MNIST_by_Soura.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPR0WCEsfy6iGN4L0pmZatv",
      "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/souraOP/MNIST-MODEL-SOURA/blob/main/MNIST_by_Soura.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "id": "lkvAD0_1ghF9"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_set = pd.read_csv('/content/train.csv')"
      ],
      "metadata": {
        "id": "1OdxeDdZgyeU"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "XauL_8bMh6pJ",
        "outputId": "8c96fd9b-7476-47d3-da1d-a6394c8e7351"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
              "0      1       0       0       0       0       0       0       0       0   \n",
              "1      0       0       0       0       0       0       0       0       0   \n",
              "2      1       0       0       0       0       0       0       0       0   \n",
              "3      4       0       0       0       0       0       0       0       0   \n",
              "4      0       0       0       0       0       0       0       0       0   \n",
              "\n",
              "   pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
              "0       0  ...         0         0         0         0         0         0   \n",
              "1       0  ...         0         0         0         0         0         0   \n",
              "2       0  ...         0         0         0         0         0         0   \n",
              "3       0  ...         0         0         0         0         0         0   \n",
              "4       0  ...         0         0         0         0         0         0   \n",
              "\n",
              "   pixel780  pixel781  pixel782  pixel783  \n",
              "0         0         0         0         0  \n",
              "1         0         0         0         0  \n",
              "2         0         0         0         0  \n",
              "3         0         0         0         0  \n",
              "4         0         0         0         0  \n",
              "\n",
              "[5 rows x 785 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-215ef320-a436-4374-a0ad-b8e314532e7b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>pixel0</th>\n",
              "      <th>pixel1</th>\n",
              "      <th>pixel2</th>\n",
              "      <th>pixel3</th>\n",
              "      <th>pixel4</th>\n",
              "      <th>pixel5</th>\n",
              "      <th>pixel6</th>\n",
              "      <th>pixel7</th>\n",
              "      <th>pixel8</th>\n",
              "      <th>...</th>\n",
              "      <th>pixel774</th>\n",
              "      <th>pixel775</th>\n",
              "      <th>pixel776</th>\n",
              "      <th>pixel777</th>\n",
              "      <th>pixel778</th>\n",
              "      <th>pixel779</th>\n",
              "      <th>pixel780</th>\n",
              "      <th>pixel781</th>\n",
              "      <th>pixel782</th>\n",
              "      <th>pixel783</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-215ef320-a436-4374-a0ad-b8e314532e7b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-215ef320-a436-4374-a0ad-b8e314532e7b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-215ef320-a436-4374-a0ad-b8e314532e7b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Loading our data\n",
        "data_set = np.array(data_set)\n",
        "data_values, features = data_set.shape\n",
        "np.random.shuffle(data_set) # shuffling our data before splitting it into data_dev and training sets\n",
        "\n",
        "data_dev = data_set[0:1000].T\n",
        "y_dev = data_dev[0]\n",
        "x_dev = data_dev[1:features]\n",
        "x_dev = x_dev/255\n",
        "\n",
        "training_data = data_set[1000:data_values].T\n",
        "y_train = training_data[0]\n",
        "x_train = training_data[1:features]\n",
        "x_train = x_train / 255\n",
        "_, m_train = x_train.shape\n"
      ],
      "metadata": {
        "id": "iytLUQMFlBqO"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "866h4Ej7HLcb",
        "outputId": "5f9b5f0e-5f18-4b4a-cef3-dbf873b897fb"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 9, 9, ..., 2, 9, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def ReLU(Z):   \n",
        "    return np.maximum(Z, 0)\n",
        "\n",
        "def softmax(Z):\n",
        "    #Compute softmax values for each sets of scores in x.\n",
        "    exp = np.exp(Z - np.max(Z)) \n",
        "    return exp / exp.sum(axis=0)\n",
        "\n",
        "def derivative_ReLU(Z):   #Derivative of Leaky ReLU\n",
        "  return Z > 0\n",
        "\n",
        "def one_hot(Y):     #took reference from other sites\n",
        "  one_hot_Y = np.zeros((Y.size, Y.max() + 1))  #9 + 1 = 10\n",
        "  one_hot_Y[np.arange(Y.size), Y] = 1    #X-Axis: creates an array from 0 to data_values (no. of training examples). \n",
        "                                         #Y-Axis: no. of columns we are accessing\n",
        "                                         #Function: for each row go to the column specified by the label Y and set it to 1\n",
        "  one_hot_Y = one_hot_Y.T  #Transpose this-> since we want every column as an example\n",
        "  return one_hot_Y\n",
        "\n",
        "def first_parameters():\n",
        "  W1 = np.random.normal(size=(10, 784)) * np.sqrt(1./(784))\n",
        "  B1 = np.random.normal(size=(10, 1)) * np.sqrt(1./10)\n",
        "  W2 = np.random.normal(size=(10, 10)) * np.sqrt(1./20)\n",
        "  B2 = np.random.normal(size=(10, 1)) * np.sqrt(1./(784))\n",
        "  return W1, B1, W2, B2\n",
        "\n",
        "def forward_propagation(W1, B1, W2, B2, X):\n",
        "  Z1 = W1.dot(X) + B1\n",
        "  A1 = ReLU(Z1)\n",
        "  Z2 = W2.dot(A1) + B2\n",
        "  A2 = softmax(Z2)\n",
        "  return Z1, Z2, A1, A2\n",
        "\n",
        "def backward_propagation(Z1, A1, Z2, A2, W1, W2, X, Y):\n",
        "  one_hot_Y = one_hot(Y)\n",
        "  m = Y.size\n",
        "  dZ2 = 2*(A2 - one_hot_Y)\n",
        "  dW2 = 1/m * dZ2.dot(A1.T)\n",
        "  dB2 = 1/m * sum(dZ2)\n",
        "  dZ1 = W2.T.dot(dZ2) * derivative_ReLU(Z1)\n",
        "  dW1 = 1/m * dZ1.dot(X.T)\n",
        "  dB1 = 1/m * sum(dZ1)\n",
        "  return dW1, dW2, dB1, dB2\n",
        "\n",
        "def parameters_update(W1, W2, B1, B2, dW1, dW2, dB1, dB2, alpha):\n",
        "  W2 = W2 - alpha * dW2\n",
        "  B2 = B2 - alpha * dB2\n",
        "  W1 = W1 - alpha * dW1\n",
        "  B1 = B1 - alpha * dB1\n",
        "  return W1, B1, W2, B2"
      ],
      "metadata": {
        "id": "29WIGRlOqOiO"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Running our gradient descent\n",
        "def gradient_descent(X, Y, alpha, iterations):\n",
        "  W1, B1, W2, B2 = first_parameters()\n",
        "  for i in range(iterations):\n",
        "    Z1, Z2, A1, A2 = forward_propagation(W1, B1, W2, B2, X)\n",
        "    dW1, dW2, dB1, dB2 = backward_propagation(Z1, A1, Z2, A2, W1, W2, X, Y)\n",
        "    W1, B1, W2, B2 = parameters_update(W1, W2, B1, B2, dW1, dW2, dB1, dB2, alpha)\n",
        "    if (i % 10 == 0):\n",
        "      print(\"No. of iterations: \", i)\n",
        "      pred = output_pred(A2)\n",
        "      print(\"Accuracy is: \", accuracy(pred, Y))\n",
        "  return W1, B1, W2, B2\n",
        "\n",
        "def output_pred(A2):\n",
        "  return np.argmax(A2, 0)\n",
        "\n",
        "def accuracy(pred, Y):\n",
        "  print(pred, Y)\n",
        "  return (np.sum(pred == Y)/Y.size)*100"
      ],
      "metadata": {
        "id": "3JusB26SDciN"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "W1, B1, W2, B2 = gradient_descent(x_train, y_train, 0.05, 500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IcGF2uaFw1f",
        "outputId": "084616a7-86de-4900-e344-f3c025f8e5d5"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of iterations:  0\n",
            "[0 2 9 ... 0 2 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  16.17560975609756\n",
            "No. of iterations:  10\n",
            "[9 9 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  29.424390243902437\n",
            "No. of iterations:  20\n",
            "[8 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  40.236585365853664\n",
            "No. of iterations:  30\n",
            "[8 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  52.57560975609756\n",
            "No. of iterations:  40\n",
            "[8 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  67.99268292682927\n",
            "No. of iterations:  50\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  75.60487804878049\n",
            "No. of iterations:  60\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  78.77804878048781\n",
            "No. of iterations:  70\n",
            "[4 4 9 ... 2 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  80.78780487804879\n",
            "No. of iterations:  80\n",
            "[4 4 9 ... 2 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  82.20975609756097\n",
            "No. of iterations:  90\n",
            "[4 4 9 ... 2 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  83.18292682926828\n",
            "No. of iterations:  100\n",
            "[4 4 9 ... 2 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  84.12926829268292\n",
            "No. of iterations:  110\n",
            "[4 4 9 ... 2 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  84.8\n",
            "No. of iterations:  120\n",
            "[4 4 9 ... 2 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  85.28536585365853\n",
            "No. of iterations:  130\n",
            "[4 4 9 ... 2 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  85.71463414634147\n",
            "No. of iterations:  140\n",
            "[4 4 9 ... 2 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  86.1609756097561\n",
            "No. of iterations:  150\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  86.51219512195122\n",
            "No. of iterations:  160\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  86.90243902439025\n",
            "No. of iterations:  170\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  87.20243902439024\n",
            "No. of iterations:  180\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  87.42682926829268\n",
            "No. of iterations:  190\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  87.6780487804878\n",
            "No. of iterations:  200\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  87.84634146341463\n",
            "No. of iterations:  210\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  88.02926829268293\n",
            "No. of iterations:  220\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  88.21463414634147\n",
            "No. of iterations:  230\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  88.3780487804878\n",
            "No. of iterations:  240\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  88.49024390243902\n",
            "No. of iterations:  250\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  88.57073170731708\n",
            "No. of iterations:  260\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  88.65853658536585\n",
            "No. of iterations:  270\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  88.82439024390244\n",
            "No. of iterations:  280\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  88.9609756097561\n",
            "No. of iterations:  290\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.04878048780488\n",
            "No. of iterations:  300\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.13414634146342\n",
            "No. of iterations:  310\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.20731707317073\n",
            "No. of iterations:  320\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.27073170731707\n",
            "No. of iterations:  330\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.34390243902439\n",
            "No. of iterations:  340\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.4219512195122\n",
            "No. of iterations:  350\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.50243902439024\n",
            "No. of iterations:  360\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.54878048780488\n",
            "No. of iterations:  370\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.60487804878049\n",
            "No. of iterations:  380\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.69512195121952\n",
            "No. of iterations:  390\n",
            "[4 4 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.72926829268293\n",
            "No. of iterations:  400\n",
            "[4 5 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.79024390243903\n",
            "No. of iterations:  410\n",
            "[4 5 9 ... 0 9 0] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.84146341463415\n",
            "No. of iterations:  420\n",
            "[4 5 9 ... 0 9 5] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.9\n",
            "No. of iterations:  430\n",
            "[4 5 9 ... 0 9 5] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  89.96829268292683\n",
            "No. of iterations:  440\n",
            "[4 5 9 ... 0 9 5] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  90.00731707317074\n",
            "No. of iterations:  450\n",
            "[4 5 9 ... 0 9 5] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  90.04634146341463\n",
            "No. of iterations:  460\n",
            "[4 5 9 ... 0 9 5] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  90.09024390243903\n",
            "No. of iterations:  470\n",
            "[4 5 9 ... 0 9 5] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  90.1219512195122\n",
            "No. of iterations:  480\n",
            "[4 5 9 ... 0 9 5] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  90.16829268292683\n",
            "No. of iterations:  490\n",
            "[4 5 9 ... 0 9 5] [5 9 9 ... 2 9 5]\n",
            "Accuracy is:  90.20487804878049\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predictionMaker(X, W1, B1, W2, B2):\n",
        "  _, _, _, A2 = forward_propagation(W1, B1, W2, B2, X)\n",
        "  pred = output_pred(A2)\n",
        "  return pred\n",
        "\n",
        "def predictionTesting(ind, W1, B1, W2, B2):\n",
        "  initialImage = x_train[:, ind, None]\n",
        "  pred = predictionMaker(x_train[:, ind, None], W1, B1, W2, B2)\n",
        "  label = y_train[ind]\n",
        "  print(\"Prediction is -> \", pred)\n",
        "  print(\"Label is: \", label)\n",
        "  initialImage = initialImage.reshape((28, 28)) * 255\n",
        "  plt.gray()\n",
        "  plt.imshow(initialImage, interpolation = 'nearest')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "O2jg-uBELnyu"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictionTesting(89, W1, B1, W2, B2)\n",
        "predictionTesting(30, W1, B1, W2, B2)\n",
        "predictionTesting(54, W1, B1, W2, B2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 868
        },
        "id": "SfzXEnkwM1lq",
        "outputId": "1a47f4bf-ffe0-49f2-ce38-1569218b38eb"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction is ->  [8 8 8 ... 8 8 8]\n",
            "Label is:  8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction is ->  [2 2 2 ... 2 2 2]\n",
            "Label is:  2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction is ->  [3 3 3 ... 3 3 3]\n",
            "Label is:  3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}