{
"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": [
"I wanted to show how Neural network is better than generic machine learning.\n",
"\n",
"1. I wanted to show with sklearn, it is difficult when we wanted to show descision boundary in non linear data.\n",
"\n",
"2. I used SVM to do this, but with a helper function like 'plot_decision_regions', can it be done with logistic regression with straight line to show that it is not possible. See [out] 13.\n",
"\n",
"3. [Out] shows that with only input 1, output 1 neuron it is not possible. It need atleast 4 input neuron to make it happen.\n",
"\n",
"3. I could not make [out] 13 look like [out] 16. \n",
"\n",
"4. Please ignore the text markups.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JzBIxKCynCim",
"colab_type": "text"
},
"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": {
"id": "4BjZLitwXUI6",
"colab_type": "code",
"outputId": "9359de0c-9ee2-4d92-cd0c-652aa90e5031",
"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",
"# Loading some example data\n",
"iris = datasets.load_iris()\n",
"X = iris.data[:, [0, 2]]\n",
"y = iris.target\n",
"\n",
"# Training a classifier\n",
"svm = SVC(C=0.5, kernel='linear')\n",
"svm.fit(X, y)\n",
"\n",
"\n",
"# Plotting decision regions\n",
"plot_decision_regions(X, y, clf=svm, legend=2)\n",
"\n",
"# Adding axes annotations\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": {
"id": "j6QY3PjWXJs4",
"colab_type": "text"
},
"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",
" 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": {
"id": "N9rRARqdXJs5",
"colab_type": "text"
},
"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": {
"id": "OCwPfWSfXJs8",
"colab_type": "text"
},
"source": [
"## ভার্সন চেক করি"
]
},
{
"cell_type": "code",
"metadata": {
"id": "dBXgoSoNXJs9",
"colab_type": "code",
"outputId": "95ad95bb-5cdd-4726-85b2-76bdb5567b61",
"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": {
"id": "-peF4S1Noc39",
"colab_type": "code",
"outputId": "6093c833-e479-43a2-fa3f-5b4f3bc9a98e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" # শুধুমাত্র 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": "code",
"metadata": {
"id": "dCGChruQXJtC",
"colab_type": "code",
"outputId": "1b6de002-4e9a-49ed-c9aa-bc93d001d68e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
"import tensorflow as tf\n",
"tf.__version__"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'2.0.0-rc2'"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"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": {
"id": "268qDbW0XJtF",
"colab_type": "text"
},
"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। চেষ্টা করি 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": "0c74d211-2d2f-442e-f392-711ff6e978cc",
"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": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
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Og0FjLQ0zvPoq0YgRSEXy7LOYDyvn2SgCyqmKqeyN+DTDG28gdcXevdBD33MPjLTFheHD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\n6E5EPYiomhBCXUQ4nYgsR6GUrrtKMvT1gL1epTQ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+IKL1ls+Po6NhRPRKQRtakjcZb4vH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"text/plain": [
"
"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZaADNuarXJtK",
"colab_type": "text"
},
"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": {
"id": "5Upc4g4pXJtL",
"colab_type": "text"
},
"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": {
"id": "1EcN9ynXXJtL",
"colab_type": "text"
},
"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": {
"id": "v9VnlkqDXJtM",
"colab_type": "text"
},
"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": {
"id": "b_vktYi3XJtN",
"colab_type": "text"
},
"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": {
"id": "4Qg-wNIHXJtN",
"colab_type": "text"
},
"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 "
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "IhGTb_eiNoKE",
"outputId": "340fd2b3-a754-482d-b578-2d947c948309",
"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": 12,
"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": 12
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9aJnyQCFOZlD"
},
"source": [
"## একটা প্লটিং দেখি "
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "MDvAOwa5Ohqz",
"outputId": "b5d1c63c-236f-4b69-9556-76fe4b02849b",
"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(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": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 13
},
{
"output_type": "display_data",
"data": {
"image/png": 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z/VbnrLRjoVwue/ZQKC1cGG0dzJlDjT4UMjRx1XAvHpzmCyjEKoTTdefuoGb4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