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Digit Recognition Using BackPropagation Algorithm.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "Digit Recognition Using BackPropagation Algorithm.ipynb", | |
| "provenance": [], | |
| "authorship_tag": "ABX9TyPTI6OKaZRHOz+pfklA+Ilf", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| }, | |
| "accelerator": "GPU" | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/sayak2k1maruti/6350cb4180983be345b2c5eedcd8a7dc/digit-recognition-using-backpropagation-algorithm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# ***Digit Recognition using Back Propagation Algorithm*** 📸😃\n", | |
| "\n", | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "AbXe9OwLL0MX" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "! nvidia-smi" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "aGW5NYkMbvoR", | |
| "outputId": "c9dc9cfb-6df9-44b0-e82b-d31c621cef9f" | |
| }, | |
| "execution_count": 34, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Sat Jan 29 15:39:03 2022 \n", | |
| "+-----------------------------------------------------------------------------+\n", | |
| "| NVIDIA-SMI 495.46 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", | |
| "|-------------------------------+----------------------+----------------------+\n", | |
| "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", | |
| "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", | |
| "| | | MIG M. |\n", | |
| "|===============================+======================+======================|\n", | |
| "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", | |
| "| N/A 32C P8 11W / 70W | 0MiB / 15109MiB | 0% Default |\n", | |
| "| | | N/A |\n", | |
| "+-------------------------------+----------------------+----------------------+\n", | |
| " \n", | |
| "+-----------------------------------------------------------------------------+\n", | |
| "| Processes: |\n", | |
| "| GPU GI CI PID Type Process name GPU Memory |\n", | |
| "| ID ID Usage |\n", | |
| "|=============================================================================|\n", | |
| "| No running processes found |\n", | |
| "+-----------------------------------------------------------------------------+\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## **Importing All Modules**" | |
| ], | |
| "metadata": { | |
| "id": "3arP7Hr-MIB3" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 35, | |
| "metadata": { | |
| "id": "ZScLlBRlZVyz" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from pandas import read_csv\n", | |
| "from matplotlib import pyplot as plt\n", | |
| "from numpy import exp\n", | |
| "from numpy.random import seed\n", | |
| "from numpy.random import rand\n", | |
| "from numpy import matmul\n", | |
| "from numpy import matrix\n", | |
| "from numpy import transpose as T\n", | |
| "from numpy import multiply\n", | |
| "from numpy import sqrt\n", | |
| "from numpy import mean\n", | |
| "from numpy import maximum\n", | |
| "from numpy import c_\n", | |
| "from numpy.random import shuffle\n", | |
| "from numpy import argmax\n", | |
| "from numpy import zeros\n", | |
| "from google.colab import data_table\n", | |
| "from google.colab import files\n", | |
| "from matplotlib.pyplot import plot\n", | |
| "data_table.enable_dataframe_formatter()\n", | |
| "from google.colab import drive" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## ***Fixing Seed for every random() events*:🙂**" | |
| ], | |
| "metadata": { | |
| "id": "uFgQzoPJMOWg" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "seed(348322342) #random" | |
| ], | |
| "metadata": { | |
| "id": "shqFK6VUuba8" | |
| }, | |
| "execution_count": 36, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## ***Importing Training Datase***" | |
| ], | |
| "metadata": { | |
| "id": "1N2NQshsPo4h" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### ***Downoading from Google drive***" | |
| ], | |
| "metadata": { | |
| "id": "OTpjwvObPzMC" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "drive.mount(\"/gdrive\")\n", | |
| "!ls \"/gdrive/MyDrive/Colab Notebooks/\"" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "vM3fpK0tPwJ4", | |
| "outputId": "3c4fcc84-dbd5-472a-f9e2-4a0de4489b8d" | |
| }, | |
| "execution_count": 37, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n", | |
| " BackPropagation.ipynb\t\t\t\t matlabPlot.ipynb\n", | |
| " bisection.ipynb\t\t\t\t matplotlib-iris.ipynb\n", | |
| "'BP_1 hidden layer.ipynb'\t\t\t Primes.ipynb\n", | |
| " BP_in_irish_dataset.ipynb\t\t\t RandomFunctions.ipynb\n", | |
| "'bpskAWGN (1).ipynb'\t\t\t\t 'Song Recommander.ipynb'\n", | |
| " bpskAWGN.ipynb\t\t\t\t\t test.csv\n", | |
| "'Central value Theorm.ipynb'\t\t\t turi_test.ipynb\n", | |
| "'Classifying images using deep features .ipynb' turi-test.ipynb\n", | |
| "'Copy of Song Recommander.ipynb'\t\t Untitled\n", | |
| " digit_recognizer_train.csv\t\t\t Untitled0.ipynb\n", | |
| " flask.ipynb\t\t\t\t\t Untitled1.ipynb\n", | |
| " FND02-NB01.ipynb\t\t\t\t Untitled2.ipynb\n", | |
| "'FND05-NB01 (1).ipynb'\t\t\t\t Untitled3.ipynb\n", | |
| " FND05-NB01.ipynb\t\t\t\t Untitled4.ipynb\n", | |
| " FND06-NB01.ipynb\t\t\t\t Untitled5.ipynb\n", | |
| "'FND06-NB02 (1).ipynb'\t\t\t\t Untitled6.ipynb\n", | |
| " FND06-NB02.ipynb\t\t\t\t Untitled7.ipynb\n", | |
| " image_recognision_BP.ipynb\t\t\t Untitled8.ipynb\n", | |
| "'Image retrival using Deep Learning.ipynb'\t vsCOdeinCollab.ipynb\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### ***Reading Data***" | |
| ], | |
| "metadata": { | |
| "id": "k2uCJ5K_P_Ex" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "raw_training_data = read_csv(\"/gdrive/MyDrive/Colab Notebooks/digit_recognizer_train.csv\",delimiter = \",\")\n", | |
| "raw_training_data.head()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 317 | |
| }, | |
| "id": "iU1i8faxQB_e", | |
| "outputId": "d9bd95f6-a177-4a95-eefe-2065d145b9e6" | |
| }, | |
| "execution_count": 38, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Warning: Total number of columns (785) exceeds max_columns (20). Falling back to pandas display.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-2fce2b62-2bda-4e13-96e7-7ff62e70b70c\">\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", | |
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| " <th>pixel18</th>\n", | |
| " <th>pixel19</th>\n", | |
| " <th>pixel20</th>\n", | |
| " <th>pixel21</th>\n", | |
| " <th>pixel22</th>\n", | |
| " <th>pixel23</th>\n", | |
| " <th>pixel24</th>\n", | |
| " <th>pixel25</th>\n", | |
| " <th>pixel26</th>\n", | |
| " <th>pixel27</th>\n", | |
| " <th>pixel28</th>\n", | |
| " <th>pixel29</th>\n", | |
| " <th>pixel30</th>\n", | |
| " <th>pixel31</th>\n", | |
| " <th>pixel32</th>\n", | |
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| " <th>pixel34</th>\n", | |
| " <th>pixel35</th>\n", | |
| " <th>pixel36</th>\n", | |
| " <th>pixel37</th>\n", | |
| " <th>pixel38</th>\n", | |
| " <th>...</th>\n", | |
| " <th>pixel744</th>\n", | |
| " <th>pixel745</th>\n", | |
| " <th>pixel746</th>\n", | |
| " <th>pixel747</th>\n", | |
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| " <th>pixel781</th>\n", | |
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| " <tr>\n", | |
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| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 785 columns</p>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fce2b62-2bda-4e13-96e7-7ff62e70b70c')\"\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-2fce2b62-2bda-4e13-96e7-7ff62e70b70c 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-2fce2b62-2bda-4e13-96e7-7ff62e70b70c');\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", | |
| " " | |
| ], | |
| "text/plain": [ | |
| " label pixel0 pixel1 pixel2 ... pixel780 pixel781 pixel782 pixel783\n", | |
| "0 1 0 0 0 ... 0 0 0 0\n", | |
| "1 0 0 0 0 ... 0 0 0 0\n", | |
| "2 1 0 0 0 ... 0 0 0 0\n", | |
| "3 4 0 0 0 ... 0 0 0 0\n", | |
| "4 0 0 0 0 ... 0 0 0 0\n", | |
| "\n", | |
| "[5 rows x 785 columns]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 38 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "raw_training_data.shape" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "HAeWaV8vcDV5", | |
| "outputId": "cf5e564d-95c9-495a-f105-bdf8629611fc" | |
| }, | |
| "execution_count": 39, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(42000, 785)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 39 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "(785 - 1)**.5" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "XyPENi_hfj5N", | |
| "outputId": "db2f4f03-7fa4-4d21-c413-cc076c5f7014" | |
| }, | |
| "execution_count": 40, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "28.0" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 40 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# i.e images are 28 pixel * 28 pixel " | |
| ], | |
| "metadata": { | |
| "id": "SyVRANAvfrNA" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### ***Converting Pandas dataframe to numpy matrix***" | |
| ], | |
| "metadata": { | |
| "id": "VsYFbC9RQMQe" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "data = matrix(raw_training_data.to_numpy())\n", | |
| "plt.imshow(data[10,1:].reshape((28, 28)) * 255)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 282 | |
| }, | |
| "id": "ad_1nW4HQMkt", | |
| "outputId": "3818e7ee-65d4-4705-91f8-842c76824fcb" | |
| }, | |
| "execution_count": 42, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f5b8244b6d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 42 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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z3jT7rpbpz/PiC7qTzXL3yyRdI+mW7O1qU/K+z2DNNHY6qGm8G2WAacbfVOa+q3X687zKCPt+SZP7PT4/W9YU3H1/dtst6Sk131TUB9+YQTe77S65nzc10zTeA00zribYd2VOf15G2NdLmmpmF5nZUEkfk7SihD5OYmYjsy9OZGYjJV2t5puKeoWkBdn9BZKeLrGXt2iWabwrTTOukvdd6dOfu3vD/yRdq75v5P9L0hfL6KFCX1Mk/Wf2t63s3iQtV9/buh71fbdxs6SzJa2WtEPSv0ka10S9/aP6pvberL5gTSypt1nqe4u+WdKm7O/asvddoq+G7Dd+LgsEwRd0QBCEHQiCsANBEHYgCMIOBEHYgSAIOxDE/wHJwHbCMPWULgAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### ***Function to Print from 28 * 28 pixel array***" | |
| ], | |
| "metadata": { | |
| "id": "uyZGPR_uQhWr" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def print_image(X):\n", | |
| " plt.imshow(X.reshape((28, 28)) * 255)\n", | |
| " plt.show()\n", | |
| "\n", | |
| "print_image(data[9,1:])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 265 | |
| }, | |
| "id": "no_7rxJyQ03F", | |
| "outputId": "3be5fb8b-8243-4b01-be10-3a7a46289183" | |
| }, | |
| "execution_count": 44, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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JU5qot59K2iZpqwaDNb2i3q7W4Cb6Vklbstuiqj+7RF8N+dz4uSwQBDvogCAIOxAEYQeCIOxAEIQdCIKwA0EQdiCI/wPfDT2RU9Mk6AAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## ***All functions defination***" | |
| ], | |
| "metadata": { | |
| "id": "Y6H9vUPRMnYH" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### ***Sigmoid Function***\n", | |
| "\n", | |
| "\n", | |
| "> Sigmoid function is used as a activation function here\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "zRIT-f3QNvPm" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def Sigmoid(x):\n", | |
| " return 1/(1+exp(-x))" | |
| ], | |
| "metadata": { | |
| "id": "XLlUolysOKlz" | |
| }, | |
| "execution_count": 45, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### ***Forward Propagartion***" | |
| ], | |
| "metadata": { | |
| "id": "taQit4GeOMHr" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def model(x,w1,w2):\n", | |
| " '''\n", | |
| " x => inputs\n", | |
| " w1,w2 => weights\n", | |
| " '''\n", | |
| " v1 = matmul(w1,x)\n", | |
| " hidden_layer_out = Sigmoid(v1)\n", | |
| " v2 = matmul(w2,hidden_layer_out)\n", | |
| " output = Sigmoid(v2)\n", | |
| " return [output,hidden_layer_out]" | |
| ], | |
| "metadata": { | |
| "id": "SoO820gJOSBy" | |
| }, | |
| "execution_count": 46, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### ***Back Propagation***" | |
| ], | |
| "metadata": { | |
| "id": "zf-vR5iVOS9x" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def backPropagation(w1,w2,inputs,target):\n", | |
| " \n", | |
| " learning_factor = .1\n", | |
| " \n", | |
| " n = inputs[:,1].size\n", | |
| " for i in range(0,n):\n", | |
| " x = inputs[i,:].transpose()\n", | |
| " target_out = target[i,:].transpose()\n", | |
| "\n", | |
| " [output,hidden_layer_out] = model(x,w1,w2)\n", | |
| "\n", | |
| " ###Error at output layer\n", | |
| " error_out = target_out - output\n", | |
| " output_ = 1- output\n", | |
| " activation_negotiation_2 = multiply(output,output_)\n", | |
| " delta_out = multiply(activation_negotiation_2,error_out)\n", | |
| "\n", | |
| " ###Back Propagationg Output layer error to hidden layer\n", | |
| " w2_t = w2.transpose()\n", | |
| " error_hidden = matmul(w2_t,delta_out)\n", | |
| " hidden_layer_out_ = 1 - hidden_layer_out \n", | |
| " activation_negotiation_1 = multiply(hidden_layer_out,hidden_layer_out_)\n", | |
| " delta_hidden = multiply(activation_negotiation_1,error_hidden)\n", | |
| "\n", | |
| "\n", | |
| " #w1 and w2 value correction\n", | |
| " hidden_layer_out_T = hidden_layer_out.transpose()\n", | |
| " x_T = x.transpose()\n", | |
| " dw2 = learning_factor * matmul(delta_out,hidden_layer_out_T)\n", | |
| " dw1 = learning_factor * matmul(delta_hidden,x_T)\n", | |
| " w2 = w2 + dw2 #updating w1\n", | |
| " w1 = w1 + dw1 #updating w1\n", | |
| "\n", | |
| " return [w1,w2]\n", | |
| "\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "8-6U7z5lt5F3" | |
| }, | |
| "execution_count": 47, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### ***Output generated from our neural network***" | |
| ], | |
| "metadata": { | |
| "id": "fpm9D7EzPJk_" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def model_output(x,w1,w2):\n", | |
| " [output, _ ] = model(x,w1,w2)\n", | |
| " return argmax(output)\n" | |
| ], | |
| "metadata": { | |
| "id": "vlEwZtgWPUFh" | |
| }, | |
| "execution_count": 48, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### ***Calculating Accuracy***" | |
| ], | |
| "metadata": { | |
| "id": "mVmtSAP5PaMb" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def accuracy(data,w1,w2):\n", | |
| " count = 0\n", | |
| " n = data[:,0].size\n", | |
| " for i in range(0,n):\n", | |
| " x = data[i,1:].transpose() / 256\n", | |
| " if model_output(x,w1,w2) == data[i,0]:\n", | |
| " count += 1 \n", | |
| " return count/n" | |
| ], | |
| "metadata": { | |
| "id": "KAe90PQ-PZvY" | |
| }, | |
| "execution_count": 49, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## ***Training Neural Netwok*** 😍😁😁😁😁\n", | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "fPV2G99yRO78" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#Shuffleing Training Data\n", | |
| "shuffle(data)\n", | |
| "\n", | |
| "\n", | |
| "N = data[:,0].size #Total numbers pf Training Data \n", | |
| "L_I = 28*28 #Number of Columns of Training input\n", | |
| "L_O = 10 #Number of columns of Training output\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "target_data = zeros([N,10])\n", | |
| "for i in range(0,N):\n", | |
| " target_data[i,data[i,0]] = 1\n", | |
| "ratio = .8\n", | |
| "\n", | |
| "## Dividing training set into Training and Testing data\n", | |
| "training_data = matrix(data[ : int(ratio * N) , : ])\n", | |
| "testing_data = matrix(data[ int(ratio * N) + 1: , : ])\n", | |
| "\n", | |
| "\n", | |
| "training_input = training_data[:, 1:] / 256 #Normalising inputs b/w 0 to 1\n", | |
| "testing_input = testing_data[:, 1:] / 256 # (as max pixel value = 256)\n", | |
| "\n", | |
| "\n", | |
| "# generating output target\n", | |
| "# if , number = 2 then, training_target[i] = [0,0,1,0,0,0,0,0,0,0]\n", | |
| "# if , number = 3 then, training_target[i] = [0,0,0,1,0,0,0,0,0,0]\n", | |
| "# if , number = 4 then, training_target[i] = [0,0,0,0,1,0,0,0,0,0]\n", | |
| "\n", | |
| "training_target = matrix(target_data[ : int(ratio * N) , : ])\n", | |
| "testing_target = matrix(target_data[ int(ratio * N) + 1: , : ])\n" | |
| ], | |
| "metadata": { | |
| "id": "9iGgD6O8pLSZ" | |
| }, | |
| "execution_count": 50, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "X = training_input #Renamimg just for ease to code\n", | |
| "D = training_target\n", | |
| "\n", | |
| "T_X = testing_input\n", | |
| "T_D = testing_target\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "num_neurones = 10 #Number of hidden layer neurones/Nodes\n", | |
| "iteration = 1 #Number of iteration\n", | |
| "\n", | |
| "o_w1 = 2 * matrix(rand(num_neurones,X[0,:].size)) - 1\n", | |
| "o_w2 = 2 * matrix(rand(D[0,:].size,num_neurones)) - 1\n", | |
| "o_training_accuracy = 0\n", | |
| "\n", | |
| "\n", | |
| "for i in range(0,iteration):\n", | |
| "\n", | |
| " w1 = 2 * matrix(rand(num_neurones,X[0,:].size)) - 1 #input weight-matrix with random value b/w -1,+1\n", | |
| " w2 = 2 * matrix(rand(D[0,:].size,num_neurones)) - 1 #hidden-layer weight-matrix with random value b/w -1,+1\n", | |
| "\n", | |
| "\n", | |
| " #epoch\n", | |
| " for epoch in range(0,50):\n", | |
| " [w1,w2] = backPropagation(w1,w2,X,D)\n", | |
| " training_accuracy = accuracy(training_data,w1,w2)\n", | |
| " print(f\"Iteration : {iteration}\",f\"Epoch : {epoch}\",\"Trainig data accuracy : \", training_accuracy , \"Testing data accuracy :\" , accuracy(testing_data,w1,w2))\n", | |
| "\n", | |
| "\n", | |
| " print(f\"Final:\",\"Trainig data accuracy : \", accuracy(training_data,w1,w2), \"Testing data accuracy :\" , accuracy(training_data,w1,w2))\n", | |
| "\n", | |
| " if (o_training_accuracy < training_accuracy):\n", | |
| " o_training_accuracy = training_accuracy\n", | |
| " o_w1 = w1\n", | |
| " o_w2 = w2\n", | |
| "\n", | |
| "w1 = o_w1\n", | |
| "w2 = o_w2\n", | |
| "print(\"Optimum Training accuracy : \", o_training_accuracy)\n", | |
| "print(\"Optimum Testing accuracy :\",accuracy(testing_data,w1,w2) )" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "HkObexSjprpQ", | |
| "outputId": "8822ff73-9e05-41ba-9888-d7d79cb0e996" | |
| }, | |
| "execution_count": 51, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Iteration : 1 Epoch : 0 Trainig data accuracy : 0.863452380952381 Testing data accuracy : 0.853434932730087\n", | |
| "Iteration : 1 Epoch : 1 Trainig data accuracy : 0.8917261904761905 Testing data accuracy : 0.8821288248601024\n", | |
| "Iteration : 1 Epoch : 2 Trainig data accuracy : 0.9033630952380952 Testing data accuracy : 0.8934396952018098\n", | |
| "Iteration : 1 Epoch : 3 Trainig data accuracy : 0.909702380952381 Testing data accuracy : 0.8990355994761281\n", | |
| "Iteration : 1 Epoch : 4 Trainig data accuracy : 0.9114583333333334 Testing data accuracy : 0.9002262174068342\n", | |
| "Iteration : 1 Epoch : 5 Trainig data accuracy : 0.9148214285714286 Testing data accuracy : 0.901774020716752\n", | |
| "Iteration : 1 Epoch : 6 Trainig data accuracy : 0.9201785714285714 Testing data accuracy : 0.9045124419573759\n", | |
| "Iteration : 1 Epoch : 7 Trainig data accuracy : 0.9212202380952381 Testing data accuracy : 0.9045124419573759\n", | |
| "Iteration : 1 Epoch : 8 Trainig data accuracy : 0.9214583333333334 Testing data accuracy : 0.90534587450887\n", | |
| "Iteration : 1 Epoch : 9 Trainig data accuracy : 0.9245833333333333 Testing data accuracy : 0.9077271103702822\n", | |
| "Iteration : 1 Epoch : 10 Trainig data accuracy : 0.9257440476190476 Testing data accuracy : 0.908084295749494\n", | |
| "Iteration : 1 Epoch : 11 Trainig data accuracy : 0.9263095238095238 Testing data accuracy : 0.9085605429217763\n", | |
| "Iteration : 1 Epoch : 12 Trainig data accuracy : 0.9245238095238095 Testing data accuracy : 0.9074889867841409\n", | |
| "Iteration : 1 Epoch : 13 Trainig data accuracy : 0.9280654761904762 Testing data accuracy : 0.9082033575425645\n", | |
| "Iteration : 1 Epoch : 14 Trainig data accuracy : 0.9279761904761905 Testing data accuracy : 0.9087986665079176\n", | |
| "Iteration : 1 Epoch : 15 Trainig data accuracy : 0.9291666666666667 Testing data accuracy : 0.9099892844386236\n", | |
| "Iteration : 1 Epoch : 16 Trainig data accuracy : 0.929672619047619 Testing data accuracy : 0.9092749136802\n", | |
| "Iteration : 1 Epoch : 17 Trainig data accuracy : 0.9292261904761905 Testing data accuracy : 0.9101083462316942\n", | |
| "Iteration : 1 Epoch : 18 Trainig data accuracy : 0.9275595238095238 Testing data accuracy : 0.9098702226455531\n", | |
| "Iteration : 1 Epoch : 19 Trainig data accuracy : 0.929375 Testing data accuracy : 0.9090367900940588\n", | |
| "Iteration : 1 Epoch : 20 Trainig data accuracy : 0.9308035714285714 Testing data accuracy : 0.9095130372663413\n", | |
| "Iteration : 1 Epoch : 21 Trainig data accuracy : 0.9310119047619048 Testing data accuracy : 0.9091558518871294\n", | |
| "Iteration : 1 Epoch : 22 Trainig data accuracy : 0.9304166666666667 Testing data accuracy : 0.9102274080247649\n", | |
| "Iteration : 1 Epoch : 23 Trainig data accuracy : 0.9283333333333333 Testing data accuracy : 0.9092749136802\n", | |
| "Iteration : 1 Epoch : 24 Trainig data accuracy : 0.9289880952380952 Testing data accuracy : 0.9096320990594118\n", | |
| "Iteration : 1 Epoch : 25 Trainig data accuracy : 0.9315476190476191 Testing data accuracy : 0.9114180259554708\n", | |
| "Iteration : 1 Epoch : 26 Trainig data accuracy : 0.9319940476190476 Testing data accuracy : 0.9109417787831885\n", | |
| "Iteration : 1 Epoch : 27 Trainig data accuracy : 0.9323214285714285 Testing data accuracy : 0.9101083462316942\n", | |
| "Iteration : 1 Epoch : 28 Trainig data accuracy : 0.9302976190476191 Testing data accuracy : 0.910465531610906\n", | |
| "Iteration : 1 Epoch : 29 Trainig data accuracy : 0.9291964285714286 Testing data accuracy : 0.908679604714847\n", | |
| "Iteration : 1 Epoch : 30 Trainig data accuracy : 0.929702380952381 Testing data accuracy : 0.9111799023693297\n", | |
| "Iteration : 1 Epoch : 31 Trainig data accuracy : 0.9283928571428571 Testing data accuracy : 0.9105845934039767\n", | |
| "Iteration : 1 Epoch : 32 Trainig data accuracy : 0.9329761904761905 Testing data accuracy : 0.9107036551970472\n", | |
| "Iteration : 1 Epoch : 33 Trainig data accuracy : 0.9336904761904762 Testing data accuracy : 0.9114180259554708\n", | |
| "Iteration : 1 Epoch : 34 Trainig data accuracy : 0.9328571428571428 Testing data accuracy : 0.9109417787831885\n", | |
| "Iteration : 1 Epoch : 35 Trainig data accuracy : 0.9355059523809524 Testing data accuracy : 0.9122514585069651\n", | |
| "Iteration : 1 Epoch : 36 Trainig data accuracy : 0.9347619047619048 Testing data accuracy : 0.9103464698178354\n", | |
| "Iteration : 1 Epoch : 37 Trainig data accuracy : 0.9341964285714286 Testing data accuracy : 0.9120133349208239\n", | |
| "Iteration : 1 Epoch : 38 Trainig data accuracy : 0.9334523809523809 Testing data accuracy : 0.9111799023693297\n", | |
| "Iteration : 1 Epoch : 39 Trainig data accuracy : 0.9342559523809524 Testing data accuracy : 0.9102274080247649\n", | |
| "Iteration : 1 Epoch : 40 Trainig data accuracy : 0.9312797619047619 Testing data accuracy : 0.9107036551970472\n", | |
| "Iteration : 1 Epoch : 41 Trainig data accuracy : 0.934375 Testing data accuracy : 0.911060840576259\n", | |
| "Iteration : 1 Epoch : 42 Trainig data accuracy : 0.9340773809523809 Testing data accuracy : 0.9099892844386236\n", | |
| "Iteration : 1 Epoch : 43 Trainig data accuracy : 0.9349404761904762 Testing data accuracy : 0.9111799023693297\n", | |
| "Iteration : 1 Epoch : 44 Trainig data accuracy : 0.9348809523809524 Testing data accuracy : 0.9099892844386236\n", | |
| "Iteration : 1 Epoch : 45 Trainig data accuracy : 0.9345833333333333 Testing data accuracy : 0.9105845934039767\n", | |
| "Iteration : 1 Epoch : 46 Trainig data accuracy : 0.9358035714285714 Testing data accuracy : 0.9112989641624003\n", | |
| "Iteration : 1 Epoch : 47 Trainig data accuracy : 0.9349404761904762 Testing data accuracy : 0.9092749136802\n", | |
| "Iteration : 1 Epoch : 48 Trainig data accuracy : 0.9361309523809523 Testing data accuracy : 0.9120133349208239\n", | |
| "Iteration : 1 Epoch : 49 Trainig data accuracy : 0.9354166666666667 Testing data accuracy : 0.9118942731277533\n", | |
| "Final: Trainig data accuracy : 0.9354166666666667 Testing data accuracy : 0.9354166666666667\n", | |
| "Optimum Training accuracy : 0.9354166666666667\n", | |
| "Optimum Testing accuracy : 0.9118942731277533\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## **Accuracy of whole training data set** 😎" | |
| ], | |
| "metadata": { | |
| "id": "0CHgScNvUU6l" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print (\"Whole Training dataset accuracy : \" , accuracy(data,w1,w2)*100 , \"%\") " | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "hmJQgJxa0rqo", | |
| "outputId": "e6457c15-711f-40cb-850a-8d2a1616ef87" | |
| }, | |
| "execution_count": 52, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Whole Training dataset accuracy : 93.07142857142857 %\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## ***Manually Checking***" | |
| ], | |
| "metadata": { | |
| "id": "RQnQmNO4Ufnx" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def manual_check(index):\n", | |
| " print(\"Predicted :\" , model_output(data[index,1:].transpose()/256,w1,w2))\n", | |
| " print_image(data[index,1:].transpose())" | |
| ], | |
| "metadata": { | |
| "id": "dQyPNhJM2lc5" | |
| }, | |
| "execution_count": 53, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "manual_check(69)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 282 | |
| }, | |
| "id": "03pMV7CyBvvZ", | |
| "outputId": "8e806d9d-f8fd-407a-8d80-b97e8af011d4" | |
| }, | |
| "execution_count": 56, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Predicted : 2\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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ZjZZ0tZpvKupVkuZnt+dL+m6JvbxKs0zjnTfNuEp+7kqf/tzdG/4j6VoNnJH/taQvlNFDTl/vkPR09rOl7N4krdTAy7o+DZzbuFHSBElrJO2Q9BNJ7U3U27ckbZK0UQPB6iiptys08BJ9o6QN2c+1ZT93ib4a8rzxcVkgCE7QAUEQdiAIwg4EQdiBIAg7EARhB4Ig7EAQ/wdmZ1HtEGqTqwAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "for i in range(50,100):\n", | |
| " manual_check(i)\n", | |
| " print(\"\\n\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "id": "zsDAciN9Bz-3", | |
| "outputId": "785f7a61-d7ba-49a4-eaa1-2c714a20d73f" | |
| }, | |
| "execution_count": 57, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Predicted : 4\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 0\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 9\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 0\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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Js3qot29LelbSMxoJ1pwu9XaeRj6iPyNpQ+Pv4m6vu0JfHVlv/FwWSIIddEAShB1IgrADSRB2IAnCDiRB2IEkCDuQxP8D1TwveqgC3bwAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 0\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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Jn1L3TUW9TNLC4vFCSXfX2MvbdMs03mXTjKvm96726c8jouM3SXM1fET+BUl/V0cPJX19SNJPi9u6unuTdLuGd+sGNHxs4yJJh0laIWmDpAckTemi3r4naY2k1RoO1rSaejtTw7voqyWtKm5z637vEn115H3jdFkgExygAzJB2IFMEHYgE4QdyARhBzJB2IFMEHYgE/8Pmt1RMgt1dq8AAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 0\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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BWmnsdFDTeDfLANOMv67K567e6c+LqiLsOyVN7Hf/jGxZS3D3ndnv3ZLuV+tNRd11bAbd7Pfuivt5XStN4z3QNONqgeeuyunPqwj7akmTzWySmQ2XdLWkhyro4zhmNir74ERmNkrS5Wq9qagfkjQvuz1P0oMV9vIGrTKNd94046r4uat8+nN3b/qPpFnq+0R+m6R/rKKHnL7eI+np7OfZqnuTtEx9L+uOqO+zjWslnSJppaTnJP2vpPYW6u1/JD0jab36gjW+ot4uVN9L9PWS1mU/s6p+7hJ9NeV54+uyQBB8QAcEQdiBIAg7EARhB4Ig7EAQhB0IgrADQfw/B1+QxS2wdgsAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 5\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 4\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANQUlEQVR4nO3df6zV9X3H8dcL5EdFXaFuQIBYLWwta1Pa3GEz3cpibNDNYNPNlCwNS91ut5SlLiSbc1nqfzPbbHXZ0uU6ibSxGhM1soZUKelCjBnzalBAu+IsVhg/dGwDVsXL5b0/7ldzwfv93Mv59T3wfj6Sm3PO933O+b79xhff7/l+vud8HBECcOGb1nQDAHqDsANJEHYgCcIOJEHYgSQu6uXKZnpWzNacXq4SSOVt/Z/eiZOeqNZW2G2vlnSvpOmS/iki7io9f7bm6Gpf184qARTsiG21tZYP421Pl/QPkm6QtFzSWtvLW30/AN3Vzmf2lZJeiYhXI+IdSQ9LWtOZtgB0WjthXyTp9XGP91fLzmB70Paw7eERnWxjdQDa0fWz8RExFBEDETEwQ7O6vToANdoJ+wFJS8Y9XlwtA9CH2gn7s5KW2b7S9kxJX5S0uTNtAei0lofeIuKU7fWSntTY0NvGiNjTsc4AdFRb4+wRsUXSlg71AqCLuFwWSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kERbUzbb3ifpuKRRSaciYqATTQHovLbCXvmNiHizA+8DoIs4jAeSaDfsIekp28/ZHpzoCbYHbQ/bHh7RyTZXB6BV7R7GXxsRB2z/gqSttn8UEdvHPyEihiQNSdJlnhdtrg9Ai9ras0fEger2iKTHJa3sRFMAOq/lsNueY/vSd+9L+pyk3Z1qDEBntXMYP1/S47bffZ/vRsT3O9IVznD4j3+1WF/8hZ/U1kZvOlF87enjx1vqCeeflsMeEa9K+mQHewHQRQy9AUkQdiAJwg4kQdiBJAg7kEQnvgiDLjv20dFi/ZGlT9TWfnvWDeU3Z+QtDfbsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AE4+zngY2r7yvWV73wu7W1uW/u7XQ7fWP60iuL9dFX6r/6mxF7diAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgnH2PjBtxfJi/YqLni7Wf3ZyZm1tbksd9YfX/7L8E9oPfPneYn3DhvW1tYsf29FST+cz9uxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kATj7H3g6Cd+rlifM83F+oK/m9XJdnpm+gfL/933rCt/j/+qi94p1mccO3XOPV3IJt2z295o+4jt3eOWzbO91fbe6vZ8vnYDSGEqh/EPSFp91rLbJW2LiGWStlWPAfSxScMeEdslHT1r8RpJm6r7myTd3OG+AHRYq5/Z50fEwer+IUnz655oe1DSoCTN1sUtrg5Au9o+Gx8RISkK9aGIGIiIgRk6P08kAReCVsN+2PZCSapuj3SuJQDd0GrYN0taV91fJ6l+zmAAfWHSz+y2H5K0StLltvdL+rqkuyQ9YvtWSa9JuqWbTWb3zNu1p0QkSdP/5fkedXLups2ZU1v76cbFxdde94GTxfovPrKhWF/6g38t1rOZNOwRsbamdF2HewHQRVwuCyRB2IEkCDuQBGEHkiDsQBJ8xRVd9Z+//8na2gtX/31b7/1LQ/9VrI+29e4XHvbsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AE4+zngUunvVV+wspPtPze//2xS4r1N64p/xzz2l8pT3385D/W/oiRlv7zHxZf++ef/V6xrjfO/mlElLBnB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkGGc/D6yaPVKuP76pWC+Z7vK/9ztPln/Oed09f1KsL35yf23t7j8tj6Pfs//6Yn30zcPFOs7Enh1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkmCcvQ988MF/K9Zvemp1sf7al5e2vO4rHi2PVZ/+yevF+oKRZ4r1EzetrK392uzyd+U3fPeqYv1yMc5+Libds9veaPuI7d3jlt1p+4DtndXfjd1tE0C7pnIY/4CkiXYt34yIFdXfls62BaDTJg17RGyXxO//AOe5dk7Qrbf9YnWYP7fuSbYHbQ/bHh5R+TprAN3Tati/JekjklZIOijp7ronRsRQRAxExMAMzWpxdQDa1VLYI+JwRIxGxGlJ90mqP+UKoC+0FHbbC8c9/Lyk3XXPBdAfJh1nt/2QpFWSLre9X9LXJa2yvUJSSNon6Std7PHCd7o8k/jo4SPF+uK/KteL793yK6cmptfXfnrqZ8XXLnji1WK9PEqPs00a9ohYO8Hi+7vQC4Au4nJZIAnCDiRB2IEkCDuQBGEHkuArrmjPtMLYmqTLbqv/iuzbUd7XnDrEV1g7iT07kARhB5Ig7EAShB1IgrADSRB2IAnCDiTBODvacmrVimJ9y7L7amsf2/5HxddeqRda6gkTY88OJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kwzo62HPpMeZafH43UT/m14GFmCOol9uxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kIQjomcru8zz4mpf17P1oX2eVR4L/+gz5Umf9534UG3trc/yu/CdtiO26Vgc9US1SffstpfY/qHtl2zvsf21avk821tt761u53a6cQCdM5XD+FOSNkTEckmfkfRV28sl3S5pW0Qsk7StegygT00a9og4GBHPV/ePS3pZ0iJJayRtqp62SdLN3WoSQPvO6dp42x+W9ClJOyTNj4iDVemQpPk1rxmUNChJs3Vxq30CaNOUz8bbvkTSo5Jui4hj42sxdpZvwjN9ETEUEQMRMTBDfPEBaMqUwm57hsaC/mBEPFYtPmx7YVVfKOlId1oE0AmTHsbbtqT7Jb0cEd8YV9osaZ2ku6rbJ7rSIRo1cs3Hi/W7F9T/VLQkffpv1tfWFoiht16aymf2ayR9SdIu2zurZXdoLOSP2L5V0muSbulOiwA6YdKwR8TTkiYcpJfEFTLAeYLLZYEkCDuQBGEHkiDsQBKEHUiCn5JG0aGruerxQsGeHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSYJwdRW//8lvF+umJf6DoPXN/PNLJdtAG9uxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kATj7Ci65NkPFOsjq8pTNs/ZdbC2dqqljtAq9uxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kMRU5mdfIunbkuZLCklDEXGv7Tsl/YGkN6qn3hERW7rVKJqxaNOeYv03f+t3ivWjX1hUW1twz/6WekJrpnJRzSlJGyLieduXSnrO9taq9s2I+NvutQegU6YyP/tBSQer+8dtvyyp/p9rAH3pnD6z2/6wpE9J2lEtWm/7Rdsbbc+tec2g7WHbwyM62VazAFo35bDbvkTSo5Jui4hjkr4l6SOSVmhsz3/3RK+LiKGIGIiIgRli3jCgKVMKu+0ZGgv6gxHxmCRFxOGIGI2I05Luk7Sye20CaNekYbdtSfdLejkivjFu+cJxT/u8pN2dbw9Ap0zlbPw1kr4kaZftndWyOySttb1CY8Nx+yR9pSsdolGj//O/xfrM68v1BXqtk+2gDVM5G/+0JE9QYkwdOI9wBR2QBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJR0TvVma/IZ3xBefLJb3ZswbOTb/21q99SfTWqk72dkVE/PxEhZ6G/X0rt4cjYqCxBgr6tbd+7Uuit1b1qjcO44EkCDuQRNNhH2p4/SX92lu/9iXRW6t60lujn9kB9E7Te3YAPULYgSQaCbvt1bb/3fYrtm9vooc6tvfZ3mV7p+3hhnvZaPuI7d3jls2zvdX23up2wjn2GurtTtsHqm230/aNDfW2xPYPbb9ke4/tr1XLG912hb56st16/pnd9nRJP5Z0vaT9kp6VtDYiXuppIzVs75M0EBGNX4Bh+9clnZD07Yj4eLXsryUdjYi7qn8o50bEn/VJb3dKOtH0NN7VbEULx08zLulmSb+nBrddoa9b1IPt1sSefaWkVyLi1Yh4R9LDktY00Effi4jtko6etXiNpE3V/U0a+5+l52p66wsRcTAinq/uH5f07jTjjW67Ql890UTYF0l6fdzj/eqv+d5D0lO2n7M92HQzE5gfEQer+4ckzW+ymQlMOo13L501zXjfbLtWpj9vFyfo3u/aiPi0pBskfbU6XO1LMfYZrJ/GTqc0jXevTDDN+Hua3HatTn/eribCfkDSknGPF1fL+kJEHKhuj0h6XP03FfXhd2fQrW6PNNzPe/ppGu+JphlXH2y7Jqc/byLsz0paZvtK2zMlfVHS5gb6eB/bc6oTJ7I9R9Ln1H9TUW+WtK66v07SEw32coZ+mca7bppxNbztGp/+PCJ6/ifpRo2dkf8PSX/RRA81fV0l6YXqb0/TvUl6SGOHdSMaO7dxq6QPSdomaa+kH0ia10e9fUfSLkkvaixYCxvq7VqNHaK/KGln9Xdj09uu0FdPthuXywJJcIIOSIKwA0kQdiAJwg4kQdiBJAg7kARhB5L4f5qz65Ozjg5iAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 0\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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7k3SvRp7W7dXIextXSTpK0ipJz0p6WNKMCvX2j5KekrROI8Ga2aPeLtDIU/R1ktbWfi7r9bEr9NWV48bHZYEkeIMOSIKwA0kQdiAJwg4kQdiBJAg7kARhB5L4P/9fN7NM+Zg/AAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 4\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 6\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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KWifpYY0Ea26XejtVIx/RH5a0trqd1e33rtBXR943TpcFkuAAHZAEYQeSIOxAEoQdSIKwA0kQdiAJwg4k8X8FfExEQVpszAAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 9\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANMklEQVR4nO3dcYwc9XnG8efBOWxhiGOH5mKZSyDGqYpQa6KTiQJpIagRQZUOIgXFUlO3glwqgUQqWoFI1CA1f1hVIaIoojqCFVNSp64CsZuigGNFoRGN4aDGNjbBYJni47AJrgJBjfH53v5xY3SY29m73dmdtd/vR1rt7ry7O69Gfjyz85vbnyNCAE59p9XdAIDuIOxAEoQdSIKwA0kQdiCJ93VzZad7fizQwm6uEkjlt3pLb8cRz1RrK+y2r5R0l6R5kr4TEWvLXr9AC3Wxr2hnlQBKbIutDWstH8bbnifp25I+J+kCSattX9Dq5wHorHa+s6+S9EJE7IuItyV9X9JQNW0BqFo7YV8m6eVpzw8Uy97F9rDtUdujR3WkjdUBaEfHz8ZHxEhEDEbEYJ/md3p1ABpoJ+xjkgamPT+nWAagB7UT9iclrbB9nu3TJX1R0uZq2gJQtZaH3iJiwvaNkh7R1NDbuoh4trLOAFSqrXH2iHhY0sMV9QKgg7hcFkiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5BEW1M2294v6U1JxyRNRMRgFU0BqF5bYS9cHhG/quBzAHQQh/FAEu2GPSQ9avsp28MzvcD2sO1R26NHdaTN1QFoVbuH8ZdGxJjtD0naYvu5iHhs+gsiYkTSiCS930uizfUBaFFbe/aIGCvuD0l6SNKqKpoCUL2Ww257oe2zjj+W9FlJu6pqDEC12jmM75f0kO3jn/MvEfHjSroCULmWwx4R+yT9QYW9AOgght6AJAg7kARhB5Ig7EAShB1Iooo/hEET8z6wqLT+y7s/Vlp/7jPfqbKdOfn4j/6ytP6RH7m0vuDfn6iyHbSBPTuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJME4ewXe9+H+0vqHf/hWaX3TQPk4+qQm59xTVZ7/k38qrY98+tzS+n88vqJh7djrh1tpCS1izw4kQdiBJAg7kARhB5Ig7EAShB1IgrADSTDOXoGxa5eX1h8cuKvJJ5T/n3vH6xeW1tft/FTDWv+m+U3WXe6mb24orV+/aF9p/X9+8sGGtcf/7uLS957x4LbSOuaGPTuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJME4+0lg471XlNaX3/14x9a9fvTy0votf/2h0vpzQ99uWNtzR/k4+te2XVNanxh7pbSOd2u6Z7e9zvYh27umLVtie4vtvcX94s62CaBdszmM/66kK09YdqukrRGxQtLW4jmAHtY07BHxmKQTfz9oSNL64vF6SVdX3BeAirX6nb0/IsaLx69KavgjbLaHJQ1L0gKd0eLqALSr7bPxERGSoqQ+EhGDETHYp/b+KANA61oN+0HbSyWpuD9UXUsAOqHVsG+WtKZ4vEbSpmraAdApTb+z294g6TJJZ9s+IOkbktZK2mj7OkkvSbq2k02iPhP79pfWz9/QZNR1qHHp904v39e8fvlHSuuLHmCcfS6ahj0iVjcolV/pAaCncLkskARhB5Ig7EAShB1IgrADSfAnrhWY99uGFxBKko5FeX3+afOqbKerTvvP/y6tr37xqoa1f13+49L3Hry0fKrqRQ+UlnEC9uxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kATj7BU4e+S/SutP3LKgtH7JgqNVttNTJqPx/mRS5ePoqBZ7diAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCv2fvgr945PrS+vND93Spk+57Zu9A4+L53esDs9iz215n+5DtXdOW3W57zPb24tZ4JgAAPWE2h/HflXTlDMu/FREri9vD1bYFoGpNwx4Rj0k63IVeAHRQOyfobrS9ozjMX9zoRbaHbY/aHj2qI22sDkA7Wg37PZKWS1opaVzSHY1eGBEjETEYEYN9mt/i6gC0q6WwR8TBiDgWEZOS7pW0qtq2AFStpbDbXjrt6TWSdjV6LYDe0HSc3fYGSZdJOtv2AUnfkHSZ7ZWSQtJ+SV/pYI8nvYFHm7xgqLz8qT97urT+4t1z66ebfvee/2tYG/3MyTsv/cmoadgjYvUMi+/rQC8AOojLZYEkCDuQBGEHkiDsQBKEHUjCEdG1lb3fS+JiX9G19Z0sHnlle2n9aBwrrV/2Vzc0rJ258Rct9dQNf7Sj8bCcJP3vxBml9R2f6N6/3ZPFttiqN+KwZ6qxZweSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJPgp6R7w8Z+tKa1fuOyV0voHRl9tWJtoqaPumNSMw8Hv+Gb/E6X1z3/086X1iZdennNPpzL27EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBOPsPeC81c+U1t9q+gmvVdXKSeWFL59TWj/364yzT8eeHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEk3DbnvA9k9t77b9rO2biuVLbG+xvbe4X9z5dgG0ajZ79glJN0fEBZI+KekG2xdIulXS1ohYIWlr8RxAj2oa9ogYj4ini8dvStojaZmkIUnri5etl3R1p5oE0L45XRtv+1xJF0naJqk/IsaL0quS+hu8Z1jSsCQtUPncXQA6Z9Yn6GyfKekHkr4aEW9Mr8XU7JAzzrIXESMRMRgRg32a31azAFo3q7Db7tNU0L8XEQ8Wiw/aXlrUl0o61JkWAVSh6WG8bUu6T9KeiLhzWmmzpDWS1hb3mzrSIU5Z9+9eVVr/m0/vLK3/7Rc2ln/+1wfm3NOpbDbf2S+R9CVJO20fn0j8Nk2FfKPt6yS9JOnazrQIoApNwx4RP5ca/pr/FdW2A6BTuIIOSIKwA0kQdiAJwg4kQdiBJPgpafSs05rsiwb6Xm/yCYyzT8eeHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSYJwdtfn9Za+U1ic1WV6PvirbOeWxZweSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJBhnR23G/3F5af3Xd77d5BOYYWgu2LMDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKzmZ99QNL9kvolhaSRiLjL9u2SvizpteKlt0XEw51qFKeeM/9tW2l999qzSusXzX+rtP7rP/1kw9qiB35R+t5T0WwuqpmQdHNEPG37LElP2d5S1L4VEf/QufYAVGU287OPSxovHr9pe4+kZZ1uDEC15vSd3fa5ki6SdPz460bbO2yvs724wXuGbY/aHj2qI201C6B1sw677TMl/UDSVyPiDUn3SFouaaWm9vx3zPS+iBiJiMGIGOzjWmagNrMKu+0+TQX9exHxoCRFxMGIOBYRk5LulbSqc20CaFfTsNu2pPsk7YmIO6ctXzrtZddI2lV9ewCqMpuz8ZdI+pKknba3F8tuk7Ta9kpNDcftl/SVjnSItK7/4XBp/fyLXi6tL35oZ8Na+Y9Un5pmczb+55I8Q4kxdeAkwhV0QBKEHUiCsANJEHYgCcIOJEHYgST4KWn0rOU3l/8ZajR5f7N6NuzZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJR3RvNNL2a5JemrbobEm/6loDc9OrvfVqXxK9tarK3j4aEb8zU6GrYX/Pyu3RiBisrYESvdpbr/Yl0VurutUbh/FAEoQdSKLusI/UvP4yvdpbr/Yl0VurutJbrd/ZAXRP3Xt2AF1C2IEkagm77Stt/9L2C7ZvraOHRmzvt73T9nbbozX3ss72Idu7pi1bYnuL7b3F/Yxz7NXU2+22x4ptt932VTX1NmD7p7Z3237W9k3F8lq3XUlfXdluXf/ObnuepOcl/bGkA5KelLQ6InZ3tZEGbO+XNBgRtV+AYfsPJf1G0v0RcWGx7O8lHY6ItcV/lIsj4pYe6e12Sb+pexrvYraipdOnGZd0taQ/V43brqSva9WF7VbHnn2VpBciYl9EvC3p+5KGauij50XEY5IOn7B4SNL64vF6Tf1j6boGvfWEiBiPiKeLx29KOj7NeK3brqSvrqgj7MskTZ+354B6a773kPSo7adsl88/VI/+iBgvHr8qqb/OZmbQdBrvbjphmvGe2XatTH/eLk7QvdelEfEJSZ+TdENxuNqTYuo7WC+Nnc5qGu9umWGa8XfUue1anf68XXWEfUzSwLTn5xTLekJEjBX3hyQ9pN6bivrg8Rl0i/tDNffzjl6axnumacbVA9uuzunP6wj7k5JW2D7P9umSvihpcw19vIfthcWJE9leKOmz6r2pqDdLWlM8XiNpU429vEuvTOPdaJpx1bztap/+PCK6fpN0labOyL8o6Wt19NCgr49Jeqa4PVt3b5I2aOqw7qimzm1cJ+mDkrZK2ivpJ5KW9FBv/yxpp6QdmgrW0pp6u1RTh+g7JG0vblfVve1K+urKduNyWSAJTtABSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBL/D43d4sP4irrYAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 4\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 6\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 8\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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r967QV1feN74uCyTBCTogCcIOJEHYgSQIO5AEYQeSIOxAEoQdSOL/AbcCNTkjDxCAAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 1\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 1\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 3\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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N0gMaPqwb0PC5jVWSTpHULWm3pB9ImtNCvX1T0nZJz2o4WPMr6u1SDR+iPytpW3ZZWvVrl+irKa8bH5cFguAEHRAEYQeCIOxAEIQdCIKwA0EQdiAIwg4E8X8TGj8HcnZRZgAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 7\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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9ImlKD/V2j6T1ktZpMFh9XertAg0eoq+TtKb6m9vt967QV0feNy6XBZLgBB2QBGEHkiDsQBKEHUiCsANJEHYgCcIOJPH/5yQoC+ndcdcAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 2\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 2\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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GS7pKrTcV9UpJ87L78yQ9UmEvb9Eq03jnTTOuil+7yqc/d/em/0m6Vn1n5P9H0h1V9JDT14WSns3+Nlfdm6QV6ntbd1R95zZuknS2pDWStkv6kaT2FurtnyU9J2mj+oI1vqLerlDfW/SNkjZkf9dW/dol+mrK68bXZYEgOEEHBEHYgSAIOxAEYQeCIOxAEIQdCIKwA0H8P/41TzT+fdBYAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 2\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANo0lEQVR4nO3de4xc9XnG8efB+IIxuL4ExwKHm9wKU1GnXZm0oRGtG5fQRhBVoNAocSqkjRSoTEsqUKoC6V8oIdCqubQmGNwIbCEFiqsiGnBQDU0wXoOxDU5j6prG1mJD3coY8P3tH3scrWHnt+uZMxf8fj/SaGbOO+eclxGPz8z5nZ2fI0IATn6ndLsBAJ1B2IEkCDuQBGEHkiDsQBKndnJnEzwxJun0Tu4SSGW/3tbBOOCRai2F3fYVkv5W0jhJ34uIO0uvn6TTdakXtrJLAAVrY3XDWtMf422Pk/RtSZ+SNE/SdbbnNbs9AO3Vynf2BZJejYhtEXFQ0kpJV9XTFoC6tRL2syX9fNjzHdWy49jutz1ge+CQDrSwOwCtaPvZ+IhYGhF9EdE3XhPbvTsADbQS9p2S5gx7fk61DEAPaiXs6yTNtX2+7QmSPitpVT1tAahb00NvEXHY9o2S/lVDQ2/LIuLl2joDUKuWxtkj4nFJj9fUC4A24nJZIAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Lo6JTNWV24blKxfsOHni7Wb1r85WL9lH978YR7Qj4c2YEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcbZO+BIuFi/eMJpxfo3Hvj7Yv0L3/qzE+5prKbsPFqsn7HyubbtG/VqKey2t0t6S9IRSYcjoq+OpgDUr44j++9ExJs1bAdAG/GdHUii1bCHpB/aXm+7f6QX2O63PWB74JAOtLg7AM1q9WP8ZRGx0/ZZkp60/dOIWDP8BRGxVNJSSTrT06PF/QFoUktH9ojYWd3vlvSopAV1NAWgfk2H3fbpts849ljSIkmb62oMQL1a+Rg/S9Kjto9t56GIeKKWrk4yP3r1V8ovOOcnxfL8iROL9Y03f+dEWxqzwcP7ivXHbhvlv61gf4wv1p9YfFmxHgMcW05E02GPiG2Sfq3GXgC0EUNvQBKEHUiCsANJEHYgCcIOJOGIzl3Udqanx6Ve2LH99YpxM2cU6298+peL9U/cuLbOdo7ztVk/LtannFL+Gex2+pd3yvv+8xV/Uqyf91flIc2T0dpYrb2xZ8S/qebIDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJ8FPSHXDkzf8p1qffXx4P3nx/nd0c7/c+d1OxfnhS+WewR3NwauP1H13y9eK6fzC5vO0lF77bTEtpcWQHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQYZ09u6oPdm3L5k5csKda3LbqvQ53kwJEdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASo4bd9jLbu21vHrZsuu0nbW+t7qe1t00ArRrLkf0BSVe8Z9mtklZHxFxJq6vnAHrYqGGPiDWS9rxn8VWSllePl0u6uua+ANSs2WvjZ0XEYPX4dUmzGr3Qdr+kfkmapFF+VAxA27R8gi6GZoZsODtkRCyNiL6I6Buvia3uDkCTmg37LtuzJam6311fSwDaodmwr5K0uHq8WNJj9bQDoF1G/c5ue4WkyyXNtL1D0u2S7pT0sO3rJb0m6dp2NokPrnEzpjesLbzopx3sBKOGPSKua1BaWHMvANqIK+iAJAg7kARhB5Ig7EAShB1Igp+SRlv97O8+0rD2+JwHWtr2pI1cfn0iOLIDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKMs6Ml42bOKNZ/6/xtTW/74X1Ti/WPfGdzsX6k6T2fnDiyA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASjLOj6PDv/kaxfsldLxTr3/jwiw1r+47uL657/zWNfth4yNG9W4p1HI8jO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kwTh7cuPOPLNY373k7WK9NI4uSQfiUMPaJU/dUFx37kvri3WcmFGP7LaX2d5te/OwZXfY3ml7Q3W7sr1tAmjVWD7GPyDpihGW3xMR86vb4/W2BaBuo4Y9ItZI2tOBXgC0USsn6G60vbH6mD+t0Yts99sesD1wSAda2B2AVjQb9u9KulDSfEmDkr7Z6IURsTQi+iKib7wmNrk7AK1qKuwRsSsijkTEUUn3SlpQb1sA6tZU2G3PHvb0M5LKv+kLoOtGHWe3vULS5ZJm2t4h6XZJl9ueLykkbZf0pTb2iDbas/KsYv3F+Stb2v68H/U3rM39IuPonTRq2CNipF8QuK8NvQBoIy6XBZIg7EAShB1IgrADSRB2IAn+xPUkd/D3+4r1FRf/zShbmFKs/vv+o8X6Bf8Qo2wfncKRHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSYJz9JHBoUeOx9IfuLY+jzz61PI4+mptv+3KxPvXZ51raPurDkR1IgrADSRB2IAnCDiRB2IEkCDuQBGEHknBE5/7e+ExPj0u9sGP7+6AYN63h7FmSpMHPXVSsP/SVuxrWLpowuamexmrN/nL99cNTG9Zueeaa4roznhvfTEtjMvmNI8X6af/0fNv23U5rY7X2xh6PVOPIDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJMM7eAf+7+DeL9T/6ylPF+i0zttbZDiQ98c7EYv2eP762vIHnN9XYTX1aGme3Pcf207Zfsf2y7SXV8um2n7S9tbovXxkCoKvG8jH+sKSbI2KepI9JusH2PEm3SlodEXMlra6eA+hRo4Y9IgYj4oXq8VuStkg6W9JVkpZXL1su6ep2NQmgdSf0G3S2z5P0UUlrJc2KiMGq9LqkWQ3W6ZfUL0mT1N7rtAE0Nuaz8banSPqBpJsiYu/wWgyd5RvxTF9ELI2IvojoG6/ySREA7TOmsNser6GgPxgRj1SLd9meXdVnS9rdnhYB1GHUj/G2Lek+SVsi4u5hpVWSFku6s7p/rC0dfgCUfspZku7/2t3F+sUTTmtp/88fONSwtu7dC4rrfu/bny7W3z2rvO8/veafyy8o+O3J5SHFZ96Z2/S2JWn5f32sYW3yt36puO7E59e1tO9eNJbv7B+X9HlJm2xvqJZ9VUMhf9j29ZJekzTKwCSAbho17BHxrKQRB+kl5btCBviA4nJZIAnCDiRB2IEkCDuQBGEHkmDK5hq8/eHyTx63Oo7+34f3Fetf+P5fNKyde9tPiuuepR831dMxq26f0fS6jyxcVKyfunp909uWpGniT4OH48gOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kwzl6D6S/9X7H+8L7G0xZL0l9v/sNiffY9E4r1c58pj6X3qlbH0XFiOLIDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBJM2QycRFqashnAyYGwA0kQdiAJwg4kQdiBJAg7kARhB5IYNey259h+2vYrtl+2vaRafoftnbY3VLcr298ugGaN5ccrDku6OSJesH2GpPW2n6xq90TEXe1rD0BdxjI/+6CkwerxW7a3SDq73Y0BqNcJfWe3fZ6kj0paWy260fZG28tsT2uwTr/tAdsDh3SgpWYBNG/MYbc9RdIPJN0UEXslfVfShZLma+jI/82R1ouIpRHRFxF94zWxhpYBNGNMYbc9XkNBfzAiHpGkiNgVEUci4qikeyUtaF+bAFo1lrPxlnSfpC0Rcfew5bOHvewzkjbX3x6AuozlbPzHJX1e0ibbG6plX5V0ne35kkLSdklfakuHAGoxlrPxz0oa6e9jH6+/HQDtwhV0QBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJDo6ZbPtNyS9NmzRTElvdqyBE9OrvfVqXxK9NavO3s6NiA+NVOho2N+3c3sgIvq61kBBr/bWq31J9NasTvXGx3ggCcIOJNHtsC/t8v5LerW3Xu1LordmdaS3rn5nB9A53T6yA+gQwg4k0ZWw277C9n/YftX2rd3ooRHb221vqqahHuhyL8ts77a9ediy6baftL21uh9xjr0u9dYT03gXphnv6nvX7enPO/6d3fY4ST+T9ElJOyStk3RdRLzS0UYasL1dUl9EdP0CDNufkLRP0j9GxK9Wy74uaU9E3Fn9QzktIm7pkd7ukLSv29N4V7MVzR4+zbikqyV9UV187wp9XasOvG/dOLIvkPRqRGyLiIOSVkq6qgt99LyIWCNpz3sWXyVpefV4uYb+Z+m4Br31hIgYjIgXqsdvSTo2zXhX37tCXx3RjbCfLennw57vUG/N9x6Sfmh7ve3+bjczglkRMVg9fl3SrG42M4JRp/HupPdMM94z710z05+3ihN073dZRPy6pE9JuqH6uNqTYug7WC+NnY5pGu9OGWGa8V/o5nvX7PTnrepG2HdKmjPs+TnVsp4QETur+92SHlXvTUW969gMutX97i738wu9NI33SNOMqwfeu25Of96NsK+TNNf2+bYnSPqspFVd6ON9bJ9enTiR7dMlLVLvTUW9StLi6vFiSY91sZfj9Mo03o2mGVeX37uuT38eER2/SbpSQ2fk/1PSX3ajhwZ9XSDpper2crd7k7RCQx/rDmno3Mb1kmZIWi1pq6SnJE3vod6+L2mTpI0aCtbsLvV2mYY+om+UtKG6Xdnt967QV0feNy6XBZLgBB2QBGEHkiDsQBKEHUiCsANJEHYgCcIOJPH/A8ANg+bpn9AAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 6\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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dZ2bDJH1e0ooK+ngLMxuZHTiRmY2U9FG131TUKyRdld2/StJDFfZygnaZxjtvmnFV/NpVPv25u7f8R9I89R+Rf07Sn1bRQ05f75b0VPazuereJN2v/rd1h9V/bOMaSWdIWiVpu6SfSepqo96+JWmjpKfVH6wJFfV2ifrfoj8taUP2M6/q1y7RV0teN06XBYLgAB0QBGEHgiDsQBCEHQiCsANBEHYgCMIOBPF/SsRVVhk0YrcAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 3\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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g7EAShB1IgrADSRB2IAnCDiRB2IEk/h9dzWQj2phVNgAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 9\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 0\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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Jz5jZi2bWWnQz3Rjq7tuy++9KGlpkM90oOY13PR0xzXjDvHblTH9eKU7QHW2Su/+OpMmSrs/erjYk7/wM1khjpz2axrteuplm/BNFvnblTn9eqSLCvlXSyC6PT8+WNQR335rd7pC0WI03FfX2wzPoZrc7Cu7nE400jXd304yrAV67Iqc/LyLsqyWNNbPRZtZH0jRJSwvo4yhmNiA7cSIzGyDpEjXeVNRLJV2b3b9W0pMF9vIrGmUa77xpxlXwa1f49OfuXvcfSVPUeUb+DUm3FtFDTl+fk/Sz7GdD0b1JelSdb+sOqPPcxtclnSqpTdImSf8lqbmBentI0jpJa9UZrGEF9TZJnW/R10pak/1MKfq1S/RVl9eNr8sCQXCCDgiCsANBEHYgCMIOBEHYgSAIOxAEYQeC+H8cqqDNOPkaDAAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 4\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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e9XaBxk/Rn5S0sfq7uNf7rtBXV/YbX5cFkuADOiAJwg4kQdiBJAg7kARhB5Ig7EAShB1I4v8Bjowx9Su4dsEAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 3\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 9\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 0\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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6snh+paRHWtjL+7TLNN5l04yrxfuu5dOfR0TTfyTNUP878r+U9I1W9FDS12RJzxU/z7e6N0n3qf+0brf639u4WtIxkhZLWi/pSUnj26i3f5a0WtIq9Qers0W9XaD+U/RVklYWPzNave8SfTVlv/FxWSATvEEHZIKwA5kg7EAmCDuQCcIOZIKwA5kg7EAm/h/95F0ftyZ9xwAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 1\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 1\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 4\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 4\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 4\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 1\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 8\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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Kmf68UvxA916XufvvSbpW0q3Zx9WG5N3fwRpp7LRP03jXSi/TjL+rnu9dudOfV6oeYd8paVyPx+dkyxqCu+/MbjslPavGm4p6z/EZdLPbzjr3865Gmsa7t2nG1QDvXT2nP69H2FdKmmRmE8xssKQbJC2pQx/vYWbDsh9OZGbDJF2txpuKeomkOdn9OZIW17GXEzTKNN5504yrzu9d3ac/d/ea/0maoe5f5H8t6S/q0UNOX+dJ+u/sb329e5O0SN0f646o+7eNGyWdJWm5pM2S/l3SiAbq7Z8lrZW0Rt3BGl2n3i5T90f0NZJWZ38z6v3eJfqqyfvG4bJAEPxABwRB2IEgCDsQBGEHgiDsQBCEHQiCsANB/D9oD3U6PX2hYwAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 9\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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Jwg4kQdiBJP4fUiojIQBA1z4AAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 7\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 1\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 0\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 6\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 5\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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6bvcm6XYNP63bqeH3NpZIOkLSSklPSvo3STN6qLd/lrRG0mMaDtasLvV2qoafoj8maXX1c063H7tCXx153Pi4LJAEb9ABSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBL/D6O5S6ph/eONAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 9\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 6\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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y5+5e9z9JF2vgE/nnJX2hET3k9HWGpJ9nf9sb3Zuk+zTwsu6oBj7bWCrpZEnrJXVL+g9JU5qot3+R9JSkJzUQrLYG9TZfAy/Rn5S0Nfu7uNHPXaKvujxvfF0WCIIP6IAgCDsQBGEHgiDsQBCEHQiCsANBEHYgiP8DHRBeHJu3kFIAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 1\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 9\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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m9vq9K/TVlfeN02WBJPiCDkiCsANJEHYgCcIOJEHYgSQIO5AEYQeS+H9NdDG1EtSA4gAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 9\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 7\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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SWklPSLpT0sIe6u3bkh6UtEGTwRrsUm8naPIUfYOk9dXf6d1+7Qp9deR14+uyQBJ8QAckQdiBJAg7kARhB5Ig7EAShB1IgrADSfw/rfkurRejpwcAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Predicted : 7\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "wC8kJf34DSkH" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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