ANN LAB Training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1+ np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_inputs = np.array([[0,0,1],\n",
    "                          [1,1,1],\n",
    "                          [1,0,1],\n",
    "                          [0,1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_outputs = np.array([[0,1,1,0]]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "synaptic_weights = 2 * np.random.random((3,1))-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.16595599],\n",
       "       [ 0.44064899],\n",
       "       [-0.99977125]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synaptic_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Starting Synaptic Weights:  [[-0.16595599]\n",
      " [ 0.44064899]\n",
      " [-0.99977125]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Random Starting Synaptic Weights: \", synaptic_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.16595599]\n",
      " [ 0.44064899]\n",
      " [-0.99977125]]\n"
     ]
    }
   ],
   "source": [
    "print(synaptic_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outputs after Training: \n",
      "[[0.2689864 ]\n",
      " [0.3262757 ]\n",
      " [0.23762817]\n",
      " [0.36375058]]\n"
     ]
    }
   ],
   "source": [
    "for iteration in range(1):\n",
    "    input_layer = training_inputs\n",
    "    outputs = sigmoid(np.dot(input_layer, synaptic_weights))\n",
    "    print(\"Outputs after Training: \")\n",
    "    print(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1+ np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid_derivative(x):\n",
    "    return x * (1 - x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Starting Synaptic Weights: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.16595599],\n",
       "       [ 0.44064899],\n",
       "       [-0.99977125]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_inputs = np.array([[0,0,1],\n",
    "                          [1,1,1],\n",
    "                          [1,0,1],\n",
    "                          [0,1,1]])\n",
    "\n",
    "training_outputs = np.array([[0,1,1,0]]).T\n",
    "np.random.seed(1)\n",
    "synaptic_weights = 2 * np.random.random((3,1))-1\n",
    "print(\"Random Starting Synaptic Weights: \")\n",
    "synaptic_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Synaptic Weights after Training: \n",
      "[[11.78376704]\n",
      " [-0.20460659]\n",
      " [-5.68765926]]\n",
      "Output after Training: \n",
      "[[0.0033761 ]\n",
      " [0.99724477]\n",
      " [0.99775343]\n",
      " [0.00275313]]\n"
     ]
    }
   ],
   "source": [
    "for iteration in range(20000):\n",
    "    input_layer = training_inputs\n",
    "    outputs = sigmoid(np.dot(input_layer, synaptic_weights))\n",
    "    error = training_outputs - outputs\n",
    "    adjustments = error * sigmoid_derivative(outputs)\n",
    "    synaptic_weights += np.dot(input_layer.T,adjustments)\n",
    "\n",
    "print(\"Synaptic Weights after Training: \")\n",
    "print(synaptic_weights)\n",
    "print(\"Output after Training: \",)\n",
    "print(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Design 2-input AND Gate training process using ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Problem Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1+ np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Starting Synaptic Weights: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[8.34044009e-01],\n",
       "       [1.44064899e+00],\n",
       "       [2.28749635e-04]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_inputs = np.array([[0,0,1],\n",
    "                          [1,1,1],\n",
    "                          [1,0,1],\n",
    "                          [0,1,1]])\n",
    "\n",
    "training_outputs = np.array([[0,1,1,0]]).T\n",
    "np.random.seed(1)\n",
    "synaptic_weights = 2 * np.random.random((3,1))-0\n",
    "print(\"Random Starting Synaptic Weights: \")\n",
    "synaptic_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Synaptic Weights after Training: \n",
      "[[10.47741794]\n",
      " [-0.20607592]\n",
      " [-5.03326221]]\n",
      "Output after Training: \n",
      "[[0.00647546]\n",
      " [0.99471749]\n",
      " [0.99569701]\n",
      " [0.00527591]]\n"
     ]
    }
   ],
   "source": [
    "for iteration in range(20000):\n",
    "    input_layer = training_inputs\n",
    "    outputs = sigmoid(np.dot(input_layer, synaptic_weights))\n",
    "    error = training_outputs - outputs\n",
    "    adjustments = error * sigmoid_derivative(outputs)\n",
    "    synaptic_weights += np.dot(input_layer.T,adjustments)\n",
    "\n",
    "print(\"Synaptic Weights after Training: \")\n",
    "print(synaptic_weights)\n",
    "print(\"Output after Training: \",)\n",
    "print(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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