11/cnn-resnet-from-scratch-top-10-0-1-2-3-4.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">MNIST DIGIT RECOGNITION </h2><a id = \"1\" ></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style='color:white; background:#0096FF ; border:0' role=\"tab\" aria-controls=\"home\"><center>Quick Navigation</center></h3>\n",
    "\n",
    "* [Importing Libraries](#1.1)\n",
    "    \n",
    "* [Data Preprocessing](#2)\n",
    "    \n",
    "    - [Load Training Dataset](#2.1)\n",
    "    \n",
    "    - [Split Data](#2.2)\n",
    "    \n",
    "    - [Image Preprocessing](#2.3)\n",
    "    \n",
    "* [Data Visualization](#3)\n",
    "\n",
    "* [Activation function](#4)\n",
    "\n",
    "* [Model Summary](#5)\n",
    "\n",
    "* [Baseline Architecture](#5.3)  \n",
    "    \n",
    "* 👆[**Resnet Architecture**](#5.4)    \n",
    "\n",
    "* [Data Augmentation](#6)\n",
    "    \n",
    "* [Save and Load Model](#6.2)\n",
    "    \n",
    "* [Plot Accuracy and Loss curve](#7)\n",
    "    \n",
    "* [Evaluation](#8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Objective**\n",
    "* To recognize digits from the MNIST dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">1. IMPORTING LIBRARIES</h1><a id = \"1.1\" ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-04-05T22:59:50.622121Z",
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    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import decomposition\n",
    "\n",
    "import tensorflow \n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Input,Dense, Activation,Flatten, Conv2D, Dropout\n",
    "from tensorflow.keras.layers import MaxPool2D, LSTM, BatchNormalization,concatenate\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau,ModelCheckpoint\n",
    "from tensorflow.keras.layers import ELU\n",
    "from tensorflow.keras.losses import sparse_categorical_crossentropy, categorical_crossentropy\n",
    "from tensorflow.keras import Model\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": false,
    "execution": {
     "iopub.execute_input": "2023-04-05T22:59:59.952905Z",
     "iopub.status.busy": "2023-04-05T22:59:59.950099Z",
     "iopub.status.idle": "2023-04-05T22:59:59.969101Z",
     "shell.execute_reply": "2023-04-05T22:59:59.968133Z",
     "shell.execute_reply.started": "2023-04-05T22:59:59.952863Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/character/Train.csv\n",
      "/kaggle/input/character/Test.csv\n",
      "/kaggle/input/digit-recognizer/sample_submission.csv\n",
      "/kaggle/input/digit-recognizer/train.csv\n",
      "/kaggle/input/digit-recognizer/test.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">2. DATA PREPROCESSING</h1><a id = \"2\" ></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2  style='color:#0096FF; background:white ; border:0' class=\"list-group-item list-group-item-action active\">2.1 LOAD TRAINING DATASET</h2><a id = \"2.1\" ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T22:59:59.975740Z",
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     "shell.execute_reply": "2023-04-05T23:00:16.329760Z",
     "shell.execute_reply.started": "2023-04-05T22:59:59.975697Z"
    }
   },
   "outputs": [],
   "source": [
    "loc = '../input/character'\n",
    "train_data = pd.read_csv(loc+'/Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:00:16.334686Z",
     "iopub.status.busy": "2023-04-05T23:00:16.333950Z",
     "iopub.status.idle": "2023-04-05T23:00:18.448902Z",
     "shell.execute_reply": "2023-04-05T23:00:18.447865Z",
     "shell.execute_reply.started": "2023-04-05T23:00:16.334645Z"
    }
   },
   "outputs": [],
   "source": [
    "test_file = loc+\"/Test.csv\"\n",
    "test_data = pd.read_csv(test_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3  style='color:#0096FF; background:#FFF ' class=\"list-group-item list-group-item-action active\">Data Dimension</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:00:18.451016Z",
     "iopub.status.busy": "2023-04-05T23:00:18.450604Z",
     "iopub.status.idle": "2023-04-05T23:00:18.457239Z",
     "shell.execute_reply": "2023-04-05T23:00:18.456099Z",
     "shell.execute_reply.started": "2023-04-05T23:00:18.450974Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train.csv size is (209966, 785)\n",
      "test.csv size is (34966, 784)\n"
     ]
    }
   ],
   "source": [
    "print(f\"train.csv size is {train_data.shape}\")\n",
    "print(f\"test.csv size is {test_data.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:00:18.459666Z",
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     "iopub.status.idle": "2023-04-05T23:00:18.487234Z",
     "shell.execute_reply": "2023-04-05T23:00:18.486259Z",
     "shell.execute_reply.started": "2023-04-05T23:00:18.459626Z"
    }
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
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       "      <th>count</th>\n",
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       "      <td>0.834946</td>\n",
       "      <td>0.263604</td>\n",
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       "      <td>2.997498</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>219.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>129.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               label         pixel0         pixel1         pixel2  \\\n",
       "count  209966.000000  209966.000000  209966.000000  209966.000000   \n",
       "mean       16.999919       0.018417       0.014988       0.015369   \n",
       "std        10.099573       0.834946       0.263604       0.317467   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         8.000000       0.000000       0.000000       0.000000   \n",
       "50%        17.000000       0.000000       0.000000       0.000000   \n",
       "75%        26.000000       0.000000       0.000000       0.000000   \n",
       "max        34.000000     148.000000      66.000000      78.000000   \n",
       "\n",
       "              pixel3         pixel4         pixel5         pixel6  \\\n",
       "count  209966.000000  209966.000000  209966.000000  209966.000000   \n",
       "mean        0.015845       0.017536       0.020813       0.030957   \n",
       "std         0.425064       0.510315       0.920157       1.481729   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max       126.000000     139.000000     243.000000     232.000000   \n",
       "\n",
       "              pixel7        pixel8  ...      pixel774       pixel775  \\\n",
       "count  209966.000000  209966.00000  ...  209966.00000  209966.000000   \n",
       "mean        0.031591       0.18181  ...       0.29872       0.244111   \n",
       "std         1.554027       2.00597  ...       6.37452       5.617072   \n",
       "min         0.000000       0.00000  ...       0.00000       0.000000   \n",
       "25%         0.000000       0.00000  ...       0.00000       0.000000   \n",
       "50%         0.000000       0.00000  ...       0.00000       0.000000   \n",
       "75%         0.000000       0.00000  ...       0.00000       0.000000   \n",
       "max       231.000000     219.00000  ...     255.00000     255.000000   \n",
       "\n",
       "            pixel776       pixel777       pixel778       pixel779  \\\n",
       "count  209966.000000  209966.000000  209966.000000  209966.000000   \n",
       "mean        0.180834       0.131097       0.104465       0.073721   \n",
       "std         4.624211       3.598397       2.997498       2.256605   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max       255.000000     253.000000     243.000000     232.000000   \n",
       "\n",
       "            pixel780       pixel781       pixel782       pixel783  \n",
       "count  209966.000000  209966.000000  209966.000000  209966.000000  \n",
       "mean        0.028852       0.016436       0.009506       0.006534  \n",
       "std         1.802888       1.281223       0.915430       0.547347  \n",
       "min         0.000000       0.000000       0.000000       0.000000  \n",
       "25%         0.000000       0.000000       0.000000       0.000000  \n",
       "50%         0.000000       0.000000       0.000000       0.000000  \n",
       "75%         0.000000       0.000000       0.000000       0.000000  \n",
       "max       229.000000     200.000000     189.000000     129.000000  \n",
       "\n",
       "[8 rows x 785 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To Check how many pixel columns are there such that all their values are zero as we can see from the describe() function.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:00:54.695018Z",
     "iopub.status.busy": "2023-04-05T23:00:54.694392Z",
     "iopub.status.idle": "2023-04-05T23:07:56.608616Z",
     "shell.execute_reply": "2023-04-05T23:07:56.607513Z",
     "shell.execute_reply.started": "2023-04-05T23:00:54.694979Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = train_data.drop(columns=['label'])\n",
    "lst,ind = [],[]\n",
    "for feature in df.columns:\n",
    "    l = len(df.loc[df[feature] ==0])\n",
    "    lst.append(l)\n",
    "# 209966 is the number of datapoints of training dataset.\n",
    "lst.count(209966)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**:\n",
    "* There are 76 pixel columns among the 784 columns , 209966 datapoints whose all values are 0. \n",
    "* For sake of dimensionality reduction, these features can be removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3  style='color:#0096FF; background:#FFF ' class=\"list-group-item list-group-item-action active\">Missing values</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:18:25.175450Z",
     "iopub.status.busy": "2023-04-05T23:18:25.174870Z",
     "iopub.status.idle": "2023-04-05T23:18:25.368167Z",
     "shell.execute_reply": "2023-04-05T23:18:25.366147Z",
     "shell.execute_reply.started": "2023-04-05T23:18:25.175412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label       0\n",
       "pixel0      0\n",
       "pixel1      0\n",
       "pixel2      0\n",
       "pixel3      0\n",
       "           ..\n",
       "pixel779    0\n",
       "pixel780    0\n",
       "pixel781    0\n",
       "pixel782    0\n",
       "pixel783    0\n",
       "Length: 785, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* No missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:18:32.686752Z",
     "iopub.status.busy": "2023-04-05T23:18:32.686164Z",
     "iopub.status.idle": "2023-04-05T23:18:32.691842Z",
     "shell.execute_reply": "2023-04-05T23:18:32.690656Z",
     "shell.execute_reply.started": "2023-04-05T23:18:32.686713Z"
    }
   },
   "outputs": [],
   "source": [
    "train_labels=train_data['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">2.2 SPLIT DATA</h2><a id = \"2.2\" ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:18:39.700158Z",
     "iopub.status.busy": "2023-04-05T23:18:39.699135Z",
     "iopub.status.idle": "2023-04-05T23:18:39.705105Z",
     "shell.execute_reply": "2023-04-05T23:18:39.703742Z",
     "shell.execute_reply.started": "2023-04-05T23:18:39.700116Z"
    }
   },
   "outputs": [],
   "source": [
    "img_rows, img_cols = 28, 28 # 28*28 dimension of image reshape\n",
    "num_classes = 35 # 35 number of labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">2.3 PREPROCESSING</h2><a id = \"2.3\" ></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3  style='color:#0096FF; background:#FFF ' class=\"list-group-item list-group-item-action active\">One hot Encoding</h3>\n",
    "\n",
    "One hot encoding is one method of converting data to prepare it for an algorithm and get a better prediction. With one-hot, we convert each categorical value into a new categorical column and assign a binary value of 1 or 0 to those columns. Each integer value is represented as a binary vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:18:46.398027Z",
     "iopub.status.busy": "2023-04-05T23:18:46.397433Z",
     "iopub.status.idle": "2023-04-05T23:18:46.889036Z",
     "shell.execute_reply": "2023-04-05T23:18:46.887988Z",
     "shell.execute_reply.started": "2023-04-05T23:18:46.397989Z"
    }
   },
   "outputs": [],
   "source": [
    "def data_prep(raw):\n",
    "    out_y = tensorflow.keras.utils.to_categorical(raw.label, num_classes)\n",
    "\n",
    "    num_images = raw.shape[0]\n",
    "    x_as_array = raw.values[:,1:]\n",
    "    x_shaped_array = x_as_array.reshape(num_images, img_rows, img_cols, 1)\n",
    "    # normalization\n",
    "    out_x = x_shaped_array / 255\n",
    "    return out_x, out_y\n",
    "\n",
    "x, y = data_prep(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3  style='color:#0096FF; background:#FFF ' class=\"list-group-item list-group-item-action active\">Data Split</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:18:54.056390Z",
     "iopub.status.busy": "2023-04-05T23:18:54.055482Z",
     "iopub.status.idle": "2023-04-05T23:18:56.270941Z",
     "shell.execute_reply": "2023-04-05T23:18:56.269861Z",
     "shell.execute_reply.started": "2023-04-05T23:18:54.056351Z"
    }
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)#ratio 90:10\n",
    "x_train, x_val, y_train, y_val= train_test_split(x_train, y_train, test_size = 1/9, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Splitting data into 90% training and 10% test data.\n",
    "* Splitting the training data into 90% training data and 10% validation data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3  style='color:#0096FF; background:#FFF ' class=\"list-group-item list-group-item-action active\">Reshaping the Data to 4 dimension</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:18:57.483691Z",
     "iopub.status.busy": "2023-04-05T23:18:57.483313Z",
     "iopub.status.idle": "2023-04-05T23:18:57.584877Z",
     "shell.execute_reply": "2023-04-05T23:18:57.583784Z",
     "shell.execute_reply.started": "2023-04-05T23:18:57.483657Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34966, 28, 28, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalization\n",
    "test_data = test_data / 255  \n",
    "# reshaping\n",
    "test_data = test_data.values.reshape(-1,28,28,1)\n",
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3  style='color:#0096FF; background:#FFF ' class=\"list-group-item list-group-item-action active\">Data Dimension</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:19:00.701318Z",
     "iopub.status.busy": "2023-04-05T23:19:00.700580Z",
     "iopub.status.idle": "2023-04-05T23:19:00.707141Z",
     "shell.execute_reply": "2023-04-05T23:19:00.706153Z",
     "shell.execute_reply.started": "2023-04-05T23:19:00.701278Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data size is (167972, 28, 28, 1)\n",
      "Training data size is (167972, 35)\n",
      "Testing data size is (20997, 28, 28, 1)\n",
      "Training data size is (20997, 35)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training data size is {x_train.shape}\")\n",
    "print(f\"Training data size is {y_train.shape}\")\n",
    "print(f\"Testing data size is {x_test.shape}\")\n",
    "print(f\"Training data size is {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">3. VISUALIZE DATA</h1><a id = \"3\" ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2023-04-05T23:19:02.704442Z",
     "iopub.status.busy": "2023-04-05T23:19:02.703396Z",
     "iopub.status.idle": "2023-04-05T23:19:02.713322Z",
     "shell.execute_reply": "2023-04-05T23:19:02.711884Z",
     "shell.execute_reply.started": "2023-04-05T23:19:02.704399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title=[j for i in range(10, 19) for j in range(0,10) if y_train[i][j] == 1]\n",
    "title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:19:22.302872Z",
     "iopub.status.busy": "2023-04-05T23:19:22.302275Z",
     "iopub.status.idle": "2023-04-05T23:19:22.627711Z",
     "shell.execute_reply": "2023-04-05T23:19:22.626716Z",
     "shell.execute_reply.started": "2023-04-05T23:19:22.302829Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,9))\n",
    "j = 3\n",
    "for i in range(1, 3):\n",
    "    plt.subplot(330 + i)\n",
    "    plt.imshow(x_train[j], cmap=plt.get_cmap('gray'))\n",
    "    plt.title(title[i-1])\n",
    "    j+=1\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:19:26.113449Z",
     "iopub.status.busy": "2023-04-05T23:19:26.112726Z",
     "iopub.status.idle": "2023-04-05T23:19:26.315255Z",
     "shell.execute_reply": "2023-04-05T23:19:26.314165Z",
     "shell.execute_reply.started": "2023-04-05T23:19:26.113395Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6000, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999, 5999]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_labels)\n",
    "plt.title('Count plot of the MNIST Labels')\n",
    "print(list(train_data.label.value_counts().sort_index()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From the above countplot label 1 is data point highest in number at 4684 points, label 5 is least with 3795 data points. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">4. ACTIVATION FUNCTION</h1><a id = \"4\" ></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **LeakyReLu**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:19:35.864704Z",
     "iopub.status.busy": "2023-04-05T23:19:35.863588Z",
     "iopub.status.idle": "2023-04-05T23:19:35.870935Z",
     "shell.execute_reply": "2023-04-05T23:19:35.869225Z",
     "shell.execute_reply.started": "2023-04-05T23:19:35.864655Z"
    }
   },
   "outputs": [],
   "source": [
    "alpha = 0.01\n",
    "def LeakyReLu(z,alpha):\n",
    "    if z >= 0:\n",
    "        return z\n",
    "    else :\n",
    "        return alpha*z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:19:38.056647Z",
     "iopub.status.busy": "2023-04-05T23:19:38.056292Z",
     "iopub.status.idle": "2023-04-05T23:19:40.700236Z",
     "shell.execute_reply": "2023-04-05T23:19:40.699279Z",
     "shell.execute_reply.started": "2023-04-05T23:19:38.056616Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "leakyrelu = tensorflow.keras.layers.LeakyReLU(alpha=0.01)\n",
    "x = np.arange(-3,4)\n",
    "pd.Series(leakyrelu(x)).plot(kind='line')\n",
    "plt.title('LeakyRelu function')\n",
    "plt.xticks(range(len(x)),x)\n",
    "plt.ylabel('LeakyRelu')\n",
    "plt.xlabel('x')\n",
    "plt.grid('on')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "* LeakyRelu is linear in the positive x axis and goes close to x axis when x<0 but does not touch x axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Swish** <br>\n",
    "Mathematical formula <br>\n",
    "Y = X * sigmoid(X)\n",
    "  = X /(1 + e^-X)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:19:48.074692Z",
     "iopub.status.busy": "2023-04-05T23:19:48.073964Z",
     "iopub.status.idle": "2023-04-05T23:19:48.313567Z",
     "shell.execute_reply": "2023-04-05T23:19:48.312514Z",
     "shell.execute_reply.started": "2023-04-05T23:19:48.074654Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.68280104e-03 -6.37735836e-03 -1.48357389e-02 -3.34642546e-02\n",
      " -7.19448398e-02 -1.42277620e-01 -2.38405844e-01 -2.68941421e-01\n",
      "  0.00000000e+00  7.31058579e-01  1.76159416e+00  2.85772238e+00\n",
      "  3.92805516e+00]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-8,5)\n",
    "swish = x/(1+np.exp(-x))\n",
    "print(swish)\n",
    "pd.Series(swish).plot(kind='line')\n",
    "plt.title('Swish function')\n",
    "plt.xticks(range(len(x)),x)\n",
    "plt.ylabel('Swish')\n",
    "plt.xlabel('x')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "* Swish function is approximately similar to Relu when x>0, when x<0 the values change.\n",
    "* Swish algorithm looks similar to the ReLu function in the positive x axis.\n",
    " \n",
    " For more details of [\"Why Swish is better than ReLu\"](https://github.com/christianversloot/machine-learning-articles/blob/main/why-swish-could-perform-better-than-relu.md)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">5. MODEL SUMMARY</h2><a id = \"5\" ></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2  style='color:#0096FF; background:#FFF ' class=\"list-group-item list-group-item-action active\">Model</h2><a id = \"5.3\" ></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline Architecture\n",
    "* activation = 'leakyrelu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2023-04-05T23:20:01.014371Z",
     "iopub.status.busy": "2023-04-05T23:20:01.013998Z",
     "iopub.status.idle": "2023-04-05T23:20:01.326325Z",
     "shell.execute_reply": "2023-04-05T23:20:01.325512Z",
     "shell.execute_reply.started": "2023-04-05T23:20:01.014337Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 64)        640       \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 28, 28, 64)       256       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 28, 28, 64)        36928     \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 28, 28, 64)       256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 14, 14, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 14, 14, 64)        0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 14, 14, 128)       73856     \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 14, 14, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 14, 14, 128)       147584    \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 14, 14, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 7, 7, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 7, 7, 128)         0         \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 7, 7, 64)          73792     \n",
      "                                                                 \n",
      " batch_normalization_4 (Batc  (None, 7, 7, 64)         256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 7, 7, 64)          0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 3136)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               803072    \n",
      "                                                                 \n",
      " batch_normalization_5 (Batc  (None, 256)              1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 35)                8995      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,147,683\n",
      "Trainable params: 1,146,275\n",
      "Non-trainable params: 1,408\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=3, padding='same',  activation = leakyrelu,input_shape=(img_rows, img_cols, 1)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(64, kernel_size=3, padding = 'same', activation = leakyrelu))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Conv2D(128, kernel_size=3, padding = 'same',activation = leakyrelu))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(128, kernel_size=3, padding = 'same',activation = leakyrelu))\n",
    "\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=3, padding = 'same',activation = leakyrelu))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(256,activation='swish'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "# Metrics of above model\n",
    "# Model Accuracy = 0.99564 for activation = leakyrelu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_kg_hide-input": false
   },
   "source": [
    "# <h1  style='color:#0096FF; background:#FFF ' class=\"list-group-item list-group-item-action active\">Resnet19 -> skip-connection architecture</h1><a id = \"5.4\" ></a> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:20:07.943636Z",
     "iopub.status.busy": "2023-04-05T23:20:07.942683Z",
     "iopub.status.idle": "2023-04-05T23:20:07.950383Z",
     "shell.execute_reply": "2023-04-05T23:20:07.949203Z",
     "shell.execute_reply.started": "2023-04-05T23:20:07.943598Z"
    }
   },
   "outputs": [],
   "source": [
    "def conv_layer(input,filter=64):\n",
    "    conv_1 = Conv2D(filter, kernel_size=3, padding='same',activation = 'selu',kernel_initializer = 'he_normal')(input)\n",
    "    bn     = BatchNormalization()(conv_1)\n",
    "    conv_2 = Conv2D(filter, kernel_size=3, padding='same',kernel_initializer = 'he_normal')(bn)\n",
    "    bn     = BatchNormalization()(conv_2)\n",
    "    act    = Activation('selu')(bn)\n",
    "    return act"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_kg_hide-input": false,
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2023-04-05T23:20:09.163563Z",
     "iopub.status.busy": "2023-04-05T23:20:09.163120Z",
     "iopub.status.idle": "2023-04-05T23:20:09.371088Z",
     "shell.execute_reply": "2023-04-05T23:20:09.370063Z",
     "shell.execute_reply.started": "2023-04-05T23:20:09.163525Z"
    }
   },
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    input  = Input((img_rows,img_cols,1))\n",
    "    conv_1 = conv_layer(input,filter=64)\n",
    "    conv_2 = conv_layer(conv_1,filter=64)\n",
    "    \n",
    "    merge1 = concatenate([conv_1,conv_2], axis = 3)\n",
    "    #pool = MaxPool2D((2,2))(merge1)\n",
    "    \n",
    "    conv_3 = conv_layer(merge1,filter=128)\n",
    "    conv_4 = conv_layer(conv_3,filter=128)\n",
    "    merge2 = concatenate([conv_3,conv_4], axis = 3)\n",
    "    #pool = MaxPool2D((2,2))(merge2)\n",
    "    \n",
    "    dense  = Flatten()(merge2)\n",
    "    dense  = Dense(256,activation='selu')(dense)\n",
    "    drop   = Dropout(0.2)(dense)\n",
    "    output = Dense(num_classes, activation='softmax')(drop)    \n",
    "\n",
    "    model = Model(inputs=[input],outputs = [output])\n",
    "    return model\n",
    "\n",
    "model = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-04-05T23:20:16.232300Z",
     "iopub.status.busy": "2023-04-05T23:20:16.231682Z",
     "iopub.status.idle": "2023-04-05T23:20:16.945265Z",
     "shell.execute_reply": "2023-04-05T23:20:16.944127Z",
     "shell.execute_reply.started": "2023-04-05T23:20:16.232260Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(model, to_file='model.png', show_shapes=True)\n",
    "from IPython.display import Image\n",
    "Image(\"model.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:20:40.338840Z",
     "iopub.status.busy": "2023-04-05T23:20:40.338216Z",
     "iopub.status.idle": "2023-04-05T23:20:40.400095Z",
     "shell.execute_reply": "2023-04-05T23:20:40.399210Z",
     "shell.execute_reply.started": "2023-04-05T23:20:40.338774Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 28, 28, 1)]  0           []                               \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 28, 28, 64)   640         ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 28, 28, 64)  256         ['conv2d_5[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 28, 28, 64)   36928       ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 28, 28, 64)  256         ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 28, 28, 64)   0           ['batch_normalization_7[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 28, 28, 64)   36928       ['activation[0][0]']             \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 28, 28, 64)  256         ['conv2d_7[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 28, 28, 64)   36928       ['batch_normalization_8[0][0]']  \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 28, 28, 64)  256         ['conv2d_8[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 28, 28, 64)   0           ['batch_normalization_9[0][0]']  \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 28, 28, 128)  0           ['activation[0][0]',             \n",
      "                                                                  'activation_1[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)              (None, 28, 28, 128)  147584      ['concatenate[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, 28, 28, 128)  512        ['conv2d_9[0][0]']               \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, 28, 28, 128)  147584      ['batch_normalization_10[0][0]'] \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 28, 28, 128)  512        ['conv2d_10[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 28, 28, 128)  0           ['batch_normalization_11[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, 28, 28, 128)  147584      ['activation_2[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 28, 28, 128)  512        ['conv2d_11[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, 28, 28, 128)  147584      ['batch_normalization_12[0][0]'] \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, 28, 28, 128)  512        ['conv2d_12[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 28, 28, 128)  0           ['batch_normalization_13[0][0]'] \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, 28, 28, 256)  0           ['activation_2[0][0]',           \n",
      "                                                                  'activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " flatten_1 (Flatten)            (None, 200704)       0           ['concatenate_1[0][0]']          \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 256)          51380480    ['flatten_1[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_4 (Dropout)            (None, 256)          0           ['dense_2[0][0]']                \n",
      "                                                                                                  \n",
      " dense_3 (Dense)                (None, 35)           8995        ['dropout_4[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 52,094,307\n",
      "Trainable params: 52,092,771\n",
      "Non-trainable params: 1,536\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">6. DATA AUGMENTATION</h1><a id = \"6\" ></a>\n",
    "\n",
    "One way to avoid overfitting and improve the accuracy is to increase the variability of existing samples. Which is also helps to compensate lack of data. Data augmentation generates data from existing samples by applying various transformations to the original dataset. This method aims to increase the number of unique input samples, which, in turn, will allow the model to show better accuracy on the validation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-04-05T23:20:45.513858Z",
     "iopub.status.busy": "2023-04-05T23:20:45.513455Z",
     "iopub.status.idle": "2023-04-05T23:20:45.520777Z",
     "shell.execute_reply": "2023-04-05T23:20:45.519578Z",
     "shell.execute_reply.started": "2023-04-05T23:20:45.513798Z"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:20:48.863857Z",
     "iopub.status.busy": "2023-04-05T23:20:48.863238Z",
     "iopub.status.idle": "2023-04-05T23:20:49.052731Z",
     "shell.execute_reply": "2023-04-05T23:20:49.051536Z",
     "shell.execute_reply.started": "2023-04-05T23:20:48.863796Z"
    }
   },
   "outputs": [],
   "source": [
    "# use data augmentation to improve accuracy and prevent overfitting\n",
    "augs_gen = ImageDataGenerator(\n",
    "        featurewise_center=False,  \n",
    "        samplewise_center=False, \n",
    "        featurewise_std_normalization=False,  \n",
    "        samplewise_std_normalization=False,  \n",
    "        zca_whitening=False,  \n",
    "        rotation_range=10,  \n",
    "        zoom_range = 0.1, \n",
    "        width_shift_range=0.1,  \n",
    "        height_shift_range=0.1, \n",
    "        horizontal_flip=False,  \n",
    "        vertical_flip=False) \n",
    "\n",
    "train_generator = augs_gen.flow(x_train, y_train, batch_size=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\"> CallBacks</h2><a id = \"6.1\" ></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2  style='color:#0096FF; background:#FFF ' class=\"list-group-item list-group-item-action active\">6.1 EarlyStopping</h2><a id = \"6.1\" ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-04-05T23:20:50.961494Z",
     "iopub.status.busy": "2023-04-05T23:20:50.961101Z",
     "iopub.status.idle": "2023-04-05T23:20:50.967024Z",
     "shell.execute_reply": "2023-04-05T23:20:50.965767Z",
     "shell.execute_reply.started": "2023-04-05T23:20:50.961459Z"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:20:51.218035Z",
     "iopub.status.busy": "2023-04-05T23:20:51.216878Z",
     "iopub.status.idle": "2023-04-05T23:20:51.223892Z",
     "shell.execute_reply": "2023-04-05T23:20:51.222936Z",
     "shell.execute_reply.started": "2023-04-05T23:20:51.217984Z"
    }
   },
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor='val_loss', min_delta=0.00001, patience=8, mode='auto', restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2  style='color:#0096FF; background:#FFF ' class=\"list-group-item list-group-item-action active\">6.2 Learning Rate Reduction</h2><a id = \"6.2\" ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:20:56.420605Z",
     "iopub.status.busy": "2023-04-05T23:20:56.419908Z",
     "iopub.status.idle": "2023-04-05T23:20:56.426247Z",
     "shell.execute_reply": "2023-04-05T23:20:56.424840Z",
     "shell.execute_reply.started": "2023-04-05T23:20:56.420567Z"
    }
   },
   "outputs": [],
   "source": [
    "lr_reduction = ReduceLROnPlateau(monitor='val_loss',patience=4, verbose=1,  factor=0.6, min_lr=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:20:57.777174Z",
     "iopub.status.busy": "2023-04-05T23:20:57.776238Z",
     "iopub.status.idle": "2023-04-05T23:20:57.782874Z",
     "shell.execute_reply": "2023-04-05T23:20:57.781616Z",
     "shell.execute_reply.started": "2023-04-05T23:20:57.777108Z"
    }
   },
   "outputs": [],
   "source": [
    "model_checkpoint = ModelCheckpoint('model.hdf5', verbose=1,monitor='val_loss',save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-04-05T23:21:10.588963Z",
     "iopub.status.busy": "2023-04-05T23:21:10.588354Z",
     "iopub.status.idle": "2023-04-05T23:21:10.594530Z",
     "shell.execute_reply": "2023-04-05T23:21:10.593088Z",
     "shell.execute_reply.started": "2023-04-05T23:21:10.588924Z"
    }
   },
   "outputs": [],
   "source": [
    "epochs = 30\n",
    "callbacks = [model_checkpoint,lr_reduction,early_stop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-05T23:21:11.019021Z",
     "iopub.status.busy": "2023-04-05T23:21:11.018531Z",
     "iopub.status.idle": "2023-04-06T00:01:21.336825Z",
     "shell.execute_reply": "2023-04-06T00:01:21.335394Z",
     "shell.execute_reply.started": "2023-04-05T23:21:11.018984Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 4.5289 - accuracy: 0.0324\n",
      "Epoch 1: val_loss improved from inf to 3.50400, saving model to model.hdf5\n",
      "560/560 [==============================] - 93s 145ms/step - loss: 4.5289 - accuracy: 0.0324 - val_loss: 3.5040 - val_accuracy: 0.0534 - lr: 0.0010\n",
      "Epoch 2/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 3.2983 - accuracy: 0.1098\n",
      "Epoch 2: val_loss improved from 3.50400 to 2.34258, saving model to model.hdf5\n",
      "560/560 [==============================] - 80s 144ms/step - loss: 3.2983 - accuracy: 0.1098 - val_loss: 2.3426 - val_accuracy: 0.3017 - lr: 0.0010\n",
      "Epoch 3/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 1.9001 - accuracy: 0.4104\n",
      "Epoch 3: val_loss improved from 2.34258 to 0.94631, saving model to model.hdf5\n",
      "560/560 [==============================] - 78s 140ms/step - loss: 1.9001 - accuracy: 0.4104 - val_loss: 0.9463 - val_accuracy: 0.7073 - lr: 0.0010\n",
      "Epoch 4/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.9732 - accuracy: 0.6972\n",
      "Epoch 4: val_loss improved from 0.94631 to 0.62507, saving model to model.hdf5\n",
      "560/560 [==============================] - 80s 143ms/step - loss: 0.9732 - accuracy: 0.6972 - val_loss: 0.6251 - val_accuracy: 0.8361 - lr: 0.0010\n",
      "Epoch 5/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.6577 - accuracy: 0.8037\n",
      "Epoch 5: val_loss improved from 0.62507 to 0.37021, saving model to model.hdf5\n",
      "560/560 [==============================] - 80s 143ms/step - loss: 0.6577 - accuracy: 0.8037 - val_loss: 0.3702 - val_accuracy: 0.8956 - lr: 0.0010\n",
      "Epoch 6/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.5204 - accuracy: 0.8486\n",
      "Epoch 6: val_loss did not improve from 0.37021\n",
      "560/560 [==============================] - 78s 140ms/step - loss: 0.5204 - accuracy: 0.8486 - val_loss: 0.5045 - val_accuracy: 0.8694 - lr: 0.0010\n",
      "Epoch 7/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.4394 - accuracy: 0.8751\n",
      "Epoch 7: val_loss did not improve from 0.37021\n",
      "560/560 [==============================] - 78s 139ms/step - loss: 0.4394 - accuracy: 0.8751 - val_loss: 0.5050 - val_accuracy: 0.8783 - lr: 0.0010\n",
      "Epoch 8/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.3873 - accuracy: 0.8926\n",
      "Epoch 8: val_loss improved from 0.37021 to 0.33153, saving model to model.hdf5\n",
      "560/560 [==============================] - 80s 143ms/step - loss: 0.3873 - accuracy: 0.8926 - val_loss: 0.3315 - val_accuracy: 0.9246 - lr: 0.0010\n",
      "Epoch 9/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.3574 - accuracy: 0.9027\n",
      "Epoch 9: val_loss improved from 0.33153 to 0.23323, saving model to model.hdf5\n",
      "560/560 [==============================] - 80s 143ms/step - loss: 0.3574 - accuracy: 0.9027 - val_loss: 0.2332 - val_accuracy: 0.9405 - lr: 0.0010\n",
      "Epoch 10/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.3187 - accuracy: 0.9144\n",
      "Epoch 10: val_loss did not improve from 0.23323\n",
      "560/560 [==============================] - 76s 135ms/step - loss: 0.3187 - accuracy: 0.9144 - val_loss: 0.8713 - val_accuracy: 0.7905 - lr: 0.0010\n",
      "Epoch 11/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.3036 - accuracy: 0.9199\n",
      "Epoch 11: val_loss did not improve from 0.23323\n",
      "560/560 [==============================] - 76s 135ms/step - loss: 0.3036 - accuracy: 0.9199 - val_loss: 0.3926 - val_accuracy: 0.8997 - lr: 0.0010\n",
      "Epoch 12/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.2749 - accuracy: 0.9280\n",
      "Epoch 12: val_loss improved from 0.23323 to 0.17958, saving model to model.hdf5\n",
      "560/560 [==============================] - 80s 142ms/step - loss: 0.2749 - accuracy: 0.9280 - val_loss: 0.1796 - val_accuracy: 0.9539 - lr: 0.0010\n",
      "Epoch 13/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.2637 - accuracy: 0.9311\n",
      "Epoch 13: val_loss did not improve from 0.17958\n",
      "560/560 [==============================] - 76s 136ms/step - loss: 0.2637 - accuracy: 0.9311 - val_loss: 0.1893 - val_accuracy: 0.9583 - lr: 0.0010\n",
      "Epoch 14/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.2491 - accuracy: 0.9357\n",
      "Epoch 14: val_loss did not improve from 0.17958\n",
      "560/560 [==============================] - 78s 139ms/step - loss: 0.2491 - accuracy: 0.9357 - val_loss: 0.1850 - val_accuracy: 0.9536 - lr: 0.0010\n",
      "Epoch 15/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.2344 - accuracy: 0.9403\n",
      "Epoch 15: val_loss did not improve from 0.17958\n",
      "560/560 [==============================] - 76s 136ms/step - loss: 0.2344 - accuracy: 0.9403 - val_loss: 0.2364 - val_accuracy: 0.9440 - lr: 0.0010\n",
      "Epoch 16/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.2209 - accuracy: 0.9433\n",
      "Epoch 16: val_loss improved from 0.17958 to 0.16092, saving model to model.hdf5\n",
      "560/560 [==============================] - 78s 139ms/step - loss: 0.2209 - accuracy: 0.9433 - val_loss: 0.1609 - val_accuracy: 0.9631 - lr: 0.0010\n",
      "Epoch 17/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.2140 - accuracy: 0.9459\n",
      "Epoch 17: val_loss improved from 0.16092 to 0.15091, saving model to model.hdf5\n",
      "560/560 [==============================] - 80s 143ms/step - loss: 0.2140 - accuracy: 0.9459 - val_loss: 0.1509 - val_accuracy: 0.9690 - lr: 0.0010\n",
      "Epoch 18/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.2023 - accuracy: 0.9488\n",
      "Epoch 18: val_loss did not improve from 0.15091\n",
      "560/560 [==============================] - 78s 139ms/step - loss: 0.2023 - accuracy: 0.9488 - val_loss: 0.1671 - val_accuracy: 0.9623 - lr: 0.0010\n",
      "Epoch 19/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1939 - accuracy: 0.9506\n",
      "Epoch 19: val_loss did not improve from 0.15091\n",
      "560/560 [==============================] - 78s 139ms/step - loss: 0.1939 - accuracy: 0.9506 - val_loss: 0.1574 - val_accuracy: 0.9689 - lr: 0.0010\n",
      "Epoch 20/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1906 - accuracy: 0.9520\n",
      "Epoch 20: val_loss improved from 0.15091 to 0.11412, saving model to model.hdf5\n",
      "560/560 [==============================] - 78s 139ms/step - loss: 0.1906 - accuracy: 0.9520 - val_loss: 0.1141 - val_accuracy: 0.9736 - lr: 0.0010\n",
      "Epoch 21/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1834 - accuracy: 0.9540\n",
      "Epoch 21: val_loss improved from 0.11412 to 0.11345, saving model to model.hdf5\n",
      "560/560 [==============================] - 80s 143ms/step - loss: 0.1834 - accuracy: 0.9540 - val_loss: 0.1134 - val_accuracy: 0.9748 - lr: 0.0010\n",
      "Epoch 22/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1729 - accuracy: 0.9566\n",
      "Epoch 22: val_loss did not improve from 0.11345\n",
      "560/560 [==============================] - 76s 135ms/step - loss: 0.1729 - accuracy: 0.9566 - val_loss: 0.1664 - val_accuracy: 0.9652 - lr: 0.0010\n",
      "Epoch 23/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1748 - accuracy: 0.9561\n",
      "Epoch 23: val_loss did not improve from 0.11345\n",
      "560/560 [==============================] - 76s 136ms/step - loss: 0.1748 - accuracy: 0.9561 - val_loss: 0.1214 - val_accuracy: 0.9692 - lr: 0.0010\n",
      "Epoch 24/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1621 - accuracy: 0.9591\n",
      "Epoch 24: val_loss did not improve from 0.11345\n",
      "560/560 [==============================] - 78s 140ms/step - loss: 0.1621 - accuracy: 0.9591 - val_loss: 0.1334 - val_accuracy: 0.9701 - lr: 0.0010\n",
      "Epoch 25/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1616 - accuracy: 0.9594\n",
      "Epoch 25: val_loss did not improve from 0.11345\n",
      "\n",
      "Epoch 25: ReduceLROnPlateau reducing learning rate to 0.0006000000284984708.\n",
      "560/560 [==============================] - 78s 139ms/step - loss: 0.1616 - accuracy: 0.9594 - val_loss: 0.1217 - val_accuracy: 0.9729 - lr: 0.0010\n",
      "Epoch 26/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1177 - accuracy: 0.9694\n",
      "Epoch 26: val_loss improved from 0.11345 to 0.10658, saving model to model.hdf5\n",
      "560/560 [==============================] - 78s 140ms/step - loss: 0.1177 - accuracy: 0.9694 - val_loss: 0.1066 - val_accuracy: 0.9758 - lr: 6.0000e-04\n",
      "Epoch 27/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1107 - accuracy: 0.9710\n",
      "Epoch 27: val_loss improved from 0.10658 to 0.08401, saving model to model.hdf5\n",
      "560/560 [==============================] - 80s 142ms/step - loss: 0.1107 - accuracy: 0.9710 - val_loss: 0.0840 - val_accuracy: 0.9808 - lr: 6.0000e-04\n",
      "Epoch 28/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1069 - accuracy: 0.9714\n",
      "Epoch 28: val_loss did not improve from 0.08401\n",
      "560/560 [==============================] - 76s 136ms/step - loss: 0.1069 - accuracy: 0.9714 - val_loss: 0.1000 - val_accuracy: 0.9805 - lr: 6.0000e-04\n",
      "Epoch 29/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1064 - accuracy: 0.9718\n",
      "Epoch 29: val_loss did not improve from 0.08401\n",
      "560/560 [==============================] - 76s 135ms/step - loss: 0.1064 - accuracy: 0.9718 - val_loss: 0.1186 - val_accuracy: 0.9779 - lr: 6.0000e-04\n",
      "Epoch 30/30\n",
      "560/560 [==============================] - ETA: 0s - loss: 0.1059 - accuracy: 0.9719\n",
      "Epoch 30: val_loss did not improve from 0.08401\n",
      "560/560 [==============================] - 78s 139ms/step - loss: 0.1059 - accuracy: 0.9719 - val_loss: 0.1255 - val_accuracy: 0.9743 - lr: 6.0000e-04\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer = 'adam',loss = categorical_crossentropy ,metrics=['accuracy'])\n",
    "model_fit = model.fit(train_generator, epochs=epochs, batch_size =300 ,validation_data=(x_val, y_val), verbose =1,callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">SAVE MODEL</h2><a id = \"6.2\" ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T00:26:23.959971Z",
     "iopub.status.busy": "2023-04-06T00:26:23.958911Z",
     "iopub.status.idle": "2023-04-06T00:26:30.372701Z",
     "shell.execute_reply": "2023-04-06T00:26:30.371493Z",
     "shell.execute_reply.started": "2023-04-06T00:26:23.959926Z"
    }
   },
   "outputs": [],
   "source": [
    "!mkdir -p saved_model\n",
    "model.save('saved_model/model.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T00:26:30.376310Z",
     "iopub.status.busy": "2023-04-06T00:26:30.375901Z",
     "iopub.status.idle": "2023-04-06T00:26:30.792859Z",
     "shell.execute_reply": "2023-04-06T00:26:30.791842Z",
     "shell.execute_reply.started": "2023-04-06T00:26:30.376266Z"
    }
   },
   "outputs": [],
   "source": [
    "model.load_weights('model.hdf5')\n",
    "load_model = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">7. PLOT ACCURACY AND LOSS CURVE</h2><a id = \"7\" ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T00:26:30.794491Z",
     "iopub.status.busy": "2023-04-06T00:26:30.794131Z",
     "iopub.status.idle": "2023-04-06T00:26:31.086906Z",
     "shell.execute_reply": "2023-04-06T00:26:31.085803Z",
     "shell.execute_reply.started": "2023-04-06T00:26:30.794452Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x71bc3051ec10>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Defining Figure\n",
    "f = plt.figure(figsize=(20,7))\n",
    "\n",
    "#For Accuracy - subplot\n",
    "f.add_subplot(121)\n",
    "\n",
    "plt.plot(model_fit.epoch,model_fit.history['accuracy'],label = \"training accuracy\") # Accuracy curve for training set\n",
    "plt.plot(model_fit.epoch,model_fit.history['val_accuracy'],label = \"validation accuracy\")\n",
    "\n",
    "plt.title(\"Accuracy Curve\",fontsize=18)\n",
    "plt.xlabel(\"Epochs\",fontsize=15)\n",
    "plt.ylabel(\"Accuracy\",fontsize=15)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T00:26:31.090359Z",
     "iopub.status.busy": "2023-04-06T00:26:31.089758Z",
     "iopub.status.idle": "2023-04-06T00:26:31.333772Z",
     "shell.execute_reply": "2023-04-06T00:26:31.332757Z",
     "shell.execute_reply.started": "2023-04-06T00:26:31.090317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HR9OvXz/mz5/v/fzwR0ueR0ALFixg4MCBxMTEcMYZZ7B7927vOTU1Ndx4440kJCSQnJzMbbfdxpQpUzjvvPP8+v2ffPJJ+vTpg9PpZMCAAbzwwgv1Pr/nnnvo1asX4eHhdO/enRtvvNH72RNPPEG/fv2IiIggPT2dCy+80K97twcFGT/0TokBYPP+8nYuiYhI+zBNk7KqmnZ5mabpUxkfe+wxRo8ezVVXXcXu3bvZvXs3mZmZ3s+nT5/OzJkzWbt2LUOGDKGkpIQzzzyThQsXsnLlSiZOnMjkyZPJzc1t9j733XcfF154Id999x1nnnkml19+OQcOHGjy+LKyMh5++GFeeOEFli5dSm5uLn/4wx+8nz/44IO89NJLPPPMM3z22WcUFRUxb948n35nj7lz53LTTTdxyy238MMPP/B///d//OpXv2LRokUAvPHGGzz66KM89dRT/PTTT8ybN49jjjkGgOXLl3PjjTdy3333sX79ej744ANOPvlkv+7fHtRHxg/9060gs7OwkspqF5HhyoEi0rWUV7sYdNeCdrn3mvsmEuVs+c9WfHw8TqeTqKgoMjIyGnx+3333cfrpp3vfJycnM3ToUO/7+++/n7lz5zJ//nyuv/76Ju8zZcoULrnkEsLCwvjLX/7CP//5T77++mvOOOOMRo+vrq7m3//+N3369AHg+uuv57777vN+/s9//pMZM2Zw/vnnA/D444/z3nvvtfj71vXwww8zdepUrr32WgCmTZvGl19+ycMPP8zPfvYzcnNzycjI4LTTTsPhcNCrVy+OO+44AHJzc4mOjubss88mNjaWrKwshg0b5tf924P+EvshNTac+EgHbhM25Ze2d3FERCQAI0eOrPe+tLSU6dOnM2jQIBISEoiJiWHdunUttsgMGTLEux0dHU1sbCx5eXlNHh8VFeUNMQDdunXzHl9YWMjevXu9oQLAbrczYsQIv363tWvXcuKJJ9bbd+KJJ7J27VoAfvGLX1BeXk7v3r256qqrmDt3rref0Omnn05WVha9e/fmyiuv5KWXXqKsrMyv+7cHtcj4wTAM+qXFsHzbQX7KK2Fwz4T2LpKIyBEV6bCz5r6J7XbvYDh89NGtt97KggULePjhh+nbty+RkZFceOGFVFVVNXsdh8NR771hGLjdbr+OP/xx2eGdg319nNbSNTz7MjMzWb9+PR999BELFy7k2muv5a9//StLliwhNjaWb7/9lsWLF/Phhx9y1113cc899/DNN9+E9BBvtcj4qV/t46Wf8krauSQiIkeeYRhEOcPa5eXPCCCn04nL5fLp2GXLljF16lTOP/98jjnmGDIyMti6dWuANRSY+Ph40tPT+frrr737XC4XK1eu9Os6AwcO5NNPP6237/PPP2fgwIHe95GRkZxzzjn84x//YPHixXzxxResXr0agLCwME477TQeeughvv/+e7Zu3conn3zSit+s7alFxk/90mqDzN7idi6JiIg0JTs7m6+++oqtW7cSExNDUlJSk8f27duXOXPmMHnyZAzD4E9/+lOzLStt5YYbbmDmzJn07duXo446in/+858cPHjQrwB36623ctFFFzF8+HDGjx/P22+/zZw5c7yjsJ599llcLhfHH388UVFRvPDCC0RGRpKVlcU777zD5s2bOfnkk0lMTOS9997D7XYzYMCAtvqVg0ItMn7yBJkNapEREQlZf/jDH7Db7QwaNIjU1NRm+7s8+uijJCYmMmbMGCZPnszEiRMZPnz4ESyt5bbbbuPSSy/ll7/8JaNHjyYmJoaJEycSERHh8zXOO+88HnvsMf76179y9NFH89RTT/HMM88wbtw4ABISEpg1axYnnngiQ4YM4eOPP+btt98mOTmZhIQE5syZw6mnnsrAgQP597//zSuvvMLRRx/dRr9xcBhmIA/gQlxRURHx8fEUFhYSFxcX1GvvLSzn+JmfYBiw9r4ziAjSM9vOzO12k5eXR1paWlCWbO8qVG+BUb0FprF6q6ioYMuWLeTk5Pj1x7Sr8EyoFxbm32MvX7ndbgYOHMhFF13E//t//y/o128v5eXlbNq0id69ezeYZDCQv996tOSnlBgncRF2iipcbMov4ejumn5aRERab9u2bXz44YeccsopVFZW8vjjj7NlyxYuu+yy9i5aSNP/rvjJMAx6J0cC8NNePV4SEZHgsNlsPPvss4waNYoTTzyR1atXs3DhwnoddaUhtcgEICcpglU7S9igDr8iIhIkmZmZfPbZZ+1djA5HLTIByKltkdmgFhkREZF2pSATgN7JVqe3jXlqkREREWlPCjIByEmyWmS2HSijotq3CZdEREQk+BRkApAUFUZilAPThI2aT0ZERKTdKMgEwDAM+npm+NXjJRERkXajIBMg7wy/6vArIiLSbhRkAtQ/PRbQXDIiIp1VdnY2f//7373vDcNg3rx5TR6/detWDMNg1apVrbpvsK7TkqlTp3Leeee16T2OBM0jE6B+erQkItKl7N69m8TExKBec+rUqRQUFNQLSJmZmezevZuUlJSg3quzUpAJkKePTO6BMsqrXEQ6teaSiEhnlpGRcUTuY7fbj9i9OgM9WgpQSozTO3JpU74eL4mIhIqnnnqKHj164Ha76+0/55xzmDJlCgCbNm3i3HPPJT09nZiYGEaNGsXChQubve7hj5a+/vprRo0aRWRkJCNHjmTlypX1jne5XPzmN78hJyeHyMhIBgwYwGOPPeb9/J577uG5557jrbfewjAMDMNg8eLFjT5aWrJkCccddxzh4eF069aN22+/nZqaGu/n48aN48Ybb2T69OkkJSWRkZHBPffc41e9VVZWcuONN5KWlkZERARjx47lm2++8X5+8OBBLr/8clJTU4mMjKRfv34888wzAFRVVXH99dfTrVs3IiIiyM7OZubMmX7dP1BqkQmQYRj0S4/l6y0H2LC3mME9tHikiHQBpgnVZe1zb0cU+LDK9C9+8QtuvPFGFi1axPjx4wHrj/CCBQt4++23ASgpKeHMM8/k/vvvJyIigueee47Jkyezfv16evXq1eI9SktLmTx5MuPGjePFF19k69at3HTTTfWOcbvd9OzZk9dee42UlBQ+//xzfve739GtWzcuuugi/vCHP7B27VqKioq8gSApKYldu3bVu87OnTs588wzmTp1Ks8//zzr1q3jqquuIiIiol5Yee6555g2bRpfffUVX3zxBVOnTuXEE0/k9NNPb/H3AZg+fTpvvvkmzz33HFlZWTz00ENMnDiRjRs3kpSUxJ/+9CfWrFnD+++/T0pKChs3bqS8vByAf/zjH8yfP5/XXnuNXr16sX37drZv3+7TfVtLQaYV+qfH8PWWA/ykuWREpKuoLoO/dG+fe/9xFzijWzwsKSmJM844g5dfftkbZF5//XWSkpK874cOHcrQoUO959x///3MnTuX+fPnc/3117d4j5deegmXy8WsWbOIi4tj8ODB7Nixg2uuucZ7jMPh4N577/W+z8nJ4fPPP+e1117joosuIiYmhsjISCorK5t9lPTEE0+QmZnJ448/jmEYHHXUUezatYvbbruNu+66C5vNergyZMgQ7r77bgD69evH448/zscff+xTkCktLeXJJ5/k2WefZdKkSQDMmjWLjz76iP/+97/ceuut5ObmMmzYMEaOHAlYnaE9cnNz6devH2PHjsUwDLKyslq8Z7Do0VIrHBq5pA6/IiKh5PLLL+fNN9+ksrISsILHJZdcgt1u9WcsLS1l+vTpDBo0iISEBGJiYli3bh25ubk+XX/t2rUMHTqUqKgo777Ro0c3OO7f//43I0eOJDU1lZiYGGbNmuXzPerea/To0Rh1WqNOPPFESkpK2LFjh3ffkCFD6p3XrVs38vLyfLrHpk2bqK6u5sQTT/TuczgcHHfccaxduxaAa665htmzZ3Pssccyffp0Pv/8c++xU6dOZdWqVQwYMIAbb7yRDz/80K/fsTXUItMKfTWXjIh0NY4oq2Wkve7to8mTJ+N2u3n33XcZNWoUy5Yt45FHHvF+fuutt7JgwQIefvhh+vbtS2RkJBdeeCFVVVU+Xd80zRaPee211/j973/P3/72N0aPHk1sbCx//etf+eqrr3z+PTz3Mg57pOa5f939Doej3jGGYTToJ9TcPQ6/3uH3njRpEtu2bePdd99l4cKFjB8/nuuuu46HH36Y4cOHs2XLFt5//30WLlzIRRddxGmnncYbb7zh1+8aCAWZVvC0yGw/qJFLItJFGIZPj3faW2RkJBdccAEvvfQSGzdupH///owYMcL7+bJly5g6dSrnn38+YPWZ2bp1q8/XHzRoEC+88ALl5eXExlp/C7788st6xyxbtowxY8Zw7bXXevdt2rSp3jFOpxOXq/k1+wYNGsSbb75ZL1R8/vnnxMbG0qNHD5/L3Jy+ffvidDr59NNPueyyywCorq5m+fLl3Hzzzd7jUlNTmTp1KlOnTuWkk07i1ltv5eGHHwYgLi6Oiy++mIsvvpgLL7yQM844gwMHDpCUlBSUMjZFj5ZaISUmnKRop9ZcEhEJQZdffjnvvvsu//vf/7jiiivqfda3b1/mzJnDqlWr+O6777jssst8br0AuOyyy7DZbPzud79jzZo1vPfee94/6HXvsXz5chYsWMCGDRv405/+VG8UEFj9TL7//nvWr1/Pvn37qK6ubnCva6+9lu3bt3PDDTewbt063nrrLe6++26mTZvm7R/TWtHR0VxzzTXceuutfPDBB6xZs4arrrqKsrIyfvOb3wBw11138dZbb7Fx40Z+/PFH3nnnHQYOHAjAo48+yuzZs1m3bh0bNmzg9ddfJyMjg4SEhKCUrzkKMq2kifFERELTqaeeSlJSEuvXr/e2Mng8+uijJCYmMmbMGCZPnszEiRMZPny4z9eOiYlh/vz5rF27luHDh3PHHXfw4IMP1jvm6quv5oILLuDiiy/m+OOPZ//+/fVaZwCuuuoqBgwY4O1H89lnnzW4V48ePXjvvff4+uuvGTp0KFdffTW/+c1vuPPOO/2ojZY98MAD/PznP+fKK69k+PDhbNy4kQULFngnAXQ6ncyYMYMhQ4Zw8sknY7fbmT17trc+HnzwQUaOHMmoUaPYunUr7733XtCCVnMM05cHfR1MUVER8fHxFBYWEhcXF9Rru91u8vLySEtLw2az8ad5P/DCl9u4+pQ+3D7pqKDeq7M4vM7EN6q3wKjeAtNYvVVUVLBlyxZycnKIiIho5xKGHtM0qampISwsrEHfEmlaeXk5mzZtonfv3vU6S0Ngf7/1b3kr9UuvbZHRyCUREZEjTkGmlfqlWZ28NujRkoiIyBGnINNK/WtbZLYfKKesqqaFo0VERCSYFGRaKTkmnORoJwCb8krbuTQiIiJdi4JMEHj6yWxQPxkR6YQ64ZgQaUfB/j4pyASB+smISGfkmSm2rKydFomUTqmsrAzTNBvMRBwozewbBP29I5c0KZ6IdB52u52EhATvej1RUVEaZlyHhl/7xzRNysrKyM/PJyIiwrvuVWuFfJCZOXMmf/zjH7npppv4+9//3t7FaVQ/z+KRapERkU7Gsyqzr4sPdiWmaeJ2u7HZbAoyfoiPjw9qfYV0kPnmm2/4z3/+02BFz1DjXXOpduRSlDOkq1VExGeGYdCtWzfS0tIanT6/K3O73ezfv5/k5GRNvugjh8OBYRhBDcYh+xe3pKSEyy+/nFmzZnH//fe3d3GalRTtJDnayf7SKjbmlTCkZ0J7F0lEJKjsdnvQHgV0Fm63G4fDQUREhIKMH/xZ08oXIRtkrrvuOs466yxOO+20FoNMZWUllZWV3vdFRUWAVVnBrjC32+1tTqyrX1oM+7ccYP2eIgZ3D+6yCB1dU3UmzVO9BUb1FhjVm/9UZ4Fprt4CqcuQDDKzZ8/m22+/bbBKaFNmzpzJvffe22B/fn4+FRUVQS2b2+2msLAQ0zTrJfCecdb/qazaksdJPZ1BvWdH11SdSfNUb4FRvQVG9eY/1Vlgmqu34mL/+5qGXJDZvn07N910Ex9++KHPi5TNmDGDadOmed8XFRWRmZlJampqmywaaRgGqamp9f4BDMkq543v8tlV4iYtLS2o9+zomqozaZ7qLTCqt8Co3vynOgtMc/UWyOKkIRdkVqxYQV5eHiNGjPDuc7lcLF26lMcff5zKysoGz2nDw8MJDw9vcC2bzdYmXy7DMBpce0CGFZh+yivRF7oRjdWZtEz1FhjVW2BUb/5TnQWmqXoLpB5DLsiMHz+e1atX19v3q1/9iqOOOorbbrstZDubeYZg7zhYTmllDdHhIVe1IiIinU7I/bWNjY1l8ODB9fZFR0eTnJzcYH8oSYp2khLjZF+JNXJpaGZCexdJRESk01NbWBB5lyrQmksiIiJHRMi1yDRm8eLF7V0En/RPj+GLzfvZmKelCkRERI4EtcgEkaefjFpkREREjgwFmSDql2YtHrlBi0eKiIgcEQoyQeRZc2lngTVySURERNqWgkwQJUY7SYmx5rP5Sf1kRERE2pyCTJD1T7ceL/2kfjIiIiJtTkEmyDyPl9QiIyIi0vYUZIKsr7fDr1pkRERE2pqCTJB5W2Q0cklERKTNKcgEmaePzM6Ccko0cklERKRNKcgEWUKUk9RYa+SSZvgVERFpWwoybcDTKqN+MiIiIm1LQaYNeBaP1BBsERGRtqUg0wb6pWupAhERkSNBQaYNHBq5pBYZERGRtqQg0wY8i0fuKqyguKK6nUsjIiLSeSnItAGNXBIRETkyFGTayKE1lxRkRERE2oqCTBvxjFzSEGwREZG2oyDTRrR4pIiISNtTkGkjhx4tqUVGRESkrSjItBHPoyWNXBIREWk7CjJtJD7KQVrtyCU9XhIREWkbCjJtSBPjiYiItC0FmTbUT0OwRURE2pSCTBvytMhs0KMlERGRNqEg04Y8SxXo0ZKIiEjbUJBpQ/1qW2R2F1ZQpJFLIiIiQacg04biIx2kx9WOXFI/GRERkaBTkGljnn4yG/P0eElERCTYFGT8se0LjHlXE/3tv30+5dCaS2qRERERCTYFGX8U7cT4/lXCty32+RTPEGwtHikiIhJ8CjL+SB8MQNiB9WC6fTqlv+aSERERaTMKMv5I7oNpd2KrLoOC7T6d0rf20dKeogoKyzVySUREJJgUZPxhd0BKf2s770efTomPdJARFwHARk2MJyIiElQKMv5KP9r6uXeNz6ccWqpA/WRERESCSUHGT2baIACMPD+CjEYuiYiItAkFGX/VBhn8CDLeDr+aS0ZERCSoFGT85Xm0tH8jVFf4dIpnqQINwRYREQkuBRl/xWTgDk/AMF2wb71Pp3j6yOwtqtTIJRERkSBSkPGXYVCTVDtyyccOv3ERdUcuqVVGREQkWBRkAlCdPMDa2PuDz+ccmuFXHX5FRESCRUEmAN4WGb86/KqfjIiISLApyASgJtm/R0twaOSSJsUTEREJHgWZANQk9bM2SvZA6X6fztHIJRERkeBTkAmA6YjGTMy23vi4VEHfNI1cEhERCTYFmUB5JsbzY+RSt3hr5JKWKhAREQkOBZlApXnWXPJ95JKnVWZzfmlblEhERKTLUZAJkJnu/1IFOSnRAGzZryAjIiISDAoygfKuubQW3G6fTslOrg0yapEREREJCgWZQCX1hrAIqC6Dg1t8OiUntTbI7FOQERERCQYFmUDZwiC1doZfHx8v9a59tLR1fylut9lWJRMREekyFGRaw9vh17cg0yMhkjCbQWWNm91Fvq2cLSIiIk1TkGmNdP9GLoXZbfRKjgLUT0ZERCQYFGRaI4CRS701cklERCRoFGRaw/No6cBmqCrz6RSNXBIREQkeBZnWiEmDqBQw3ZC/zqdTDo1c0uKRIiIiraUg0xqG4ffjpRzvyCXfWnBERESkaQoyreXnyCVPkMk9UEa1y7eJ9ERERKRxCjKt5efIpfTYCCIddlxuk+0H1CojIiLSGgoyreXnoyWbzSC7zsR4IiIiEjgFmdZKHQgYUJoPJXk+nZKTYs0lo1WwRUREWkdBprWcUda6SwB7f/TpFO8q2FpzSUREpFUUZILB75FLMYAeLYmIiLSWgkww+D1yScsUiIiIBENIBpknn3ySIUOGEBcXR1xcHKNHj+b9999v72I1zc+RS54WmV2FFZRXudqqVCIiIp1eSAaZnj178sADD7B8+XKWL1/OqaeeyrnnnsuPP/rWB+WI8wSZ/HXgbjmYJEY5iI90ALDtgFplREREAhWSQWby5MmceeaZ9O/fn/79+/PnP/+ZmJgYvvzyy/YuWuMSsyEsEmoq4MCWFg83jENDsPV4SUREJHBh7V2AlrhcLl5//XVKS0sZPXp0o8dUVlZSWVnpfV9UVASA2+3G7Q7u7LlutxvTNA+7roGRNhBj17e496w+NIqpGTnJUXy3vYBN+SVBL2OoabzOpCWqt8Co3gKjevOf6iwwzdVbIHUZskFm9erVjB49moqKCmJiYpg7dy6DBg1q9NiZM2dy7733Ntifn59PRUVFUMvldrspLCzENE1stkMNWnFxOUTt+payLd9QktJ44KorLdL6uW7HfvLyYoNaxlDTVJ1J81RvgVG9BUb15j/VWWCaq7fi4mK/rxeyQWbAgAGsWrWKgoIC3nzzTaZMmcKSJUsaDTMzZsxg2rRp3vdFRUVkZmaSmppKXFxcUMvldrsxDIPU1NT6/wB6jYB1bxJduo2otLQWr3N0rxr4Yhe7S92k+XB8R9ZknUmzVG+BUb0FRvXmP9VZYJqrt4iICL+vF7JBxul00rdvXwBGjhzJN998w2OPPcZTTz3V4Njw8HDCw8Mb7LfZbG3y5TIMo+G1MwZbn+39EcOHe/ZJs1phtu0v7RL/AjRaZ9Ii1VtgVG+BUb35T3UWmKbqLZB67DA1b5pmvX4wIcczcungVqhquQOvp7PvvpIqCsur27BgIiIinVdIBpk//vGPLFu2jK1bt7J69WruuOMOFi9ezOWXX97eRWtadApEpwEm5K1r8fCY8DBSY61WpK1aqkBERCQgIRlk9u7dy5VXXsmAAQMYP348X331FR988AGnn356exeteX5PjKdVsEVERFojJPvI/Pe//23vIgQm/WjYvMjnNZd6p0Tz9ZYDWgVbREQkQCHZItNhpdWOqPJxFexsrYItIiLSKgoyweR9tPQjmGaLh+vRkoiISOsoyART6gAwbFB+AEr2tnh4Tp1lCkwfgo+IiIjUpyATTI5ISOpjbfvweKlXUhSGAcWVNewrqWrjwomIiHQ+CjLBVvfxUgsiHHZ6JFhrFejxkoiIiP8UZILNE2R8HLmUo1WwRUREAqYgE2x+jlzyBJnNGrkkIiLiNwWZYPO0yOSvB1dNi4d7Ry4pyIiIiPhNQSbYErLAEQ2uSjiwqcXDNZeMiIhI4BRkgs1mg7SB1rYPj5d615lLxu3WEGwRERF/KMi0BT9GLvVIiMRhN6iscbO7qKKNCyYiItK5KMi0BT9GLoXZbWQmRQEauSQiIuIvBZm24OfIpd7efjIlbVUiERGRTklBpi14WmQKtkFlcYuHe+eS2VfWlqUSERHpdBRk2kJUEsR2s7bz1rZ4eLZaZERERAKiINNW/Hi8lKMh2CIiIgFRkGkrfoxc6p0SA8D2g+VUu9xtWSoREZFORUGmrfgxcik9LpxIhx2X22T7AfWTERER8ZWCTFup+2jJbH6iO8MwNMOviIhIABRk2krqADDsUFEAxbtbPLy3goyIiIjfFGTaSlg4pPSztn3oJ5OdUjspnoKMiIiIzxRk2pJfI5esDr8KMiIiIr5TkGlLfnT49QzB3qogIyIi4jMFmbbkxxBsT5DZVVhBeZWrLUslIiLSaSjItCXPo6X89eCqbvbQxCgH8ZEOALYdUKuMiIiILxRk2lJCL3DGgrsa9m9s9lDDMA7N8KtVsEVERHyiINOWDAPS/V+qYLP6yYiIiPikTYJMTU0NTz31FNdffz1//etfKSwsbIvbdAwBrLmkDr8iIiK+aVWQue+++7Db7SxZssS7zzRNxo8fz7XXXssTTzzB7bffzqhRoygqKmp1YTukAEYuaQi2iIiIb1oVZD766CN69OjBKaec4t03Z84cli1bxjHHHMNTTz3F+eefz8aNG/nXv/7V6sJ2SAGMXFKQERER8U2rgszmzZsZOHBgvX1vvPEGhmEwe/ZsrrrqKl5//XV69erF66+/3qqCdlhptfVTuB0qmn/E5llvaX9pFYXlzY9yEhERkVYGmf3795Oamlpv37Jly+jfvz9HHXUUYI3GGTlyJNu2bWvNrTquyESI62lt561t9tCY8DBSY8MB9ZMRERHxRauCTGpqKvn5+d73mzdvZteuXfUeNQE4nU6qqqpac6uOzTty6YcWD9XjJREREd+1KsgMGjSIZcuWsX37dgBmzZqFYRiceeaZ9Y7bunUr3bp1a82tOjbvyKWWO/xqFWwRERHftSrITJs2jYqKCoYMGcLw4cN58MEHycnJ4YwzzvAeU1hYyLfffsvQoUNbXdgOK32w9dOHkUvZCjIiIiI+a1WQmThxIk888QTx8fGsX7+esWPHMnfuXJxOp/eY559/nqqqKsaPH9/qwnZY6XVaZEyz2UP1aElERMR3Ya29wNVXX83VV1/d5Oe//e1v+eUvf0lMTExrb9VxJfcDWxhUFkLhDkjIbPLQ3nUmxTNNE8MwjlQpRUREOpw2X6IgMjKS+Ph47HZ7W98qdIU5IWWAtd3C46XMpCgMA4ora9hX0oU7SIuIiPigVUGmrKyM3NxcSkvrPwYpLCxkxowZnH322Vx33XVs2bKlVYXsFHwcuRThsNMjIRLQ4yUREZGWtCrI3H///eTk5LB27aH5Uaqqqhg9ejQPPfQQ7733Hk8++SSjR49m7969rS5sh+bHyCWtuSQiIuKbVgWZjz/+mJycHEaOHOnd9/LLL7Nu3Tp+9rOfsWDBAm6++Wby8vJ49NFHW13YDs2PkUtaBVtERMQ3rQoyubm59O/fv96+efPmYbPZePbZZzn99NN55JFHGDBgAO+++26rCtrheR4t7dsANc33fTk0cqmkrUslIiLSobUqyBw8eJDExMR6+z7//HOOOeYYevbs6d03ZMgQ76R5XVZcDwiPB3eNFWaacejRUtmRKJmIiEiH1aogk5GRwa5du7zvf/zxR/bt29dgiQINIQYM49ACkvnrmj3U2yKzvxS3u/l5Z0RERLqyVgWZYcOG8dlnn7Fq1SoAHn30UQzD4Oyzz6533E8//UT37t1bc6vOIbmv9fPA5mYP65EQicNuUFXjZldh+REomIiISMfUqiBz++2343a7GTlyJMnJyfzvf/9j6NChnHrqqd5j8vLy+O677xgxYkSrC9vhJfexfu7f2OxhYXYbvZKiAD1eEhERaU6rgszxxx/PW2+9xdixY8nIyOCKK65g/vz52GyHLvvyyy8TGxtbb/2lLsvTItNCkAF1+BUREfFFq5coOOusszjrrLOa/Pzmm2/m5ptvbu1tOoe6QcY0rX4zTdAQbBERkZa1+RIFUkdSjvWzohDKDjR7aE6KtTaVJsUTERFpWqtbZACqq6uZO3cuy5YtY9euXRiGQbdu3TjppJM4//zzcTgcwbhNx+eIhPhMKNxutcpEJzd5aHaK1UdGyxSIiIg0rdVB5rPPPuOyyy5jx44dmGb9ocJPPPEEmZmZvPzyy4wZM6a1t+ockvscCjK9jm/ysN61LTLbD5ZT7XLjsKvxTERE5HCtCjIbNmxg0qRJlJSUMGLECK644gqys7MB2LZtGy+++CLLly9n0qRJLF++nH79+gWjzB1bUh/YvBgObGr2sPS4cCIddsqrXWw/UEbv1JgjUz4REZEOpFVB5s9//jMlJSU8+uij3HTTTQ0+v/HGG/nHP/7BzTffzJ///GeeffbZ1tyuc/Bx5JJhGGSnRLN2dxFb9pUqyIiIiDSi1YtGDhs2rNEQ43HjjTcybNgwFi5c2JpbdR7eINN8iwxAb+8QbPWTERERaUyrgkx+fj5HHXVUi8cdddRR7Nu3rzW36jw8k+Id2Axud7OH5ijIiIiINKtVQSY5OZkNG5pfABGsvjRJSUmtuVXnkZAFtjCoLoPi3c0emq0gIyIi0qxWBZmf/exnfPvtt8yaNavJY2bNmsWKFSvqLVvQpdnDIDHb2m6hn8yhVbAVZERERBrTqs6+d955J/PmzePqq6/m5Zdf5rLLLiM7OxvDMNiyZQsvvfQSy5YtIyoqijvuuCNYZe74kvtaIWb/Ruh9SpOHefrI7CqsoLzKRaTTfqRKKCIi0iG0KsgMHDiQ+fPnc/nll7NkyRKWLl1a73PTNElPT+ell15i4MCBrSpop5JUp59MMxKjncRHOigsr2br/lIGdos7AoUTERHpOFo9Id748ePZvHkzr732mndmX4Du3btz0kkncdFFFxEVFdXqgnYqPq6CDdbjpVXbC9i6T0FGRETkcEFZoiAqKoqpU6cyderURj9//fXX2b17NzfeeGMwbtfx+bkK9qrtBVo8UkREpBFHZN77Rx55hN///vdH4lYdgyfIHNwKrppmD9UQbBERkaZpAZ/2ENsNwiLBXQMF25o9VCOXREREmqYg0x5stjr9ZJqf4VctMiIiIk1TkGkvPnb49UyKt7+0isLy6rYulYiISIcSkkFm5syZjBo1itjYWNLS0jjvvPNYv359excruLxDsJtvkYkJDyMtNhzQ4yUREZHDhWSQWbJkCddddx1ffvklH330ETU1NUyYMIHS0k70h9yPkUtaqkBERKRxQRl+HWwffPBBvffPPPMMaWlprFixgpNPPrmdShVkfq6C/fWWAxqCLSIichi/gozd3j5T5BcWFgI0ufBkZWUllZWV3vdFRUUAuN1u3C2sMO0vt9uNaZqtv25Sb6s5rHA77spScEQ2eWh2sjWh4Jb8kqD/PkdC0Oqsi1G9BUb1FhjVm/9UZ4Fprt4CqUu/goxpmn7fwMMwjIDOM02TadOmMXbsWAYPHtzoMTNnzuTee+9tsD8/P5+KioqA7tsUt9tNYWEhpmlis7XiyZxpkuaMw1ZVxIFNK6hJ6t/koYlh1lwzP+0pJC8vL/B7tpOg1VkXo3oLjOotMKo3/6nOAtNcvRUXF/t9Pb+CTHukzuuvv57vv/+eTz/9tMljZsyYwbRp07zvi4qKyMzMJDU1lbi44E7r73a7MQyD1NTUVn9xjZR+sGsFSeZBSEtr8rhjzUhgEzsKK0lNTQ04FLaXYNZZV6J6C4zqLTCqN/+pzgLTXL1FRET4fb2Q7CPjccMNNzB//nyWLl1Kz549mzwuPDyc8PDwBvttNlubfLkMwwjOtVP6wq4V2A5ssuaWaUJ2agyGASWVLg6U1ZAa2/B3DXVBq7MuRvUWGNVbYFRv/lOdBaapegukHkOy5k3T5Prrr2fOnDl88skn5OTktHeR2oaPQ7DDw+z0SLD60GjkkoiIyCEhGWSuu+46XnzxRV5++WViY2PZs2cPe/bsoby8vL2LFlw+zu4LdWf4LWnLEomIiHQoIRlknnzySQoLCxk3bhzdunXzvl599dX2Llpw+TGXTG9vkClryxKJiIh0KCHZR6Y1o6M6FE+LTGk+VBRCRHyTh2arRUZERKSBkGyR6TLCYyEm3dr2cfHIjXkKMiIiIh4KMu3Nxxl+B/ewWms25Zdq8UgREZFaCjLtzcdVsFNiwumVZM3w+932gjYulIiISMegINPefByCDTC8VwIA3+YebMMCiYiIdBwKMu3Nj5FLw7MSAfg2t6ANCyQiItJxKMi0t7p9ZFoYrTW8lxVkVuYexO3uIiO7REREmqEg096ScgADKousYdjNGJARS4TDRnFFDZvyNXpJREREQaa9hYVDQqa13cLIJYfdxpCeCQCs1OMlERERBZmQ4E8/mV6efjLq8CsiIqIgEwr8CjIJgIKMiIgIKMiEBj+GYA+rbZH5Ka+EogpNjCciIl2bgkwo8HF2X4DU2HAykyIxTVilfjIiItLFKciEAu/svpvA7W7xcPWTERERsSjIhIKEXmBzgKsSina0ePih+WQK2rhgIiIioU1BJhTY7LXzyeDT4yVNjCciImJRkAkVfoxcOqqbNTFeUUUNm/dpYjwREem6FGRCRd1+Mi1w2G0M6ZEAwLfbCtquTCIiIiFOQSZU+DEEG2BYVgKgDr8iItK1KciECj8eLYE6/IqIiICCTOjwBJmD26CmqsXDPUFmQ16xJsYTEZEuS0EmVMRmgCMaTBcUbGvx8LoT4323vaDtyyciIhKCFGRChWFAcm9r24cOv1BnYjx1+BURkS5KQSaU+NlPZlhmAqAOvyIi0nUpyIQSfzv8ZlktMqu2F2hiPBER6ZIUZEKJn0OwB3aLI8Jho7C8ms37StuwYCIiIqFJQSaU+LEKNhw2MZ4eL4mISBekIBNKPLP7Fu2EKt9aWDwT461UkBERkS5IQSaURCVBpNXvhQObfTplWKZGLomISNelIBNq/Hy8NLy2RWZDXjHFmhhPRES6GAWZUOPnyKW02Ah6Jnomxitsw4KJiIiEHgWZUOPHKtge3onx1E9GRES6GAWZUOPnEGyA4b0SAAUZERHpehRkQo2fj5bg0MR4K3M1MZ6IiHQtCjKhJql2vaWy/VB2wKdTjsqIIzzMmhhvy35NjCciIl2HgkyoCY+B2G7Wto9DsJ1hNob0jAfg2216vCQiIl2Hgkwo8nMINtTt8FvQBgUSEREJTQoyocg7csn3fjLDenn6yahFRkREug4FmVAUSIff2pFL6/dqYjwREek6FGRCUQBDsNPiIuiRoInxRESka1GQCUV1+8iYvg+nPjQMW4+XRESka1CQCUWJ2WDYoKoESvb6fJomxhMRka5GQSYUhTkhoZe17Vc/mdoWme0FmH605IiIiHRUCjKhKoAh2AO7WRPjFZRVs3mfJsYTEZHOT0EmVAUwcskZZuOYHpoYT0REug4FmVAVQIsM1Onwu70gyAUSEREJPQoyocqz5pIfQ7ChTodftciIiEgXoCATqjwtMgc2g9vl82meDr8b9hZTUlnTFiUTEREJGQoyoSq+J9jDwVUFhdt9Ps0zMZ7bhO/0eElERDo5BZlQZbNDUo617UeHXzjUT0aPl0REpLNTkAll3g6/m/06bVhmAqAOvyIi0vkpyISyAFbBhvpLFWhiPBER6cwUZEJZAHPJAAyqnRjvYFk1WzQxnoiIdGIKMqEsgFWw4bCJ8XILglwoERGR0KEgE8o8LTIFuVBT6dep3g6/WkBSREQ6MQWZUBaTBs5YMN1wcKtfp3o7/KpFRkREOjEFmVBmGJBcO8NvgB1+1+8p0sR4IiLSaSnIhLoA11xKrzMx3vcahi0iIp2UgkyoC3DkEsAwz7pL6icjIiKdlIJMqAuwRQYOrbukkUsiItJZKciEugCHYIMmxhMRkc5PQSbUeTr7Fu+GyhK/Th3ULQ5n7cR4W/eXtUHhRERE2peCTKiLTISoFGu7NRPjaQFJERHphBRkOoIA11wCGK4OvyIi0okpyHQEAa6CDerwKyIinVtIBpmlS5cyefJkunfvjmEYzJs3r72L1L5a0yKjifFERKQTC8kgU1paytChQ3n88cfbuyihoRVzyaTHRdA9PsKaGG9HQXDLJSIi0s7C2rsAjZk0aRKTJk1q72KEjqTAW2QAhmUlsuv73azMLWBMn5QgFkxERKR9hWSLjBwmqXYIdkUBlB3w+3RvPxmNXBIRkU4mJFtk/FVZWUllZaX3fVFREQButxu32x3Ue7ndbkzTDPp1mxUWgRHXA6NoJ+78DZB5nF+nD8u0hmCvzD2Iy+XCMIy2KGWT2qXOOgHVW2BUb4FRvflPdRaY5uotkLrsFEFm5syZ3HvvvQ325+fnU1FREdR7ud1uCgsLMU0Tm+3INWglxmYSXrSToq0rqQjP9uvc1DA3TrvBgbJqVmzYTq/EiLYpZBPaq846OtVbYFRvgVG9+U91Fpjm6q24uNjv63WKIDNjxgymTZvmfV9UVERmZiapqanExcUF9V5utxvDMEhNTT2iX1wjYyDs/JL46nzi0tL8Pv+YHvGsyC3g693VjBzQqw1K2LT2qrOOTvUWGNVbYFRv/lOdBaa5eouI8P9/tDtFkAkPDyc8PLzBfpvN1iZfLsMw2uzaTUrpZ937wCaMAO57yXG9WJFbwD8/2chZQ7qTkxId7BI2q13qrBNQvQVG9RYY1Zv/VGeBaareAqnHkKz5kpISVq1axapVqwDYsmULq1atIjc3t30L1p48Q7B3LIdq/x+XXTiiJyf1S6Gyxs1tb36P261FJEVEpOMLySCzfPlyhg0bxrBhwwCYNm0aw4YN46677mrnkrWjrBMhJgOKdsDSh/w+3TAM/nL+MUQ57Xy95QAvfd2FQ6GIiHQaIRlkxo0bh2maDV7PPvtsexet/YTHwFkPW9ufPQZ7Vvt9icykKG6dOACAB95by86C8mCWUERE5IgLySAjTRg42Xq5a2D+DeB2+X2JKaOzGZGVSGmVizvmrsY09YhJREQ6LgWZjmbSXyE8HnathK/+7ffpNpvBgz8fgjPMxuL1+cxdubMNCikiInJkKMh0NHHdYMJ91vYn98PBbX5fom9aDDeNt0ZB3ffOGvKLK1s4QwJSWQIL74G9a9q7JCIinZaCTEc07JeQNRaqy+Cd30MAj4d+d3Jvju4eR0FZNXfP/6ENCil8+QR8+ijM/V1A/4xERKRlCjIdkc0Gkx8Dezhs+hi+f83vSzjsNh66cAhhNoP3Vu/hgx92t0FBu7g1862fe1ZD7hftWxYRkU5KQaajSukLp0y3tj+4HUr3+X2Jo7vHc/Up1srad877kYKyqmCWsGs7sBn21hlZFkB/JhERaZmCTEd24k2QdjSUH4APZgR0iRvG96VPajT7Sir5f++sDXIBu7C1b1s/PSuXr30HCra3X3lERDopBZmOzO6Ac/4Jhg1WvwY/feT3JcLD7Dx04VAMA978dgeL1+e1QUG7IM9jpdHXQfZJYLpg+X/bt0wiIp2QgkxH13MEHH+Ntf3O762RMn4akZXI1DHZANwx9wdKKmuCWMAuqHAn7FwOGHDU2XD81db+Fc9CtSYhFBEJJgWZzuDUOyChFxRut4ZkB+DWiQPITIpkZ0E5D32wLsgF7GLWvWP9zDweYjNgwCSI7wXlB2H16+1bNhGRTkZBpjNwRsPZj1rbX/3bWljST1HOMB64YAgAz3+xja+3HAhmCbsWT/+YQedYP212OO4qa/urpzQUW0QkiBRkOou+p8GQSwDTWr6gxv8RSCf2TeGSUZkA3Pbm91RU+78EQpdXug+2fWZtH3X2of3DrwRHFOz94dDnIiLSagoyncnEv0BUMuStsRaWDMAfzxpIelw4W/aV8ujCDUEuYBew7l0w3dDtWEjMOrQ/MhGGXGxtayi2iEjQKMh0JtHJcMaD1vbShyDf/yASF+Hgz+cdA8CspZv5fkdBEAvYBaytHa3keaxU1/H/Z/1c9y4U5B65MomIdGIKMp3NMRdC39PBVQVv3wRut9+XOG1QOpOHdsdtwvQ3vqeqxv9rdEnlBbB5ibU9sJEgkzYQck6xWmy+efqIFk1EpLNSkOlsDAPOfgQc0ZD7OXz7bECXuWfyIJKinazbU8yTizcFt4yd1YYF4K6G1IGQ0q/xY7xDsZ+DqrIjVzYRkU5KQaYzSugF4++ytj+6G4p2+X2J5Jhw7p48CIDHF/3E+j3FwSxh5+R5rDRwctPH9J8ICVlQUWBNYigiIq2iINNZHXcV9BgJlUXw7h8CGvJ7ztDunDYwjWqXyfQ3v8fl1rDhJlWVwsaF1nZj/WM8bHY47nfWtoZii4i0moJMZ2Wzwzn/AFsYrH/3UGuBHwzD4P7zjiE2PIzvthfwn6Wb26CgncRPH0FNBSRmQ/rg5o8ddoU1FDtvDWxddkSKJyLSWSnIdGbpR8PY31vb791qzSzrp4z4CO44ayAAD36wjlte+47C8upglrJz8EyCN/Acq59ScyITYOil1vZXT7VpsUREOjsFmc7upD9Acj8o2Qsf3RXQJS4elcn/ndLbu7DkhEeXsGidFpf0qqm0OvpC46OVGuN5vLT+PTi4tU2KJSLSFSjIdHaOCOsRE8C3z8NPC/2+hGEYzJg0kNf/bzQ5KdHsLarkV89+wx9eV+sMAJsXQ1UxxHaHHiN8OyftKOj9Mw3FFhFpJQWZriBrDIyqXevnzV/D/sCGU4/MTuK9G0/iN2NzMAx4Y8UOJj66VK0zazyjlc4Gmx//SnmGYn/7vNVZWERE/KYg01VM/DP0HAUVhTD7cqgMbDh1pNPOn84exGu1rTN7iiq6duuMq8bqTA2+P1by6DcBEnOsfybfvxr8somIdAEKMl1FWDhc9ALEZED+Wph7dUCz/nqMaqp1Zn0Xa53Z9qnViToqGXqN9u9cm63OUOz/aCi2iEgAFGS6krhucPGLYHfCundg2cOtulzd1pns5CirdeaZb7i1K7XOeEYrHXUW2MP8P3/Y5dYszPlrYcvS4JZNRKQLUJDpajJHwVmPWNuL/gzr3mv1JUdlJ/H+TSfz6xOt1pnXu0rrjNsNa9+xtv19rOQREQ/HXmZtayi2iIjfFGS6ouFXHnqkMed3kL++1ZeMdNq5a3LD1pnpb3xHUUUnbZ3Z8Q2U7IHwOGsxyEBpKLaISMAUZLqqiX+BrLHWsOFXLrVWbg6Cw1tnXlu+g0mPfcoXWwuDcv2Q4pktuf8ZEOYM/Dqp/aHPeMCEr2cFpWgiIl2FgkxXZXfARc9BfCYc2ARv/hbcrqBc+vDWmd2FFfx+3kZ+8e8veH35dsqqaoJyn3ZlmoeCTHNrK/nKOxT7Bagsaf31RES6iAB6J0qnEZ0Cl7wE/50IGz+CT/4fnHZP0C4/KjuJ968/ga+ev4P+u+bh3FsNb0PF2yZmmJ3wMIMwT5Q2TaB21I5Jne3anz2Gw/lPWR2WQ8Hu76Ag11ozqc/41l+v72mQ1BsObLaGYo/6TeuvKSLSBahFpqvrNhTOfdza/vRR+GFO8K69cwWRz5zKuN3/pbuxnxSjiBSjiCSjmGhXAWGVB62hy+UHoaLAmk+lohAqC61VuyuLrEdfVcWwZQnMOhV2rQpe+VrDM1qp72ngjGr99Ww2OO7/rG2tii0i4jO1yAgcc6HVwvD5P+Ct6yClH2QcE/j1qsutEVFf/AtMN2ZUCoUnTCeu/4mAwaodhbz/w14+/WkfVW7rD3Z4mJ1TBqRx1pBuHN09HsOok7ErCmHeNZC/Dp6ZBBfMsmbRbU+ex0qBjlZqzLGXWa1i+9Zbyx60pgOxiEgXoSAjltPugb0/wKZP4JXL4HeLITrZ/+ts/QzmX289IgE45heYE2dSUeIiLi0Nm83G8AwYPhIOlFYx59sdzP5mO2vySljzAzz5w176pZVyyXG9uGBYDxKjazvR/uZDeH2qVb5Xr4DT7oYTb255pem2kLcO9m2w5uPpPzF4142Ig2Mvh6+fslplFGRERFqkR0tisdnhwv9ZU+YX5sLrU6zp931VWQzv3gLPnmmFmNhucOls+PnT1qy3jUiKdvLbk3rz0e9P5s1rRnPhiJ5EOGz8lFfC/3tnDcf/5WNueGUln2/ch9sZB5e9XrtmlAkL74G3roeaqqD8+n7xPFbqPc4KH8HkGYq94QM4sCW41xYR6YQUZOSQyES49BVrptmty+DDO307b+NCeGL0oVWch/8Srv0SBkzy6XTDMBiRlcTDvxjK13ecxv3nDWZwjziqXG7e/m4Xlz39FaMf+JibX1/Nq2k3cuDkP2MaNlj1IrxwHpTuD+z3DdTat6yfwXys5JHSF/qeDpgYWhVbRKRFCjJSX9pAuKB2htmvnoRVLzd9bNkBmHsNvPhzKNwOCVnwy7fgnH9CZEJAt4+LcHDFCVm8c8NJvHPDWK44oRex4WHsLapk3qpd3PbmaoZ/mMMtYXdQbouGbZ9R859TIX9DQPfz24EtsGc1GHYYcGbb3MMzFHvVCxjVWhVbRKQ5CjLS0MDJcMpt1vbbN8OOFQ2PWfs2PHECfPcyYMDx18C1X1iPW4JkcI947j/vGL658zRe/u3x3HBqX0ZmJeKwG8wpHsg55Xez3Z1KWOFWiv91Cv959r/MXbmDPYUVQStDA57HStknBtaHyBd9ToXkvhiVxUSsn9c29xAR6STU2Vcad8rtVsvD+veszrW/Wwyx6VCSB+/dCmvmWccl94Nz/wW9jm+zokQ47Izpm8KYvikAlFXVsGLbQb7Y1Ic7N2ZyY97djLBt4Ndb/sDdP/3A712nkZMSzQm9kxndJ5kTcpJIi4sITmE8QaYtHit5eIZiv38r0T+8CD+7Cf0/h4hI4xRkpHE2mzUB3dPjrRE6r10JI6bCgj9a874YdjjxJqvlxhGkkOCjKGcYJ/VL5aR+qcBRlJSezJ7XryVj61v82fE/+hi7uX/f5WzZV8orX+cCkBjlICclmuyUaHKSa3/Wvo8J9/Ffg6JdsONra/uoNh7+feylmB/fR1jBZsyF98CE/9c+I7REREKcgow0LSIOLnnFmohu+1fWC6w5Zs55HLof267F84iJjiFmynOw7GH45H5+HfY+52SW87+MO1myrYI1u4s4WFbNwdwCvs0taHB+amx4bbiJ8gadnNRospKiiXTaDx247l3rZ+bxbT/DcHgs5sS/YLx9A8YX/7Qm3fvZH9v2niIiHZCCjDQvpa81hPqVi8EWBqdMt+ZvsTvau2T1GQacfCsk94W5V5OyezHTXXlM/+WrlEV1Z+u+MrbsK2Xr/lK27LNeW/eVsr+0ivziSvKLK/l664EGl+0WH0Gf1BgGdY/jqs2vkwq4BpyNvWEJgm/YFRQd2EPcZ3+GJQ9CWAScNO1I3FlEpMNQkJGW9Z8A13wOzhhIyGzv0jTv6PMhoZe1onfeGph1KlGXvMKgzFEM6t5wzpfC8mq2HRZuPNtFFTXsLqxgd2EFP27czG3hX4MBEz6IJ2bVpwzqHsegbnEM6h7HURlxRPv6iMoPZcf8kpiIMGwf3wsf32uFmdHXBv0+IiIdlYKM+CZtYHuXwHc9RsBVn8DLl8De1fDsWXDyH+C4q6y5cuqIj3QwpGcCQ3om1NtvmiYHy6rZsq+EDXtLiFj9EvbtJmvMbDbVpMCOQr7bUeg93jAgJzmagXXCzdHd4oLTyfjEm6GmEpY8AAtmQFi4FpUUEamlICOdU3xP+PUHMOcqa+TVoj/DZ/+wAsDo6yAmrdnTDcMgKdpJUnQSI7KS4KdvATjq1CtYdPQ41uwqYs3uQn7cVcSaXUXkFVeyeV8pm/eV8u73u73XSYlxkh4XQWKUk/goB4lRDhKjnCREOb3b8bU/E6McxEU4sNka6dQ77naoqYDP/g7vTrNaZoZdHswaExHpkBRkpPMKj4GLX4If58Cyv1mPmj77O3z1b2v24TE3WI+hWlJRCJsWAWAbdA45tSOezhpyqMNvfnEla3cXsWZ3UW3IKWJzfgn7SqrYV+L7Mgo2w2olSohykhDpINZh0r/7AbJSosnOuo5jS4qJ/e6/1npWYeHWgp8iIl2Ygox0bjab9cf+6Aus9YuWPQw7V8DX/4Hl/4MhF8PY31srfjdlw4fgroaUAZA6oNFDUmPDSY1N5eT+qd595VUuNuaVsK+kkoLyKg6WVlNQVmWNoCqroqCsut7+0ioXbpPaz6u911m6ubDOnU7lAcdWLrF/jOvNq3jj272U9T2T7ORospKj6JkYhTNMc86ISNehICNdg80GR51prf+0ZYnVQrNlKax6yVqGYdC5cNIt0G1Iw3O9aytN9uuWkU47x/SM9/n4yhoXhWXVFJRXc7C0igOllWzcuY8D1Xa2Hyhj6/4ycveXMaP6Vzio4uf2ZZy/+U/8bv1B7nUPs35NA7onRJKdHE2v5ChSop3ERIQRE+6o/Wm3tsPDiI0IIyY8jJiIMBx2hR8R6ZgUZKRrMQxrGYXe42D7N/DpI1YfmjXzrFe/CVag6XWCdXxVGfy00Noe1Iaz+QLhYXbS4uzeDsJut5thqTbS0tKw2ayg4XKb7CmqYFv+KLZ9ciNZuxcwK/zv3BV9N28V9aOsysWOg+XsOFgOG/25t61esIkJt8JPQpSDhEgHidHO2kdeDhIinSREObzvY8LDMDRZn4i0EwUZ6boyR1mrfe/5AT591OpL89OH1itrrDVnS1Up1JRbfWkyGmmtOcLsNoMeCZH0SIiE3i/Ba1NwrH+XmRV/5i+/foP85JHk7ve03pRSUF5NSUUNxZU1lFTUUFJpvYoraiiprKai2g1AZY2bSj/783iE2Yw6wcbq2xNfG3gSo6ywE1/bmdkTghSARCRYFGREMgbDhf+1Zs797O+w6hXY9qn1Cou0jhl4TugtEWB3wC+egdmXwcaFGC9fTNqV80jLHsXI7CSfLlHtclPqDTa1r9rgU1ReTWHtq8Dbp6e69vGX1denqsZNjdus06nZ99W67TaDBE8rT5STpAgbg2zbGFz9PWmuPPZljKU08xRio6KIi7SCkuelfkAi4qEgI+KR3AfO+ae1YOYXj8PyZ6zWGGjbRSJbIywcLn4RXr7I6vPz4s9hynyfl49w2G1WK0qUM6DbV1S7KKjTebmwvKr2ff0AdLCsqva9tV1Z4wZ3Dd3LNnFCxRpOKFjLKNs64ozyQxff/Rr7vo1jvmsMb7pO4kczG7DCZITDVi/YxEdaQ9fjIh3ERYRhVleQnFBOlDOMcIeN8DA74Q4bEWF2ImrfRzhshDvsRIQd+hmmvkIiHY6CjMjh4nvAGTOtvjLL/2eFhczj2rtUTXNEwqWz4YULYPuX8ML5MPUdSD+6zW8d4bCTEW8nI96Hif9cNbDnO9j6Ka4tn2LkfoGtqrjeIZX2GHJjj2WfLZmjC5eS4jrIr8M+4NdhH/ATmbxRPZZ5rhPZW51ERXUle4sqg/r72G0GkQ470eF2YiMOdYqOq7MdExFGbISD2MPex4SHERcRRnR4GBEOO/bG5gMSkaBTkBFpSnSKtbZUR+CMhstfhxfOs4aXP38uTH0PUvv7fy3ThOoyazZhW5j1CMvmAJvdv8drdYILWz+FbV9AbXDxrlUVHg9ZYyB7LGSPJTzjGPrZ7PQDcFVb8/d89wqse5d+ru3McLzC7Y7ZVGSexL7e57M9fTwHa5wUVdR/DHawqBTsDqpcJhXVLipr3PV+VlS7qaxxUVntpsrlPlRkt+l9xNbakBQeZiPSaSfKYSfSaa/dDiPisH2RDjtRdbbD7DbCbAZ2m1Hnp837Psxe5729/nEOe51WJ4f102m3qS+SdGoKMiKdRUQcXPEmPDcZ9qyG58+BU26zZgSuKrE6LntfJU1s174wG7mBcSjU2B11tsPA7qy/bdggb503uBwqYzxknegNLqQPtgJSY+wOa52v/hOgvADWvAXfzcbI/ZzI7UvJ3L6UTEe0NZps6CWQfRLY7LjdbvLy8uqN9mqO221anZ1rrIBTXu2itLKGoorajtIVNRRXVHs7SRd7+hRVVHv7FtU9xl1bddY13RRQ3XwB2phh4A03EQ47EQ474XUep0U4PI/bbNhc1aQkHKgzcs1qYfL8jA6319sXHqaQJO1PQUakM4lMhCvnWetL5a+Dd24O4sVNcFVZL1//NvsTXJoTmQAjplivA1vg+9eslpqDW6yf370CcT1gyEVwzEWAb52dAWw2w9s64hfTtEJg2X7rVbofd2khNTU1VNoiqbRFUE4k5UYEZURQakZQYoZT7A6nvMakvKqG8io3ZdU1VFS5KKtyUV7twuU2qXGb1LisjtSe94d+uqlxWe/rf+amqsZNRbWbihoXpnmomOXV1rV9+we3z+cqsNsMYp0Gxzq2081exHqjN/uMRNymidtt4jbBZZqYZu2228Rtmph1tt21n0U57NZjPG9/J+uRXmP74iId3kd+cZEOopx2bIbhbTD0RCvDMOpso9DVSRmmaTb2v14dWlFREfHx8RQWFhIX13DF49bw9//2RHUWqFbVW/Fe+Pg+KM23Hjs5oyE89tC2M6bOdt33MYe2w8LBXVMbXqp93K62frqqa4esHxNYcPGFacL2r60Q8+McaymJWq6IJGyRcRjO2t853PP7Hf6+zu8bXrsdFm61AJXth7IDh4JKYy+X/8PVAXBEHVbndf4ZpQ20Fj7tPhyikwOsGpNql0lFjct6pFZ96JGaZ1+Fd5+Liho35VU15B8oBEcEpVUuSipqKK19zFZaVUNppcvarqymR3UuY2w/Mtq2hhNsa0gwDo1W2+5OZaXZl5Xuvqx092ONmUUVjsDqqQ3VDT3d4iM5tlcCx/ZM4NheCQzuHu9TsNV/2wLTXL0F8vdbQcZP+uL6T3UWGNWbH6orrCUovn8V86cPMdw1R+7eYREQlQJRSVa/KluYNZFiVUnDR3imu+Xr1ZWYbQWaHiOsV7eh4Ixqk1+jye+baVotX1uW1r6WQWlevXNdjliqotKJKNyEcdhjSbfNSWXK0VSkD6cyYwRVGcMx4zOx2WwYBtgMA5thUFZlPaIrqqimqNzzs5qi2kd41naNd19RufU4r7TKFfS6sNsMBqTH1gs3fVJjGnTg1r+jgQl2kNGjJRHp+BwRcPR5cPR5mKUH2L/1e5JiwrFVlx0KE5Ul9cNFU+9rKiAiwQolUUkQlXzYK6k2uNS+9zVYmGZtf6XaUFNZ0rCPUtl+2PM97PwW9v8EB7darx/nWNcw7JA2CHrUCTepR1l9k4KpcOeh4LJ1GRRur/95WKQ1+3XOyZBzCvZuQ4m0h0FFEexaCTu+gR3LYcfX2Mr2E5m3ksi8lbD6v9b50WnQcxT0HGn97D4MYqOguhQqSqGyyLpWZZHV0uZ9X3zYZ0WYFYW4ayoxnTGYzljM8BjM8Dhr2xmHGR6L2xmL6YzFHR5b+z7O+95lj2LzvjJW7ShgVW4Bq7YXkFdcaS0Au7uIl7/KBSAmPIxjesRzbK8EhvZMYFivBFJjApu2oEMwTau1tbrc+t7WVFj/w1BT5xWVfERGR7ZELTJ+UgL3n+osMKq3wHSaeisvsELBzhVWsNm5Akr2NDzOEWW11PQYYc2FZNgAo/bZiVFnpNnh+zzPVqxtd00lFRuXErlnOcaBTfXvYXNYUxBkn2SFl54jrUdwLTFNK4jtWF4bbr6xglqDFjNPGdvjz5Fh9eWKSoLIJIhMpCwsnryaKHIrIvipyMGPBWHsrYmiwIylwIzmILGUEU63+Ej6JIeTEhdNpNNeOz+R1Zn6UCfqQx2tG9vvtNux2w0cNsMasWavHZ1ms+GwG4H36zFNK+yV5FstaCW1L892ab4VEqvLrRGKNbU/675vqQXxmIvg57P8LppaZEREuoLIBOjzM+vlUbSrNtjUvnatsv5Y5X5hvVrBBnjblgyb1UqSc7L1yjze6sPjL8OApBzrNeQX1r7qctj9/aFgs2M5FO2oUxCHNQIvPBbC46yQER5Xuy/usM/irCH8YeG1rVzFtS04tS03lcX1W3IOb9lx1wAmVBRYLzZDbT1k175O9lTOYY0vlWYYBRUxHNgRy14zid1mEnvMJHaTxG4z2fu+hNY9CrR7htbbDCJsLlLspSQbxSQZpSTbikgyC0k0C0gyC0gyD5JoFpBYuy+cAPtwNaIKB1WG03oRTrXhYO9+JyODdofAKciIiHQUcd2tl2cldrcb9m88FGyKdgGm9X/j3p80sq/OkKbafSYGZTFZRA6aiC1nrBUg2oIjEnodb708SvKsskTEWX2OjsToIs+jvooiK8SUHYDyAw1/lh+EsoP197mqCDdqSKeAdKOAgWxv8jblRhQH7Cnss6WQbySzByv07HInsr0miUoXxJlFxJlFJFBMIsUkGcUkGsUkUUyiUUIi1vs4ysHPLkElZgT5Zjz7iGefeeiVTwKFZjQVOKjESYXpoAKntY2DStPp/ayKMEwatm6eE9tdQUZERFrBZrMmPUztD8de2qpLmW43xXl5RKalWdc9kmLSjuz9wApLjkjrFZvu+3mmafVnKj+Au3Q/Bbs2kmArx1a8G4p2WmGyaJe1XVFApFlGj5pcepDb+PX8HfVv2HCFJ1ITkUi1M4HqyBTrFZFq/QxPoSoyheqIFKoiUnCF1c66bUIc1ivHk2MxDw3T9/565qEHfI0dZx46NiPOhxm9jwAFGREREV8ZhjVUPzwG4npSZe8GTYW/qlIoqhtwdtQPOkW7rH4oDTqT13kfmVTvMyMigTCbjTAgNGJE+1OQERERaQvOaEjpa72kzYRsl/4nnniCnJwcIiIiGDFiBMuWLWvvIomIiEiICckg8+qrr3LzzTdzxx13sHLlSk466SQmTZpEbm4TzxhFRESkSwrJIPPII4/wm9/8ht/+9rcMHDiQv//972RmZvLkk0+2d9FEREQkhIRcH5mqqipWrFjB7bffXm//hAkT+Pzzzxs9p7KyksrKSu/7oqIiwJp0x+32c0rwFrjdbmsBtCBftzNTnQVG9RYY1VtgVG/+U50Fprl6C6QuQy7I7Nu3D5fLRXp6/eFw6enp7NnTyKyWwMyZM7n33nsb7M/Pz6eioiKo5XO73RQWFmKaZseeNfQIUp0FRvUWGNVbYFRv/lOdBaa5eisuLvb7eiEXZDwOn5bZNM0mp2qeMWMG06ZN874vKioiMzOT1NTUNlmiwDAMUlNT9cX1keosMKq3wKjeAqN685/qLDDN1VtEhP+DykMuyKSkpGC32xu0vuTl5TVopfEIDw8nPLzhuh82m61NvlyGYbTZtTsr1VlgVG+BUb0FRvXmP9VZYJqqt0DqMeRq3ul0MmLECD766KN6+z/66CPGjBnTTqUSERGRUBRyLTIA06ZN48orr2TkyJGMHj2a//znP+Tm5nL11Ve3d9FEREQkhIRkkLn44ovZv38/9913H7t372bw4MG89957ZGVltXfRREREJISEZJABuPbaa7n22mvbuxgiIiISwkKuj4yIiIiIrxRkREREpMNSkBEREZEOK2T7yLSGaZrAoaUKgsntdlNcXExERITmDfCR6iwwqrfAqN4Co3rzn+osMM3Vm+fvtufvuC86ZZDxTHGcmZnZziURERERfxUXFxMfH+/TsYbpT+zpINxuN7t27SI2NrbJZQ0C5Vn+YPv27UFf/qCzUp0FRvUWGNVbYFRv/lOdBaa5ejNNk+LiYrp37+5zK1enbJGx2Wz07NmzTe8RFxenL66fVGeBUb0FRvUWGNWb/1RngWmq3nxtifHQQz0RERHpsBRkREREpMNSkPFTeHg4d999d6OrbUvjVGeBUb0FRvUWGNWb/1RngQl2vXXKzr4iIiLSNahFRkRERDosBRkRERHpsBRkREREpMNSkBEREZEOS0HGD0888QQ5OTlEREQwYsQIli1b1t5FCmn33HMPhmHUe2VkZLR3sULO0qVLmTx5Mt27d8cwDObNm1fvc9M0ueeee+jevTuRkZGMGzeOH3/8sX0KG0JaqrepU6c2+P6dcMIJ7VPYEDFz5kxGjRpFbGwsaWlpnHfeeaxfv77eMfq+NeRLven7Vt+TTz7JkCFDvJPejR49mvfff9/7eTC/ZwoyPnr11Ve5+eabueOOO1i5ciUnnXQSkyZNIjc3t72LFtKOPvpodu/e7X2tXr26vYsUckpLSxk6dCiPP/54o58/9NBDPPLIIzz++ON88803ZGRkcPrpp3vXFOuqWqo3gDPOOKPe9++99947giUMPUuWLOG6667jyy+/5KOPPqKmpoYJEyZQWlrqPUbft4Z8qTfQ962unj178sADD7B8+XKWL1/OqaeeyrnnnusNK0H9npnik+OOO868+uqr6+076qijzNtvv72dShT67r77bnPo0KHtXYwOBTDnzp3rfe92u82MjAzzgQce8O6rqKgw4+PjzX//+9/tUMLQdHi9maZpTpkyxTz33HPbpTwdRV5engmYS5YsMU1T3zdfHV5vpqnvmy8SExPNp59+OujfM7XI+KCqqooVK1YwYcKEevsnTJjA559/3k6l6hh++uknunfvTk5ODpdccgmbN29u7yJ1KFu2bGHPnj31vnvh4eGccsop+u75YPHixaSlpdG/f3+uuuoq8vLy2rtIIaWwsBCApKQkQN83Xx1ebx76vjXO5XIxe/ZsSktLGT16dNC/ZwoyPti3bx8ul4v09PR6+9PT09mzZ087lSr0HX/88Tz//PMsWLCAWbNmsWfPHsaMGcP+/fvbu2gdhuf7pe+e/yZNmsRLL73EJ598wt/+9je++eYbTj31VCorK9u7aCHBNE2mTZvG2LFjGTx4MKDvmy8aqzfQ960xq1evJiYmhvDwcK6++mrmzp3LoEGDgv4965SrX7cVwzDqvTdNs8E+OWTSpEne7WOOOYbRo0fTp08fnnvuOaZNm9aOJet49N3z38UXX+zdHjx4MCNHjiQrK4t3332XCy64oB1LFhquv/56vv/+ez799NMGn+n71rSm6k3ft4YGDBjAqlWrKCgo4M0332TKlCksWbLE+3mwvmdqkfFBSkoKdru9QVLMy8trkCiladHR0RxzzDH89NNP7V2UDsMzykvfvdbr1q0bWVlZ+v4BN9xwA/Pnz2fRokX07NnTu1/ft+Y1VW+N0fcNnE4nffv2ZeTIkcycOZOhQ4fy2GOPBf17piDjA6fTyYgRI/joo4/q7f/oo48YM2ZMO5Wq46msrGTt2rV069atvYvSYeTk5JCRkVHvu1dVVcWSJUv03fPT/v372b59e5f+/pmmyfXXX8+cOXP45JNPyMnJqfe5vm+Na6neGqPvW0OmaVJZWRn871kQOiJ3CbNnzzYdDof53//+11yzZo158803m9HR0ebWrVvbu2gh65ZbbjEXL15sbt682fzyyy/Ns88+24yNjVWdHaa4uNhcuXKluXLlShMwH3nkEXPlypXmtm3bTNM0zQceeMCMj48358yZY65evdq89NJLzW7duplFRUXtXPL21Vy9FRcXm7fccov5+eefm1u2bDEXLVpkjh492uzRo0eXrrdrrrnGjI+PNxcvXmzu3r3b+yorK/Meo+9bQy3Vm75vDc2YMcNcunSpuWXLFvP77783//jHP5o2m8388MMPTdMM7vdMQcYP//rXv8ysrCzT6XSaw4cPrzf0Thq6+OKLzW7dupkOh8Ps3r27ecEFF5g//vhjexcr5CxatMgEGrymTJlimqY1JPbuu+82MzIyzPDwcPPkk082V69e3b6FDgHN1VtZWZk5YcIEMzU11XQ4HGavXr3MKVOmmLm5ue1d7HbVWH0B5jPPPOM9Rt+3hlqqN33fGvr1r3/t/XuZmppqjh8/3htiTDO43zPDNE0zgBYiERERkXanPjIiIiLSYSnIiIiISIelICMiIiIdloKMiIiIdFgKMiIiItJhKciIiIhIh6UgIyIiIh2WgoyI+MQwjBZfU6dObe9itmjq1KkYhsHixYvbuygiEgRa/VpE/DJlypQmPxs7duwRLImIiIKMiPjp2Wefbe8iiIh46dGSiIiIdFgKMiLSZgzDIDs7m6qqKu6++2769OlDREQEvXv35q677qKioqLR8/bv38+tt95Kv379iIiIICkpiTPOOIMPP/ywyXvt27ePGTNmMHjwYKKjo0lISODYY4/ljjvuYP/+/Y2es3TpUk499VRiY2OJi4vjrLPOYs2aNQ2OM02T2bNnc/LJJ5ORkUFERASZmZmcdtpp/Otf/wqsckQkKLRopIj4xDAMwPqj7s85vXr1YujQoSxcuJDx48fjdDr5+OOPKSwsZPz48SxYsAC73e49Z+fOnZx88sls3ryZXr16MXr0aPLz81myZAkul4tHHnmE3//+9/Xus2bNGiZMmMDOnTvp1q0bo0ePxuVysX79etatW8eiRYsYN24cYHX2fe6555g2bRqPPfYYgwcPpm/fvqxevZoNGzaQnJzMDz/8QEZGhvf6t99+Ow8++CCxsbGMHTuWhIQEdu/ezQ8//EB0dDRbt24NvGJFpHVav1i3iHQFgOnvfzI85/Ts2dPctGmTd39eXp45ePBgEzAfe+yxeuecffbZJmBeeeWVZlVVlXf/smXLzKioKNNut5vfffedd391dbV51FFHmYB5yy231DvHNE3z22+/Nbdv3+59P2XKFBMwbTab+fLLL3v319TUmD//+c9NwPzTn/7k3V9eXm6Gh4eb2dnZ5v79++tdu7q62lyyZIlfdSIiwaUgIyI+8YSS5l5z585t9Jz//Oc/Da73/vvvm4DZv39/775NmzaZgBkXF2cePHiwwTnTpk0zAfP//u//vPteffVVEzCHDBliulyuFn8PT5C54oorGny2YsUKEzBPOeUU7769e/eagHnuuee2eG0ROfI0aklE/NLc8OtevXo1uv+SSy5psO+MM84gMTGRDRs2kJ+fT2pqKp9++ikAZ555JgkJCQ3OufLKK3nkkUdYtmyZd9/ChQsBuOqqq7DZfO/2N2HChAb7+vfvD8Du3bu9+9LS0ujZsyfvvvsuf/3rX7n88svp3r27z/cRkbalICMifvF3+HViYiKxsbGNfpaVlcXBgwfZtWsXqamp7Nq1C4Ds7OxGj/fs9xwHsH37dgD69OnjV7l69uzZYF9MTAwAlZWV9fY/99xzXHLJJUyfPp3p06eTk5PDySefzGWXXdZoIBKRI0ejlkSk3ZhNdBz2dCxuan9jnzd1TlP8Of7UU09l48aNvPTSS1x55ZW4XC6ee+45Jk6cyMUXX+zXfUUkuBRkRKRNHTx4kOLi4kY/y83NBaBbt24A3kc2W7ZsafR4z+ggz/EAmZmZAGzcuDEo5W1KXFwcl112Gc8//zzbtm3jiy++oGfPnrz22mu8//77bXpvEWmagoyItLlXX321wb4FCxZw8OBB+vXrR1paGnBoiYN3332XgoKCBue8+OKLAJx00knefaeddhoATz/9tF9Dw1vrhBNO4MorrwRg9erVR+y+IlKfgoyItLn77ruv3lwr+/btY/r06QBce+213v29e/fmrLPOori4mJtuuonq6mrvZ1988QVPPvkkdru93jkXXHAB/fv357vvvuP222+npqam3r1XrVrFjh07Ai57bm4uzz77LGVlZfX2V1ZWsmjRIqDpTs4i0vY0IZ6I+MTTp6SlUUv33XdfvXN69erFkCFD+OSTTxg/fjwOh4NPPvmEgoICfvazn/Hhhx8SFnZo3MHOnTs56aST2LJlC1lZWd4J8RYvXozL5eJvf/sb06ZNq3ffH374gdNPP509e/bQvXt3xowZQ01NDevXr2ft2rWNTohXd9/hv2dWVpY3eK1atYphw4YRFRXFyJEj6dmzJ6WlpXz++efk5+dz3HHHsWzZMpxOZ4A1KyKt0r6jv0Wko8CHeWSGDh3a4JysrCyzoqLC/OMf/2hmZ2ebTqfTzMrKMu+44w6zrKys0Xvt27fPvOWWW8w+ffqYTqfTTEhIMCdMmGAuWLCgyfLt2bPHvOWWW8x+/fqZ4eHhZmJionnsscead955Z72J7DzzyCxatKjJ3zMrK8v7vqioyHz44YfNM88808zOzjYjIiLMlJQUc9SoUeY//vGPJn8HETky1CIjIm3m8NYNEZFgUx8ZERER6bAUZERERKTDUpARERGRDktLFIhIm1EXPBFpa2qRERERkQ5LQUZEREQ6LAUZERER6bAUZERERKTDUpARERGRDktBRkRERDosBRkRERHpsBRkREREpMNSkBEREZEO6/8D4JXG4CT40fIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Adding Subplot (For Loss)\n",
    "f.add_subplot(122)\n",
    "\n",
    "plt.plot(model_fit.epoch,model_fit.history['loss'],label=\"training loss\") # Loss curve for training set\n",
    "plt.plot(model_fit.epoch,model_fit.history['val_loss'],label=\"validation loss\")\n",
    "\n",
    "plt.title(\"Loss Curve\",fontsize=18)\n",
    "plt.xlabel(\"Epochs\",fontsize=15)\n",
    "plt.ylabel(\"Loss\",fontsize=15)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h1  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">8. EVALUATION</h1><a id = \"8\" ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T00:26:31.338345Z",
     "iopub.status.busy": "2023-04-06T00:26:31.335256Z",
     "iopub.status.idle": "2023-04-06T00:26:36.897453Z",
     "shell.execute_reply": "2023-04-06T00:26:36.896484Z",
     "shell.execute_reply.started": "2023-04-06T00:26:31.338313Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "657/657 [==============================] - 5s 8ms/step - loss: 0.0776 - accuracy: 0.9815\n",
      "\n",
      "Accuracy = 98.1521189%\n",
      "Loss     = 0.077588171\n"
     ]
    }
   ],
   "source": [
    "evaluate_test = model.evaluate(x_test, y_test, verbose=1)\n",
    "\n",
    "print(\"\\nAccuracy =\", \"{:.7f}%\".format(evaluate_test[1]*100))\n",
    "print(\"Loss     =\" ,\"{:.9f}\".format(evaluate_test[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T00:26:36.899371Z",
     "iopub.status.busy": "2023-04-06T00:26:36.898962Z",
     "iopub.status.idle": "2023-04-06T00:26:42.346726Z",
     "shell.execute_reply": "2023-04-06T00:26:42.345657Z",
     "shell.execute_reply.started": "2023-04-06T00:26:36.899329Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "657/657 [==============================] - 4s 6ms/step\n"
     ]
    }
   ],
   "source": [
    "y_predict = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T00:26:42.350118Z",
     "iopub.status.busy": "2023-04-06T00:26:42.348421Z",
     "iopub.status.idle": "2023-04-06T00:26:42.359485Z",
     "shell.execute_reply": "2023-04-06T00:26:42.358369Z",
     "shell.execute_reply.started": "2023-04-06T00:26:42.350075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 28, 12, ..., 25, 15,  3])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_max = np.argmax(y_predict,axis=1) \n",
    "y_predict_max"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h2  style='color:white; background:#0096FF ; border:0;text-align: center' class=\"list-group-item list-group-item-action active\">8.1 SUBMISSION</h2><a id = \"8.1\" ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T00:26:42.362057Z",
     "iopub.status.busy": "2023-04-06T00:26:42.361686Z",
     "iopub.status.idle": "2023-04-06T00:26:52.965699Z",
     "shell.execute_reply": "2023-04-06T00:26:52.964664Z",
     "shell.execute_reply.started": "2023-04-06T00:26:42.362021Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1093/1093 [==============================] - 6s 6ms/step\n"
     ]
    }
   ],
   "source": [
    "submission_label = np.argmax(model.predict(test_data), axis=1)\n",
    "submission_label = pd.Series(submission_label, name=\"Label\")\n",
    "\n",
    "image_id = pd.Series(range(1,len(test_data)+1))\n",
    "image_id = pd.Series(image_id, name=\"ImageId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T00:26:52.971117Z",
     "iopub.status.busy": "2023-04-06T00:26:52.970371Z",
     "iopub.status.idle": "2023-04-06T00:26:53.063968Z",
     "shell.execute_reply": "2023-04-06T00:26:53.062973Z",
     "shell.execute_reply.started": "2023-04-06T00:26:52.971078Z"
    }
   },
   "outputs": [
    {
     "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>ImageId</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ImageId  Label\n",
       "0        1      0\n",
       "1        2      0\n",
       "2        3      0\n",
       "3        4      0\n",
       "4        5      0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.concat([image_id,submission_label],axis = 1)\n",
    "submission.to_csv(\"submission.csv\", index=False)\n",
    "pd.read_csv(\"submission.csv\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### If you like my work, please don't forget to upvote. Thank you - `@tejasurya`"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}