{
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   "cell_type": "markdown",
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     "status": "completed"
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   "source": [
    "![](https://wallpaperaccess.com/full/803470.jpg)"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "# Soil-Type-Classification for Crops Suggestion 🌳🌲🎄🎋🌴\n",
    "<br>Problem Statement: Classifying the type of the Soil from Input Image.\n",
    "# Model deployed as Web-Application API at:🎯🔗📳  <br>\n",
    "https://soilnet.herokuapp.com/\n",
    "\n",
    "# GitHub Repo <br>\n",
    "https://github.com/OMIII1997/Soil-Type-Classification \n",
    "\n",
    "# Dataset available at: 📚📓🗞💾 <br>\n",
    "https://www.kaggle.com/omkargurav/soil-classification-image-data\n",
    "<br>\n",
    "\n",
    "\n",
    "For this project a deep learning model is trained with 903 images of four different types soil. \"Alluvial\", \"Black\", \"Clay\" , \"Red\". All images are collected from Google Search Engine  and crafted and filtered. \n",
    "<br>\n",
    "\n",
    "Based on the type of the Soil Crops will be suggested. Model is deployed on Heroku Platform\n",
    "\n",
    "# Acknoledgement: 🎓💡🏷🔖📌  <br>\n",
    "I am expressing my gratitude towards Sir Krish Naik for his super clear explanation about Neural Network in\n",
    " Deep Learning Playlist and Model Deployment Tutorial on YouTube.\n",
    "\n",
    "I am also thankful to Sir Akash Zade for his model deployment explanation at AI in Agri Playlist.\n",
    "\n",
    "\n",
    "# Click on Below Link for Project Demo: 😍👀🔍📺📡 <br>\n",
    "https://youtu.be/gnKmbgbPRJA\n",
    "\n",
    "# Sample:🖥🖨✔🖼📷 <br>\n",
    "![Image](https://soilnet.herokuapp.com/static/css/images/Clay_2.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow==2.0.0\r\n",
      "  Downloading tensorflow-2.0.0-cp37-cp37m-manylinux2010_x86_64.whl (86.3 MB)\r\n",
      "\u001b[K     |████████████████████████████████| 86.3 MB 50 kB/s \r\n",
      "\u001b[?25hCollecting tensorboard<2.1.0,>=2.0.0\r\n",
      "  Downloading tensorboard-2.0.2-py3-none-any.whl (3.8 MB)\r\n",
      "\u001b[K     |████████████████████████████████| 3.8 MB 50.9 MB/s \r\n",
      "\u001b[?25hRequirement already satisfied: absl-py>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow==2.0.0) (0.10.0)\r\n",
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      "Collecting gast==0.2.2\r\n",
      "  Downloading gast-0.2.2.tar.gz (10 kB)\r\n",
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      "  Downloading astor-0.8.1-py2.py3-none-any.whl (27 kB)\r\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2020.6.20)\r\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.24.3)\r\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.1.1)\r\n",
      "Requirement already satisfied: rsa<4.1,>=3.1.4 in /opt/conda/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (4.0)\r\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.2.7)\r\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.0.1)\r\n",
      "Requirement already satisfied: pyasn1>=0.1.3 in /opt/conda/lib/python3.7/site-packages (from rsa<4.1,>=3.1.4->google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.4.8)\r\n",
      "Building wheels for collected packages: gast\r\n",
      "  Building wheel for gast (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for gast: filename=gast-0.2.2-py3-none-any.whl size=7539 sha256=1d0f11427ddef58f989c239b1795ca5a5f4f34940f9183defba2d473542084ea\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/21/7f/02/420f32a803f7d0967b48dd823da3f558c5166991bfd204eef3\r\n",
      "Successfully built gast\r\n",
      "Installing collected packages: tensorboard, gast, astor, keras-applications, tensorflow-estimator, tensorflow\r\n",
      "  Attempting uninstall: tensorboard\r\n",
      "    Found existing installation: tensorboard 2.3.0\r\n",
      "    Uninstalling tensorboard-2.3.0:\r\n",
      "      Successfully uninstalled tensorboard-2.3.0\r\n",
      "  Attempting uninstall: gast\r\n",
      "    Found existing installation: gast 0.3.3\r\n",
      "    Uninstalling gast-0.3.3:\r\n",
      "      Successfully uninstalled gast-0.3.3\r\n",
      "  Attempting uninstall: tensorflow-estimator\r\n",
      "    Found existing installation: tensorflow-estimator 2.3.0\r\n",
      "    Uninstalling tensorflow-estimator-2.3.0:\r\n",
      "      Successfully uninstalled tensorflow-estimator-2.3.0\r\n",
      "  Attempting uninstall: tensorflow\r\n",
      "    Found existing installation: tensorflow 2.3.0\r\n",
      "    Uninstalling tensorflow-2.3.0:\r\n",
      "      Successfully uninstalled tensorflow-2.3.0\r\n",
      "\u001b[31mERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.\r\n",
      "\r\n",
      "We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\r\n",
      "\r\n",
      "tensorflow-probability 0.11.0 requires gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\r\n",
      "Successfully installed astor-0.8.1 gast-0.2.2 keras-applications-1.0.8 tensorboard-2.0.2 tensorflow-2.0.0 tensorflow-estimator-2.0.1\r\n",
      "\u001b[33mWARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.\r\n",
      "You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\r\n",
      "Collecting imutils\r\n",
      "  Downloading imutils-0.5.3.tar.gz (17 kB)\r\n",
      "Building wheels for collected packages: imutils\r\n",
      "  Building wheel for imutils (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for imutils: filename=imutils-0.5.3-py3-none-any.whl size=25850 sha256=d9e8df780eda97485481761e273fe0355875ea26fa228b7f61bedd2f8e6e7b64\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/fc/9c/6d/1826267c72afa51b564c9c6e0f66abc806879338bc593a2270\r\n",
      "Successfully built imutils\r\n",
      "Installing collected packages: imutils\r\n",
      "Successfully installed imutils-0.5.3\r\n",
      "\u001b[33mWARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.\r\n",
      "You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\r\n",
      "Collecting python-telegram-bot\r\n",
      "  Downloading python_telegram_bot-12.8-py2.py3-none-any.whl (375 kB)\r\n",
      "\u001b[K     |████████████████████████████████| 375 kB 2.8 MB/s \r\n",
      "\u001b[?25hRequirement already satisfied: decorator>=4.4.0 in /opt/conda/lib/python3.7/site-packages (from python-telegram-bot) (4.4.2)\r\n",
      "Requirement already satisfied: cryptography in /opt/conda/lib/python3.7/site-packages (from python-telegram-bot) (2.8)\r\n",
      "Requirement already satisfied: certifi in /opt/conda/lib/python3.7/site-packages (from python-telegram-bot) (2020.6.20)\r\n",
      "Collecting tornado>=5.1\r\n",
      "  Downloading tornado-6.0.4.tar.gz (496 kB)\r\n",
      "\u001b[K     |████████████████████████████████| 496 kB 12.3 MB/s \r\n",
      "\u001b[?25hRequirement already satisfied: cffi!=1.11.3,>=1.8 in /opt/conda/lib/python3.7/site-packages (from cryptography->python-telegram-bot) (1.14.0)\r\n",
      "Requirement already satisfied: six>=1.4.1 in /opt/conda/lib/python3.7/site-packages (from cryptography->python-telegram-bot) (1.14.0)\r\n",
      "Requirement already satisfied: pycparser in /opt/conda/lib/python3.7/site-packages (from cffi!=1.11.3,>=1.8->cryptography->python-telegram-bot) (2.20)\r\n",
      "Building wheels for collected packages: tornado\r\n",
      "  Building wheel for tornado (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \bdone\r\n",
      "\u001b[?25h  Created wheel for tornado: filename=tornado-6.0.4-cp37-cp37m-linux_x86_64.whl size=428639 sha256=f22cff20e0be88089c55e70987b682b7bee311511f159d76364ad38f941c72ca\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/7d/14/fa/d88fb5da77d813ea0ffca38a2ab2a052874e9e1142bad0b348\r\n",
      "Successfully built tornado\r\n",
      "Installing collected packages: tornado, python-telegram-bot\r\n",
      "  Attempting uninstall: tornado\r\n",
      "    Found existing installation: tornado 5.0.2\r\n",
      "    Uninstalling tornado-5.0.2:\r\n",
      "      Successfully uninstalled tornado-5.0.2\r\n",
      "\u001b[31mERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.\r\n",
      "\r\n",
      "We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\r\n",
      "\r\n",
      "jupyterlab-git 0.10.0 requires nbdime<2.0.0,>=1.1.0, but you'll have nbdime 2.0.0 which is incompatible.\r\n",
      "dask-xgboost 0.1.11 requires xgboost<=0.90, but you'll have xgboost 1.2.0 which is incompatible.\u001b[0m\r\n",
      "Successfully installed python-telegram-bot-12.8 tornado-6.0.4\r\n",
      "\u001b[33mWARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.\r\n",
      "You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow==2.0.0\n",
    "!pip install imutils\n",
    "!pip install python-telegram-bot #For training updates on Telegram\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras.applications import MobileNetV2\n",
    "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
    "from tensorflow.keras.layers import Dropout, MaxPooling2D, AveragePooling2D, Dense, Flatten, Input, Conv2D, add, Activation\n",
    "from tensorflow.keras.layers import (Dense, Dropout, Activation, Flatten, Reshape, Layer,\n",
    "                          BatchNormalization, LocallyConnected2D,\n",
    "                          ZeroPadding2D, Conv2D, MaxPooling2D, Conv2DTranspose,\n",
    "                          GaussianNoise, UpSampling2D, Input)\n",
    "\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.models import Sequential , Model , load_model\n",
    "from tensorflow.keras.preprocessing.image import load_img , img_to_array , ImageDataGenerator\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import cv2\n",
    "from imutils import paths\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "execution": {
     "iopub.execute_input": "2020-09-29T17:11:25.960434Z",
     "iopub.status.busy": "2020-09-29T17:11:25.959592Z",
     "iopub.status.idle": "2020-09-29T17:11:25.965117Z",
     "shell.execute_reply": "2020-09-29T17:11:25.965741Z"
    },
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     "end_time": "2020-09-29T17:11:25.965908",
     "exception": false,
     "start_time": "2020-09-29T17:11:25.876356",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version:  2.0.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Tensorflow version: \",tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.074112,
     "end_time": "2020-09-29T17:11:26.116897",
     "exception": false,
     "start_time": "2020-09-29T17:11:26.042785",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Creating class for Telegram Bot Message Updates along with Graph**\n",
    "\n",
    "For complete guidanc and example of making Telegram bot visit here:<br>\n",
    "https://github.com/OMIII1997/Telegram-Bot-for-Model-Training-Updates <br>\n",
    "\n",
    "Get access_token from Telegram app: \n",
    "* Open Telegram mobile app \n",
    "* Search for \"BotFather\" \n",
    "* Send \"/start\"\n",
    "* After reply from BotFather send \"/newbot\"\n",
    "* Give name to your Bot Eg: Example_Bot\n",
    "* Give username to your Bot Eg: My_Example_bot *Note: User name must end with '_bot'* \n",
    "* Done...Congratulations You have crated your own Telegram bot. Now you will get Token to access the HTTP API. Copy that Token Key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-29T17:11:26.299907Z",
     "iopub.status.busy": "2020-09-29T17:11:26.298934Z",
     "iopub.status.idle": "2020-09-29T17:11:26.301406Z",
     "shell.execute_reply": "2020-09-29T17:11:26.302009Z"
    },
    "papermill": {
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     "end_time": "2020-09-29T17:11:26.302167",
     "exception": false,
     "start_time": "2020-09-29T17:11:26.192950",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import tensorflow as tf\n",
    "\n",
    "import tensorflow.keras.utils as np_utils\n",
    "\n",
    "access_token = '' #Access token here\n",
    "\n",
    "class botCallback(tf.keras.callbacks.Callback):\n",
    "    def __init__(self,access_token):\n",
    "        self.access_token = access_token\n",
    "        self.ping_url = 'https://api.telegram.org/bot'+str(self.access_token)+'/getUpdates'\n",
    "        self.response = requests.get(self.ping_url).json()\n",
    "        #print(self.response)\n",
    "        self.chat_id = self.response['result'][0]['message']['chat']['id']\n",
    "        #self.chat_id = self.response['result']\n",
    "\n",
    "    def send_message(self,message):\n",
    "        #print('sending message')\n",
    "        self.ping_url = 'https://api.telegram.org/bot'+str(self.access_token)+'/sendMessage?'+\\\n",
    "                        'chat_id='+str(self.chat_id)+\\\n",
    "                        '&parse_mode=Markdown'+\\\n",
    "                        '&text='+message\n",
    "        self.response = requests.get(self.ping_url)\n",
    "    \n",
    "    def send_photo(self,filepath):\n",
    "        imagefile= open(filepath,\"rb\")\n",
    "        file_dict = {'photo':imagefile}\n",
    "        self.ping_url = 'https://api.telegram.org/bot'+str(self.access_token)+'/sendPhoto?chat_id='+str(self.chat_id)\n",
    "        self.response = requests.post(self.ping_url, files = file_dict)\n",
    "        imagefile.close()\n",
    "\n",
    "    def on_train_batch_begin(self, batch, logs=None):\n",
    "        pass\n",
    "    \n",
    "    def on_train_batch_end(self, batch, logs=None):\n",
    "        message = ' Iteration/Batch {}\\n Training Accuracy : {:7.2f}\\n Training Loss : {:7.2f}\\n'.format(batch,logs['accuracy'],logs['loss'])\n",
    "        #print(logs)\n",
    "        try:\n",
    "            message += ' Validation Accuracy : {:7.2f}\\n Validation Loss : {:7.2f}\\n'.format(logs['val_accuracy'],logs['val_loss'])\n",
    "            self.send_message(message)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "    def on_test_batch_begin(self, batch, logs=None):\n",
    "        pass\n",
    "    \n",
    "    def on_test_batch_end(self, batch, logs=None):\n",
    "        message = ' Iteration/Batch {}\\n Training Accuracy : {:7.2f}\\n Training Loss : {:7.2f}\\n'.format(batch,logs['accuracy'],logs['loss'])\n",
    "        try:\n",
    "            message += ' Validation Accuracy : {:7.2f}\\n Validation Loss : {:7.2f}\\n'.format(logs['val_accuracy'],logs['val_loss'])\n",
    "            self.send_message(message)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "    def on_epoch_begin(self, epoch, logs=None):\n",
    "        pass\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "\n",
    "        message = ' Epoch {}\\n Training Accuracy : {:7.2f}\\n Training Loss : {:7.2f}\\n'.format(epoch,logs['accuracy'],logs['loss'])\n",
    "        try:\n",
    "            message += ' Validation Accuracy : {:7.2f}\\n Validation Loss : {:7.2f}\\n'.format(logs['val_accuracy'],logs['val_loss'])\n",
    "            self.send_message(message)        \n",
    "        except:\n",
    "            pass\n",
    "\n",
    "class Plotter(botCallback):\n",
    " def __init__(self,access_token):\n",
    "    \n",
    "     super().__init__(access_token)\n",
    " def on_train_begin(self,logs=None):\n",
    "     self.batch = 0\n",
    "     self.epoch = []\n",
    "     self.train_loss = []\n",
    "     self.val_loss = []\n",
    "     self.train_acc = []\n",
    "     self.val_acc = []\n",
    "     self.fig = plt.figure(figsize=(200,100))\n",
    "     self.logs = []\n",
    "\n",
    " def on_epoch_end(self, epoch, logs=None):\n",
    "     self.logs.append(logs)\n",
    "     self.epoch.append(epoch)\n",
    "     self.train_loss.append(logs['loss'])\n",
    "     self.val_loss.append(logs['val_loss'])\n",
    "     self.train_acc.append(logs['accuracy'])\n",
    "     self.val_acc.append(logs['val_accuracy'])\n",
    "     f,(ax1,ax2) = plt.subplots(1,2,sharex=True)\n",
    "     #clear_output(wait=True)\n",
    "     ax1.plot(self.epoch, self.train_loss, label='Training Loss')\n",
    "     ax1.plot(self.epoch, self.val_loss, label='Validation Loss')\n",
    "     ax1.legend()\n",
    "     ax2.plot(self.epoch, self.train_acc, label='Training Accuracy')\n",
    "     ax2.plot(self.epoch, self.val_acc, label='Validation Accuracy')\n",
    "     ax2.legend()\n",
    "     plt.savefig('Accuracy and Loss plot.jpg')\n",
    "     self.send_photo('Accuracy and Loss plot.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-29T17:11:26.464215Z",
     "iopub.status.busy": "2020-09-29T17:11:26.463241Z",
     "iopub.status.idle": "2020-09-29T17:11:26.466428Z",
     "shell.execute_reply": "2020-09-29T17:11:26.465925Z"
    },
    "papermill": {
     "duration": 0.084621,
     "end_time": "2020-09-29T17:11:26.466535",
     "exception": false,
     "start_time": "2020-09-29T17:11:26.381914",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "train_dir = '/kaggle/input/soil-classification-image-data/Soil_Dataset/Train'\n",
    "test_dir = '/kaggle/input/soil-classification-image-data/Soil_Dataset/Test'\n",
    "\n",
    "image_size = 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-29T17:11:26.626716Z",
     "iopub.status.busy": "2020-09-29T17:11:26.626063Z",
     "iopub.status.idle": "2020-09-29T17:11:26.745847Z",
     "shell.execute_reply": "2020-09-29T17:11:26.745284Z"
    },
    "papermill": {
     "duration": 0.204086,
     "end_time": "2020-09-29T17:11:26.745959",
     "exception": false,
     "start_time": "2020-09-29T17:11:26.541873",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 715 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                            rotation_range=45,\n",
    "                            zoom_range=0.40,\n",
    "                            width_shift_range=0.2,\n",
    "                            height_shift_range=0.2,\n",
    "                            shear_range=0.15,\n",
    "                            horizontal_flip=True,\n",
    "                            vertical_flip= True,\n",
    "                            fill_mode=\"nearest\")\n",
    "\n",
    "train_data = train_datagen.flow_from_directory(train_dir,\n",
    "                                              target_size=(150,150),\n",
    "                                              batch_size=32,\n",
    "                                              class_mode=\"categorical\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-29T17:11:26.924273Z",
     "iopub.status.busy": "2020-09-29T17:11:26.922329Z",
     "iopub.status.idle": "2020-09-29T17:11:27.037434Z",
     "shell.execute_reply": "2020-09-29T17:11:27.037904Z"
    },
    "papermill": {
     "duration": 0.204345,
     "end_time": "2020-09-29T17:11:27.038044",
     "exception": false,
     "start_time": "2020-09-29T17:11:26.833699",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 188 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "test_data = test_datagen.flow_from_directory(test_dir,\n",
    "                                            target_size=(150,150),\n",
    "                                            batch_size=32,\n",
    "                                            class_mode=\"categorical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-29T17:11:27.211649Z",
     "iopub.status.busy": "2020-09-29T17:11:27.210629Z",
     "iopub.status.idle": "2020-09-29T17:11:28.303223Z",
     "shell.execute_reply": "2020-09-29T17:11:28.303723Z"
    },
    "papermill": {
     "duration": 1.189673,
     "end_time": "2020-09-29T17:11:28.303898",
     "exception": false,
     "start_time": "2020-09-29T17:11:27.114225",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"SoilNet\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 150, 150, 32)      896       \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 150, 150, 32)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 50, 50, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 50, 50, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 50, 50, 64)        32832     \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 50, 50, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 50, 50, 64)        65600     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 50, 50, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 25, 25, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 25, 25, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 25, 25, 128)       131200    \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 25, 25, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 25, 25, 128)       262272    \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 25, 25, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 18432)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              18875392  \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 4100      \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 4)                 0         \n",
      "=================================================================\n",
      "Total params: 19,376,388\n",
      "Trainable params: 19,374,340\n",
      "Non-trainable params: 2,048\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#=================================================================\n",
    "chanDim = 1\n",
    "model = Sequential(name=\"SoilNet\")\n",
    "model.add(Conv2D(32, (3, 3), padding=\"same\",input_shape=(150,150,3)))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(64, (4, 4), padding=\"same\"))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add(Conv2D(64, (4, 4), padding=\"same\"))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(128, (4, 4), padding=\"same\"))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add(Conv2D(128, (4, 4), padding=\"same\"))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1024))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(4))\n",
    "model.add(Activation(\"softmax\"))\n",
    "\n",
    "\n",
    "model.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
    "reduction_lr = ReduceLROnPlateau(monitor = \"val_accuracy\",patience = 2 ,verbose = 1, factor = 0.2, min_lr = 0.00001)\n",
    "model.summary()\n",
    "plot_model(model,show_shapes=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-29T17:11:28.500252Z",
     "iopub.status.busy": "2020-09-29T17:11:28.499356Z",
     "iopub.status.idle": "2020-09-29T17:11:28.502531Z",
     "shell.execute_reply": "2020-09-29T17:11:28.501987Z"
    },
    "papermill": {
     "duration": 0.119525,
     "end_time": "2020-09-29T17:11:28.502632",
     "exception": false,
     "start_time": "2020-09-29T17:11:28.383107",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "callback_list = [reduction_lr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-29T17:11:28.721691Z",
     "iopub.status.busy": "2020-09-29T17:11:28.720379Z",
     "iopub.status.idle": "2020-09-29T17:50:38.344386Z",
     "shell.execute_reply": "2020-09-29T17:50:38.343457Z"
    },
    "papermill": {
     "duration": 2349.761742,
     "end_time": "2020-09-29T17:50:38.344572",
     "exception": false,
     "start_time": "2020-09-29T17:11:28.582830",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "30/30 [==============================] - 127s 4s/step - loss: 1.6986 - accuracy: 0.6635 - val_loss: 10.5414 - val_accuracy: 0.3138\n",
      "Epoch 2/20\n",
      "30/30 [==============================] - 114s 4s/step - loss: 0.9081 - accuracy: 0.7941 - val_loss: 4.6568 - val_accuracy: 0.5266\n",
      "Epoch 3/20\n",
      "30/30 [==============================] - 112s 4s/step - loss: 0.5484 - accuracy: 0.7955 - val_loss: 0.8508 - val_accuracy: 0.7553\n",
      "Epoch 4/20\n",
      "30/30 [==============================] - 110s 4s/step - loss: 0.4851 - accuracy: 0.8211 - val_loss: 3.1642 - val_accuracy: 0.4840\n",
      "Epoch 5/20\n",
      "29/30 [============================>.] - ETA: 2s - loss: 0.4300 - accuracy: 0.8363\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "30/30 [==============================] - 107s 4s/step - loss: 0.4273 - accuracy: 0.8366 - val_loss: 0.6723 - val_accuracy: 0.7500\n",
      "Epoch 6/20\n",
      "30/30 [==============================] - 110s 4s/step - loss: 0.3249 - accuracy: 0.8786 - val_loss: 0.5604 - val_accuracy: 0.7926\n",
      "Epoch 7/20\n",
      "30/30 [==============================] - 113s 4s/step - loss: 0.2949 - accuracy: 0.8903 - val_loss: 0.5716 - val_accuracy: 0.8032\n",
      "Epoch 8/20\n",
      "30/30 [==============================] - 105s 4s/step - loss: 0.2673 - accuracy: 0.9052 - val_loss: 0.6583 - val_accuracy: 0.7713\n",
      "Epoch 9/20\n",
      "30/30 [==============================] - 108s 4s/step - loss: 0.2655 - accuracy: 0.9137 - val_loss: 0.4773 - val_accuracy: 0.8511\n",
      "Epoch 10/20\n",
      "30/30 [==============================] - 108s 4s/step - loss: 0.2251 - accuracy: 0.9116 - val_loss: 0.5565 - val_accuracy: 0.7926\n",
      "Epoch 11/20\n",
      "29/30 [============================>.] - ETA: 2s - loss: 0.2377 - accuracy: 0.9063\n",
      "Epoch 00011: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "30/30 [==============================] - 111s 4s/step - loss: 0.2421 - accuracy: 0.9052 - val_loss: 0.5537 - val_accuracy: 0.8085\n",
      "Epoch 12/20\n",
      "30/30 [==============================] - 110s 4s/step - loss: 0.2089 - accuracy: 0.9041 - val_loss: 0.4529 - val_accuracy: 0.8564\n",
      "Epoch 13/20\n",
      "30/30 [==============================] - 112s 4s/step - loss: 0.2337 - accuracy: 0.9159 - val_loss: 0.4746 - val_accuracy: 0.8777\n",
      "Epoch 14/20\n",
      "30/30 [==============================] - 117s 4s/step - loss: 0.2696 - accuracy: 0.9085 - val_loss: 0.3749 - val_accuracy: 0.8511\n",
      "Epoch 15/20\n",
      "29/30 [============================>.] - ETA: 3s - loss: 0.2159 - accuracy: 0.9343\n",
      "Epoch 00015: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "30/30 [==============================] - 119s 4s/step - loss: 0.2179 - accuracy: 0.9333 - val_loss: 0.4204 - val_accuracy: 0.8670\n",
      "Epoch 16/20\n",
      "30/30 [==============================] - 129s 4s/step - loss: 0.2311 - accuracy: 0.9212 - val_loss: 0.4488 - val_accuracy: 0.8670\n",
      "Epoch 17/20\n",
      "30/30 [==============================] - 135s 5s/step - loss: 0.2141 - accuracy: 0.9270 - val_loss: 0.4064 - val_accuracy: 0.8777\n",
      "Epoch 18/20\n",
      "30/30 [==============================] - 128s 4s/step - loss: 0.1759 - accuracy: 0.9412 - val_loss: 0.4084 - val_accuracy: 0.8830\n",
      "Epoch 19/20\n",
      "30/30 [==============================] - 133s 4s/step - loss: 0.2152 - accuracy: 0.9292 - val_loss: 0.4224 - val_accuracy: 0.8723\n",
      "Epoch 20/20\n",
      "30/30 [==============================] - 141s 5s/step - loss: 0.1928 - accuracy: 0.9325 - val_loss: 0.4349 - val_accuracy: 0.8723\n",
      "Total train time:  39.15497325261434  mins\n"
     ]
    }
   ],
   "source": [
    "#bot_callback = botCallback(access_token)\n",
    "#plotter = Plotter(access_token)\n",
    "#callback_list = [bot_callback,plotter] callbacks=callback_list\n",
    "start = time.time()\n",
    "\n",
    "history = model.fit_generator(train_data,\n",
    "                    steps_per_epoch = 30,\n",
    "                    validation_data = test_data,\n",
    "                    validation_steps = 30,\n",
    "                    epochs=20,\n",
    "                    callbacks = callback_list)\n",
    "end = time.time()\n",
    "print(\"Total train time: \",(end-start)/60,\" mins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-29T17:50:38.975288Z",
     "iopub.status.busy": "2020-09-29T17:50:38.973867Z",
     "iopub.status.idle": "2020-09-29T17:50:39.416650Z",
     "shell.execute_reply": "2020-09-29T17:50:39.417236Z"
    },
    "papermill": {
     "duration": 0.76281,
     "end_time": "2020-09-29T17:50:39.417403",
     "exception": false,
     "start_time": "2020-09-29T17:50:38.654593",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cAABAGSA0FxJt5wAAAMoCobmQ0m3n6KABAABQ0gjNhZRuO0evZgAAgJJGaC6kTNs5RpoBAABKGaG50JoWEJoBAABKHKG50Gg7BwAAUPIIzYVG2zkAAICSR2gutEwHDU4GBAAAKFWE5kLL9GpmXjMAAECpIjQXWkOMtnMAAAAljtBcaOEIbecAAABKHKG5GGg7BwAAUNIIzcVA2zkAAICSRmguBtrOAQAAlDRCczFk2s4xRQMAAKAUEZqLIdN2jg4aAAAApYjQXAzptnOMNAMAAJQkQnMxpNvO0asZAACgJBGaiyXdQQMAAAAlh9BcLOlezbSdAwAAKDmE5mKh7RwAAEDJIjQXC23nAAAAShahuVgyoZmTAQEAAEoNoblYprTQdg4AAKBEEZqLhbZzAAAAJYvQXEyNrdKBLX5XAQAAgHEiNBcTbecAAABKEqG5mBpbpYFj0tE9flcCAACAcSA0F1O6gwbzmgEAAEoKobmY6NUMAABQkgjNxZRpO8dIMwAAQCkhNBdTOCJNm8v0DAAAgBJDaC62xgW0nQMAACgxhOZia2yl7RwAAECJITQXW9MC2s4BAACUGEJzsTXO95bMawYAACgZhOZia1zgLWk7BwAAUDIIzcVG2zkAAICSQ2gutnTbOUaaAQAASgah2Q+NC6T9hGYAAIBSQWj2A23nAAAASgqh2Q+0nQMAACgphGY/0HYOAACgpBCa/UDbOQAAgJJCaPbDlBYpFKHtHAAAQIkgNPshHJGmzWOkGQAAoEQQmv3S2ErbOQAAgBJBaPZL4wLazgEAAJQIQrNfaDsHAABQMgjNfkm3nWNeMwAAQOD5EprNbKqZ3W9mz5vZJjO71I86fJVuO0evZgAAgMCL+PS+/yjpYefce8ysStIkn+rwT6btHCPNAAAAQVf00GxmDZKWS/qwJDnn+iX1F7sO32XazjHSDAAAEHR+TM9oldQt6dtm9nsz+1czm+xDHf5rbGWkGQAAoAT4EZojki6S9A3n3IWSjkm6dfhOZvYxM+s0s87u7u5i11gcjQu8Xs20nQMAAAg0P0Jzl6Qu59zTqfv3ywvRQzjnbnfOtTvn2pubm4taYNE0ttJ2DgAAoAQUPTQ753ZL2m5m56Y2vUnSc8WuIxCaWr0lUzQAAAACza8+zZ+SdI+ZrZN0gaT/41Md/mpMhWbazgEAAASaLy3nnHNrJLX78d6BMuVM2s4BAACUAK4I6KdwRJo6l7ZzAAAAAUdo9lvTAkaaAQAAAo7Q7DfazgEAAAQeodlvmbZze/2uBAAAAKMgNPst03aOec0AAABBRWj2WyO9mgEAAIKO0Oy3dNs5ejUDAAAEFqHZb7SdAwAACDxCcxDQdg4AACDQCM1B0NhK2zkAAIAAIzQHQeMC2s4BAAAEGKE5CBppOwcAABBkYwrNZvYZM2swzx1m9qyZvbXQxVWMJtrOAQAABNlYR5o/4pw7Iumtkpol3SDpKwWrqtLQdg4AACDQxhqaLbV8h6RvO+fWZm3DRGXazjHSDAAAEERjDc2rzewReaH552ZWLylZuLIqUNMC5jQDAAAEVGSM+90o6QJJm51zx82sUd4UDeRLY6v06n95beeMQXwAAIAgGetI86WSXnDOHTKzD0j635IOF66sCtS4QOo/Sts5AACAABpraP6GpONmtlTSn0l6VdLdBauqEjXSQQMAACCoxhqa4845J+lqSf/onPtHSfWFK6sCNdGrGQAAIKjGOqe5x8w+J+l6SZeZWVhStHBlVSDazgEAAATWWEear5PUJ69f825JcyR9rWBVVSLazgEAAATWmEJzKijfI2mKmV0pqdc5x5zmfGtsZXoGAABAAI31MtrXSvqdpD+SdK2kp83sPYUsrCI1LZAObPHazgEAACAwxjqn+c8ldTjn9kqSmTVL+qWk+wtVWEVqbB1sO1c/w+9qAAAAkDLWOc2hdGBO2T+O52KsGhd4S+Y1AwAABMpYR5ofNrOfS/p+6v51kh4qTEkVLLvt3NxL/a0FAAAAGWMKzc65m83s3ZJeK8kk3e6c+2FBK6tE6bZzjDQDAAAEylhHmuWc+4GkHxSwFqTbztGrGQAAIFBOGZrNrEfSSK0cTJJzzjUUpKpK1tjKSDMAAEDAnDI0O+e4VHaxNS2Qtj3ptZ0z87saAAAAiA4YwZNuO3es2+9KAAAAkEJoDpp02znmNQMAAAQGoTloGud7Sy6nDQAAEBiE5qCZOpe2cwAAAAFDaA6acESaeibTMwAAAAKE0BxEjQsYaQYAAAgQQnMQpXs1u5FaZAMAAKDYCM1B1LSAtnMAAAABQmgOItrOAQAABAqhOYgybeeY1wwAABAEhOYgyrSdY6QZAAAgCAjNQZRuO8dIMwAAQCAQmoOqcQFzmgEAAAKC0BxUtJ0DAAAIDEJzUNF2DgAAIDAIzUF1xjnecvd6f+sAAAAAoTmw5lwsyaSuTr8rAQAAqHiE5qCqaZCmL5K6nvG7EgAAgIpHaA6yWLsXmpNJvysBAACoaITmIIstk3oPcZETAAAAnxGagyzW4S23/87fOgAAACocoTnIzjhHqp7CvGYAAACfEZqDLBSSYhcTmgEAAHxGaA662DJp73NSX4/flQAAAFQsQnPQxTokl5R2POt3JQAAABWL0Bx0sYu9JVM0AAAAfENoDrraad4JgYRmAAAA3xCaS0FsmReanfO7EgAAgIpEaC4FsXbp+H7pwGa/KwEAAKhIhOZS0LLMW3Z1+lsHAABAhSI0l4LmhVJVvdTFlQEBAAD8QGguBaGwNOciTgYEAADwCaG5VMQ6pN0bpP5jflcCAABQcQjNpaJlmeQS0s41flcCAABQcQjNpWJOu7dkXjMAAEDREZpLxeQmqXEBHTQAAAB84FtoNrOwmf3ezH7iVw0lJ9Yhbf8dFzkBAAAoMj9Hmj8jaZOP7196WjqkY3ulQ9v8rgQAAKCi+BKazSwm6Q8l/asf71+yYh3ektZzAAAAReXXSPM/SPozSUmf3r80TW+TopMIzQAAAEVW9NBsZldK2uucW51jv4+ZWaeZdXZ3dxepuoALR6TZF3nzmgEAAFA0fow0v1bSVWa2VdK9kt5oZt8dvpNz7nbnXLtzrr25ubnYNQZXS4e0e500cMLvSgAAACpG0UOzc+5zzrmYc26epJWSfuWc+0Cx6yhZsQ4pGZd2rfW7EgAAgIpBn+ZSw8mAAAAARRfx882dc7+R9Bs/ayg5ddOlqXOZ1wwAAFBEjDSXopZlXBkQAACgiAjNpSjWIfXslA53+V0JAABARSA0lyLmNQMAABQVobkUzVgsRWqk7YRmAACAYiA0l6JIlTT7QkaaAQAAioTQXKpi7dKuNVK8z+9KAAAAyh6huVTFlkmJfmn3er8rAQAAKHuE5lKVPhmQfs0AAAAFR2guVQ2zpCktzGsGAAAoAkJzKYu1E5oBAACKgNBcymLLpMPbpSO7/K4EAACgrBGaSxkXOQEAACgKQnMpm3W+FK4iNAMAABQYobmURaqlWUsJzQAAAAVGaC51sWXSzt9LiQG/KwEAAChbhOZSF2uX4r1c5AQAAKCACM2lrmWZt+zq9LcOAACAMkZoLnUNc6T6WVIXVwYEAAAoFEJzqTPzWs9xMiAAAEDBEJrLQaxDOrhVOtrtdyUAAABlidBcDrjICQAAQEERmsvB7AukUIR5zQAAAAVCaC4H0Vpp5hI6aAAAABQIoblcxJZJO1ZLibjflQAAAJQdQnO5iHVIA8elvc/5XQkAAEDZITSXi5b0yYDMawYAAMg3QnO5mDpXmtxcPvOaTxyUfnaLtwQAAPAZoblcmHnzmreXyUjzs/8mPf1Nae29flcCAABAaC4rsXbpwCvS8QN+VzJx61Z5y40/9LcOAAAAEZrLS8syb1nqFznZvUHas0FqXCBtf1o63OV3RQAAoMIRmsvJ7AslC5d+aF53r3exlhXf8u5v/JG/9QAAgIpHaC4nVZOlGW2lPa85mZDW3y+d9RavI8ispUzRAAAAviM0l5tYh7TjWS98lqItv5V6dklLr/Put62QdnRKB1/1ty4AAFDRCM3lpmWZ1N8jdT/vdyWnZ+0qqXqKdM7bvfttK7zlcz/2ryYAAFDxCM3lJpa+yEkJzmvuPyZt+g+p7WopWuNtmzZPmn2RtPEBX0sDAACVjdBcbhpbpdpGaXsJhuZNP5EGjknnrxy6vW2FtPP30oEt/tQFAAAqHqG53Jh5o82lONK87l5pypnSmZcO3d72Lm/5HF00AACAPwjN5ailQ9r3Qmldgrpnt7T5N9L510qhYR/LqWd6XwQ2MEUDAAD4g9BcjtLzmnes9reO8Vj/75JLSktXjvx42wpp9zpp/yvFrQsAAECE5vI052JJVlrzmtet8k74O+PskR8/72pvSc9mAADgA0JzOaqul6afVzrzmvc8J+1eP/oosyRNiUktl3B1QAAA4AtCc7lq6ZC6OqVk0u9KcktfNnvxu0+9X9sKac96ad9LxakLAAAghdBcrmIdUt9haX/AA2YyIa37d+msN0uTzzj1vuddJcmYogEAAIqO0FyuYsu85fbf+VtHLlsfl3p2el0zcmmY7bWjIzQDAIAiIzSXq6azpJopwZ/XvHaVVN0gnfuOse2/+Bpp73PS3hK9TDgAAChJhOZyFQoF/yIn/celTQ960y6itWN7ziKmaAAAgOIjNJezWIe0d5PUe8TvSkb2/E+l/qMnXzb7VOpnSPNe54Vm5wpXGwAAQBZCczmLdUhywb3Iybp7pSkt0tzXju95bSu8Kx7ufa4wdQEAAAxDaC5ncy72ll2d/tYxkp490iu/kpb80cmXzc5l0VWShZiiAQAAiobQXM5qp0rNC6WuAHbQ2HD/qS+bfSp1zdK8y5iiAQAAiobQXO5i7d7JgEELl2vvlWZdIDWfe3rPb1sh7X9Z2rMhv3UBAACMgNBc7mLLpBMHpf2v+F3JoL2bpN3rTm+UOW3RVZKFpQ0P5K8uAACAURCay12sw1sGqfXc2nu9wLv4Paf/GpObpNbLmaIBAACKgtBc7poXehcPCcq85mRSWv/v0llv8uYmT0TbCungFmnX2vzUBgAAMApCc7kLhaQ5FwVnpHnr49KRHdL51038tRZeKYUi0kamaAAAgMIiNFeC2DJpz0ap76jflUjrVklV9dLCP5z4a01qlFrfwBQNAABQcITmShDr8Nq77fy9v3X0H5eee1A67+qxXzY7l7YV0qFt0s5n8/N6AAAAIyA0V4JYu7f0e17zCw9J/T3S0jxMzUhb+A4pFOVCJwAAoKAIzZVgUqPUdJb/VwZct0pqiElzX5e/16yd5p1UuPFHTNEAAAAFQ2iuFLFl0vbf+Rcsj+6VXn5UOv80LpudS9sK6fB2/78UAACAskVorhSxdun4PungVn/ef8MPJJeQzp/ABU1Gc+7bpXAVUzQAAEDBEJorRcsyb+lX67m190qzlkrTF+b/tWumSGe9xQvNyWT+Xx8AAFQ8QnOlaF4kRSf7E5q7X5B2rclPb+bRtK2Qenb6f7IjAAAoS4TmShGOeBc52e5DqFx7r2ShiV02O5dzr5DC1UzRAAAABUForiSxDmnPBq9fcrGkL5u94I1S/YzCvU91vXT2W7wuGslE4d4HAABUJEJzJWlZJiXj3lSJYnn1P73OFoU4AXC4xddIR3dL254q/HsBAICKQmiuJHPSFzkp4rzmdfdKVXX5uWx2Lme/TYrUMkUDAADkHaG5ktQ1S9PmF29e88AJ77LZi66SqiYV/v2q66Rz3io992OmaAAAgLwqemg2sxYz+7WZbTKzjWb2mWLXUNFiHd5IczEucvLCQ1LfkfxeNjuXtmukY3u9aSEAAAB54sdIc1zS/3TOLZJ0iaT/bmbn+VBHZWpZJh3d480zLrS1q6T62dK8ywr/Xmlnv1WKTmKKBgAAyKuih2bn3C7n3LOp9R5JmyTNKXYdFStWpHnNR7ull3+Zumx2uLDvla1qknTOFd60kES8eO8LAADKmq9zms1snqQLJT09wmMfM7NOM+vs7u4udmnla8Zi72S57QUOzYW8bHYubSu8S4Zvfbz47w0AAMqSb6HZzOok/UDSZ51zR4Y/7py73TnX7pxrb25uLn6B5SoclWZfWPiR5jzki+sAACAASURBVHX3SjOXSDN8mHlz9lu8jh1M0QAAAHniS2g2s6i8wHyPc+4BP2qoaC0d0q610kBvYV6/+0Vp5+/9GWWWpGitdO7bpU3/ISUG/KkBAACUFT+6Z5ikOyRtcs59vdjvD3kdNJID0u51hXn9dau8y2YvKeBls3NpWyGdOCBtecy/GgAAQNnwY6T5tZKul/RGM1uTur3DhzoqV6zDWxaiX3MyKa27T2p9g1Q/M/+vP1YL3iRVN0gb+UMGAACYOD+6ZzzhnDPn3PnOuQtSt4eKXUdFq58pTTmzMPOatz0pHd4mLfVpakZatEY69x3Spp9I8X5/awEAACWPKwJWqpaOwoTmdfdK0cnFuWx2Lm0rpN5D0pbf+l0JAAAocYTmShXrkI7skA7vyN9rDvRKG38snXeVVDU5f697uha8QaqeIm1gigYAAJgYQnOlii3zlvkcbX7xZ1LfYen8Il42+1Qi1dKiK6XnfyrF+/yuBgAAlDBC8yicc3pxT4/fZRTOzCVSuDq/oXntKql+ljR/ef5ec6LaVnhB/pVf+10JAAAoYYTmUdzz9Da94x8f18/W7/K7lMKIVEmzL8hfaD62T3r5F9KSIl82O5f5l0s1U+miAQAAJoTQPIqrL5itpS1T9Sff/71+vCaP836DJNYh7VyTn+4SGx6QkvHgTM1Ii1Slpmg8VLiLuQAAgLJHaB5FfU1Ud39kmS6eO003rVqj+1d3+V1S/sU6pESftHv9xF9r3b3SjMXSzMUTf618a7tG6u+RXnnU70oAAECJIjSfwuTqiO66oUOXLmjSzfev1b2/2+Z3SfmVvsjJRKdo7HtJ2rE6eKPMafOXS7WN0sYf+l0JAAAoUYTmHCZVRXTHhzq0/Oxm3frAev3bk1v9Lil/psyRGuZIXRO8MmDmstl/lJ+68i0clRa9U3rhZ9LACb+rAQAAJYjQPAY10bBu/+DFevOiGfr8jzfqjie2+F1S/sTaJzbSnEx6oXn+5VLDrPzVlW+Lr5H6j0ov/cLvSgAAQAkiNI9RdSSs//f+i/T2xTP1pZ88p2/85hW/S8qP2DLp0DapZ8/pPX/7U97z/b5sdi5zXydNOoMpGgAA4LQQmsehKhLSP7/3Qr1z6Wz9zcPP658efcnvkiZuovOa194rRSdJC6/MX02FEI54Vyp88WGp/5jf1QAAgBJDaB6nSDikf7juAl1z0Rx9/Rcv6u8eeUHOOb/LOn2zlkqh6OnNax7olTb+yJsvXF2X/9ryrW2FNHBceukRvysBAAAlJuJ3AaUoHDL97XuWqioc0j//6mX1x5O69e0LZWZ+lzZ+0Rpp1vlSV+f4n/viw8G6bHYuc18rTZ7uTdFoW+F3NQAAoIQQmk9TKGT6PyuWKBoO6VuPbVZ/IqkvXHleaQbn2DJp9V1SYsDrNDFW6+6T6mZKra8vUGF5FgpL510t/f67Ut/R0hgdBwAAgcD0jAkIhUx/eXWbPvLa+fr2f27V53+8QclkCU7ViLVL8RPSno1jf87xA940hyXvCdZls3NpW+H9rC8+7HclAACghBCaJ8jM9PkrF+njly/Qd5/aps89sF6JUgvOLcu85XhOBtzwAyk5EPyuGcOdeak3Ok4XDQAAMA6E5jwwM91yxbn69JvO1qrO7br539cqnkj6XdbYTWmR6maMLzSvWyVNb5NmLilcXYUQCklt7/L6Nff1+F0NAAAoEYTmPDEz/Y+3nKM/fes5euD3O/TZVWs0UCrB2cxrPbd9jB009r/iBeylJXIC4HBtK6REn3eFQAAAgDEgNOfZn7zxbH3u7Qv1k3W79Knv/V798RIJzrEO6eAW6di+3PuuWyXJgnvZ7Fxiy6T62UzRAAAAY0ZoLoA/vnyBvnDleXp442598p7V6osn/C4pt7Fe5MQ5LzS3Xi41zC58XYUQCnmjzS//Uuo97Hc1AACgBBCaC+Qjr5uvL79rsX65aa8+evdq9Q4EPDjPvlCycO7QvP1p6eBW6fwSOwFwuLYVUqJfev4hvysBAAAlgNBcQB+4ZK6++u7z9fhL3frIXc/oeH/c75JGVzVJmrk497zm9GWzF72zOHUVSqzdOwGSKRoAAGAMCM0Fdm1Hi75+7VI9tXm/PnznMzraF+DgHFsm7XhWSo4yKh7v80LmwitL/8IgZl4XjVd+JZ046Hc1AAAg4AjNRbDiwpj+ceWFWr3toD54x9M60jvgd0kji3VIA8ekvc+N/PiLP5d6D5XOZbNzaVvh9ZpmigYAAMiB0Fwk71w6W7e970Kt33FY1//r0zp8PIDBuSXHyYDrVkmTp5fOZbNzmX2RNPVMaeMDflcCAAACjtBcRFcsnqVvfuBibdrVo/f+y1M6cKzf75KGmjZfmtQkbR8hNB8/4I00L/kjKRwpfm2FYOaNNm/+jffzAQAAjILQXGRvWjRD//Khdr3SfVTvvf0pdff0+V3SIDNvXvNII80bH0hdNrtMpmaktV0jJePS8z/xuxIAABBghGYfXH5Os7794Q5tO3BcK29/UnuP9Ppd0qBYu7T/pZNHXteukpoXSTPP96euQpm11Bth38AUDQAAMDpCs09ec9YZuuuGDu0+3Kvrbn9Kuw6f8LskT8syb7lj9eC2/a9IXb/zRpnN/KmrUNJTNLY8NrarIQIAgIpEaPbRH7Q26e4b/0D7evp07bee1PYDx/0uyTs5zkJD+zWvu0/eZbOv9a2sglp8jeQS0qb/8LsSAAAQUIRmn108d5q++9/+QIePD2jl7U/p1f3H/C2ouk6a3jY4rzl92ez5l0lT5vhbW6HMWCw1nUUXDQAAMCpCcwAsbZmq7330Eh3vj+vabz2pV7qP+ltQrN2bnpFMeuH54JbSv2z2qaSnaGx9Qjq61+9qAABAABGaA2LxnCn6/scuUSLpdN23ntJLe3r8K6ZlmdR3RNr3gnfZ7EitdN5V/tVTDG0rJJeUNj3odyUAACCACM0BsnBmg+792CUKmbTy9qe0adcRfwqJpS5ysvUJb8rCwj+Uquv9qaVYpp8nnXGutPFHflcCAAACiNAcMGdNr9eqP75UVZGQrvvWk7rt1y/rYLEvgtJ0llQzVXriH6QTB6WlZTw1Iy17ikbPbr+rAQAAAUNoDqD5Z0zWfX98qZa2TNXXfv6CLv3Ko/pfP1yvl/cWaa6zmTfafKQrddnsNxTnff3WtkKSk55jigYAABiK0BxQLY2T9G83/oEeuWm53nXBHN2/uktv/vpv9eFv/06Pv9Qt51yBC0j1a17ynvK5bHYu0xd60zQ2/tDvSgAAQMAQmgPunBn1+sq7z9eTt75R/+Mt52jDjiO6/o7f6Yp/eFyrntmm3oFEYd74rDdJ0UnShdcX5vWDqm2FtO1J6chOvysBAAABYgUfscyD9vZ219nZ6XcZgdAXT+g/1u7Svz6+Wc/v7lHT5Cq9/5K5uv6SuWqur87vmyWTUqjCvld1vyjd1iFd8RXpkk/4XQ0AACgyM1vtnGs/aTuhuTQ55/TkK/t1xxNb9Ojze1UVDumqC2brxtfN16JZDX6XV9q+8VqparJ04yN+VwIAAIpstNBcIZNVy4+Z6TVnnaHXnHWGNncf1bf/c6vuX92l+1d36TULmnTj6+brDedOVyhkfpdaetpWSL/6knS4S5oS87saAAAQABX2t/fy1Npcpy+9a7Ge/NwbdcsVC7W5+5hu/E6n3vz13+rfnnpVx/vjfpdYWtpWeEt6NgMAgBSmZ5ShgURSD63fpTue2KJ1XYc1pTaq9/3BmfrQpfM0c0qN3+WVhm9eJoWrpI8+6nclAACgiJjTXIGcc+p89aDueHyLHnlut0Jm+sPzZ+nG183X+bGpfpcXbE/8vfTLL0qX/U/vYi/T5knT5kt1Myrv5Eg/OOddyr26wesbDgBAkRCaK9z2A8f17f/cqvs6t+toX1wd86bpxtfN11vOm6kw855PdniHdPdV0v5XJGX9jkRqpKlzUyE669Y439teNcmXcktK/zHpyC6pJ+uWub9b6tnpLRP9Um2jNOdiKdbu3eZcLNVO8/snAACUMUIzJEk9vQNa9cx23fVfW9V18IRaGmv14dfM17XtMdXXRP0uL3ji/dLh7dLBLdLBrUNvB7ZK/T1D96+bcXKgrpRR6sSAdHRPjkC8yxtBHq6qTqqfJdXP9JYNs6RJTdK+F6Wu1VL388p8eWk6S5qTFaJnLJYiVUX9UQEA5YvQjCHiiaR+8dwe3fHEFnW+elD11RFd29GiD79mnloaGS0dE+ek4wdSIXp4qH7Vuwy5Sw7uX6qj1MmkdOLAsAA8PBDvko7t05BReUkKRQeDcP1MqWF21v1ZgwG5uv7UNfQekXY+K3V1SjtWe8tje73HIjXSrKWpIH2xt5x6JtM6AACnhdCMUa3dfkh3PLFFP12/S845va1tpv7bZfN10ZnTZASP0zfRUeopLVIoIrmElIxLyYQXwtPryXjqsWTWevqxROp+9n7Z28ewX6LfC8I9u6TkwMk/3+TmVACePXogntRUmNF157xjmwnRz0i71krx3lRt0wdHomPt0uyLpJoS6V+eTEjHuqWje1PHPfU7aOatj7jUKR7Lfv5orzPC8y3kTY9hFB9AhSE0I6ddh0/oO//1qr739Ks60htX2+wGnTOjXtPrq9Wcuk2vr/GWDdWqr44Qqk+Xc9KJg9KBLWMbpR4LC0kW9oJ2KJxaT98iqfuhrPX0fqHB9SH7RQeD8fBAXDcjeGEqMSDt2TB0NHr/S6kHTWo+d3A0OtYhNS+SwkVqVZ8+sfHoXm8Ky9E9Uk9qmdmWWh7fN/5/+4Ix7999Ssz7Eje1xVtm1mNSzRS/iwSAvCI0Y8yO98f1g9VdenDtTu063Ku9PX3qj5/8P/GaaCgTpKdnQvVgsE6H66bJ1ZxsOF7xfu+EOJccY8gNMx1hJCcOpgL0amlHpxekTxzwHotOkmZfODgaPaddmjJnfK8f7/emiWQC8J6hwfjoXunobm+ZHgXPFop6X0DqpnvL+hmD9ydP99oeSpKcF7xHWkqjPKbB5ek83yW9Ee9D271R/cPbvQv+JPqH/gzVU7LCdOzkYD15ennP5QdQdgjNOG3OOR05EVf30V7tPdKnvT196u7p096e3qz1Pu090qsjvSdfSCVkUlNd9YjBenoqWDfX1Wh6Q7VqomEffkJUDOe8kf2uVIDe0SntWjc4/aR+9uC86NkXesFxSAgeFoxPHBz5fWobvRHadBjOLLNv071OIKX0ZSeZCtKHt0uHtg0G6exg3Xt46HPCVamR6pg05czBEep0qG6YI0Wq/fl5hnPOm6YU7/O+HFTXS2FOkAYqDaEZRdE7kMiE6O6ePnUPD9Y9veru6dO+o/1KJE/+7NXXRDJhurm+RpOrwppUFdGkqrBqq8KalLrVVkU0KRrO2j50n9pomKkjGJt4n7R7fSpIP+MF6YNbT94vUjt0JLhuhlQ3QjCe3By8qSvF1HskFaqzgvShVLg+vN3rojLkhFHzjtuQ0eozvWW4yguviX5vVD/Rl7Weup9ZT+/X503VGfJYals6DJ9qvyHMm46UGUGPDY6ip7cxPSW/EgPeX2UGer2/oFXVVfbvE3xBaEagJJJOB471nzRi3T0sWB/ti+tEf0LH++MaIWOfUu2QUD00aE+q9tZPCuJZobuuOqLpDTWaPbVGk6qKNPcVwXBsn3diYaRmcNpEVV1pjQoHVbxPOrJj6Ah1rikguYSrpHC1NyocSS3D1cPWq0bYL7VtyHrV4PqJg4Nh/9B2r+6Tpqc0DIbo4dNTpsS8vziESvAvaMmkF17Tt4ETWeu9UvyE9285kFrGT6S2Z+8/wvbMc0Z5LZc4uZZwlff7V10nVdWnlnWDy+z16vrc90vx36OQnBs8GTw5MPRk88wtMcL27BPIR9tv2Otkn3ieuSWH3Y97f+WrmSq9/hZfDgmhGSXNOae+eNIL0AMJneiP63h/Qsf7E6lQ7QXrEwOJrO3xzOPHstaP9ydS+w1ui+dI5NMmRTV7aq1mT63VnKm1mj21RrOn1mrWFO9+cz3ztoG8yJ4CkoyPHIAz4Ta1rVhfZpJJbw57dpBOr6cD//ApO6GIdyJtdpAeEq5jUtXk/NWYGJD6erwTT/uOptbT93tGuKW29x89+bHhLSTHzKRorfdvFqmVojXeF9BIzfi2JxNel6G+o6n6jg7WOeT+UW+/sZ5AG6kdFryzgnjV5MGgHanKOgRu6DkC0sjnBWTWR3hOzudr6LobFi4T8ZPD5YiPDQwNpolh97MfTwyM/EXFD9nn7oQiXhepjz/uTymEZmB0/ZlAPhike3rj2nOkVzsOndDOzK1XOw+dUE/f0LnbkZBp5pSak0L17Cm1qbBdw8VjgErQ1+NdUfRwl3R4W9aIepd3O7Lj5JBS2zh0Wko6TIciQ8PtSMF2eBge6YTTk5gXDIffquq8kfPqdIic7AXMSHUq1KYDbo6wG64q/l9lnPNGqjOh+tjQQD08YOcK4v1HT6OIYS0cpRHaPg5rATli28f0esjr8BOKZIXJ6OD9IY+NcBvyeHQwjIYi3pfN7PvZj4dT72GhrPXwsP3T9YywzU5nv2CdzE5oBvLoSO+AdqUC9PBQvePQCe0+0nvSnO36mkgqUHshOj1Knb4/o6FG0TBdBoCyloh7HVVGGqVOT1MZLbCFol6/8UzQbRiccpC97aRA3OCF4PT96GQ6muSSTGZ9uRkl9AYo5CG/RgvNTNQETkNDTVQNM6M6d+bIV7JLJJ329vRmRqbTtx2p+89uO6hDx4deMCRk0oyG9LQPb1lXHVFtNKya1Dzr2mhYtVUh1USGbwurJrUeDRsnQQJBFY4MTtEYiXNeB5LD273pBtlBOChdRipBKCSJLxYYitAMFEA4ZJo1xZvzfPHcaSPuc6wvrl2HvSC9a1ioXr/jsB55bs+I/bHH8t61US9E10RDJ4Xqk+5XhbL2H3y8NhpWdSSk6mhI1ZHUeiScuu+tV0VCzOUG8slMqp3q3QAECqEZ8Mnk6ojOml6vs6aPPFotSQOJpHoHvBMXe/uTOpFaP9GfUG88od7USY2ZbZl1b9++gaGPHzrer11Z+6T3H6n931hFQpYK1+lgPXK4Hu2x7GBeFRkeyL33sMyfRAcX6dH0zAxAG9zXhu6e9TzL7Dv4PBv2/PS6qSYa0uR0V5VUx5UQXxIAoCIRmoEAi4ZDioZDBT+JcCCRTAXzoSG7L570bgMJ9SeS6htI3Y+nHhvIWo8n1J/Zf3D7iYGEDp3oH/Lc9H69A4lxtxL0W200rMnVg73BJ1WFNbnaW59cFdGkrMfS9zPBe9j99POqI6G8TalJJJ0GEknFk07xRFIDCTfitngytUxtH0gkFc/ankg6RcMhbzpQ1Ks9+y8V6b9GlNtfGpxz3mc9nlR/PKmBRFIDcW9bPDm4PpB164+7ofcTTgPxYfcTycy2zP0cz4+GTZOqIpnP2+TqiNe7PmtZl34s67M1uXrws1cVzt9nC6h0hGYAmXDe4EOHj3giORjO44kh4dq5wY5Pzrms9fSz3ZD7Lns9tZL9nMwrDF1kPd9l1pOpNofH++M61pfILE8MJHSsz+uykl4e7Yuru6fPa23Y57U47B0Y+9SakGlowK4Oa1I0IieXCbheoB0aeuOJ7DDsNJBMqtjndldFQkPD9EjLYUH7pH2ytk9KTx2qCnv/Bqf8ouZ9oRvp89MfH22/3K9TkOMUDikaNkUj3u9aVTikSNgyv3tVWeu1VSFFQ6Z40ul4f1w7Dg0M+Rwe7x97i7BIyDSpyus7nwnb6WBdHUkF7sEgPrl6MKSHzashkXRKOKdE6jOXSLrM9njSKZm5nxyyPZH6XCad93lN3x/6/KQSSY34XJM33SyaOlaRkHcMI+GQIiHzbqnjGgml90ltC2XtFx5lW47Xq4qEMn37a/P85TZonHPqHUhm2rGe6E/ISZkv/+X+848VoRmAryLhkCLhkCaX2TlOiVTg8fqEZwXtVLBOh5/soJ3e93hfXMf64zKFVBNNhYas8BAOmaKhwdA1UniIjhQKsred4vnR1Ht4oW3o1J90n/PerJ7nven/0Q4kM/se749r/7H+wcf6E+odSKo/UZhQmq0qHDppPn5V1hSiuuqImiaPPk0oPZWoKhVwo6ljU5Vejwy7Hw6pKmJZ+3qhOBoZPL75DBvJpMt8eRvps3Us9fkZ/sUuO3jvTAXxo6cRxHMJmRQJeec7pG+R7GXqcxYOmcKW2h4eul806s3NGkh4v0feX0MGg/vAsC+Sw7cVQsiUCtAR1VaFNCkaUU1VOHOhrNph65kLbKWfk3XBrewviOltNZHRp3+lQ23636o365oEvVm/l5nrEwwMvS5B+iJhI21P/3Ux1xfucOoL2OCUtWF/VasavHhY9tV80/drqwb/ElJbNbhPVaR0TrgkNANAAYRDpvqaKP25h4knBufm9/YndXwgPvg/7mHLkOWeL39SOA6Hyn7eeShkqRHh/P0vPBPEU8H7aF9czmnk4JsJuKGTHgub+X78nRscsR5IJFPTk4b+dSZ7W3o6UjyR1EDWX3P6E0mdSH3hO57+XKbWe4cFz709vZnH018uT+evFjXRUGYqlKQhgXi8qlMj5ZOqIoOvWxXW1ElVmj115GCfPbJuJi+Yp76cpQP70C/5cR041q/tB44Pfunvj2sgMfYvLtGwpaa9ZU1jqworNm2S/u7apeP+uQuJ0AwAKJpIOKT6IszTx/gMCeKjn5tcEszSUzKkmqh/l8xOJF0m9GaH3/RfXk4Mf2zI40k5uVED7dD1yMkj1z6ftJy+YJj3F4/0Xz1S09xSV+zNnm6U/VeR9D7Hhl1ELAgIzQAAAHkWLsBfBEpFVWp605RJ5fXluHQmkgAAAAA+8SU0m9kVZvaCmb1sZrf6UQMAAAAwVkUPzWYWlnSbpLdLOk/Se83svGLXAQAAAIyVHyPNyyS97Jzb7Jzrl3SvpKt9qAMAAAAYEz9C8xxJ27Pud6W2AQAAAIHkR2geqQfKSQ39zOxjZtZpZp3d3d1FKAsAAAAYmR+huUtSS9b9mKSdw3dyzt3unGt3zrU3NzcXrTgAAABgOD9C8zOSzjaz+WZWJWmlpAd9qAMAAAAYk6J33HbOxc3sTyT9XFJY0p3OuY3FrgMAAAAYK18uU+Oce0jSQ368NwAAADBeXBEQAAAAyIHQDAAAAORAaAYAAAByIDQDAAAAORCaAQAAgBwIzQAAAEAOhGYAAAAgB0IzAAAAkIM55/yuIScz65b0qg9vfYakfT68b7ng+E0cx3BiOH4Tw/GbGI7fxHD8Jobjd/rmOueah28sidDsFzPrdM61+11HqeL4TRzHcGI4fhPD8ZsYjt/EcPwmhuOXf0zPAAAAAHIgNAMAAAA5EJpP7Xa/CyhxHL+J4xhODMdvYjh+E8PxmxiO38Rw/PKMOc0AAABADow0AwAAADkQmiWZ2RVm9oKZvWxmt47wuJnZP6UeX2dmF/lRZxCZWYuZ/drMNpnZRjP7zAj7vN7MDpvZmtTtC37UGlRmttXM1qeOTecIj/P5G4WZnZv1uVpjZkfM7LPD9uHzN4yZ3Wlme81sQ9a2RjP7hZm9lFpOG+W5p/zvZSUY5fh9zcyeT/2O/tDMpo7y3FP+vleCUY7fF81sR9bv6TtGeS6fv5GP36qsY7fVzNaM8tyK//xNiHOuom+SwpJekdQqqUrSWknnDdvnHZJ+JskkXSLpab/rDspN0ixJF6XW6yW9OMLxe72kn/hda1BvkrZKOuMUj/P5G9txDEvaLa+/ZvZ2Pn8nH6vlki6StCFr21cl3Zpav1XS34xyjE/538tKuI1y/N4qKZJa/5uRjl/qsVP+vlfCbZTj90VJf5rjeXz+Rjl+wx7/O0lfGOWxiv/8TeTGSLO0TNLLzrnNzrl+SfdKunrYPldLutt5npI01cxmFbvQIHLO7XLOPZta75G0SdIcf6sqO3z+xuZNkl5xzvlxIaSS4px7TNKBYZuvlvSd1Pp3JL1rhKeO5b+XZW+k4+ece8Q5F0/dfUpSrOiFlYhRPn9jwedPpz5+ZmaSrpX0/aIWVSEIzV7A2551v0snh76x7FPxzGyepAslPT3Cw5ea2Voz+5mZtRW1sOBzkh4xs9Vm9rERHufzNzYrNfr/KPj85TbDObdL8r4MS5o+wj58FsfmI/L+OjSSXL/vlexPUtNb7hxlehCfv9wuk7THOffSKI/z+ZsAQrP3J+/hhrcUGcs+Fc3M6iT9QNJnnXNHhj38rLw/mS+V9M+SflTs+gLutc65iyS9XdJ/N7Plwx7n85eDmVVJukrSv4/wMJ+//OGzmIOZ/bmkuKR7Rtkl1+97pfqGpAWSLpC0S94Ug+H4/OX2Xp16lJnP3wQQmr1vqi1Z92OSdp7GPhXLzKLyAvM9zrkHhj/unDvinDuaWn9IUtTMzihymYHlnNuZWu6V9EN5f4LMxucvt7dLetY5t2f4A3z+xmxPetpParl3hH34LJ6CmX1I0pWS3u9SE0iHG8Pve0Vyzu1xziWcc0lJ/6KRjwufv1Mws4ikayStGm0fPn8TQ2iWnpF0tpnNT41WrZT04LB9HpT0wVQXg0skHU7/GbPSpeZP3SFpk3Pu66PsMzO1n8xsmbzP3f7iVRlcZjbZzOrT6/JOJtowbDc+f7mNOrrC52/MHpT0odT6hyT9eIR9xvLfy4pkZldIukXSVc6546PsM5bf94o07DyNFRr5uPD5O7U3S3reOdc10oN8/iYu4ncBfnPOxc3sTyT9XN6ZuXc65zaa2cdTj39T0kPyOhi8LOm4pBv8qjeAXivpeknrs1rc/C9JZ0qZ4/ceSZ8ws7ikE5JWjjYKU4FmSPphKtNFJH3POfcwn7+xM7NJkt4i6Y+ztmUfPz5/w5jZ9+V1FTnDzLok/YWkr0i6z8xulLRN0h+l9p0t6V+dc+8Y7b+XfvwMfhrlMONt9gAAAm5JREFU+H1OUrWkX6R+n59yzn08+/hplN93H34EX41y/F5vZhfIm26xVanfZz5/Jxvp+Dnn7tAI53Xw+csvrggIAAAA5MD0DAAAACAHQjMAAACQA6EZAAAAyIHQDAAAAORAaAYAAAByIDQDQACZWcLM1mTdbs3ja88zM/qzAsA4VHyfZgAIqBPOuQv8LgIA4GGkGQBKiJltNbO/MbPfpW5npbbPNbNHzWxdanlmavsMM/uhma1N3V6Teqmwmf2LmW00s0fMrDa1/6fN7LnU69zr048JAIFDaAaAYKodNj3juqzHjjjnlkn6v5L+IbXt/0q62zl3vqR7JP1Tavs/Sfqtc26ppIskpa+gdrak25xzbZIOSXp3avutki5Mvc7HC/XDAUCp4YqAABBAZnbUOVc3wvatkt7onNtsZlFJu51zTWa2T9Is59xAavsu59wZZtYtKeac68t6jXmSfuGcOzt1/xZJUefcl83sYUlHJf1I0o+cc0cL/KMCQElgpBkASo8bZX20fUbSl7We0OA5Ln8o6TZJF0tabWac+wIAIjQDQCm6Lmv5ZGr9vyStTK2/X9ITqfVHJX1CkswsbGYNo72omYUktTjnfi3pzyRNlXTSaDcAVCJGEAAgmGrNbE3W/Yedc+m2c9Vm9rS8gY/3prZ9WtKdZnazpG5JN6S2f0bS7WZ2o7wR5U9I2jXKe4YlfdfMpkgySX/vnDuUt58IAEoYc5oBoISk5jS3O+f2+V0LAFQSpmcAAAAAOTDSDAD/fzt2IAMAAAAgzN86hAR+ihYADKcZAACGaAYAgCGaAQBgiGYAABiiGQAAhmgGAIAR/p7vMU6VqycAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_graph(history,string):\n",
    "    plt.figure(figsize=(12,8))\n",
    "    plt.plot(history.history[string],label=str(string))\n",
    "    plt.plot(history.history[\"val_\"+str(string)],label=\"val_\"+str(string))\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(str(string))\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "plot_graph(history,\"accuracy\")\n",
    "plot_graph(history,\"loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-29T17:50:40.091321Z",
     "iopub.status.busy": "2020-09-29T17:50:40.090260Z",
     "iopub.status.idle": "2020-09-29T17:50:40.403052Z",
     "shell.execute_reply": "2020-09-29T17:50:40.402481Z"
    },
    "papermill": {
     "duration": 0.656959,
     "end_time": "2020-09-29T17:50:40.403228",
     "exception": false,
     "start_time": "2020-09-29T17:50:39.746269",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.save(\"SoilNet.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-29T17:50:41.048859Z",
     "iopub.status.busy": "2020-09-29T17:50:41.048188Z",
     "iopub.status.idle": "2020-09-29T17:50:41.055077Z",
     "shell.execute_reply": "2020-09-29T17:50:41.054485Z"
    },
    "papermill": {
     "duration": 0.3298,
     "end_time": "2020-09-29T17:50:41.055201",
     "exception": false,
     "start_time": "2020-09-29T17:50:40.725401",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href='SoilNet.h5' target='_blank'>SoilNet.h5</a><br>"
      ],
      "text/plain": [
       "/kaggle/working/SoilNet.h5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import FileLink\n",
    "FileLink('SoilNet.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.320658,
     "end_time": "2020-09-29T17:50:41.704291",
     "exception": false,
     "start_time": "2020-09-29T17:50:41.383633",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.321807,
     "end_time": "2020-09-29T17:50:42.348843",
     "exception": false,
     "start_time": "2020-09-29T17:50:42.027036",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.488903,
     "end_time": "2020-09-29T17:50:43.238464",
     "exception": false,
     "start_time": "2020-09-29T17:50:42.749561",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  },
  "papermill": {
   "duration": 2431.440235,
   "end_time": "2020-09-29T17:50:44.814809",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2020-09-29T17:10:13.374574",
   "version": "2.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}