neuralNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fb959b52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from math import exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9a47631e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9820137900379085, 0.9999991684719722, 0.9999999992417439, 1.0, 0.9999399336076413, 0.9955037268390589]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fbf191ecd00>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xi = [4, 7, 3, 6, 8.1, 1]\n",
    "wi = [1, 2, 7, 9, 1.2, 5.4]\n",
    "\n",
    "neurons = np.multiply(xi, wi)\n",
    "bias = 2\n",
    "n = sum(neurons) + bias\n",
    "\n",
    "def sigmoid(ls: list):\n",
    "    \n",
    "    ls1 = []\n",
    "    \n",
    "    for x in ls:\n",
    "        sigmoid_x = 1 / (1 + np.exp(-x))\n",
    "        ls1.append(sigmoid_x)\n",
    "    \n",
    "    return ls1\n",
    "\n",
    "\n",
    "scope = [i for i in neurons]\n",
    "print(sigmoid(scope))\n",
    "plt.plot(scope, sigmoid(scope))\n",
    "\n",
    "plt.axhline(0, color='black', linewidth=.5)\n",
    "plt.axvline(0, color='black', linewidth=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85ae9f5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 16])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2])\n",
    "b = np.array([9, 8])\n",
    "np.multiply(a,b)"
   ]
  }
 ],
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