ASL2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ASL.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Arshad221b/Sign-Language-Recognition-/blob/master/ASL2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "imJ4pdxMFuob",
        "colab_type": "code",
        "outputId": "00e50642-4322-4f4e-e497-011de77529b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import cv2 \n",
        "from keras.models import Sequential \n",
        "from keras.layers import Conv2D,MaxPooling2D, Dense,Flatten\n",
        "from keras.datasets import mnist \n",
        "import matplotlib.pyplot as plt\n",
        "from keras.utils import np_utils\n",
        "from keras.optimizers import SGD"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "qIP4zEpGGYl1",
        "colab_type": "code",
        "outputId": "fc05d15f-6713-4e3b-f9e6-849225009a4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install PyDrive"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting PyDrive\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/e0/0e64788e5dd58ce2d6934549676243dc69d982f198524be9b99e9c2a4fd5/PyDrive-1.3.1.tar.gz (987kB)\n",
            "\r\u001b[K    1% |▎                               | 10kB 17.4MB/s eta 0:00:01\r\u001b[K    2% |▋                               | 20kB 4.6MB/s eta 0:00:01\r\u001b[K    3% |█                               | 30kB 6.5MB/s eta 0:00:01\r\u001b[K    4% |█▎                              | 40kB 4.4MB/s eta 0:00:01\r\u001b[K    5% |█▋                              | 51kB 5.3MB/s eta 0:00:01\r\u001b[K    6% |██                              | 61kB 6.2MB/s eta 0:00:01\r\u001b[K    7% |██▎                             | 71kB 7.0MB/s eta 0:00:01\r\u001b[K    8% |██▋                             | 81kB 7.7MB/s eta 0:00:01\r\u001b[K    9% |███                             | 92kB 8.6MB/s eta 0:00:01\r\u001b[K    10% |███▎                            | 102kB 7.3MB/s eta 0:00:01\r\u001b[K    11% |███▋                            | 112kB 7.5MB/s eta 0:00:01\r\u001b[K    12% |████                            | 122kB 10.2MB/s eta 0:00:01\r\u001b[K    13% |████▎                           | 133kB 10.2MB/s eta 0:00:01\r\u001b[K    14% |████▋                           | 143kB 17.6MB/s eta 0:00:01\r\u001b[K    15% |█████                           | 153kB 17.9MB/s eta 0:00:01\r\u001b[K    16% |█████▎                          | 163kB 18.0MB/s eta 0:00:01\r\u001b[K    17% |█████▋                          | 174kB 17.9MB/s eta 0:00:01\r\u001b[K    18% |██████                          | 184kB 18.3MB/s eta 0:00:01\r\u001b[K    19% |██████▎                         | 194kB 18.4MB/s eta 0:00:01\r\u001b[K    20% |██████▋                         | 204kB 20.6MB/s eta 0:00:01\r\u001b[K    21% |███████                         | 215kB 19.5MB/s eta 0:00:01\r\u001b[K    22% |███████▎                        | 225kB 19.8MB/s eta 0:00:01\r\u001b[K    23% |███████▋                        | 235kB 20.2MB/s eta 0:00:01\r\u001b[K    24% |████████                        | 245kB 20.1MB/s eta 0:00:01\r\u001b[K    25% |████████▎                       | 256kB 20.0MB/s eta 0:00:01\r\u001b[K    26% |████████▋                       | 266kB 19.0MB/s eta 0:00:01\r\u001b[K    27% |█████████                       | 276kB 19.6MB/s eta 0:00:01\r\u001b[K    29% |█████████▎                      | 286kB 19.6MB/s eta 0:00:01\r\u001b[K    30% |█████████▋                      | 296kB 19.6MB/s eta 0:00:01\r\u001b[K    31% |██████████                      | 307kB 36.6MB/s eta 0:00:01\r\u001b[K    32% |██████████▎                     | 317kB 43.0MB/s eta 0:00:01\r\u001b[K    33% |██████████▋                     | 327kB 41.8MB/s eta 0:00:01\r\u001b[K    34% |███████████                     | 337kB 42.3MB/s eta 0:00:01\r\u001b[K    35% |███████████▎                    | 348kB 38.9MB/s eta 0:00:01\r\u001b[K    36% |███████████▋                    | 358kB 42.3MB/s eta 0:00:01\r\u001b[K    37% |████████████                    | 368kB 49.5MB/s eta 0:00:01\r\u001b[K    38% |████████████▎                   | 378kB 49.3MB/s eta 0:00:01\r\u001b[K    39% |████████████▋                   | 389kB 49.5MB/s eta 0:00:01\r\u001b[K    40% |█████████████                   | 399kB 34.9MB/s eta 0:00:01\r\u001b[K    41% |█████████████▎                  | 409kB 34.5MB/s eta 0:00:01\r\u001b[K    42% |█████████████▋                  | 419kB 34.3MB/s eta 0:00:01\r\u001b[K    43% |██████████████                  | 430kB 34.2MB/s eta 0:00:01\r\u001b[K    44% |██████████████▎                 | 440kB 33.4MB/s eta 0:00:01\r\u001b[K    45% |██████████████▋                 | 450kB 33.6MB/s eta 0:00:01\r\u001b[K    46% |███████████████                 | 460kB 33.3MB/s eta 0:00:01\r\u001b[K    47% |███████████████▎                | 471kB 33.5MB/s eta 0:00:01\r\u001b[K    48% |███████████████▋                | 481kB 33.3MB/s eta 0:00:01\r\u001b[K    49% |████████████████                | 491kB 33.4MB/s eta 0:00:01\r\u001b[K    50% |████████████████▎               | 501kB 46.6MB/s eta 0:00:01\r\u001b[K    51% |████████████████▋               | 512kB 44.5MB/s eta 0:00:01\r\u001b[K    52% |█████████████████               | 522kB 45.5MB/s eta 0:00:01\r\u001b[K    53% |█████████████████▎              | 532kB 46.7MB/s eta 0:00:01\r\u001b[K    54% |█████████████████▋              | 542kB 47.9MB/s eta 0:00:01\r\u001b[K    55% |██████████████████              | 552kB 53.5MB/s eta 0:00:01\r\u001b[K    57% |██████████████████▎             | 563kB 54.3MB/s eta 0:00:01\r\u001b[K    58% |██████████████████▋             | 573kB 53.5MB/s eta 0:00:01\r\u001b[K    59% |███████████████████             | 583kB 54.5MB/s eta 0:00:01\r\u001b[K    60% |███████████████████▎            | 593kB 53.2MB/s eta 0:00:01\r\u001b[K    61% |███████████████████▋            | 604kB 51.3MB/s eta 0:00:01\r\u001b[K    62% |████████████████████            | 614kB 53.1MB/s eta 0:00:01\r\u001b[K    63% |████████████████████▎           | 624kB 48.5MB/s eta 0:00:01\r\u001b[K    64% |████████████████████▋           | 634kB 46.0MB/s eta 0:00:01\r\u001b[K    65% |█████████████████████           | 645kB 46.2MB/s eta 0:00:01\r\u001b[K    66% |█████████████████████▎          | 655kB 45.4MB/s eta 0:00:01\r\u001b[K    67% |█████████████████████▋          | 665kB 37.2MB/s eta 0:00:01\r\u001b[K    68% |██████████████████████          | 675kB 36.7MB/s eta 0:00:01\r\u001b[K    69% |██████████████████████▎         | 686kB 36.7MB/s eta 0:00:01\r\u001b[K    70% |██████████████████████▋         | 696kB 37.5MB/s eta 0:00:01\r\u001b[K    71% |███████████████████████         | 706kB 38.2MB/s eta 0:00:01\r\u001b[K    72% |███████████████████████▎        | 716kB 39.0MB/s eta 0:00:01\r\u001b[K    73% |███████████████████████▋        | 727kB 41.1MB/s eta 0:00:01\r\u001b[K    74% |████████████████████████        | 737kB 43.0MB/s eta 0:00:01\r\u001b[K    75% |████████████████████████▎       | 747kB 43.4MB/s eta 0:00:01\r\u001b[K    76% |████████████████████████▋       | 757kB 44.1MB/s eta 0:00:01\r\u001b[K    77% |████████████████████████▉       | 768kB 54.5MB/s eta 0:00:01\r\u001b[K    78% |█████████████████████████▏      | 778kB 52.6MB/s eta 0:00:01\r\u001b[K    79% |█████████████████████████▌      | 788kB 51.2MB/s eta 0:00:01\r\u001b[K    80% |█████████████████████████▉      | 798kB 51.3MB/s eta 0:00:01\r\u001b[K    81% |██████████████████████████▏     | 808kB 50.8MB/s eta 0:00:01\r\u001b[K    82% |██████████████████████████▌     | 819kB 50.5MB/s eta 0:00:01\r\u001b[K    83% |██████████████████████████▉     | 829kB 51.0MB/s eta 0:00:01\r\u001b[K    85% |███████████████████████████▏    | 839kB 49.5MB/s eta 0:00:01\r\u001b[K    86% |███████████████████████████▌    | 849kB 49.7MB/s eta 0:00:01\r\u001b[K    87% |███████████████████████████▉    | 860kB 45.8MB/s eta 0:00:01\r\u001b[K    88% |████████████████████████████▏   | 870kB 46.4MB/s eta 0:00:01\r\u001b[K    89% |████████████████████████████▌   | 880kB 49.2MB/s eta 0:00:01\r\u001b[K    90% |████████████████████████████▉   | 890kB 50.2MB/s eta 0:00:01\r\u001b[K    91% |█████████████████████████████▏  | 901kB 49.9MB/s eta 0:00:01\r\u001b[K    92% |█████████████████████████████▌  | 911kB 50.5MB/s eta 0:00:01\r\u001b[K    93% |█████████████████████████████▉  | 921kB 50.4MB/s eta 0:00:01\r\u001b[K    94% |██████████████████████████████▏ | 931kB 50.0MB/s eta 0:00:01\r\u001b[K    95% |██████████████████████████████▌ | 942kB 50.3MB/s eta 0:00:01\r\u001b[K    96% |██████████████████████████████▉ | 952kB 50.0MB/s eta 0:00:01\r\u001b[K    97% |███████████████████████████████▏| 962kB 55.1MB/s eta 0:00:01\r\u001b[K    98% |███████████████████████████████▌| 972kB 55.7MB/s eta 0:00:01\r\u001b[K    99% |███████████████████████████████▉| 983kB 55.0MB/s eta 0:00:01\r\u001b[K    100% |████████████████████████████████| 993kB 21.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.6/dist-packages (from PyDrive) (1.6.7)\n",
            "Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from PyDrive) (4.1.3)\n",
            "Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.6/dist-packages (from PyDrive) (3.13)\n",
            "Requirement already satisfied: six<2dev,>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (1.11.0)\n",
            "Requirement already satisfied: httplib2<1dev,>=0.9.2 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (0.11.3)\n",
            "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (3.0.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.4.5)\n",
            "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.2.4)\n",
            "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (4.0)\n",
            "Building wheels for collected packages: PyDrive\n",
            "  Building wheel for PyDrive (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/fa/d2/9a/d3b6b506c2da98289e5d417215ce34b696db856643bad779f4\n",
            "Successfully built PyDrive\n",
            "Installing collected packages: PyDrive\n",
            "Successfully installed PyDrive-1.3.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ijUASkTROl6D",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2yEzz9S4RdR9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Z6DDOrSbRqA3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "download = drive.CreateFile({'id': '1wG0gS-bqjV6yz1YveuxkvHT5_2DOuT05'})\n",
        "download.GetContentFile('train.csv')\n",
        "train = pd.read_csv('train.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2Rc5CNhVRs4b",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "download = drive.CreateFile({'id': '1q_Zwlu3RncjKq1YpiVtkiMPxIIueGRYB'})\n",
        "download.GetContentFile('test.csv')\n",
        "test = pd.read_csv('test.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Bzui153jR012",
        "colab_type": "code",
        "outputId": "fa16d255-5c14-4976-b736-839e39a32c61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        }
      },
      "cell_type": "code",
      "source": [
        "display(train.info())\n",
        "\n",
        "display(test.info())\n",
        "\n",
        "display(train.head(n = 2))\n",
        "display(test.head(n = 2))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 27455 entries, 0 to 27454\n",
            "Columns: 785 entries, label to pixel784\n",
            "dtypes: int64(785)\n",
            "memory usage: 164.4 MB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7172 entries, 0 to 7171\n",
            "Columns: 785 entries, label to pixel784\n",
            "dtypes: int64(785)\n",
            "memory usage: 43.0 MB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>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>pixel9</th>\n",
              "      <th>...</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",
              "      <th>pixel784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>107</td>\n",
              "      <td>118</td>\n",
              "      <td>127</td>\n",
              "      <td>134</td>\n",
              "      <td>139</td>\n",
              "      <td>143</td>\n",
              "      <td>146</td>\n",
              "      <td>150</td>\n",
              "      <td>153</td>\n",
              "      <td>...</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>207</td>\n",
              "      <td>206</td>\n",
              "      <td>206</td>\n",
              "      <td>206</td>\n",
              "      <td>204</td>\n",
              "      <td>203</td>\n",
              "      <td>202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>155</td>\n",
              "      <td>157</td>\n",
              "      <td>156</td>\n",
              "      <td>156</td>\n",
              "      <td>156</td>\n",
              "      <td>157</td>\n",
              "      <td>156</td>\n",
              "      <td>158</td>\n",
              "      <td>158</td>\n",
              "      <td>...</td>\n",
              "      <td>69</td>\n",
              "      <td>149</td>\n",
              "      <td>128</td>\n",
              "      <td>87</td>\n",
              "      <td>94</td>\n",
              "      <td>163</td>\n",
              "      <td>175</td>\n",
              "      <td>103</td>\n",
              "      <td>135</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
              "0      3     107     118     127     134     139     143     146     150   \n",
              "1      6     155     157     156     156     156     157     156     158   \n",
              "\n",
              "   pixel9    ...     pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
              "0     153    ...          207       207       207       207       206   \n",
              "1     158    ...           69       149       128        87        94   \n",
              "\n",
              "   pixel780  pixel781  pixel782  pixel783  pixel784  \n",
              "0       206       206       204       203       202  \n",
              "1       163       175       103       135       149  \n",
              "\n",
              "[2 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>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>pixel9</th>\n",
              "      <th>...</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",
              "      <th>pixel784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>149</td>\n",
              "      <td>149</td>\n",
              "      <td>150</td>\n",
              "      <td>150</td>\n",
              "      <td>150</td>\n",
              "      <td>151</td>\n",
              "      <td>151</td>\n",
              "      <td>150</td>\n",
              "      <td>151</td>\n",
              "      <td>...</td>\n",
              "      <td>138</td>\n",
              "      <td>148</td>\n",
              "      <td>127</td>\n",
              "      <td>89</td>\n",
              "      <td>82</td>\n",
              "      <td>96</td>\n",
              "      <td>106</td>\n",
              "      <td>112</td>\n",
              "      <td>120</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>126</td>\n",
              "      <td>128</td>\n",
              "      <td>131</td>\n",
              "      <td>132</td>\n",
              "      <td>133</td>\n",
              "      <td>134</td>\n",
              "      <td>135</td>\n",
              "      <td>135</td>\n",
              "      <td>136</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>104</td>\n",
              "      <td>194</td>\n",
              "      <td>183</td>\n",
              "      <td>186</td>\n",
              "      <td>184</td>\n",
              "      <td>184</td>\n",
              "      <td>184</td>\n",
              "      <td>182</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
              "0      6     149     149     150     150     150     151     151     150   \n",
              "1      5     126     128     131     132     133     134     135     135   \n",
              "\n",
              "   pixel9    ...     pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
              "0     151    ...          138       148       127        89        82   \n",
              "1     136    ...           47       104       194       183       186   \n",
              "\n",
              "   pixel780  pixel781  pixel782  pixel783  pixel784  \n",
              "0        96       106       112       120       107  \n",
              "1       184       184       184       182       180  \n",
              "\n",
              "[2 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Yim557wpbSf_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_Y = train['label']\n",
        "test_Y = test['label']\n",
        "train_X = train.drop(['label'],axis = 1)\n",
        "test_X = test.drop(['label'],axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PyiXHi2DkT4r",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_X = train_X.astype('float32') / 255\n",
        "test_X = test_X.astype('float32')/255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RpKOAcYvi-wS",
        "colab_type": "code",
        "outputId": "d640461b-356f-4c33-e1e4-1a46cd289040",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1062
        }
      },
      "cell_type": "code",
      "source": [
        "display(train_Y)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0         3\n",
              "1         6\n",
              "2         2\n",
              "3         2\n",
              "4        13\n",
              "5        16\n",
              "6         8\n",
              "7        22\n",
              "8         3\n",
              "9         3\n",
              "10       18\n",
              "11       10\n",
              "12       16\n",
              "13       22\n",
              "14       20\n",
              "15       16\n",
              "16       17\n",
              "17       13\n",
              "18       13\n",
              "19       19\n",
              "20       18\n",
              "21       21\n",
              "22       16\n",
              "23       23\n",
              "24        3\n",
              "25       23\n",
              "26       24\n",
              "27       18\n",
              "28       22\n",
              "29        1\n",
              "         ..\n",
              "27425    21\n",
              "27426    21\n",
              "27427     4\n",
              "27428    17\n",
              "27429     5\n",
              "27430    12\n",
              "27431    19\n",
              "27432     2\n",
              "27433    22\n",
              "27434    12\n",
              "27435     1\n",
              "27436    19\n",
              "27437    16\n",
              "27438    21\n",
              "27439    11\n",
              "27440     5\n",
              "27441    18\n",
              "27442     0\n",
              "27443    21\n",
              "27444    18\n",
              "27445    14\n",
              "27446    19\n",
              "27447    11\n",
              "27448    12\n",
              "27449    20\n",
              "27450    13\n",
              "27451    23\n",
              "27452    18\n",
              "27453    17\n",
              "27454    23\n",
              "Name: label, Length: 27455, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zRdBQOJ0bVvF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "train_X = train_X.values.reshape(27455,784)\n",
        "test_X = test_X.values.reshape(7172,784)\n",
        "train_Y = keras.utils.to_categorical(train_Y,26)\n",
        "test_Y = keras.utils.to_categorical(test_Y,26)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "00fdB0Yvl1d8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(units=128,activation=\"relu\",input_shape=(784,)))\n",
        "model.add(Dense(units=128,activation=\"relu\"))\n",
        "model.add(Dense(units=128,activation=\"relu\"))\n",
        "model.add(Dense(units=128,activation=\"relu\"))\n",
        "model.add(Dense(units=128,activation=\"relu\"))\n",
        "model.add(Dense(units=26,activation=\"softmax\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QIfU7or-fcAM",
        "colab_type": "code",
        "outputId": "a570dd53-470a-4dd2-f202-090fdd291622",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3359
        }
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=SGD(0.001),loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
        "model.fit(train_X,train_Y,batch_size=32,epochs=100,verbose=1)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "27455/27455 [==============================] - 5s 171us/step - loss: 3.2353 - acc: 0.0505\n",
            "Epoch 2/100\n",
            "27455/27455 [==============================] - 4s 162us/step - loss: 3.1936 - acc: 0.0478\n",
            "Epoch 3/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 3.1550 - acc: 0.0547\n",
            "Epoch 4/100\n",
            "27455/27455 [==============================] - 4s 164us/step - loss: 3.1044 - acc: 0.1023\n",
            "Epoch 5/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 3.0325 - acc: 0.1639\n",
            "Epoch 6/100\n",
            "27455/27455 [==============================] - 4s 162us/step - loss: 2.9165 - acc: 0.1772\n",
            "Epoch 7/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 2.7596 - acc: 0.2104\n",
            "Epoch 8/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 2.5975 - acc: 0.2416\n",
            "Epoch 9/100\n",
            "27455/27455 [==============================] - 4s 162us/step - loss: 2.4310 - acc: 0.2705\n",
            "Epoch 10/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 2.2569 - acc: 0.3055\n",
            "Epoch 11/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 2.0973 - acc: 0.3460\n",
            "Epoch 12/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 1.9587 - acc: 0.3804\n",
            "Epoch 13/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 1.8474 - acc: 0.4154\n",
            "Epoch 14/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 1.7498 - acc: 0.4412\n",
            "Epoch 15/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 1.6630 - acc: 0.4679\n",
            "Epoch 16/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 1.5820 - acc: 0.4972\n",
            "Epoch 17/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 1.5068 - acc: 0.5221\n",
            "Epoch 18/100\n",
            "27455/27455 [==============================] - 5s 165us/step - loss: 1.4339 - acc: 0.5454\n",
            "Epoch 19/100\n",
            "27455/27455 [==============================] - 4s 163us/step - loss: 1.3638 - acc: 0.5645\n",
            "Epoch 20/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 1.2983 - acc: 0.5874\n",
            "Epoch 21/100\n",
            "27455/27455 [==============================] - 4s 158us/step - loss: 1.2351 - acc: 0.6061\n",
            "Epoch 22/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 1.1770 - acc: 0.6237\n",
            "Epoch 23/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 1.1224 - acc: 0.6407\n",
            "Epoch 24/100\n",
            "27455/27455 [==============================] - 4s 162us/step - loss: 1.0739 - acc: 0.6607\n",
            "Epoch 25/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 1.0249 - acc: 0.6773\n",
            "Epoch 26/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 0.9869 - acc: 0.6908\n",
            "Epoch 27/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 0.9424 - acc: 0.7049\n",
            "Epoch 28/100\n",
            "27455/27455 [==============================] - 4s 156us/step - loss: 0.9103 - acc: 0.7168\n",
            "Epoch 29/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.8732 - acc: 0.7308\n",
            "Epoch 30/100\n",
            "27455/27455 [==============================] - 4s 163us/step - loss: 0.8374 - acc: 0.7415\n",
            "Epoch 31/100\n",
            "27455/27455 [==============================] - 4s 164us/step - loss: 0.8066 - acc: 0.7533\n",
            "Epoch 32/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 0.7762 - acc: 0.7609\n",
            "Epoch 33/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.7474 - acc: 0.7722\n",
            "Epoch 34/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 0.7178 - acc: 0.7811\n",
            "Epoch 35/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.6899 - acc: 0.7915\n",
            "Epoch 36/100\n",
            "27455/27455 [==============================] - 4s 158us/step - loss: 0.6633 - acc: 0.8000\n",
            "Epoch 37/100\n",
            "27455/27455 [==============================] - 4s 158us/step - loss: 0.6386 - acc: 0.8073\n",
            "Epoch 38/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.6133 - acc: 0.8168\n",
            "Epoch 39/100\n",
            "27455/27455 [==============================] - 4s 158us/step - loss: 0.5913 - acc: 0.8231\n",
            "Epoch 40/100\n",
            "27455/27455 [==============================] - 4s 158us/step - loss: 0.5666 - acc: 0.8306\n",
            "Epoch 41/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.5423 - acc: 0.8384\n",
            "Epoch 42/100\n",
            "27455/27455 [==============================] - 4s 163us/step - loss: 0.5203 - acc: 0.8475\n",
            "Epoch 43/100\n",
            "27455/27455 [==============================] - 4s 158us/step - loss: 0.4932 - acc: 0.8568\n",
            "Epoch 44/100\n",
            "27455/27455 [==============================] - 4s 158us/step - loss: 0.4753 - acc: 0.8616\n",
            "Epoch 45/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.4544 - acc: 0.8674\n",
            "Epoch 46/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.4407 - acc: 0.8723\n",
            "Epoch 47/100\n",
            "27455/27455 [==============================] - 4s 153us/step - loss: 0.4182 - acc: 0.8793\n",
            "Epoch 48/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.4004 - acc: 0.8829\n",
            "Epoch 49/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.3776 - acc: 0.8908\n",
            "Epoch 50/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.3684 - acc: 0.8944\n",
            "Epoch 51/100\n",
            "27455/27455 [==============================] - 4s 153us/step - loss: 0.3441 - acc: 0.9026\n",
            "Epoch 52/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.3355 - acc: 0.9072\n",
            "Epoch 53/100\n",
            "27455/27455 [==============================] - 4s 157us/step - loss: 0.3129 - acc: 0.9139\n",
            "Epoch 54/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 0.3030 - acc: 0.9149\n",
            "Epoch 55/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 0.2831 - acc: 0.9237\n",
            "Epoch 56/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 0.2709 - acc: 0.9253\n",
            "Epoch 57/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 0.2573 - acc: 0.9336\n",
            "Epoch 58/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 0.2349 - acc: 0.9386\n",
            "Epoch 59/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 0.2274 - acc: 0.9410\n",
            "Epoch 60/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 0.2357 - acc: 0.9405\n",
            "Epoch 61/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.1994 - acc: 0.9508\n",
            "Epoch 62/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.2149 - acc: 0.9467\n",
            "Epoch 63/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.1785 - acc: 0.9577\n",
            "Epoch 64/100\n",
            "27455/27455 [==============================] - 4s 158us/step - loss: 0.1691 - acc: 0.9587\n",
            "Epoch 65/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.1673 - acc: 0.9591\n",
            "Epoch 66/100\n",
            "27455/27455 [==============================] - 4s 156us/step - loss: 0.1456 - acc: 0.9671\n",
            "Epoch 67/100\n",
            "27455/27455 [==============================] - 4s 153us/step - loss: 0.1659 - acc: 0.9623\n",
            "Epoch 68/100\n",
            "27455/27455 [==============================] - 4s 153us/step - loss: 0.1253 - acc: 0.9752\n",
            "Epoch 69/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.1507 - acc: 0.9704\n",
            "Epoch 70/100\n",
            "27455/27455 [==============================] - 4s 155us/step - loss: 0.1172 - acc: 0.9772\n",
            "Epoch 71/100\n",
            "27455/27455 [==============================] - 4s 153us/step - loss: 0.1226 - acc: 0.9737\n",
            "Epoch 72/100\n",
            "27455/27455 [==============================] - 4s 152us/step - loss: 0.1002 - acc: 0.9824\n",
            "Epoch 73/100\n",
            "27455/27455 [==============================] - 4s 153us/step - loss: 0.0935 - acc: 0.9842\n",
            "Epoch 74/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.1086 - acc: 0.9820\n",
            "Epoch 75/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.0944 - acc: 0.9844\n",
            "Epoch 76/100\n",
            "27455/27455 [==============================] - 4s 152us/step - loss: 0.0905 - acc: 0.9866\n",
            "Epoch 77/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.0715 - acc: 0.9904\n",
            "Epoch 78/100\n",
            "27455/27455 [==============================] - 4s 152us/step - loss: 0.0683 - acc: 0.9917\n",
            "Epoch 79/100\n",
            "27455/27455 [==============================] - 4s 153us/step - loss: 0.0631 - acc: 0.9927\n",
            "Epoch 80/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.1267 - acc: 0.9803\n",
            "Epoch 81/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.0588 - acc: 0.9934\n",
            "Epoch 82/100\n",
            "27455/27455 [==============================] - 4s 153us/step - loss: 0.0526 - acc: 0.9957\n",
            "Epoch 83/100\n",
            "27455/27455 [==============================] - 4s 153us/step - loss: 0.0949 - acc: 0.9861\n",
            "Epoch 84/100\n",
            "27455/27455 [==============================] - 4s 152us/step - loss: 0.0489 - acc: 0.9952\n",
            "Epoch 85/100\n",
            "27455/27455 [==============================] - 4s 153us/step - loss: 0.0505 - acc: 0.9945\n",
            "Epoch 86/100\n",
            "27455/27455 [==============================] - 4s 152us/step - loss: 0.0440 - acc: 0.9963\n",
            "Epoch 87/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.0410 - acc: 0.9965\n",
            "Epoch 88/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.0395 - acc: 0.9970\n",
            "Epoch 89/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.0360 - acc: 0.9980\n",
            "Epoch 90/100\n",
            "27455/27455 [==============================] - 4s 155us/step - loss: 0.0362 - acc: 0.9970\n",
            "Epoch 91/100\n",
            "27455/27455 [==============================] - 4s 155us/step - loss: 0.1516 - acc: 0.9810\n",
            "Epoch 92/100\n",
            "27455/27455 [==============================] - 4s 154us/step - loss: 0.0352 - acc: 0.9979\n",
            "Epoch 93/100\n",
            "27455/27455 [==============================] - 4s 153us/step - loss: 0.0317 - acc: 0.9983\n",
            "Epoch 94/100\n",
            "27455/27455 [==============================] - 4s 158us/step - loss: 0.0309 - acc: 0.9983\n",
            "Epoch 95/100\n",
            "27455/27455 [==============================] - 4s 160us/step - loss: 0.0282 - acc: 0.9985\n",
            "Epoch 96/100\n",
            "27455/27455 [==============================] - 4s 161us/step - loss: 0.0279 - acc: 0.9984\n",
            "Epoch 97/100\n",
            "27455/27455 [==============================] - 4s 159us/step - loss: 0.0315 - acc: 0.9973\n",
            "Epoch 98/100\n",
            "27455/27455 [==============================] - 4s 157us/step - loss: 0.0245 - acc: 0.9989\n",
            "Epoch 99/100\n",
            "27455/27455 [==============================] - 4s 158us/step - loss: 0.0246 - acc: 0.9986\n",
            "Epoch 100/100\n",
            "27455/27455 [==============================] - 4s 158us/step - loss: 0.1198 - acc: 0.9827\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f16d0030ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "tmXKzSMSfgDS",
        "colab_type": "code",
        "outputId": "226ed580-45e6-479e-9576-2fc469348fd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "cell_type": "code",
      "source": [
        "accuracy = model.evaluate(x=test_X,y=test_Y,batch_size=32)\n",
        "print(\"Accuracy: \",accuracy[1])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7172/7172 [==============================] - 0s 69us/step\n",
            "Accuracy:  0.6868377021751255\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QFWBt201hlEf",
        "colab_type": "code",
        "outputId": "14a91e7e-72a3-479d-ec39-789c06ad6168",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "img = test_X[1]\n",
        "test_img = img.reshape((1,784))\n",
        "img_class = model.predict_classes(test_img)\n",
        "prediction = img_class[0]\n",
        "classname = img_class[0]\n",
        "print(\"Class: \",classname)\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Class:  5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5Yzwqb7Fhojk",
        "colab_type": "code",
        "outputId": "a22d2978-ca5e-4dd3-fee1-afdb1a1a82e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        }
      },
      "cell_type": "code",
      "source": [
        "img = img.reshape((28,28))\n",
        "plt.imshow(img)\n",
        "plt.title(classname)\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAFZCAYAAAARqQ0OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYECyBAADkiUAGPw/byBJH7fr3p4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3NldTeSghsHB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 94
        },
        "outputId": "2ce7b919-50c1-4cde-b2c7-05a8f2d8d717"
      },
      "cell_type": "code",
      "source": [
        "model.save_weights('model_weights.h5')\n",
        "weights_file = drive.CreateFile({'title' : 'model_weights.h5'})\n",
        "weights_file.SetContentFile('model_weights.h5')\n",
        "weights_file.Upload()\n",
        "drive.CreateFile({'id': weights_file.get('id')})"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GoogleDriveFile({'id': '1RvY2_T0hQ-8fO7Rgj5C3FgoWkuCA-ICa'})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "b3MJfJrWyGNH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 94
        },
        "outputId": "9ccecde1-5cca-4397-f338-72155c24d8a2"
      },
      "cell_type": "code",
      "source": [
        "model.save('model.h5')\n",
        "weights_file = drive.CreateFile({'title' : 'model.h5'})\n",
        "weights_file.SetContentFile('model.h5')\n",
        "weights_file.Upload()\n",
        "drive.CreateFile({'id': weights_file.get('id')})"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GoogleDriveFile({'id': '1Cs0mkCbgKFYgFYIr9JPgVq_iZb0V3psf'})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "WFgvZoNXqsCJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "b5894724-4551-4efd-ffe9-41c446c360f7"
      },
      "cell_type": "code",
      "source": [
        "!pip install h5py pyyaml "
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (2.8.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (3.13)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py) (1.11.0)\n",
            "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.6/dist-packages (from h5py) (1.14.6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "y7SmbytZx6Wa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}