main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:32:00.542215400Z",
     "start_time": "2024-03-21T10:32:00.526089200Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5125a674efe7597f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:01:49.910146800Z",
     "start_time": "2024-03-21T10:01:49.887554Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dataset is loaded into a pandas dataframe\n",
    "df = pd.read_csv(\"Crop_recommendation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bbbb0293b02ea38b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T09:48:59.133870900Z",
     "start_time": "2024-03-21T09:48:59.116067900Z"
    },
    "collapsed": false
   },
   "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>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall label\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Preprocessing\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "12f7410c90a4cd99",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T09:49:01.105739800Z",
     "start_time": "2024-03-21T09:49:01.094510300Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c3044087043675f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T09:49:40.459985Z",
     "start_time": "2024-03-21T09:49:40.449998200Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N                int64\n",
       "P                int64\n",
       "K                int64\n",
       "temperature    float64\n",
       "humidity       float64\n",
       "ph             float64\n",
       "rainfall       float64\n",
       "label           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d08d1fa67d741806",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:08:38.969620900Z",
     "start_time": "2024-03-21T10:08:38.964592500Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#splitting the data\n",
    "X = df.drop(\"label\", axis=1)\n",
    "y = df[\"label\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd6d63b02705a7c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:08:39.845836800Z",
     "start_time": "2024-03-21T10:08:39.839721900Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    N   P   K  temperature   humidity        ph    rainfall\n",
      "0  90  42  43    20.879744  82.002744  6.502985  202.935536\n",
      "1  85  58  41    21.770462  80.319644  7.038096  226.655537\n",
      "2  60  55  44    23.004459  82.320763  7.840207  263.964248\n",
      "3  74  35  40    26.491096  80.158363  6.980401  242.864034\n",
      "4  78  42  42    20.130175  81.604873  7.628473  262.717340\n",
      "0    rice\n",
      "1    rice\n",
      "2    rice\n",
      "3    rice\n",
      "4    rice\n",
      "Name: label, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X.head())\n",
    "print(y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bd9a808d50a96455",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:13:27.595083700Z",
     "start_time": "2024-03-21T10:13:27.584826Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "#One Hot Encoding the target variable\n",
    "enc = OneHotEncoder()\n",
    "y_oh = enc.fit_transform(y.values.reshape(-1,1)).toarray()\n",
    "print(y_oh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb5da500dee30439",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:13:54.489174Z",
     "start_time": "2024-03-21T10:13:54.487782500Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "y = y_oh\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2e6359fedc7d002e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:19:31.811769600Z",
     "start_time": "2024-03-21T10:19:31.776175100Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Splitting the data into training, validation and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42,stratify=y)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size=0.5, random_state=42,stratify=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c9f413a91b37982c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:19:57.002053900Z",
     "start_time": "2024-03-21T10:19:56.990525500Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape:  (1540, 7)\n",
      "X_val shape:  (330, 7)\n",
      "X_test shape:  (330, 7)\n",
      "y_train shape:  (1540, 22)\n",
      "y_val shape:  (330, 22)\n",
      "y_test shape:  (330, 22)\n"
     ]
    }
   ],
   "source": [
    "print('X_train shape: ', X_train.shape)\n",
    "print('X_val shape: ', X_val.shape)\n",
    "print('X_test shape: ', X_test.shape)\n",
    "print('y_train shape: ', y_train.shape)\n",
    "print('y_val shape: ', y_val.shape)\n",
    "print('y_test shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1d4da71677d260b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:20:13.417796400Z",
     "start_time": "2024-03-21T10:20:13.406586500Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output shape:  22\n",
      "input shape:  (7,)\n"
     ]
    }
   ],
   "source": [
    "output_shape = y_train.shape[1]\n",
    "input_shape = (X_train.shape[1], )\n",
    "\n",
    "print('output shape: ', output_shape)\n",
    "print('input shape: ', input_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ceaaf282665a4f11",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:28:05.269088Z",
     "start_time": "2024-03-21T10:28:05.262069Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# initialize a deep neural network model with 3 hidden layers of 1024 neurons each and relu activation function\n",
    "def build_model(input_shape, output_shape, seed = 69):\n",
    "  tf.random.set_seed(seed)\n",
    "  model = tf.keras.Sequential([\n",
    "                             tf.keras.layers.Dense(1024, activation='relu', input_shape=input_shape),\n",
    "                             tf.keras.layers.Dense(1024, activation='relu'),\n",
    "                             tf.keras.layers.Dense(1024, activation='relu'),\n",
    "                             tf.keras.layers.Dense(output_shape, activation= 'softmax')])\n",
    "\n",
    "\n",
    "  optimizer = tf.keras.optimizers.RMSprop()\n",
    "  loss = tf.keras.losses.BinaryCrossentropy(from_logits = False)\n",
    "  metrics = ['accuracy']\n",
    "\n",
    "  model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
    "\n",
    "  return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "155473924ab6d6f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:28:06.029931600Z",
     "start_time": "2024-03-21T10:28:05.954547700Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = build_model(input_shape, output_shape)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5615459b16903753",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:30:28.802108200Z",
     "start_time": "2024-03-21T10:30:02.116775100Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 19ms/step - accuracy: 0.2784 - loss: 0.9204 - val_accuracy: 0.4364 - val_loss: 0.1765 - learning_rate: 0.0010\n",
      "Epoch 2/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.6553 - loss: 0.0999 - val_accuracy: 0.7061 - val_loss: 0.1271 - learning_rate: 0.0010\n",
      "Epoch 3/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.7661 - loss: 0.0687 - val_accuracy: 0.7758 - val_loss: 0.1080 - learning_rate: 0.0010\n",
      "Epoch 4/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.8455 - loss: 0.0473 - val_accuracy: 0.8121 - val_loss: 0.0915 - learning_rate: 0.0010\n",
      "Epoch 5/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.8843 - loss: 0.0383 - val_accuracy: 0.8333 - val_loss: 0.0760 - learning_rate: 0.0010\n",
      "Epoch 6/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.8967 - loss: 0.0318 - val_accuracy: 0.8364 - val_loss: 0.0684 - learning_rate: 0.0010\n",
      "Epoch 7/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9121 - loss: 0.0289 - val_accuracy: 0.8636 - val_loss: 0.0603 - learning_rate: 0.0010\n",
      "Epoch 8/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9200 - loss: 0.0253 - val_accuracy: 0.8515 - val_loss: 0.0680 - learning_rate: 0.0010\n",
      "Epoch 9/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9254 - loss: 0.0244 - val_accuracy: 0.8394 - val_loss: 0.0678 - learning_rate: 0.0010\n",
      "Epoch 10/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9263 - loss: 0.0248 - val_accuracy: 0.8515 - val_loss: 0.0578 - learning_rate: 0.0010\n",
      "Epoch 11/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9274 - loss: 0.0207 - val_accuracy: 0.9091 - val_loss: 0.0340 - learning_rate: 0.0010\n",
      "Epoch 12/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9433 - loss: 0.0163 - val_accuracy: 0.8818 - val_loss: 0.0508 - learning_rate: 0.0010\n",
      "Epoch 13/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9384 - loss: 0.0182 - val_accuracy: 0.8758 - val_loss: 0.0494 - learning_rate: 0.0010\n",
      "Epoch 14/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9300 - loss: 0.0188 - val_accuracy: 0.8758 - val_loss: 0.0389 - learning_rate: 0.0010\n",
      "Epoch 15/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9383 - loss: 0.0163 - val_accuracy: 0.8606 - val_loss: 0.0519 - learning_rate: 0.0010\n",
      "Epoch 16/200\n",
      "\u001b[1m45/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9335 - loss: 0.0172\n",
      "Epoch 16: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9347 - loss: 0.0168 - val_accuracy: 0.8848 - val_loss: 0.0421 - learning_rate: 0.0010\n",
      "Epoch 17/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9197 - loss: 0.0247 - val_accuracy: 0.9606 - val_loss: 0.0093 - learning_rate: 1.0000e-04\n",
      "Epoch 18/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9649 - loss: 0.0077 - val_accuracy: 0.9667 - val_loss: 0.0083 - learning_rate: 1.0000e-04\n",
      "Epoch 19/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9676 - loss: 0.0067 - val_accuracy: 0.9667 - val_loss: 0.0078 - learning_rate: 1.0000e-04\n",
      "Epoch 20/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9704 - loss: 0.0063 - val_accuracy: 0.9697 - val_loss: 0.0076 - learning_rate: 1.0000e-04\n",
      "Epoch 21/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9726 - loss: 0.0060 - val_accuracy: 0.9697 - val_loss: 0.0075 - learning_rate: 1.0000e-04\n",
      "Epoch 22/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9737 - loss: 0.0058 - val_accuracy: 0.9758 - val_loss: 0.0074 - learning_rate: 1.0000e-04\n",
      "Epoch 23/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9737 - loss: 0.0056 - val_accuracy: 0.9758 - val_loss: 0.0074 - learning_rate: 1.0000e-04\n",
      "Epoch 24/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9744 - loss: 0.0055 - val_accuracy: 0.9758 - val_loss: 0.0074 - learning_rate: 1.0000e-04\n",
      "Epoch 25/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9748 - loss: 0.0054 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-04\n",
      "Epoch 26/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9752 - loss: 0.0053 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-04\n",
      "Epoch 27/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9790 - loss: 0.0052 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-04\n",
      "Epoch 28/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9796 - loss: 0.0051 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-04\n",
      "Epoch 29/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9796 - loss: 0.0050 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-04\n",
      "Epoch 30/200\n",
      "\u001b[1m45/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9803 - loss: 0.0050\n",
      "Epoch 30: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0050 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-04\n",
      "Epoch 31/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9796 - loss: 0.0047 - val_accuracy: 0.9788 - val_loss: 0.0073 - learning_rate: 1.0000e-05\n",
      "Epoch 32/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0046 - val_accuracy: 0.9788 - val_loss: 0.0073 - learning_rate: 1.0000e-05\n",
      "Epoch 33/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9801 - loss: 0.0046 - val_accuracy: 0.9788 - val_loss: 0.0073 - learning_rate: 1.0000e-05\n",
      "Epoch 34/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9801 - loss: 0.0045 - val_accuracy: 0.9788 - val_loss: 0.0073 - learning_rate: 1.0000e-05\n",
      "Epoch 35/200\n",
      "\u001b[1m46/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9799 - loss: 0.0045\n",
      "Epoch 35: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9801 - loss: 0.0045 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-05\n",
      "Epoch 36/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0045 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-06\n",
      "Epoch 37/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0045 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-06\n",
      "Epoch 38/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0045 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-06\n",
      "Epoch 39/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0045 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-06\n",
      "Epoch 40/200\n",
      "\u001b[1m47/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9803 - loss: 0.0045\n",
      "Epoch 40: ReduceLROnPlateau reducing learning rate to 1.0000001111620805e-07.\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0045 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-06\n",
      "Epoch 41/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-07\n",
      "Epoch 42/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-07\n",
      "Epoch 43/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-07\n",
      "Epoch 44/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-07\n",
      "Epoch 45/200\n",
      "\u001b[1m47/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9803 - loss: 0.0044\n",
      "Epoch 45: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-08.\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-07\n",
      "Epoch 46/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-08\n",
      "Epoch 47/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-08\n",
      "Epoch 48/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-08\n",
      "Epoch 49/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-08\n",
      "Epoch 50/200\n",
      "\u001b[1m46/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9802 - loss: 0.0044\n",
      "Epoch 50: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-09.\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-08\n",
      "Epoch 51/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-09\n",
      "Epoch 52/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-09\n",
      "Epoch 53/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-09\n",
      "Epoch 54/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-09\n",
      "Epoch 55/200\n",
      "\u001b[1m48/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9803 - loss: 0.0044\n",
      "Epoch 55: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-10.\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-09\n",
      "Epoch 56/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-10\n",
      "Epoch 57/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-10\n",
      "Epoch 58/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-10\n",
      "Epoch 59/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-10\n",
      "Epoch 60/200\n",
      "\u001b[1m47/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9803 - loss: 0.0044\n",
      "Epoch 60: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-11.\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-10\n",
      "Epoch 61/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-11\n",
      "Epoch 62/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-11\n",
      "Epoch 63/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-11\n",
      "Epoch 64/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-11\n",
      "Epoch 65/200\n",
      "\u001b[1m47/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9803 - loss: 0.0044\n",
      "Epoch 65: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-12.\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-11\n",
      "Epoch 66/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-12\n",
      "Epoch 67/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-12\n",
      "Epoch 68/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-12\n",
      "Epoch 69/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-12\n",
      "Epoch 70/200\n",
      "\u001b[1m48/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9803 - loss: 0.0044\n",
      "Epoch 70: ReduceLROnPlateau reducing learning rate to 1.0000001044244145e-13.\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-12\n",
      "Epoch 71/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-13\n",
      "Epoch 72/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-13\n",
      "Epoch 73/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-13\n",
      "Epoch 74/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-13\n",
      "Epoch 75/200\n",
      "\u001b[1m46/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9802 - loss: 0.0044\n",
      "Epoch 75: ReduceLROnPlateau reducing learning rate to 1.0000001179769417e-14.\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-13\n",
      "Epoch 76/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-14\n",
      "Epoch 77/200\n",
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.9804 - loss: 0.0044 - val_accuracy: 0.9758 - val_loss: 0.0073 - learning_rate: 1.0000e-14\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(\n",
    "    x = X_train,\n",
    "    y = y_train,\n",
    "    batch_size = 32,\n",
    "    epochs = 200,\n",
    "    validation_data = (X_val, y_val),\n",
    "    callbacks = [\n",
    "        tf.keras.callbacks.EarlyStopping(\n",
    "            monitor='val_loss',\n",
    "            patience=25,\n",
    "            restore_best_weights=True\n",
    "        ),\n",
    "        tf.keras.callbacks.ReduceLROnPlateau(\n",
    "            monitor='val_loss',\n",
    "            factor=.1,\n",
    "            patience=5,\n",
    "            verbose=1\n",
    "        )\n",
    "    ]\n",
    ").history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "60a486167c0fd2bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:32:05.431237200Z",
     "start_time": "2024-03-21T10:32:05.152397Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and display plots for training and validation loss\n",
    "plt.figure(figsize=(15,3))\n",
    "plt.plot(history['loss'], label='Training Loss', color='grey', alpha=.8)\n",
    "plt.plot(history['val_loss'], label='Validation Loss', color='#fec100', alpha=.8)\n",
    "plt.title('Loss', fontsize=16)\n",
    "plt.legend(fontsize=13)\n",
    "plt.grid(alpha=.3)\n",
    "\n",
    "# Create and display plots for training and validation accuracy\n",
    "plt.figure(figsize=(15,3))\n",
    "plt.plot(history['accuracy'], label='Training Accuracy',color='grey' , alpha=.8)\n",
    "plt.plot(history['val_accuracy'], label='Validation Accuracy',color='#fec100', alpha=.8)\n",
    "plt.title('Accuracy', fontsize=16)\n",
    "plt.legend(fontsize=13)\n",
    "plt.grid(alpha=.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4ccb094354b26918",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-21T10:32:42.671001500Z",
     "start_time": "2024-03-21T10:32:42.394340400Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m24/49\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9799 - loss: 0.0049"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9808 - loss: 0.0047\n",
      "Accuracy of the model on Training Data is -  98.3116865158081\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9768 - loss: 0.0058 \n",
      "Accuracy of the model on Testing Data is -  97.57575988769531\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of the model on Training Data is - \" , model.evaluate(X_train,y_train)[1]*100)\n",
    "print(\"Accuracy of the model on Testing Data is - \" , model.evaluate(X_test,y_test)[1]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c81dd7c514107800",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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