nn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOJTY+mwbc0ocjXyPZSkLO0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mohitmaurya2023/AI_Intern_at_IBM/blob/main/nn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "-UnXDzLrdup_"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "train_input = np.array(\n",
        "    [\n",
        "        [1, 1, 0],\n",
        "        [0, 1, 0],\n",
        "        [0, 0, 1],\n",
        "        [1, 0, 0]\n",
        "    ]\n",
        ")\n",
        "train_output = np.array(\n",
        "    [\n",
        "        [1],\n",
        "        [0],\n",
        "        [0],\n",
        "        [1]\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weight = 2*np.random.random((3,1))-1"
      ],
      "metadata": {
        "id": "OO86rISjeZHO"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weight"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWJrQ6DFekPO",
        "outputId": "14b2d77f-2f30-4057-bcb6-c40789abfb45"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.76857316],\n",
              "       [ 0.41280464],\n",
              "       [-0.9123852 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#summation\n",
        "# def summation(a, b):\n",
        "#     return np.data(a, b)\n",
        "# activation\n",
        "\n",
        "def activation(x):\n",
        "    return (1/(1+np.exp(-x)))"
      ],
      "metadata": {
        "id": "z3JpoRhfemMW"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_output = activation(np.dot(train_input, weight))"
      ],
      "metadata": {
        "id": "qb33_6KmgXl9"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uD0lvdZngktG",
        "outputId": "3c7ee770-c77f-4a11-f60a-0987fc6b7479"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.76519545],\n",
              "       [0.60176019],\n",
              "       [0.286512  ],\n",
              "       [0.68321216]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.round(pred_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jptyZJdgoR-",
        "outputId": "f7b3ea16-3c12-4f6f-e08d-63e71222eed4"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.],\n",
              "       [1.],\n",
              "       [0.],\n",
              "       [1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "error = train_output-pred_output\n",
        "error"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56CHQ-5Zg0f-",
        "outputId": "ab392e40-8dae-4442-9b43-80a7c92f0eb3"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.23480455],\n",
              "       [-0.60176019],\n",
              "       [-0.286512  ],\n",
              "       [ 0.31678784]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# error normalisation so we use derivaties\n",
        "def derivative(x):\n",
        "  return (x*(1-x))"
      ],
      "metadata": {
        "id": "dpgEeYbqqSvS"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "adjusted_value = error * derivative(pred_output)"
      ],
      "metadata": {
        "id": "3rP70JnEqhxe"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weight += np.dot(train_input.T, adjusted_value)\n",
        "weight"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ijPwVjlirJ--",
        "outputId": "8c4615d6-4c39-4c75-86cb-8bddf24b1e38"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.87932426],\n",
              "       [ 0.31078356],\n",
              "       [-0.97095481]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(10):\n",
        "  pred_output = activation(np.dot(train_input, weight)) # output 1\n",
        "  error = train_output-pred_output\n",
        "  updates = error*derivative(pred_output)\n",
        "  weight +=np.dot(train_input.T, updates)"
      ],
      "metadata": {
        "id": "XxRk8lFrikgG"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.round(pred_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NcLVTUMtrfKm",
        "outputId": "9ed516f4-0ba5-4f0f-d353-2fb22ef16bcd"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.],\n",
              "       [0.],\n",
              "       [0.],\n",
              "       [1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "qnwG8M5XltZW"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2zyWxwnn8uT",
        "outputId": "bd74343b-8d51-472a-ee90-f4539f734b2f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "au6mXlSIooRW",
        "outputId": "4ffaea79-3886-457a-89bd-ecf3b4d150cf"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JKru7rwao4wW",
        "outputId": "18096e11-1868-4ccd-ab3a-b611a93f1b50"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(4,4))\n",
        "plt.matshow(x_train[4])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "dhdHWu_Io70W",
        "outputId": "e6f5c4e2-c1d0-446a-a64c-8c773b9efadb"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f190a5dd360>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 480x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEE2J20upFCN",
        "outputId": "8c0ce300-3c35-4fdb-a9c4-5e22713673e6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i6L262Y-psh-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}