5_neuralnetwork_fromscratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "118dfdaf-5c48-479f-922c-d8596ec1a727",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "819860ad-fea4-4751-98fe-2a0a26291289",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"insurance_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8bfb7ba8-7e90-4241-9095-0204aeeaece7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df[[\"age\",\"affordibility\"]],df.bought_insurance,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6958926d-836b-4ccf-896d-4f316e73fd9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# since ages are 1 to 100, ages will be divided to 100 in order to scale them\n",
    "\n",
    "X_train_scaled = X_train.copy()\n",
    "X_train_scaled[\"age\"] = X_train_scaled[\"age\"] / 100\n",
    "\n",
    "X_test_scaled = X_test.copy()\n",
    "X_test_scaled[\"age\"] = X_test_scaled[\"age\"] / 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b546b699-5237-49df-80fe-a5381100b1b7",
   "metadata": {},
   "source": [
    "# Tensorflow Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b75f892-21ed-4ed5-961e-5efda1e53d30",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5000\n",
      "1/1 [==============================] - 0s 189ms/step - loss: 0.7146 - accuracy: 0.5000\n",
      "Epoch 2/5000\n",
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      "Epoch 3/5000\n",
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      "Epoch 4/5000\n",
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      "Epoch 5/5000\n",
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      "Epoch 6/5000\n",
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      "Epoch 7/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7123 - accuracy: 0.5000\n",
      "Epoch 8/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7119 - accuracy: 0.5000\n",
      "Epoch 9/5000\n",
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      "Epoch 10/5000\n",
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      "Epoch 11/5000\n",
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      "Epoch 12/5000\n",
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      "Epoch 13/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7101 - accuracy: 0.5000\n",
      "Epoch 14/5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-12 09:09:33.796723: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7097 - accuracy: 0.5000\n",
      "Epoch 15/5000\n",
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      "Epoch 3964/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4999 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 0.4998 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 0.4998 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 0.4998 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 0.4996 - accuracy: 0.8182\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 0.4810 - accuracy: 0.9091\n",
      "Epoch 4844/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4810 - accuracy: 0.9091\n",
      "Epoch 4845/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4810 - accuracy: 0.9091\n",
      "Epoch 4846/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4809 - accuracy: 0.9091\n",
      "Epoch 4847/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4809 - accuracy: 0.9091\n",
      "Epoch 4848/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4809 - accuracy: 0.9091\n",
      "Epoch 4849/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4809 - accuracy: 0.9091\n",
      "Epoch 4850/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4809 - accuracy: 0.9091\n",
      "Epoch 4851/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4809 - accuracy: 0.9091\n",
      "Epoch 4852/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4808 - accuracy: 0.9091\n",
      "Epoch 4853/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4808 - accuracy: 0.9091\n",
      "Epoch 4854/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4808 - accuracy: 0.9091\n",
      "Epoch 4855/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4808 - accuracy: 0.9091\n",
      "Epoch 4856/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4808 - accuracy: 0.9091\n",
      "Epoch 4857/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4807 - accuracy: 0.9091\n",
      "Epoch 4858/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4807 - accuracy: 0.9091\n",
      "Epoch 4859/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4807 - accuracy: 0.9091\n",
      "Epoch 4860/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4807 - accuracy: 0.9091\n",
      "Epoch 4861/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4807 - accuracy: 0.9091\n",
      "Epoch 4862/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4806 - accuracy: 0.9091\n",
      "Epoch 4863/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4806 - accuracy: 0.9091\n",
      "Epoch 4864/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4806 - accuracy: 0.9091\n",
      "Epoch 4865/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4806 - accuracy: 0.9091\n",
      "Epoch 4866/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4806 - accuracy: 0.9091\n",
      "Epoch 4867/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4806 - accuracy: 0.9091\n",
      "Epoch 4868/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4805 - accuracy: 0.9091\n",
      "Epoch 4869/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4805 - accuracy: 0.9091\n",
      "Epoch 4870/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4805 - accuracy: 0.9091\n",
      "Epoch 4871/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4805 - accuracy: 0.9091\n",
      "Epoch 4872/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4805 - accuracy: 0.9091\n",
      "Epoch 4873/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4804 - accuracy: 0.9091\n",
      "Epoch 4874/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4804 - accuracy: 0.9091\n",
      "Epoch 4875/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4804 - accuracy: 0.9091\n",
      "Epoch 4876/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4804 - accuracy: 0.9091\n",
      "Epoch 4877/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4804 - accuracy: 0.9091\n",
      "Epoch 4878/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4803 - accuracy: 0.9091\n",
      "Epoch 4879/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4803 - accuracy: 0.9091\n",
      "Epoch 4880/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4803 - accuracy: 0.9091\n",
      "Epoch 4881/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4803 - accuracy: 0.9091\n",
      "Epoch 4882/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4803 - accuracy: 0.9091\n",
      "Epoch 4883/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4802 - accuracy: 0.9091\n",
      "Epoch 4884/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4802 - accuracy: 0.9091\n",
      "Epoch 4885/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4802 - accuracy: 0.9091\n",
      "Epoch 4886/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4802 - accuracy: 0.9091\n",
      "Epoch 4887/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4802 - accuracy: 0.9091\n",
      "Epoch 4888/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4802 - accuracy: 0.9091\n",
      "Epoch 4889/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4801 - accuracy: 0.9091\n",
      "Epoch 4890/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4801 - accuracy: 0.9091\n",
      "Epoch 4891/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4801 - accuracy: 0.9091\n",
      "Epoch 4892/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4801 - accuracy: 0.9091\n",
      "Epoch 4893/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4801 - accuracy: 0.9091\n",
      "Epoch 4894/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4800 - accuracy: 0.9091\n",
      "Epoch 4895/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4800 - accuracy: 0.9091\n",
      "Epoch 4896/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4800 - accuracy: 0.9091\n",
      "Epoch 4897/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4800 - accuracy: 0.9091\n",
      "Epoch 4898/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4800 - accuracy: 0.9091\n",
      "Epoch 4899/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4799 - accuracy: 0.9091\n",
      "Epoch 4900/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4799 - accuracy: 0.9091\n",
      "Epoch 4901/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4799 - accuracy: 0.9091\n",
      "Epoch 4902/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4799 - accuracy: 0.9091\n",
      "Epoch 4903/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4799 - accuracy: 0.9091\n",
      "Epoch 4904/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4799 - accuracy: 0.9091\n",
      "Epoch 4905/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4798 - accuracy: 0.9091\n",
      "Epoch 4906/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4798 - accuracy: 0.9091\n",
      "Epoch 4907/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4798 - accuracy: 0.9091\n",
      "Epoch 4908/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4798 - accuracy: 0.9091\n",
      "Epoch 4909/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4798 - accuracy: 0.9091\n",
      "Epoch 4910/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4797 - accuracy: 0.9091\n",
      "Epoch 4911/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4797 - accuracy: 0.9091\n",
      "Epoch 4912/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4797 - accuracy: 0.9091\n",
      "Epoch 4913/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4797 - accuracy: 0.9091\n",
      "Epoch 4914/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4797 - accuracy: 0.9091\n",
      "Epoch 4915/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4797 - accuracy: 0.9091\n",
      "Epoch 4916/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4796 - accuracy: 0.9091\n",
      "Epoch 4917/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4796 - accuracy: 0.9091\n",
      "Epoch 4918/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4796 - accuracy: 0.9091\n",
      "Epoch 4919/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4796 - accuracy: 0.9091\n",
      "Epoch 4920/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4796 - accuracy: 0.9091\n",
      "Epoch 4921/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4795 - accuracy: 0.9091\n",
      "Epoch 4922/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4795 - accuracy: 0.9091\n",
      "Epoch 4923/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4795 - accuracy: 0.9091\n",
      "Epoch 4924/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4795 - accuracy: 0.9091\n",
      "Epoch 4925/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4795 - accuracy: 0.9091\n",
      "Epoch 4926/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4794 - accuracy: 0.9091\n",
      "Epoch 4927/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4794 - accuracy: 0.9091\n",
      "Epoch 4928/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4794 - accuracy: 0.9091\n",
      "Epoch 4929/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4794 - accuracy: 0.9091\n",
      "Epoch 4930/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4794 - accuracy: 0.9091\n",
      "Epoch 4931/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4794 - accuracy: 0.9091\n",
      "Epoch 4932/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4793 - accuracy: 0.9091\n",
      "Epoch 4933/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4793 - accuracy: 0.9091\n",
      "Epoch 4934/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4793 - accuracy: 0.9091\n",
      "Epoch 4935/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4793 - accuracy: 0.9091\n",
      "Epoch 4936/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4793 - accuracy: 0.9091\n",
      "Epoch 4937/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4792 - accuracy: 0.9091\n",
      "Epoch 4938/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4792 - accuracy: 0.9091\n",
      "Epoch 4939/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4792 - accuracy: 0.9091\n",
      "Epoch 4940/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4792 - accuracy: 0.9091\n",
      "Epoch 4941/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4792 - accuracy: 0.9091\n",
      "Epoch 4942/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.9091\n",
      "Epoch 4943/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.9091\n",
      "Epoch 4944/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.9091\n",
      "Epoch 4945/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.9091\n",
      "Epoch 4946/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.9091\n",
      "Epoch 4947/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.9091\n",
      "Epoch 4948/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4790 - accuracy: 0.9091\n",
      "Epoch 4949/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4790 - accuracy: 0.9091\n",
      "Epoch 4950/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4790 - accuracy: 0.9091\n",
      "Epoch 4951/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4790 - accuracy: 0.9091\n",
      "Epoch 4952/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4790 - accuracy: 0.9091\n",
      "Epoch 4953/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4954/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4955/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4956/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4957/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4958/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4959/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4788 - accuracy: 0.9091\n",
      "Epoch 4960/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4788 - accuracy: 0.9091\n",
      "Epoch 4961/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4788 - accuracy: 0.9091\n",
      "Epoch 4962/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4788 - accuracy: 0.9091\n",
      "Epoch 4963/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4788 - accuracy: 0.9091\n",
      "Epoch 4964/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4965/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4966/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4967/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4968/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4969/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4970/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.9091\n",
      "Epoch 4971/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.9091\n",
      "Epoch 4972/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.9091\n",
      "Epoch 4973/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.9091\n",
      "Epoch 4974/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.9091\n",
      "Epoch 4975/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4976/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4977/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4978/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4979/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4980/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4981/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.9091\n",
      "Epoch 4982/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.9091\n",
      "Epoch 4983/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.9091\n",
      "Epoch 4984/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.9091\n",
      "Epoch 4985/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.9091\n",
      "Epoch 4986/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4783 - accuracy: 0.9091\n",
      "Epoch 4987/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4783 - accuracy: 0.9091\n",
      "Epoch 4988/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4783 - accuracy: 0.9091\n",
      "Epoch 4989/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4783 - accuracy: 0.9091\n",
      "Epoch 4990/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4783 - accuracy: 0.9091\n",
      "Epoch 4991/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4992/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4993/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4994/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4995/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4996/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4997/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4781 - accuracy: 0.9091\n",
      "Epoch 4998/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4781 - accuracy: 0.9091\n",
      "Epoch 4999/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4781 - accuracy: 0.9091\n",
      "Epoch 5000/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4781 - accuracy: 0.9091\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x285d5cfa0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kernel_initializer refers initial weights\n",
    "\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(1, input_shape=(2,), activation=\"sigmoid\", kernel_initializer=\"ones\", bias_initializer=\"zeros\")\n",
    "])\n",
    "\n",
    "model.compile(optimizer=\"adam\",\n",
    "              loss=\"binary_crossentropy\",\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(X_train_scaled, y_train, epochs=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5dfaee1e-bec8-481c-a054-f18a036b9697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 103ms/step - loss: 0.3153 - accuracy: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-12 09:12:05.653317: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3152589201927185, 1.0]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_scaled,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "709f9b1a-24e7-4a9d-98fe-97e92ca0278d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 41ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-12 09:12:15.139755: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.16282065],\n",
       "       [0.41183808],\n",
       "       [0.72550565],\n",
       "       [0.82668287],\n",
       "       [0.365132  ],\n",
       "       [0.80450743]], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "81b669f3-a58d-4d06-bc95-93e9b0133ed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[4.9196653],\n",
       "        [1.4286071]], dtype=float32),\n",
       " array([-2.8673053], dtype=float32))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# weights and bias\n",
    "coef, intercept = model.get_weights()\n",
    "coef, intercept"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "401887cc-fe1d-4412-851e-f9bcbcb8a1de",
   "metadata": {},
   "source": [
    "# My Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "128f102e-eda4-4e95-bd50-c1762af6f302",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid_numpy(X):\n",
    "    return 1/(1+np.exp(-X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dbda405f-b938-46c0-adf8-3e552fb4d85f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_loss(y_true, y_predicted):\n",
    "    epsilon = 1e-15\n",
    "    y_predicted_new = [max(i,epsilon) for i in y_predicted]\n",
    "    y_predicted_new = [min(i,1-epsilon) for i in y_predicted_new]\n",
    "    y_predicted_new = np.array(y_predicted_new)\n",
    "    return -np.mean(y_true*np.log(y_predicted_new)+(1-y_true)*np.log(1-y_predicted_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8a9c70e9-df60-4b43-8d0e-f2983b261aed",
   "metadata": {},
   "outputs": [],
   "source": [
    "class myNN:\n",
    "    def __init__(self):\n",
    "        self.w1 = 1\n",
    "        self.w2 = 1\n",
    "        self.bias = 0\n",
    "        \n",
    "    def fit(self, X, y, epochs, loss_threshold):\n",
    "        self.w1, self.w2, self.bias = self.gradient_descent(X[\"age\"],X[\"affordibility\"],y, epochs, loss_threshold)\n",
    "        print(f\"Final weights and bias: w1:{self.w1}, w2:{self.w2}, bias:{self.bias}\")\n",
    "        \n",
    "    def predict(self, X_test):\n",
    "        weighted_sum = self.w1*X_test[\"age\"] + self.w2*X_test[\"affordibility\"] + self.bias\n",
    "        return sigmoid_numpy(weighted_sum)\n",
    "    \n",
    "    def gradient_descent(self, age, affordability, y_true, epochs, loss_threshold):\n",
    "        w1 = w2 = 1\n",
    "        bias = 0\n",
    "        rate = 0.5\n",
    "        n = len(age)\n",
    "        for i in range(epochs):\n",
    "            weighted_sum = w1*age + w2*affordability + bias\n",
    "            y_predicted = sigmoid_numpy(weighted_sum)\n",
    "            loss = log_loss(y_true, y_predicted)\n",
    "            \n",
    "            w1d = (1/n)*np.dot(np.transpose(age), (y_predicted-y_true))\n",
    "            w2d = (1/n)*np.dot(np.transpose(affordability), (y_predicted-y_true))\n",
    "            bias_d = np.mean(y_predicted-y_true)\n",
    "            \n",
    "            w1 = w1 - rate*w1d\n",
    "            w2 = w2 - rate*w2d\n",
    "            bias = bias - rate*bias_d\n",
    "            \n",
    "            if i%50==0:\n",
    "                print(f\"Epoch:{i}, w1:{w1}, w2:{w2}, bias:{bias}, loss:{loss}\")\n",
    "                \n",
    "            if loss<=loss_threshold:\n",
    "                print(f\"Epoch:{i}, w1:{w1}, w2:{w2}, bias:{bias}, loss:{loss}\")\n",
    "                break\n",
    "                \n",
    "        return w1, w2, bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b73cd5c9-c9a6-4f1d-9062-ff21856f8729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:0, w1:0.973391914875179, w2:0.9484790550524685, bias:-0.1133436520447742, loss:0.714610156590683\n",
      "Epoch:50, w1:1.434118908813528, w2:1.1205902839299346, bias:-1.213046034118164, loss:0.5746014191876968\n",
      "Epoch:100, w1:2.079276479842114, w2:1.3122169122569924, bias:-1.6236034220276634, loss:0.5494269324988902\n",
      "Epoch:150, w1:2.6881856069465204, w2:1.3899807229711387, bias:-1.9333767096348242, loss:0.5304146897215756\n",
      "Epoch:200, w1:3.251444113258653, w2:1.4247457568978774, bias:-2.1914685336563027, loss:0.5149521957697487\n",
      "Epoch:250, w1:3.7684786837804563, w2:1.44358672453857, bias:-2.4182835285402966, loss:0.502142692070128\n",
      "Epoch:300, w1:4.2420074958184, w2:1.4570679388107317, bias:-2.623099364061675, loss:0.4914526974026106\n",
      "Epoch:350, w1:4.675802877729502, w2:1.4692197476438587, bias:-2.8106134907801184, loss:0.48248435593518396\n",
      "Epoch:378, w1:4.902834960491424, w2:1.476022916232298, bias:-2.909122064576124, loss:0.478094272032023\n",
      "Final weights and bias: w1:4.902834960491424, w2:1.476022916232298, bias:-2.909122064576124\n"
     ]
    }
   ],
   "source": [
    "customModel = myNN()\n",
    "customModel.fit(X_train_scaled, y_train, epochs=8000, loss_threshold=0.4781)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e136b350-537d-40b3-abe5-cb325586cfea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predictions comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b35dc3a5-4417-47a0-bf9b-7ad9e647f060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     0.156644\n",
       "0     0.412298\n",
       "14    0.724978\n",
       "9     0.826013\n",
       "19    0.365728\n",
       "17    0.803852\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customModel.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "47dd6f38-8abf-415c-b1f6-0617ce307ebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 10ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.16282065],\n",
       "       [0.41183808],\n",
       "       [0.72550565],\n",
       "       [0.82668287],\n",
       "       [0.365132  ],\n",
       "       [0.80450743]], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5985f82-7c87-445d-b9a5-3858ec970033",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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