{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
},
"colab": {
"name": "deep_learning_decision_boundary.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tTHOELyYaau8",
"colab_type": "text"
},
"source": [
"# ডিপ লার্নিং দিয়ে ডিসিশন বাউন্ডারি, চাঁদ আকারের ডাটাসেট\n",
"\n",
"যেকোনো প্রজেক্ট এর কাজের শুরুতে আমার একটা অভ্যাস হচ্ছে সেই ডাটাসেটকে প্লট করে দেখা। একটা ডাটাসেটের ঠিকমতো প্লটিং দেখতে পারলে তার সমস্যার অনেকটাইকুল কিনারা করা যায়। এজন্যই ডাটা ভিজুয়ালাইজেশন এতটাই দরকারি। আমরা যখন সাইকিট-লার্ন নিয়ে কাজ করছিলাম তখন এধরনের ডাটার অনেক প্লটিং দেখেছিলাম। \n",
"\n",
" চিত্রঃ বিভিন্ন ধরনের ডেটার প্লটিং "
]
},
{
"cell_type": "code",
"metadata": {
"id": "msN8Wuy6aau-",
"colab_type": "code",
"colab": {}
},
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "pKwEWPS7aavB",
"colab_type": "text"
},
"source": [
"## অল্প এবং বেশি গভীর ডিপ নিউরাল নেটওয়ার্কের কিছু কাজ\n",
"\n",
"ডাক্তার প্লটিং এর পাশাপাশি প্রয়োজনে দরকারি ডাটাসেট না পেলে অনেক ধরনের ডাটাসেট স্যাম্পল জেনারেট করতে হয় যা আসলে আমাদের দরকারি সাইজ এবং কম্প্লেক্সিটি বুঝেই করতে হয়। এই ধরনের জেনারেটর আমাদের ফিচার এবং তার করেসপন্ডিং ‘ডিসক্রিট’ টার্গেটের একটা ম্যাট্রিক্স তৈরি করে দেয়। পাশাপাশি কিছু নয়েজও ঢোকাতে হয় রিয়ালিস্টিক করার জন্য। লিনিয়ার ডাটাসেটের পাশাপাশি কিছু নন-লিনিয়ার ডাটা নিয়ে কাজ করতে গেলে আলাদা জেনারেটর ব্যবহার করি আমরা। যেমন, দুইটা বৃত্তাংশ একটা আরেকটার মধ্যে ঢুকে গেছে। দুটা অর্ধেক চাঁদ। সাইকিট-লার্নে এটার একটা ফাংশন আছে, দেখুন এই উদাহরণে।"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7r6bqn8U9hSL",
"colab_type": "text"
},
"source": [
"আমাদের কাজ হবে এই ধরনের একটা নন-লিনিয়ার ডাটাসেটে কিভাবে ডিপ লার্নিং দিয়ে সুন্দর ক্লাসিফিকেশন করা যায়। এর আগেও করেছি, তবে এই ডেটা জেনারেটর দিয়ে তৈরি, এবং আরো কমপ্লেক্স। একে sklearn.datasets থেকে ইমপোর্ট করছি এই নতুন ফাংশন দিয়ে। ফিচার এবং টার্গেট ভ্যারিয়েবল হিসেবে দুটো আলাদা আলাদা স্যাম্পল জেনারেট করছি। এরপর প্লটিং। \n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "fwu3F2vfaavB",
"colab_type": "code",
"outputId": "d6fdbe94-1240-41a1-e405-ea40b99a6179",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 287
}
},
"source": [
"from sklearn.datasets import make_moons\n",
"\n",
"X, y = make_moons(n_samples=1000, noise=0.1, random_state=0)\n",
"\n",
"plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
"plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
"plt.legend(['0', '1'])"
],
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 2
},
{
"output_type": "display_data",
"data": {
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MjyImxkQChYfz3PPn0zQifRLmzKF5RDQXXyGTQnilaF1kJJ3lyckkiLNm0b8h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Me/PQfAS2GO/Vd43Eh+ZQKllAIwHcCHzRyPg4NDrMBVMbdbNFcx5AK4Rin1CYBJ/tdQSmUqpR6T\ng5RS6QBSAazxfP5vSqktALYA6APg/zZzPA4ODg4OzUSzspK01p8BuDrI/nIAt1qvdwFIDnLct5tz\nfQcHBweHlofjSnJwcHBwCIBTDA4ODg4OAXCKwcHBwcEhAO2yg5tS6gCAyja6fB8AB9vo2mcLN+bW\ngRtz68CN+eyRprWOWAjWLhVDW0IpVR4N10gswY25deDG3DpwYz73cK4kBwcHB4cAOMXg4ODg4BAA\npxjOHPltPYCzgBtz68CNuXXgxnyO4WIMDg4ODg4BcBaDg4ODg0MAnGKIAKVUjlJqq1LqtFIqZFaB\nUmqKUupjpdSn/m52bYZoWq76j2u02qq+3Nrj9I8h7LwppboppVb431/r591qU0Qx5rlKqQPW3N4a\n7DytBaXUE0qpWqVUUJJKRfzJ/302K6WuaO0xBhlTpDF/Syl1xJrj37T2GIOMKVUptVop9ZFfZvxH\nkGNibq6DQmvt/sL8AbgUwBCwO11miGPiAOwAcBGArgA2AbisDce8BMAi//YiAA+EOO5oG89txHkD\n8CMAf/FvzwKwoh2MeS6A/27LcXrGkwXgCgAfhng/G8DrABSAsQDWtoMxfwtsC9zm82uNaSCAK/zb\n5wOoCPJsxNxcB/tzFkMEaK23aa0/jnDYGACfaq13aq1PAigA2562FaaBrVbh/z+9DccSDtHMm/1d\nngdwtZ+mva0Qa/c6IrTWJQAOhTlkGoCnNVEGIEEo8dsKUYw55qC13qu1/od/+wsA29CUPDTm5joY\nnGJoGSQDqLJeVyMIm2wrItqWq/H+dqllSqm2UB7RzNs/j9FanwJwBMCFrTK64Ij2Xv/Q7yp4XimV\n2jpDO2vE2vMbLcYppTYppV5XSg1r68HY8Ls8RwFY63mrXcx1s2i3OwqUUqsADAjy1q+01uGaD7UZ\nwo3ZfqG11kqpUKlnaVrrPUqpiwC8rZTaorXe0dJj7YR4BcCzWusTSqn5oMXjKOZbFv8An9+jSqls\nAC8CuKSNxwQAUEr1BPB3AD/TWn/e1uM5GzjFgPDtS6PEHrARkSDFv++cIdyYo225qrXe4/+/Uyn1\nDrjCaU3FEM28yTHVSqmvALgAwGetM7ygiDhmzT4lgsfAmE8so9Wf3+bCFrha6yKl1CNKqT5a6zbl\nI1JKfRVUCn/TWv9PkEPaxVw7V1LLYB2AS5RSGUqprmCQtE2yfPyI2HJVKZWolOrm3+4DYDyAj1pt\nhEQ082Z/l+sAvK39Ubw2QsQxe3zG3wd9zbGMlwH8H3/GzFgARyxXZExCKTVAYk1KqTGgLGvLBYO0\nKH4cwDat9R9CHNY+5rqto9+x/gdgBugHPAFgP4Bi//4kAEXWcdlgFsIO0AXVlmO+EMBbAD4BsApA\nb//+TACP+bevAluqbvL/v6WNxtpk3gDcB+D7/u14AIUAPgXwAYCLYuCZiDTm3wPY6p/b1QCGtvF4\nnwWwF8CX/mf5FgC3A7jd/74C8Gf/99mCENl3MTbmf7fmuAzAVTEw5m8C0AA2A9jo/8uO9bkO9ucq\nnx0cHBwcAuBcSQ4ODg4OAXCKwcHBwcEhAE4xODg4ODgEwCkGBwcHB4cAOMXg4ODg4BAApxgcHBwc\nHALgFIODg4ODQwCcYnBwcHBwCMD/B8EefoxettCRAAAAAElFTkSuQmCC\n",
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