5rU5cJ1yxLefx/lPtX3\n4XSiBCwz8+TJxvKqtWqZX+vWW7WlTgMB5t69tc9Vnr9qlfa5+v3MN92EOY71HGVr3Zr5lluYlyxh\nDgZRknbqVFy/YkXmUaNwPav44APtvQYCzOPGFXmKEwpKRMnRBg1Qb9dCS0R/JdmSPoDiaMXFIF57\nzVibuEqV+K6Rm8vcoQOIiMuFD1nWDZ41y/xjbtgQzOPyy7U1hZs1Yz561NjHZ58p9YX1BDc7G8xA\nv++ee5jnzTMSr1j1hI8dQ5+DBmn7TElh3rbN2pzk5+M+atVS7s/vZ7700vjmtjTgvffM53DuXOzP\nzGRu0QLHuN34O2+e+bXatTN/F/TbKlZknjaNefZs7K9alXnkSBDzcBjPycrznDAB/W7ezFynjvEd\n8vuZx4yxPhdDhhj7aNu2SNObcCSEQTRsiAdgoZU1BlEGZZ7kQSa+UyNaRbGTJxG1u2qVIlrPnQvD\nsozWzc5WIlL/8x/z3EB//w3PpVmzoKJ47z2oLbxeqA+WLMFxS5fCljFihPm4hMA9DBqkdUX1eqHi\nuvRSayod6RW1cKFSJezLL7V9hsMo/2kFLhdUEKtXw223Wzeo4orD/lDc6N/f/Bk+9BD+BgIwEL/2\nGgLkli1DhTwzNG2qtWt6vfBM0quTTp6Eferxx2HzOHQIHnBSXfn+++a1QfSQXnQDB8KOon+HsrNR\nf8MqqlY1alXKRf1pM5RTFVPZG3ESMWiQNpLU7wcxNsNff+EDHzKE6PzzEdeQmwvPIb0eViZki1RE\nJxRC3089BZfHw4cRNfzHH8jVP3AgiMDFF4P4RGIO7dqBEL/1FphBWprinqmP2I0Evx++9P/+i3uS\n0EfYOhyx6z3oUbs2KsCtWAHiWRajdmvWNE8SqA5eDARgq7n//ugpS157DY4Kch7y8uCG/M03mHu5\nXVaV273bPNbmoovwbkRDSgrcaINBeM8xmx+Xnh79OhLZ2aiFon6nUlIQVV/uUI69mGwGEQdatkTA\nWPv2CCi65RZtaoRgEEbsUAiG2UOH4Fp46hQI96RJcOFULyTcbiVqumvXyO9QOAyvmClTkDZBvdKX\n1dcirf4dDnjtfPUVfvv9IMQnTpgncdND1oxwu+Eb37AhJJVq1cC4/v0X7q1yZev1gpDJwjanG26+\n2Wioj5Xifc8evE/DhsHRgAhzOHq0tib0m2/Co+3NN41EPCdHm6cpLw8ODD5f5KSDPh8I9znnYLEz\na1bkRIdeLyKxN28GI/ryy8iLiscfxwJGwuXC+xApBUqZRjnOxZR0HVdxtOKyQUTDN9/AGO3zQSdc\ntapR/3rTTTh2wQIYc71e5vPPZz5yBNsPHmRu1cp4nt7gabbdTF9NBIPzI49EHndWllHX7PPBWD18\nOPPgwczz5zNnZMB+ceAAjNNq43fnzrjWF18w33UX8/PPMx8/XrzzXdrx6acw0teqxXzvvbCzRML+\n/cyVKyvPNhBgHj8e+7p0MT7T7t3N7QoOB3PPnsy//45zzzsv+rtEhHd22jQY0S+/XGtgd7lgEJfN\n54MROxDAcampzBdfrBjg1ejTx9hXt26wkzRpAieGsWPNzy1JUCJsEGecgRuz0BLRX0m2sifzlEIc\nOYIgJqlGyMlRJEppswgEICEQYdWtdw8lQjqO+fORniOSr3skN0F9tTAipWj9Aw9gpbd+PaSMdu2U\nFa7fj9rSjzyi1Ci48UZskwiHkU/qm29wP+pVY34+/P+PH4c9ZMCAyPN0OmHoUPNa1mb48ENImfLZ\nZmVBxXbvvXgnZHwLEZ7RP/+Yx5uEw8gl1aULVuo//xy775MnEa0fDsOmpK4VQgQ1llRZ5uQYg/9+\n+AGSxMUXY4xS2mnTBrY3ea7Hg7ic669X+njlFbxvZuVQyxzKonRgAUm9KyFEfyHEFiHEdiHEQyb7\nbxBCHBJC/FbQRiVjnLGwebNRNeTzIYI5EIBoPnQo9M567NgB9dAbb4DRNGmCVNlS9CcCEZcGRzM4\nnebvZ4MGYAoeD1QNPXrAiNqsmVLvYds2qI2YQeyZwSDUuOMOMJBFi2Ak1ycDZIa66tJLMf4hQ6Bq\ns2EN+flGVY1cWLz4ImxFXq/yTuij7tVgBlG2whwksrKwiNDn/nI4Yuf5Coehdq1YEcdXqgSG9+yz\neM9SUzH+xo2Rll3NgLKyYDtTY+tWRHFHU1+VOpRjFVPSJAghhJOIXieiC4hoLxH9KoRYwMz6JA4f\nM3Opzjxft67RMBwMKtlO/X7zdBirVyPHfl4eiPy4cViN33UXiO3u3fjwtmwBQb/33ujjCAQUO0Qg\ngGC9YBCR0f/8oxyXmQkbybffgjmoczTJiOnvvlPu4623FIIVCimEIxwG87n7bgTZyWCsPXtQcnTT\nprJpaC5pDB0KiU3OscuF51C9OhLnbdgA7yEhYOSN5MxgBU6nuRRqlkgxL0/r/eRyYQzqYkKhEOxi\nkrkcO4b37tNPYXdbtw7Htm+P98rhMBquJRYsgB1EZtvt1QsZgHfsQPDnmWfGzgWWFETKtlgOkEyW\n1oWItjPzX8ycR0SziCiCw1/pRoMGKFATCMDLw++HFFCrFozZkXIl3XkniHNeHlZWR45gxUiEDKu9\neiGl97BhkaOaifCRyhoJXi+MySNGIJX0gw8a1VnhsJKS+99/jSs1ddJAqUHWn6/edugQ6ixIApef\nj/QQGzdGHvPpgLw8EMkNG6Kvhps1g6tynz5KOo3cXMzrQw9BVXPnnZDk1M9GomnTyKkx3G5cf/Bg\n5J0aORIlXq2WS61TB8dKwq5eBPt8kDb198YMxuZ2Q93VtauykEhLUxhAIACHCwmpfjp1Cm3ZMqLh\nwzHeIUPwLcmFS6mDLUEkHHWISC0s7yWiribHDRNC9CGirUR0DzObCthCiNFENJqIqH79+gkeamw8\n+ihsC1u3wtvJSqrqw4e1v4NBIzH/8Ud8FJG8UCQ8HqJ77oGnFJGiGtDXa5b7pHvllVeimJFa8lB7\n3Ljd+EgXLFBqNaiZRl4e1AR6SSEUOr2lh4MHIUVlZGAuunSBKibSnHTuDH1+y5ba90KmHBk+HL97\n9sT7IFfsgQCe+Z498EzbswfSYk4OnpXPB0lRfhK9e+PvzJlYUMRKfbF7N46Rz11Kym433HR79FBS\nxathlotMqjwnT4YUe+WVSp2UcNiYtDA/H+lK8vMV1dSll4JJFjXFfcJRBom/FZT2u1pIRA2ZuS0R\nfUtEEXNMMvNbzNyJmTtVS5Ic2rYtPmSrdQwuucRYKnPgQKgbuneHofHCC/FbX4RHDSFgAHz9deh5\na9bE/0QwSOuJUoUKCKQiQp6m557DOdWqIQBPr8qaMQPBfO3a4R71bpDhMMYr78Xvx+9WrazNQ3nE\nLbeAWJ88CSK/ahXR+PGxz9PXeHY6tfEkM2eC2TgceK5PP42cVqNGQQrZuhWr986d4TCwcqXCHNS4\n5hqotmIxcclA9FJkfj6kxgsvNHeLlfXG9ahfH/aJ117TFtFyOGDYVhdzYjZKOtnZkRNhJg3l2AaR\nzBHvI6J6qt91C7b9fzDzEWaW2v23iahjCY2tRPDccyDQfj8+qCefxOrw+efxYa9Zo5SxjLbSEwLq\nqLfeQiK3jAyolmbOBGGScQ4OB1RWu3YhAIsIRKxJE6wyMzJgB9G/xx4PVF/r1kFFofaw8vkg/i9a\nhEC+K67AfSxaVApXeSWIDRu08SXZ2Zi/WHjlFejlJaEMhcCgn3gCvytXRvW4rCxc8557tOc7nVDV\nVKiA9+e22yAFSBw9isVGdjbel7ZtldrTVavieVqhYx4PpAe9TUHCzKsuFhYuRM1upxPjeOwx4zEV\nKxbu2sWOcsogkuZfS1Bv/UVEjYjIQ0S/E9GZumNqqf4fSkQrLfklJyEOQo9t25hvuAFxBLNmWTsn\nFNLmWrLaPB4kBdRvb9RIm+DP7Wbu0QP+59u3Iz9QIIDcTH4/80MPRR/fmjXGRH81aiA+woYWw4Zp\nkw/6/YgPsYI//zQ+z5QU5u++i35ebi7zl1/imciYCqcTeZWyspgnTULMS1oanvmKFcjVdOAA4lbm\nzbOet8npROzGtm3m++++29q9BoMYAzPG8eabyAm1dy+2TZigjLlSJeZffrF2XaugRMRBNGvGvHix\npZaI/kqyJbdzogEE28IOInq0YNs4Irqk4P/niGhjAfNYQkQtLD2wJDOIXbsQTCYT7wUC+DhjobAM\nIhAwBuY5nUgkaHa8wwFCoA+QCwSYf/st8vgmTjSe43QqH3iJ4eBBRBv+8EPyI60iICODuXlzzHMg\nwNyvH7LlWoX+PXA6kf03Ek6eZG7TxjxRYFoakuzps+VWrqydvhdesJ75lUgJntNv9/mYv/46+v0d\nOYLMtA4Hxvz00xiP34/zK1Rg3rIFxx4+zLxpE5hcopEwBrF0qaVW1hhEUmUeZl7EzM2YuTEzP1Ow\n7XFmXlDw/8PMfCYzn8XM5zLz5mSO1ypmzNBWcMvKQiqKWHA4oMKJ5JFiprLx+40GPocD3lS9epl7\nq4TDSrJANVwupWiQGWrUMHrzVahQwqqk1avhtnPNNYjOuvDC4ik9V0RUq4ZUE8uXQ7X01VfWPYeI\nkJdKjVAIzgCRvKHGj4f9wSzdyqlTsDnpU6pkZirurZmZUCPGE3vAbAzodDhg/L7gAvNzNmxArYyB\nA6FGDYcx5ieegPE5OxvXPHkSalIi2NVatozuyZdU2DYIG/FABpypYZWGvf46Ppa2bY3vk/6aLhcM\n3S6XNshJCBCnt99G3qS0NPNr6bcFg7BRRMKll8L7KTVVqVoXTw3nhOCaa8ANZZKrFStgbCmFcLth\neG3WLH4manZLO3cqmXv12LrVPEmjZOhm+baEQCK/9u1h94qVk8sK3G4Y6M3u99VX4fI6ahQem7q/\nUEjLnMLhMhZsmUAGESuAuOCYy4UQm4QQG4UQHyb0XlQon9EdScaIEVjRqVdzhw9j1fTJJ9qVUG4u\nPDp+/x3V4269lWj7dgSZxVrNeTyoRqZPu8CMICu3Gyu2devQ75tvKmNyuxFrsXcvjILM2N+kSeT+\nXC4QqAULcD89e1r32EoY9u3T/s7KgtVd4tdfER5+5plwoI+EEyeI1q4Ft+vQodSt7s48E89Xzfgd\nDvM4CCJ4jKlTcgihRC+vWqU9VghcmxmGbCIYrq0sYlwuJaVGOGx8R91uI6P65Re4Uj/+eGQm5Hbj\nmmr33UsuiT2eUoEElhy1EkAshGhKRA8TUU9mPiqEqJ6Qzs2QbB1XcbRk2yCYmVeuZG7aVFv9y+dD\nlTBm5tWrma+/XknaJ20ATZua65GdTuiQpW7a4TBWFpMG63POMY4nHIaet3Zt6Hnlddxu5nr1UC2v\nTKB3b23GwpQU5s8/x75HH8XkpaXhr6yAo8fmzTDaVKiA8/v1U7LprV2LsnsTJjD/+2/J3JMJwmEk\nblTfamqqYrzVo2tX7fvgcDC/8QbsRmrHAvkexWNrkO/uvffCSBwOo917r7ZPIZjr19c6LTz+ON5v\nvf1DNq8X99W+Pa4nbRB33FEy5iVKhA2iRQt88BZarP6IqDsRfa36/TARPaw75kUiGlXUcVtpSSfm\nxdHiYRCHDjFfcAFoSuPGzD/9ZPnUmLj0UuMH0bIlykNGqtxmRvSdTuZzz4UBuV8/EPk2bcw9Tvr3\nN68yJ3HsmPFjTUuD9wsz6OTkycx33qlk+SxV2LcPJdl8PtzI2LHYvm2b0WLq9Zpzvk6dtBMdCDBP\nmYJJCAQw4T4fJlqm2k0C/vkH2Vn9flSK+/nnyMempZm/Nykp2Od243ekbMB6Jwb1O+LzMV9zjXm/\n776L91AITN38+dgeDqNUbrR+AgHmV15BotPc3MTPnxUkhEG0bIkVn4VGRLuIaLWqjVZfi4iGE9Hb\nqt/XEtEk3THzCpjEz0S0koj6F/UeIrXTXsU0cCA0Dfn5MIz1748UEQ0aFP3ajRpp1QQOB9JvXH55\n9NoN6lQW0uC8fj38wj/6CL7w339vXols8ODoRV3M1AIyWVwwiPtfsQLjCwSgwtInVEsqateG/u3g\nQaiHZBTZvn2YbLXV1OVCCHr79og6lIrxnTu1Bp2sLCS8eukl5cGEQsh1cfXV0MV1744AgxJURdWq\nhXQTVtCoEexO6tuShYSIiJo3h+pz3LjY1/L7oQ564QUYjQcORL4lib//RoBeRgYi9WWsTlYWpmvT\nJnxDH3xgfn23W6lRcf751u6v1MP6e3GYmYtaFcNFRE2J6BxC/NiPQog2zBxBAVl4lC7FawkjJ8dY\nMEcIpLdIBB59FIn80tLQKlaE7l5mUtVD2gVSU5X3LRxGcNORI0hc1qEDCPnZZ5vr/8eMITrvPGNm\nTomsLKOxOycHfVSogEA9SSNlmoe9ewt3/8UGIRD6vWoVMsCNHw+3IX2uhsxMJADq3RseT5Iztm2r\nDdlNScHEmuV6WLyYaOpUhJLffHPx3lcR8Npr0WnUli0gyvqoZ59POxVEeA/vugvvXFYW0ezZimfd\nwYPgt++8QzRvHsw/atuFEGBqW7eaB3f6fHhsR4+WI+aQWC+mmAHEBNvEAmbOZ+adhFCBpgm5Fx1O\nawbhdhs/DqLERWpWqoRV3YwZ8Cj65hvYT/UE2uEAfRs4EAa9Nm3MrxcOIyp20CCc8+ij5q5/q1Yh\nP09mpjG//65dxlKggVL4a8YAACAASURBVABon5lU43KZ1x5IOqZOhRXzxRcxETLJkB7Z2bCafvml\nkilu+3ZMuHz4bjd+Dx5snFBJ/bKy4FpklvY0JycxLkBFwAMPxD5m7VosWAIB3HIggAWAdJzw+/HO\nLloUuYb1xx9ra1fowYzvp3Vro0u0wwEHh/btzb+7Mo3EMYhfiaipEKKREMJDRCOIaIHumHkE6YGE\nEFWJqBkh6DjxKC7dVTJbPDaI8eOhC5U61C5d4gtoigcTJ5rre6UaXSKSQU+tE962DbZWfWSzWTv3\nXMW4eeCA8RyfT1slTq3DPuOM4puPIqFCBeNExmN1NVOI//ILwt/T0xG2q58ov5/577+VMZw8iZKA\nTies/g88kISoQXRp5fZbtWI+dYp56lTml1/WBkXm5ODWolW+Y8b3oo7O17c2bZRr/N//KVHQVasy\n//FH8c1BYUGJsEG0asW8YYOlZqU/ih1ALIjoFSLaRER/ENGIot5DxLEU14WT2eL1Yvr2W+YnnoCd\nMicnrlMt47vvjB+TEMxnnWX8KM3KlapbaipKnI4di3KfVuifx8O8bBmuP3s2aF1aGoyYb71lzmh6\n947sNVNiOHkSA374YVg0pWeRnkpZsb5Ga04n3Lwkjh3Dg5CU1+0G9VNb7a+7ThtanpLC/P77JTs/\nBahcOfrtud3MI0YUrY+cHOYrrojch9fLPGOG9px//gFjKK3pWBLCIM48E6HeFloi+ivJlvQBFEcr\nDW6uelx4oflHZeYk89lniruf36+lfS4X6hwXJiVH/fpKH8eOYVFz4gR+P/641kP01VdLZl4iYvly\nJBFS34DLhWLG//7LPGSIMe9HUVuPHsoDOXUKxP6sszDhgwcjWdGsWXDVad/enCldf31SpmvhQrwr\nKSmYFiGU4Xk8yMu1b5+1a4XDWDDJNCG3346cSaNGRZdYAwHk6ypLSAiDaN2aeetWS62sMYjT3oup\npBCp4JSZvWPIEKQh+OEHBDp16wbj86ZNsE/88EPhskuoa01UqKDt+6mnoNLftg1BV23bxn/9IiMY\nhDJ92jTkdGY27s/IQPj2zJnIHW0WPlxYLF+O0PPbbkMNWFkIoWVLjOXqq3GcvuaqhNeL85OAgQMR\nELliBcwpTZuiFO60aaipkJEB3f8PPyBjajS8847Woevdd5Hp9bPPjDYttxu2i7w8+At06IDtR48i\nvcyOHag2eOutigpeFsmqVKkcZfwtZYGWCUOyOVRxtKJKEOoMk2bIzIQNwErysF9+gYZk7lxjjEPf\nvtpjjx/HdaOpuU6ejK5NkatHM3VWKRSstHj44cgBIur26KNIeZpI6SGWfsaKkr9CBWSWKwVYssRc\nVVm7NrL2vvce3nMzDBhgPK99e+YGDbTbPB7mBx9kPu88CHvnnce8Ywe+j8aNFS1gIMA8ejS+qTFj\nIAh6PMzdu0OSTSYoURLEX39ZaonoryRb0gdQHK2wDOLwYeZevZQMk1OnGo/59FPsS0mBCP7ttwjy\nmT+f+cMPtWL8XXfh2PR0iOaPPIJI6Ro1IK5PnszcrRsC9R56CMQ9JYW5YkXmq66CXrlGDW0m2HAY\nH7n+A/b5mP/zHxjCb73VuN/pRJbZUo0zzjAO3IzTrViBsNuSYhDxMJJ+/Zh3707qNO7ejfco2lAD\nAWjNzBZCN96oXYQIARXpp5/iPRYCBL5WLea2bRVG4HAwV6uG70AfuOd04n1X83+Pp+h2kaIiIQyi\nTRt8XBZaSTMIQjqOmNsinl+Sgy2pVlgGccEFWg+iQEAx7DIz799vXOCmpDC3bg1mkZaGtno1MjaY\nLYYdDtDB+++3tliW45g7VxnH+vVI5S0lhQoVmGfOxMd+/LjRwYcIq8lSj/btY0+Gw4FiFom2PySq\nCYGXYfnypE3jRx+ZR1abvVe//45zQiG8k34/ptbnQ5NeSNIDacUKLHRefBHb9MHr6emoBaEXuFwu\n5quvNo6hfn0ldUcykDAGsWePpZYEBrHWyrZIzbZBqLBsmdadPS8PQXM9e+L31q3GlM3BILarA9Nu\nugllQs3sDuEw0V9/oXKY1dTKWVnwP7/4YqL//Q+J1f77X8Q0jB8Pdf011yC4qXVrY0lGIWDH2Lgx\nCcn1rOLoUYT7/vYbaEckuFwohpxI20MiwQwl+8iRMBolAVWqRJ9CCZcL2QOIiCZOhNlF2hj8fpQy\nPfdc2MRkZoFu3ZRSoUeOGN/hcBixDvrtPXrAlOPzKcHuQqAFAviOLruM6L33IsdglFokMFlfoiCE\n6E5EPYiomhBCXUQ4nYgsR6GUrrtKMvT1gL1epTQ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+IKL1ls+Po6NhRPRKQRtakjcZb4vH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+A4ffuiatvrxXX4fLhfcpw+DXpLXVUuOgMGAqjzyMyEpxhtTBaMwXn8GgZxg/R+xtg4xphIL5hM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\ncBhehYWLE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"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "OYyAr4ijeyRl",
"outputId": "8ff255ca-1958-4d02-b212-dc8f1fe6a36c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87
}
},
"source": [
"# এখানে সাপোর্ট ভেক্টর মেশিন ব্যবহার করছি\n",
"from sklearn.svm import SVC\n",
"\n",
"clf = SVC(gamma='auto')\n",
"clf.fit(X, y)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
" tol=0.001, verbose=False)"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "C1ExvWGje9jx",
"colab": {}
},
"source": [
"# প্লটিং এর কিছু লাইব্রেরি ব্যবহার করছি, plot_decision_regions লাইব্রেরিটা বেশ ভালো\n",
"import matplotlib.pyplot as plt\n",
"from mlxtend.plotting import plot_decision_regions"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "84b_2PR_Yerq",
"outputId": "3a338b7e-8eae-47e3-b4d4-610eb34b7665",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284
}
},
"source": [
"plot_decision_regions(X=X, y=y, clf=clf, legend=2)\n",
"plt.xlabel(\"x\", size=5)\n",
"plt.ylabel(\"y\", size=5)\n",
"plt.title('SVM Decision Region Boundary', size=6)\n",
"plt.show()"
],
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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cP3PG7e0k9sxdON1xQOCm+gN3UbAa9QyNn/Fz3A/WDWqCUv/gt2hedQ1OdxuR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"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ZaADNuarXJtK"
},
"source": [
"মেশিন লার্নিং এ ফিচার ক্রসের সুবিধা থাকলেও আমার পছন্দের ব্যাপার হচ্ছে মডেলে নন লিনিয়ারিটি ঢুকানো। মডেলে নন লিনিয়ারিটি ঢুকাতে গেলে নিউরাল নেটওয়ার্ক ভালো একটা উপায়। আমরা একটা ছবি আঁকি লিনিয়ার মডেলের। তিনটে ইনপুট ফিচার। ইনপুট ফিচারের সাথে ওয়েটকে যোগ করে নিয়ে এলাম আউটপুটে। আপনার কি মনে হয় এভাবে মডেলে নন-লিনিয়ারিটি ঢুকানোর সম্ভব? আপনি বলুন।\n",
"\n",
" \n",
"\n",
"চিত্রঃ তিনটা ইনপুট যাচ্ছে একটা নিউরাল নেটওয়ার্কে "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5Upc4g4pXJtL"
},
"source": [
"পরের ছবিতে আমরা একটা হিডেন লেয়ার যোগ করি। হিডেন লেয়ারের অর্থ হচ্ছে এর মধ্যে কিছু মাঝামাঝি ভ্যালু যোগ করা। আগের ইনপুট লেয়ার থেকে এই লেয়ারে তাদের ওয়েটগুলোর যোগফল পাঠিয়ে দিচ্ছে সামনের লেয়ারে। এখানে সামনে লেয়ার হচ্ছে আউটপুট। ইনপুট থেকে ওয়েট যোগ করে সেগুলোকে পাঠিয়ে দিচ্ছে আউটপুট লেয়ারে। ইংরেজিতে আমরা বলি 'ওয়েটেড সাম অফ প্রিভিয়াস নোডস'। এখনো কি মডেলটা লিনিয়ার? মডেল অবশ্যই লিনিয়ার হবে কারণ আমরা এ পর্যন্ত যা করেছি তা সব লিনিয়ার ইনপুটগুলোকেই একসাথে করেছি। নন-লিনিয়ারিটি যোগ করার মতো এখনো কিছু করিনি। \n",
"\n",
" \n",
"চিত্রঃ যোগ করলাম প্রথম হিডেন লেয়ার, লিনিয়ারিটি বজায় থাকবে?\n",
"\n",
"এরকম করে আমরা যদি আরেকটা হিডেন লেয়ার যোগ করি তাহলে কি হবে? নন লিনিয়ার কিছু হতে পারে? না। আমরা যতই লেয়ার বাড়াই না কেন এই আউটপুট হচ্ছে আসলে ইনপুটের একটা ফাংশন। মানে হচ্ছে ইনপুটের ওয়েট গুলোর একটা যোগফল। যাই যোগফল হোকনা কেন সবই লিনিয়ার। এই যোগফল আসলে আমাদের নন লিনিয়ার সমস্যা মেটাবে না। \n",
"\n",
" \n",
"চিত্রঃ যোগ করলাম দ্বিতীয় হিডেন লেয়ার, লিনিয়ারিটি বজায় থাকবে?"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1EcN9ynXXJtL"
},
"source": [
"একটা নন লিনিয়ার সমস্যাকে মডেল করতে গেলে আমাদের মডেলে যোগ করতে হবে নন লিনিয়ার কিছু ফাংশন। ব্যাপারটা আমাদেরকে নিজেদেরকেই ঢোকাতে হবে। সবচেয়ে মজার কথা হচ্ছে আমরা এই ইনপুটগুলোকে পাইপ করে হিডেন লেয়ারের শেষে একটা করে নন লিনিয়ার ফাংশন যোগ করে দিতে পারি। এই ছবিটা দেখুন। আমরা এক নাম্বার হিডেন লেয়ার এর পর একটা করে নন লিনিয়ার ফাংশন যোগ করে দিয়েছি যাতে সেটার আউটপুট সে পাঠাতে পারে দ্বিতীয় হিডেন লেয়ারে। এই ধরনের নন লিনিয়ার ফাংশনকে আমরা এর আগেও বলেছি অ্যাক্টিভেশন ফাংশন। "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "v9VnlkqDXJtM"
},
"source": [
"আমাদের পছন্দের অ্যাক্টিভেশন ফাংশন হচ্ছে রেল্যু, রেকটিফাইড লিনিয়ার ইউনিট অ্যাক্টিভেশন ফাংশন। কাজে এটা স্মার্ট, অনেকের থেকে ভালো আর সে কারণে এর ব্যবহার অনেক বেশি। ভুল হবার চান্স কম। ডায়াগ্রাম দেখলেই বুঝতে পারবেন - যদি ইনপুট শূন্য হয় তাহলে আউটপুট ০ আর ইনপুটের মান ০ থেকে বেশি হয় তাহলে সেটার আউটপুটে যাবে পরের লেয়ারে যাওয়ার জন্য। \n",
" \n",
"চিত্রঃ ইনপুটের সবকিছুর 'ওয়েটেড সাম' থেকে ০ আসলে সেটাই থাকবে বেশি হলে ১, মানে পরের লেয়ারে পার "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "b_vktYi3XJtN"
},
"source": [
"আমরা যখন অ্যাক্টিভেশন ফাংশন যোগ করব তার সঙ্গে বেশি বেশি লেয়ার মডেলে ভালো কাজ করে। একটা নন লিনিয়ারিটি আরেকটা নন লিনিয়ারিটির উপর থাকাতে মডেল অনেক কমপ্লেক্স সম্পর্ক ধরতে পারে ইনপুট থেকে আউটপুট পর্যন্ত। মডেলের প্রতিটা লেয়ার তার অংশে কমপ্লেক্স জিনিসগুলো এক্সট্রাক্ট করতে পারে সেই কারণেই। অ্যাক্টিভেশন ফাংশন ছাড়া একটা নিউরাল নেটওয়াক আসলে আরেকটা লিনিয়ার রিগ্রেশন মডেল। এদিকে অ্যাক্টিভেশন ফাংশনে ব্যাক-প্রপাগেশন সম্ভব করে কারণ এর গ্রেডিয়েন্ট, তার এরর, ওয়েট এবং বায়াসকে আপডেট পাঠায়। শুরুর দিকের অ্যাক্টিভেশন ফাংশন হচ্ছে সিগময়েড। যার কাজ হচ্ছে যাই পাক না কেন সেটাকে ০ অথবা ১ এ পাঠিয়ে দেবে। \n",
" \n",
"চিত্রঃ ইনপুটের সবকিছুর 'ওয়েটেড সাম' পাল্টে দেবে ০ থেকে ১ এর মধ্যে এই সিগময়েড \n",
"\n",
"আবারো বলছি - সিগময়েড অ্যাক্টিভেশন ফাংশন লেয়ারগুলোর ইনপুটের/আউটপুটের যোগফলকে ০ অথবা ১ এর মধ্যে ফেলে দেয়। হয় এসপার না হলে ওসপার। লিনিয়ারিটির কোন স্কোপ থাকবে না। একটা ছবি দেখুন। ইকুয়েশন সহ। "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4Qg-wNIHXJtN"
},
"source": [
"আমার আরেকটা পছন্দের অ্যাক্টিভেশন ফাংশন হচ্ছে সফটম্যাক্স। এটা সাধারণত আমরা ব্যবহার করি দুইয়ের বেশি ক্লাসিফিকেশন সমস্যা হ্যান্ডেল করতে। সিগময়েড ভালো যখন আমরা দুটো ক্লাসিফিকেশন করি, তবে মাল্টিপল ক্লাসিফিকেশন এর জন্য সফটম্যাক্স অসাধারণ। যখন আউটপুট লেয়ার একটার সাথে আরেকটা 'মিউচুয়ালি এক্সক্লুসিভ' হয়, মানে কোন আউটপুট একটার বেশী আরেকটার ঘরে পড়বে না তাহলে সেটা 'সফটম্যাক্'স হ্যান্ডেল করবে। আমাদের যেকোনো শার্ট অথবা হাতে লেখা MNIST ইমেজগুলো যেকোন একটা ক্লাসেই পড়বে তার বাইরে নয়। "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "CFszInLxtAz6"
},
"source": [
"## ট্রেনিং/টেস্ট স্প্লিট করা "
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "pKVNZo7FtYNX",
"colab": {}
},
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
" test_size = 0.3, random_state=0)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Ea_iDHeYNeI_"
},
"source": [
"## একটা লজিস্টিক রিগ্রেশন করি \n",
"\n",
"আপনার মনে হচ্ছে কি হবে?"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "IhGTb_eiNoKE",
"outputId": "70949e75-f65f-480f-fbd2-0cafe4cb3763",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 159
}
},
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"lr = LogisticRegression()\n",
"lr.fit(X_train, y_train)"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
" multi_class='warn', n_jobs=None, penalty='l2',\n",
" random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
" warm_start=False)"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9aJnyQCFOZlD"
},
"source": [
"## একটা প্লটিং দেখি \n",
"\n",
"কিছুই হয়নি।"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "MDvAOwa5Ohqz",
"outputId": "7d75023c-9b71-442f-cfad-d2adc41b3b94",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 275
}
},
"source": [
"hticks = np.linspace(-2, 2, 101)\n",
"vticks = np.linspace(-2, 2, 101)\n",
"aa, bb = np.meshgrid(hticks, vticks)\n",
"ab = np.c_[aa.ravel(), bb.ravel()]\n",
"\n",
"c = lr.predict_proba(ab)[:,1]\n",
"cc = c.reshape(aa.shape)\n",
"\n",
"ax = df.plot(kind='scatter', c='target', x='lat', y='lon', cmap='bwr')\n",
"ax.contourf(aa, bb, cc, cmap='bwr', alpha=0.5)"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 12
},
{
"output_type": "display_data",
"data": {
"image/png": 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HKfW4SiBA5XfsmM5a22PRQzVgsiuIuCGlfB7A8wAwfPiUDskJbi3aaj0sX64L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\n/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"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gw_IZFTjkOCG"
},
"source": [
"## ২. একদম বেসিক নিউরাল নেটওয়ার্কে দেখি\n",
"১টা ইনপুট লেয়ার, ১টা নিউরন, ১টা আউটপুট।"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "DTcyMT39kOCO",
"outputId": "7668cb2b-8700-4393-8644-997e383005c0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 800
}
},
"source": [
"import tensorflow as tf\n",
"model = tf.keras.models.Sequential([\n",
" tf.keras.layers.Dense(1, input_dim=2, activation='tanh'),\n",
" tf.keras.layers.Dense(1, activation='sigmoid')\n",
"])\n",
"model.compile(tf.keras.optimizers.SGD(lr=0.5), 'binary_crossentropy', metrics=['accuracy'])\n",
"result = model.fit(X_train, y_train, epochs=20, validation_split=0.1)"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 945 samples, validate on 105 samples\n",
"Epoch 1/20\n",
"WARNING:tensorflow:From /tensorflow-2.0.0-rc2/python3.6/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
"945/945 [==============================] - 1s 1ms/sample - loss: 0.6553 - accuracy: 0.6275 - val_loss: 0.6547 - val_accuracy: 0.6381\n",
"Epoch 2/20\n",
"945/945 [==============================] - 0s 60us/sample - loss: 0.6446 - accuracy: 0.6582 - val_loss: 0.6548 - val_accuracy: 0.6381\n",
"Epoch 3/20\n",
"945/945 [==============================] - 0s 64us/sample - loss: 0.6440 - accuracy: 0.6582 - val_loss: 0.6544 - val_accuracy: 0.6381\n",
"Epoch 4/20\n",
"945/945 [==============================] - 0s 73us/sample - loss: 0.6452 - accuracy: 0.6582 - val_loss: 0.6559 - val_accuracy: 0.6381\n",
"Epoch 5/20\n",
"945/945 [==============================] - 0s 86us/sample - loss: 0.6428 - accuracy: 0.6582 - val_loss: 0.6557 - val_accuracy: 0.6381\n",
"Epoch 6/20\n",
"945/945 [==============================] - 0s 63us/sample - loss: 0.6443 - accuracy: 0.6582 - val_loss: 0.6564 - val_accuracy: 0.6381\n",
"Epoch 7/20\n",
"945/945 [==============================] - 0s 73us/sample - loss: 0.6434 - accuracy: 0.6582 - val_loss: 0.6550 - val_accuracy: 0.6381\n",
"Epoch 8/20\n",
"945/945 [==============================] - 0s 59us/sample - loss: 0.6440 - accuracy: 0.6582 - val_loss: 0.6566 - val_accuracy: 0.6381\n",
"Epoch 9/20\n",
"945/945 [==============================] - 0s 62us/sample - loss: 0.6431 - accuracy: 0.6582 - val_loss: 0.6577 - val_accuracy: 0.6381\n",
"Epoch 10/20\n",
"945/945 [==============================] - 0s 62us/sample - loss: 0.6429 - accuracy: 0.6582 - val_loss: 0.6546 - val_accuracy: 0.6381\n",
"Epoch 11/20\n",
"945/945 [==============================] - 0s 61us/sample - loss: 0.6446 - accuracy: 0.6582 - val_loss: 0.6549 - val_accuracy: 0.6381\n",
"Epoch 12/20\n",
"945/945 [==============================] - 0s 57us/sample - loss: 0.6431 - accuracy: 0.6582 - val_loss: 0.6572 - val_accuracy: 0.6381\n",
"Epoch 13/20\n",
"945/945 [==============================] - 0s 61us/sample - loss: 0.6451 - accuracy: 0.6582 - val_loss: 0.6543 - val_accuracy: 0.6381\n",
"Epoch 14/20\n",
"945/945 [==============================] - 0s 62us/sample - loss: 0.6439 - accuracy: 0.6582 - val_loss: 0.6547 - val_accuracy: 0.6381\n",
"Epoch 15/20\n",
"945/945 [==============================] - 0s 70us/sample - loss: 0.6426 - accuracy: 0.6582 - val_loss: 0.6551 - val_accuracy: 0.6381\n",
"Epoch 16/20\n",
"945/945 [==============================] - 0s 57us/sample - loss: 0.6440 - accuracy: 0.6582 - val_loss: 0.6544 - val_accuracy: 0.6381\n",
"Epoch 17/20\n",
"945/945 [==============================] - 0s 67us/sample - loss: 0.6432 - accuracy: 0.6582 - val_loss: 0.6553 - val_accuracy: 0.6381\n",
"Epoch 18/20\n",
"945/945 [==============================] - 0s 59us/sample - loss: 0.6448 - accuracy: 0.6582 - val_loss: 0.6542 - val_accuracy: 0.6381\n",
"Epoch 19/20\n",
"945/945 [==============================] - 0s 55us/sample - loss: 0.6428 - accuracy: 0.6582 - val_loss: 0.6544 - val_accuracy: 0.6381\n",
"Epoch 20/20\n",
"945/945 [==============================] - 0s 60us/sample - loss: 0.6422 - accuracy: 0.6582 - val_loss: 0.6539 - val_accuracy: 0.6381\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "sM0uZCaBQ-eF"
},
"source": [
"## অ্যাক্যুরেসি প্লটিং দেখি "
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "HWNaNWmOkOCS",
"outputId": "26e0218f-9a2e-4bfb-c17d-964bfdbe01ba",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 287
}
},
"source": [
"pd.DataFrame(result.history).plot(ylim=(-0.05, 1.05))"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 14
},
{
"output_type": "display_data",
"data": {
"image/png": 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U8NxzzzW5vgEDBgDwxBNPZKaPGzeOOXPmZMbT7wZOO+00tm/fztNPP83UqVNz7Z4WUbiL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\n7iIiEaRwFxGJIIW7iEgEKdxFRCLo/wOOMKDS/bBbYQAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "k_IrXnd4kOCy"
},
"source": [
"## ডিসিশন বাউন্ডারি কি ঠিক হলো?"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "kM3NKQijkOCz",
"outputId": "49eac514-9be4-4baf-cede-8e526804bfc6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 275
}
},
"source": [
"hticks = np.linspace(-2, 2, 101)\n",
"vticks = np.linspace(-2, 2, 101)\n",
"aa, bb = np.meshgrid(hticks, vticks)\n",
"ab = np.c_[aa.ravel(), bb.ravel()]\n",
"\n",
"# c = model.predict_proba(ab)[:,1]\n",
"c = model.predict_proba(ab)\n",
"cc = c.reshape(aa.shape)\n",
"\n",
"ax = df.plot(kind='scatter', c='target', x='lat', y='lon', cmap='bwr')\n",
"ax.contourf(aa, bb, cc, cmap='bwr', alpha=0.5)"
],
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 15
},
{
"output_type": "display_data",
"data": {
"image/png": 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rU0FbKzSNCuTpp2PdHkqwqO2Njawv+Id/YPuLn/2Mgn3jRnZjdaIoWlv1aXQA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\nBeHChQsXLmzx/wFIUWh/fMZXMwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "fKokeS3XkOC5",
"outputId": "14b6c209-e117-4065-e454-e42d63624d4a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 748
}
},
"source": [
"model = tf.keras.models.Sequential([\n",
" tf.keras.layers.Dense(4, input_dim=2, activation='tanh'),\n",
" # এই লেয়ার পরে যোগ করে আমরা দেখবো \n",
" # tf.keras.layers.Dense(4, activation='tanh'),\n",
" tf.keras.layers.Dense(1, activation='sigmoid')\n",
"])\n",
"model.compile(tf.keras.optimizers.SGD(lr=0.5), 'binary_crossentropy', metrics=['accuracy'])\n",
"h = model.fit(X_train, y_train, epochs=20, validation_split=0.1)"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 945 samples, validate on 105 samples\n",
"Epoch 1/20\n",
"945/945 [==============================] - 0s 451us/sample - loss: 0.6364 - accuracy: 0.6349 - val_loss: 0.6158 - val_accuracy: 0.6381\n",
"Epoch 2/20\n",
"945/945 [==============================] - 0s 61us/sample - loss: 0.5631 - accuracy: 0.6529 - val_loss: 0.5067 - val_accuracy: 0.6000\n",
"Epoch 3/20\n",
"945/945 [==============================] - 0s 60us/sample - loss: 0.4626 - accuracy: 0.8042 - val_loss: 0.4039 - val_accuracy: 0.8952\n",
"Epoch 4/20\n",
"945/945 [==============================] - 0s 62us/sample - loss: 0.3872 - accuracy: 0.8815 - val_loss: 0.3407 - val_accuracy: 0.8857\n",
"Epoch 5/20\n",
"945/945 [==============================] - 0s 59us/sample - loss: 0.3417 - accuracy: 0.8709 - val_loss: 0.3043 - val_accuracy: 0.8857\n",
"Epoch 6/20\n",
"945/945 [==============================] - 0s 61us/sample - loss: 0.3178 - accuracy: 0.8847 - val_loss: 0.2835 - val_accuracy: 0.9048\n",
"Epoch 7/20\n",
"945/945 [==============================] - 0s 60us/sample - loss: 0.3011 - accuracy: 0.8794 - val_loss: 0.2738 - val_accuracy: 0.8952\n",
"Epoch 8/20\n",
"945/945 [==============================] - 0s 64us/sample - loss: 0.2896 - accuracy: 0.8878 - val_loss: 0.2539 - val_accuracy: 0.8762\n",
"Epoch 9/20\n",
"945/945 [==============================] - 0s 61us/sample - loss: 0.2747 - accuracy: 0.8878 - val_loss: 0.2697 - val_accuracy: 0.8952\n",
"Epoch 10/20\n",
"945/945 [==============================] - 0s 61us/sample - loss: 0.2516 - accuracy: 0.8942 - val_loss: 0.2094 - val_accuracy: 0.8952\n",
"Epoch 11/20\n",
"945/945 [==============================] - 0s 61us/sample - loss: 0.2020 - accuracy: 0.9270 - val_loss: 0.1599 - val_accuracy: 0.9714\n",
"Epoch 12/20\n",
"945/945 [==============================] - 0s 62us/sample - loss: 0.1459 - accuracy: 0.9725 - val_loss: 0.1202 - val_accuracy: 0.9905\n",
"Epoch 13/20\n",
"945/945 [==============================] - 0s 56us/sample - loss: 0.1057 - accuracy: 0.9947 - val_loss: 0.0910 - val_accuracy: 0.9905\n",
"Epoch 14/20\n",
"945/945 [==============================] - 0s 57us/sample - loss: 0.0816 - accuracy: 0.9989 - val_loss: 0.0727 - val_accuracy: 1.0000\n",
"Epoch 15/20\n",
"945/945 [==============================] - 0s 62us/sample - loss: 0.0669 - accuracy: 0.9989 - val_loss: 0.0616 - val_accuracy: 1.0000\n",
"Epoch 16/20\n",
"945/945 [==============================] - 0s 60us/sample - loss: 0.0563 - accuracy: 1.0000 - val_loss: 0.0534 - val_accuracy: 1.0000\n",
"Epoch 17/20\n",
"945/945 [==============================] - 0s 54us/sample - loss: 0.0488 - accuracy: 1.0000 - val_loss: 0.0468 - val_accuracy: 1.0000\n",
"Epoch 18/20\n",
"945/945 [==============================] - 0s 57us/sample - loss: 0.0432 - accuracy: 1.0000 - val_loss: 0.0424 - val_accuracy: 1.0000\n",
"Epoch 19/20\n",
"945/945 [==============================] - 0s 67us/sample - loss: 0.0388 - accuracy: 1.0000 - val_loss: 0.0383 - val_accuracy: 1.0000\n",
"Epoch 20/20\n",
"945/945 [==============================] - 0s 62us/sample - loss: 0.0352 - accuracy: 1.0000 - val_loss: 0.0349 - val_accuracy: 1.0000\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "TAttI_euXJtl"
},
"source": [
"## একটা কনফিউশন ম্যাট্রিক্স তৈরি করি \n"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "aionVhhKXJtm",
"colab": {}
},
"source": [
"from sklearn.metrics import confusion_matrix, classification_report\n",
"\n",
"y_pred = model.predict_classes(X_test)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "ReiF4Z_0XJto",
"outputId": "860b2099-2a33-44d7-9a23-e4773e7e4997",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 110
}
},
"source": [
"cm = confusion_matrix(y_test, y_pred)\n",
"\n",
"pd.DataFrame(cm,\n",
" index=[\"Miss\", \"Hit\"],\n",
" columns=['pred_Miss', 'pred_Hit'])"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" pred_Miss \n",
" pred_Hit \n",
" \n",
" \n",
" \n",
" \n",
" Miss \n",
" 311 \n",
" 0 \n",
" \n",
" \n",
" Hit \n",
" 0 \n",
" 139 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" pred_Miss pred_Hit\n",
"Miss 311 0\n",
"Hit 0 139"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "UCp6QtU2XJtq",
"outputId": "89981d11-1003-46bb-c78d-85d27dd0d23b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 69
}
},
"source": [
"train_score = model.evaluate(X_train, y_train, verbose=0)[1]\n",
"test_score = model.evaluate(X_test, y_test, verbose=0)[1]\n",
"\n",
"print(\"\"\"Accuracy scores:\n",
" Train:\\t{:0.3}\n",
" Test:\\t{:0.3}\"\"\".format(train_score, test_score))"
],
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy scores:\n",
" Train:\t1.0\n",
" Test:\t1.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "TkA1ITQYvSn4"
},
"source": [
"## নতুন ডিসিশন বাউন্ডারি, অল্প লেয়ারেই \n",
"\n",
"একদম পারফেক্ট হয়েছে বলতে গেলে। এতো অল্প লেয়ারে। লেয়ার বাড়িয়ে দেখুন, কমেন্ট সরিয়ে আবার চালান মডেল। আরো ভালো রেজাল্ট পাবেন। "
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "3H0Eum25uDWv",
"outputId": "58abe259-9743-4cf5-8ecb-f1b8421fe49d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 275
}
},
"source": [
"hticks = np.linspace(-2, 2, 101)\n",
"vticks = np.linspace(-2, 2, 101)\n",
"aa, bb = np.meshgrid(hticks, vticks)\n",
"ab = np.c_[aa.ravel(), bb.ravel()]\n",
"\n",
"c = model.predict(ab)\n",
"cc = c.reshape(aa.shape)\n",
"\n",
"ax = df.plot(kind='scatter', c='target', x='lat', y='lon', cmap='bwr')\n",
"ax.contourf(aa, bb, cc, cmap='bwr', alpha=0.5)"
],
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 20
},
{
"output_type": "display_data",
"data": {
"image/png": 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YC6a5frNvVtq7WLSI99uOHRRKixfHWgeXXUaNPhQyNPFEM50BeztwM5wK4SIR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"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
}
]
}