trainmodel copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "19ce7749",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "from keras_preprocessing.image import load_img\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02e9c221",
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_DIR = 'images/train'\n",
    "TEST_DIR = 'images/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ede9c1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def createdataframe(dir):\n",
    "    image_paths = []\n",
    "    labels = []\n",
    "    for label in os.listdir(dir):\n",
    "        for imagename in os.listdir(os.path.join(dir,label)):\n",
    "            image_paths.append(os.path.join(dir,label,imagename))\n",
    "            labels.append(label)\n",
    "        print(label, \"completed\")\n",
    "    return image_paths,labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3a8f1a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "angry completed\n",
      "disgust completed\n",
      "fear completed\n",
      "happy completed\n",
      "neutral completed\n",
      "sad completed\n",
      "surprise completed\n"
     ]
    }
   ],
   "source": [
    "train = pd.DataFrame()\n",
    "train['image'], train['label'] = createdataframe(TRAIN_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8a0cd29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                image     label\n",
      "0            images/train\\angry\\0.jpg     angry\n",
      "1            images/train\\angry\\1.jpg     angry\n",
      "2           images/train\\angry\\10.jpg     angry\n",
      "3        images/train\\angry\\10002.jpg     angry\n",
      "4        images/train\\angry\\10016.jpg     angry\n",
      "...                               ...       ...\n",
      "28816  images/train\\surprise\\9969.jpg  surprise\n",
      "28817  images/train\\surprise\\9985.jpg  surprise\n",
      "28818  images/train\\surprise\\9990.jpg  surprise\n",
      "28819  images/train\\surprise\\9992.jpg  surprise\n",
      "28820  images/train\\surprise\\9996.jpg  surprise\n",
      "\n",
      "[28821 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f9688fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "angry completed\n",
      "disgust completed\n",
      "fear completed\n",
      "happy completed\n",
      "neutral completed\n",
      "sad completed\n",
      "surprise completed\n"
     ]
    }
   ],
   "source": [
    "test = pd.DataFrame()\n",
    "test['image'], test['label'] = createdataframe(TEST_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7f504f5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              image     label\n",
      "0       images/test\\angry\\10052.jpg     angry\n",
      "1       images/test\\angry\\10065.jpg     angry\n",
      "2       images/test\\angry\\10079.jpg     angry\n",
      "3       images/test\\angry\\10095.jpg     angry\n",
      "4       images/test\\angry\\10121.jpg     angry\n",
      "...                             ...       ...\n",
      "7061  images/test\\surprise\\9806.jpg  surprise\n",
      "7062  images/test\\surprise\\9830.jpg  surprise\n",
      "7063  images/test\\surprise\\9853.jpg  surprise\n",
      "7064  images/test\\surprise\\9878.jpg  surprise\n",
      "7065   images/test\\surprise\\993.jpg  surprise\n",
      "\n",
      "[7066 rows x 2 columns]\n",
      "0         images/test\\angry\\10052.jpg\n",
      "1         images/test\\angry\\10065.jpg\n",
      "2         images/test\\angry\\10079.jpg\n",
      "3         images/test\\angry\\10095.jpg\n",
      "4         images/test\\angry\\10121.jpg\n",
      "                    ...              \n",
      "7061    images/test\\surprise\\9806.jpg\n",
      "7062    images/test\\surprise\\9830.jpg\n",
      "7063    images/test\\surprise\\9853.jpg\n",
      "7064    images/test\\surprise\\9878.jpg\n",
      "7065     images/test\\surprise\\993.jpg\n",
      "Name: image, Length: 7066, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(test)\n",
    "print(test['image'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "693324b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f52ccbbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_features(images):\n",
    "    features = []\n",
    "    for image in tqdm(images):\n",
    "        img = load_img(image,grayscale =  True )\n",
    "        img = np.array(img)\n",
    "        features.append(img)\n",
    "    features = np.array(features)\n",
    "    features = features.reshape(len(features),48,48,1)\n",
    "    return features\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "942a9d40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c00fc29588d4991ade8b8ff79431108",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/28821 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayank Yadav\\anaconda\\lib\\site-packages\\keras_preprocessing\\image\\utils.py:107: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
      "  warnings.warn('grayscale is deprecated. Please use '\n"
     ]
    }
   ],
   "source": [
    "train_features = extract_features(train['image']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c1b294ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de89d51da31a42fb9619368fc4ab34fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/7066 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_features = extract_features(test['image'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f78b5d34",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train_features/255.0\n",
    "x_test = test_features/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c660fca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5384a25b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LabelEncoder</label><div class=\"sk-toggleable__content\"><pre>LabelEncoder()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "le.fit(train['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "50ece229",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = le.transform(train['label'])\n",
    "y_test = le.transform(test['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5e284299",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = to_categorical(y_train,num_classes = 7)\n",
    "y_test = to_categorical(y_test,num_classes = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3977223a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayank Yadav\\anaconda\\lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# convolutional layers\n",
    "model.add(Conv2D(128, kernel_size=(3,3), activation='relu', input_shape=(48,48,1)))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Conv2D(256, kernel_size=(3,3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Conv2D(512, kernel_size=(3,3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Conv2D(512, kernel_size=(3,3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Flatten())\n",
    "# fully connected layers\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.3))\n",
    "# output layer\n",
    "model.add(Dense(7, activation='softmax'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5cd0b130",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 430ms/step - accuracy: 0.1974 - loss: 2.2618 - val_accuracy: 0.2586 - val_loss: 1.8869\n",
      "Epoch 2/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 453ms/step - accuracy: 0.3567 - loss: 1.6653 - val_accuracy: 0.4343 - val_loss: 1.4958\n",
      "Epoch 3/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m217s\u001b[0m 480ms/step - accuracy: 0.4602 - loss: 1.4050 - val_accuracy: 0.4689 - val_loss: 1.4158\n",
      "Epoch 4/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m207s\u001b[0m 458ms/step - accuracy: 0.5092 - loss: 1.2835 - val_accuracy: 0.5085 - val_loss: 1.2902\n",
      "Epoch 5/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 446ms/step - accuracy: 0.5446 - loss: 1.2007 - val_accuracy: 0.5416 - val_loss: 1.2231\n",
      "Epoch 6/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 452ms/step - accuracy: 0.5610 - loss: 1.1512 - val_accuracy: 0.5725 - val_loss: 1.1459\n",
      "Epoch 7/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 438ms/step - accuracy: 0.5814 - loss: 1.1050 - val_accuracy: 0.5621 - val_loss: 1.1671\n",
      "Epoch 8/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 440ms/step - accuracy: 0.5994 - loss: 1.0611 - val_accuracy: 0.5984 - val_loss: 1.0846\n",
      "Epoch 9/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 443ms/step - accuracy: 0.6207 - loss: 1.0086 - val_accuracy: 0.5975 - val_loss: 1.0797\n",
      "Epoch 10/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 454ms/step - accuracy: 0.6359 - loss: 0.9706 - val_accuracy: 0.5945 - val_loss: 1.0933\n",
      "Epoch 11/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 451ms/step - accuracy: 0.6486 - loss: 0.9345 - val_accuracy: 0.5903 - val_loss: 1.1140\n",
      "Epoch 12/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 452ms/step - accuracy: 0.6692 - loss: 0.8860 - val_accuracy: 0.5885 - val_loss: 1.1067\n",
      "Epoch 13/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 453ms/step - accuracy: 0.6789 - loss: 0.8489 - val_accuracy: 0.5893 - val_loss: 1.1339\n",
      "Epoch 14/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 455ms/step - accuracy: 0.6930 - loss: 0.8224 - val_accuracy: 0.6301 - val_loss: 1.0359\n",
      "Epoch 15/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m203s\u001b[0m 450ms/step - accuracy: 0.7098 - loss: 0.7771 - val_accuracy: 0.6298 - val_loss: 1.0511\n",
      "Epoch 16/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 454ms/step - accuracy: 0.7221 - loss: 0.7528 - val_accuracy: 0.6288 - val_loss: 1.0448\n",
      "Epoch 17/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 455ms/step - accuracy: 0.7339 - loss: 0.7102 - val_accuracy: 0.6108 - val_loss: 1.1514\n",
      "Epoch 18/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 452ms/step - accuracy: 0.7429 - loss: 0.6900 - val_accuracy: 0.6243 - val_loss: 1.1004\n",
      "Epoch 19/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 449ms/step - accuracy: 0.7549 - loss: 0.6687 - val_accuracy: 0.6301 - val_loss: 1.1185\n",
      "Epoch 20/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 452ms/step - accuracy: 0.7651 - loss: 0.6324 - val_accuracy: 0.6251 - val_loss: 1.1542\n",
      "Epoch 21/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m214s\u001b[0m 474ms/step - accuracy: 0.7763 - loss: 0.6074 - val_accuracy: 0.6173 - val_loss: 1.1278\n",
      "Epoch 22/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 476ms/step - accuracy: 0.7832 - loss: 0.5916 - val_accuracy: 0.6318 - val_loss: 1.1247\n",
      "Epoch 23/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m214s\u001b[0m 475ms/step - accuracy: 0.7930 - loss: 0.5583 - val_accuracy: 0.6329 - val_loss: 1.1364\n",
      "Epoch 24/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m218s\u001b[0m 483ms/step - accuracy: 0.8055 - loss: 0.5355 - val_accuracy: 0.6286 - val_loss: 1.2006\n",
      "Epoch 25/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 444ms/step - accuracy: 0.8089 - loss: 0.5248 - val_accuracy: 0.6271 - val_loss: 1.2124\n",
      "Epoch 26/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 454ms/step - accuracy: 0.8151 - loss: 0.5093 - val_accuracy: 0.6230 - val_loss: 1.2114\n",
      "Epoch 27/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 446ms/step - accuracy: 0.8282 - loss: 0.4760 - val_accuracy: 0.6221 - val_loss: 1.2679\n",
      "Epoch 28/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 445ms/step - accuracy: 0.8253 - loss: 0.4769 - val_accuracy: 0.6363 - val_loss: 1.1916\n",
      "Epoch 29/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 469ms/step - accuracy: 0.8343 - loss: 0.4540 - val_accuracy: 0.6302 - val_loss: 1.2071\n",
      "Epoch 30/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m217s\u001b[0m 480ms/step - accuracy: 0.8360 - loss: 0.4470 - val_accuracy: 0.6257 - val_loss: 1.2117\n",
      "Epoch 31/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m216s\u001b[0m 479ms/step - accuracy: 0.8478 - loss: 0.4309 - val_accuracy: 0.6344 - val_loss: 1.2786\n",
      "Epoch 32/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m217s\u001b[0m 481ms/step - accuracy: 0.8499 - loss: 0.4147 - val_accuracy: 0.6356 - val_loss: 1.2425\n",
      "Epoch 33/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m217s\u001b[0m 481ms/step - accuracy: 0.8538 - loss: 0.4116 - val_accuracy: 0.6349 - val_loss: 1.2546\n",
      "Epoch 34/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m216s\u001b[0m 478ms/step - accuracy: 0.8541 - loss: 0.4009 - val_accuracy: 0.6269 - val_loss: 1.2501\n",
      "Epoch 35/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m216s\u001b[0m 480ms/step - accuracy: 0.8607 - loss: 0.3785 - val_accuracy: 0.6142 - val_loss: 1.3962\n",
      "Epoch 36/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m214s\u001b[0m 474ms/step - accuracy: 0.8646 - loss: 0.3766 - val_accuracy: 0.6405 - val_loss: 1.3056\n",
      "Epoch 37/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m217s\u001b[0m 482ms/step - accuracy: 0.8698 - loss: 0.3652 - val_accuracy: 0.6309 - val_loss: 1.3598\n",
      "Epoch 38/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m218s\u001b[0m 484ms/step - accuracy: 0.8736 - loss: 0.3516 - val_accuracy: 0.6339 - val_loss: 1.3183\n",
      "Epoch 39/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m217s\u001b[0m 480ms/step - accuracy: 0.8701 - loss: 0.3575 - val_accuracy: 0.6380 - val_loss: 1.3327\n",
      "Epoch 40/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m216s\u001b[0m 480ms/step - accuracy: 0.8761 - loss: 0.3376 - val_accuracy: 0.6216 - val_loss: 1.4211\n",
      "Epoch 41/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 477ms/step - accuracy: 0.8743 - loss: 0.3443 - val_accuracy: 0.6370 - val_loss: 1.3431\n",
      "Epoch 42/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 476ms/step - accuracy: 0.8820 - loss: 0.3293 - val_accuracy: 0.6306 - val_loss: 1.3813\n",
      "Epoch 43/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 476ms/step - accuracy: 0.8852 - loss: 0.3177 - val_accuracy: 0.6419 - val_loss: 1.3594\n",
      "Epoch 44/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 476ms/step - accuracy: 0.8838 - loss: 0.3178 - val_accuracy: 0.6359 - val_loss: 1.3492\n",
      "Epoch 45/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 476ms/step - accuracy: 0.8877 - loss: 0.3158 - val_accuracy: 0.6353 - val_loss: 1.3648\n",
      "Epoch 46/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 477ms/step - accuracy: 0.8923 - loss: 0.3040 - val_accuracy: 0.6336 - val_loss: 1.4063\n",
      "Epoch 47/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m215s\u001b[0m 477ms/step - accuracy: 0.8915 - loss: 0.3045 - val_accuracy: 0.6400 - val_loss: 1.3906\n",
      "Epoch 48/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m209s\u001b[0m 462ms/step - accuracy: 0.8949 - loss: 0.2871 - val_accuracy: 0.6367 - val_loss: 1.4112\n",
      "Epoch 49/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m210s\u001b[0m 465ms/step - accuracy: 0.8959 - loss: 0.2907 - val_accuracy: 0.6458 - val_loss: 1.4457\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 50/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m209s\u001b[0m 464ms/step - accuracy: 0.8997 - loss: 0.2786 - val_accuracy: 0.6373 - val_loss: 1.4021\n",
      "Epoch 51/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m208s\u001b[0m 461ms/step - accuracy: 0.9061 - loss: 0.2690 - val_accuracy: 0.6380 - val_loss: 1.4192\n",
      "Epoch 52/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m209s\u001b[0m 463ms/step - accuracy: 0.9031 - loss: 0.2718 - val_accuracy: 0.6282 - val_loss: 1.4277\n",
      "Epoch 53/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m217s\u001b[0m 481ms/step - accuracy: 0.9054 - loss: 0.2705 - val_accuracy: 0.6390 - val_loss: 1.4172\n",
      "Epoch 54/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m218s\u001b[0m 484ms/step - accuracy: 0.9092 - loss: 0.2539 - val_accuracy: 0.6329 - val_loss: 1.4734\n",
      "Epoch 55/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m220s\u001b[0m 487ms/step - accuracy: 0.9054 - loss: 0.2647 - val_accuracy: 0.6325 - val_loss: 1.4234\n",
      "Epoch 56/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m218s\u001b[0m 483ms/step - accuracy: 0.9071 - loss: 0.2616 - val_accuracy: 0.6360 - val_loss: 1.4491\n",
      "Epoch 57/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m218s\u001b[0m 482ms/step - accuracy: 0.9064 - loss: 0.2640 - val_accuracy: 0.6265 - val_loss: 1.4953\n",
      "Epoch 58/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m218s\u001b[0m 483ms/step - accuracy: 0.9141 - loss: 0.2427 - val_accuracy: 0.6240 - val_loss: 1.5339\n",
      "Epoch 59/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m218s\u001b[0m 483ms/step - accuracy: 0.9160 - loss: 0.2406 - val_accuracy: 0.6363 - val_loss: 1.5155\n",
      "Epoch 60/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m208s\u001b[0m 460ms/step - accuracy: 0.9106 - loss: 0.2496 - val_accuracy: 0.6262 - val_loss: 1.6106\n",
      "Epoch 61/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 441ms/step - accuracy: 0.9187 - loss: 0.2331 - val_accuracy: 0.6357 - val_loss: 1.5194\n",
      "Epoch 62/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 441ms/step - accuracy: 0.9179 - loss: 0.2350 - val_accuracy: 0.6288 - val_loss: 1.5288\n",
      "Epoch 63/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 452ms/step - accuracy: 0.9190 - loss: 0.2328 - val_accuracy: 0.6404 - val_loss: 1.5214\n",
      "Epoch 64/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 455ms/step - accuracy: 0.9162 - loss: 0.2387 - val_accuracy: 0.6394 - val_loss: 1.5032\n",
      "Epoch 65/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m203s\u001b[0m 450ms/step - accuracy: 0.9229 - loss: 0.2184 - val_accuracy: 0.6395 - val_loss: 1.5144\n",
      "Epoch 66/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 447ms/step - accuracy: 0.9217 - loss: 0.2222 - val_accuracy: 0.6425 - val_loss: 1.5478\n",
      "Epoch 67/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 447ms/step - accuracy: 0.9216 - loss: 0.2224 - val_accuracy: 0.6302 - val_loss: 1.6058\n",
      "Epoch 68/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 448ms/step - accuracy: 0.9240 - loss: 0.2108 - val_accuracy: 0.6354 - val_loss: 1.5540\n",
      "Epoch 69/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 448ms/step - accuracy: 0.9252 - loss: 0.2097 - val_accuracy: 0.6395 - val_loss: 1.5412\n",
      "Epoch 70/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 447ms/step - accuracy: 0.9226 - loss: 0.2148 - val_accuracy: 0.6378 - val_loss: 1.5261\n",
      "Epoch 71/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 447ms/step - accuracy: 0.9274 - loss: 0.2065 - val_accuracy: 0.6401 - val_loss: 1.5615\n",
      "Epoch 72/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 449ms/step - accuracy: 0.9224 - loss: 0.2195 - val_accuracy: 0.6390 - val_loss: 1.5152\n",
      "Epoch 73/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 448ms/step - accuracy: 0.9258 - loss: 0.2089 - val_accuracy: 0.6439 - val_loss: 1.5079\n",
      "Epoch 74/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m213s\u001b[0m 471ms/step - accuracy: 0.9262 - loss: 0.2035 - val_accuracy: 0.6486 - val_loss: 1.5105\n",
      "Epoch 75/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 443ms/step - accuracy: 0.9259 - loss: 0.2153 - val_accuracy: 0.6391 - val_loss: 1.5436\n",
      "Epoch 76/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m232s\u001b[0m 515ms/step - accuracy: 0.9288 - loss: 0.2028 - val_accuracy: 0.6391 - val_loss: 1.5294\n",
      "Epoch 77/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m251s\u001b[0m 556ms/step - accuracy: 0.9284 - loss: 0.2026 - val_accuracy: 0.6333 - val_loss: 1.5353\n",
      "Epoch 78/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 540ms/step - accuracy: 0.9282 - loss: 0.2033 - val_accuracy: 0.6411 - val_loss: 1.5800\n",
      "Epoch 79/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 541ms/step - accuracy: 0.9308 - loss: 0.1946 - val_accuracy: 0.6309 - val_loss: 1.6001\n",
      "Epoch 80/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m241s\u001b[0m 535ms/step - accuracy: 0.9294 - loss: 0.2021 - val_accuracy: 0.6418 - val_loss: 1.5878\n",
      "Epoch 81/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m232s\u001b[0m 515ms/step - accuracy: 0.9314 - loss: 0.1941 - val_accuracy: 0.6405 - val_loss: 1.5704\n",
      "Epoch 82/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m258s\u001b[0m 573ms/step - accuracy: 0.9333 - loss: 0.1886 - val_accuracy: 0.6414 - val_loss: 1.5742\n",
      "Epoch 83/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m257s\u001b[0m 570ms/step - accuracy: 0.9299 - loss: 0.1991 - val_accuracy: 0.6472 - val_loss: 1.6052\n",
      "Epoch 84/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m250s\u001b[0m 555ms/step - accuracy: 0.9329 - loss: 0.1853 - val_accuracy: 0.6448 - val_loss: 1.5876\n",
      "Epoch 85/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m245s\u001b[0m 543ms/step - accuracy: 0.9368 - loss: 0.1805 - val_accuracy: 0.6415 - val_loss: 1.6105\n",
      "Epoch 86/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m240s\u001b[0m 532ms/step - accuracy: 0.9343 - loss: 0.1822 - val_accuracy: 0.6388 - val_loss: 1.6012\n",
      "Epoch 87/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m236s\u001b[0m 524ms/step - accuracy: 0.9334 - loss: 0.1977 - val_accuracy: 0.6003 - val_loss: 1.6918\n",
      "Epoch 88/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m241s\u001b[0m 534ms/step - accuracy: 0.9353 - loss: 0.1837 - val_accuracy: 0.6332 - val_loss: 1.6263\n",
      "Epoch 89/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 541ms/step - accuracy: 0.9351 - loss: 0.1855 - val_accuracy: 0.6469 - val_loss: 1.6347\n",
      "Epoch 90/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m240s\u001b[0m 533ms/step - accuracy: 0.9367 - loss: 0.1765 - val_accuracy: 0.6370 - val_loss: 1.6749\n",
      "Epoch 91/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m225s\u001b[0m 499ms/step - accuracy: 0.9414 - loss: 0.1754 - val_accuracy: 0.6407 - val_loss: 1.6719\n",
      "Epoch 92/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 538ms/step - accuracy: 0.9410 - loss: 0.1657 - val_accuracy: 0.6456 - val_loss: 1.6562\n",
      "Epoch 93/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 542ms/step - accuracy: 0.9405 - loss: 0.1695 - val_accuracy: 0.6485 - val_loss: 1.6315\n",
      "Epoch 94/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 541ms/step - accuracy: 0.9422 - loss: 0.1707 - val_accuracy: 0.6483 - val_loss: 1.6506\n",
      "Epoch 95/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 540ms/step - accuracy: 0.9374 - loss: 0.1749 - val_accuracy: 0.6476 - val_loss: 1.6121\n",
      "Epoch 96/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 540ms/step - accuracy: 0.9375 - loss: 0.1759 - val_accuracy: 0.6451 - val_loss: 1.6625\n",
      "Epoch 97/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m242s\u001b[0m 537ms/step - accuracy: 0.9411 - loss: 0.1641 - val_accuracy: 0.6462 - val_loss: 1.6737\n",
      "Epoch 98/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 539ms/step - accuracy: 0.9385 - loss: 0.1760 - val_accuracy: 0.6340 - val_loss: 1.6876\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 541ms/step - accuracy: 0.9400 - loss: 0.1708 - val_accuracy: 0.6387 - val_loss: 1.6754\n",
      "Epoch 100/100\n",
      "\u001b[1m451/451\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 539ms/step - accuracy: 0.9435 - loss: 0.1589 - val_accuracy: 0.6162 - val_loss: 1.7910\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "# Define early stopping criteria\n",
    "early_stop = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model with early stopping\n",
    "            history = model.fit(x= x_train, y = y_train, epochs=100, batch_size=64, validation_data=(x_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "abc791f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: [0.23170605301856995, 0.38770341873168945, 0.47069844603538513, 0.5149717330932617, 0.54307621717453, 0.5607369542121887, 0.5791263580322266, 0.5981055498123169, 0.6144130825996399, 0.6272162795066833, 0.6412338018417358, 0.6610457897186279, 0.6726345419883728, 0.684535562992096, 0.6993858814239502, 0.7137503623962402, 0.7247840166091919, 0.7331459522247314, 0.7470247149467468, 0.7586135268211365, 0.7704104781150818, 0.7772804498672485, 0.7839075922966003, 0.7967454195022583, 0.8042052388191223, 0.8114222288131714, 0.8189514875411987, 0.8220741748809814, 0.8287706971168518, 0.8321362733840942, 0.8429964184761047, 0.8419902324676514, 0.8475417494773865, 0.8508726358413696, 0.8564241528511047, 0.8586447238922119, 0.8633981943130493, 0.8668332099914551, 0.8668332099914551, 0.8723153471946716, 0.872766375541687, 0.8778321146965027, 0.8804690837860107, 0.8793588280677795, 0.8836265206336975, 0.8877901434898376, 0.8890392184257507, 0.8898025751113892, 0.895319402217865, 0.8954928517341614, 0.8995524048805237, 0.8977828621864319, 0.9014260172843933, 0.904722273349762, 0.9029874205589294, 0.9058325290679932, 0.9036466479301453, 0.907324492931366, 0.9099962115287781, 0.9081919193267822, 0.9130842089653015, 0.9141598343849182, 0.9156864881515503, 0.913569986820221, 0.9186357259750366, 0.9188439249992371, 0.9183928370475769, 0.9198848009109497, 0.921584963798523, 0.9198501110076904, 0.92449951171875, 0.9232851266860962, 0.924777090549469, 0.9248117804527283, 0.9260608553886414, 0.9261302351951599, 0.9265466332435608, 0.9273099303245544, 0.9296693205833435, 0.9283855557441711, 0.9300510287284851, 0.9307102560997009, 0.928767204284668, 0.931959331035614, 0.9352208375930786, 0.9324104189872742, 0.9324451088905334, 0.9353943467140198, 0.9326532483100891, 0.9351861476898193, 0.9390721917152405, 0.9387599229812622, 0.9374067783355713, 0.9395232796669006, 0.9360882639884949, 0.9412928223609924, 0.9403212666511536, 0.9404253959655762, 0.9371291995048523, 0.9406335949897766]\n",
      "Validation Accuracy: [0.2585621178150177, 0.4343334138393402, 0.46886497735977173, 0.5084913969039917, 0.541607677936554, 0.5724596381187439, 0.562128484249115, 0.5983583331108093, 0.5975092053413391, 0.5945371985435486, 0.5902915596961975, 0.5884517431259155, 0.5893008708953857, 0.6300594210624695, 0.6297764182090759, 0.6287857294082642, 0.6108123660087585, 0.6242570281028748, 0.6300594210624695, 0.625106155872345, 0.6173223853111267, 0.6317577362060547, 0.6328898668289185, 0.6286442279815674, 0.6270874738693237, 0.6229832768440247, 0.6221341490745544, 0.6362864375114441, 0.630200982093811, 0.6256722211837769, 0.6344466209411621, 0.6355788111686707, 0.6348711848258972, 0.6269459128379822, 0.6142088770866394, 0.6405321359634399, 0.6309085488319397, 0.6338805556297302, 0.6379846930503845, 0.6215680837631226, 0.6369940638542175, 0.6306255459785461, 0.641947329044342, 0.635861873626709, 0.6352958083152771, 0.6335974931716919, 0.6399660110473633, 0.6367110013961792, 0.6457684636116028, 0.6372771263122559, 0.6379846930503845, 0.6282196640968323, 0.6389753818511963, 0.6328898668289185, 0.6324653029441833, 0.6360033750534058, 0.6265213489532471, 0.6239739656448364, 0.6362864375114441, 0.6262383460998535, 0.6357203722000122, 0.6287857294082642, 0.6403905749320984, 0.6393999457359314, 0.6395414471626282, 0.6425134539604187, 0.630200982093811, 0.6354373097419739, 0.6395414471626282, 0.6378431916236877, 0.6401075720787048, 0.6389753818511963, 0.6439286470413208, 0.6485989093780518, 0.6391168832778931, 0.6391168832778931, 0.6333144903182983, 0.6410982012748718, 0.6309085488319397, 0.6418058276176453, 0.6405321359634399, 0.6413812637329102, 0.6471837162971497, 0.6447778344154358, 0.6415227651596069, 0.6388338804244995, 0.6003396511077881, 0.6331729292869568, 0.6469006538391113, 0.6369940638542175, 0.6406736373901367, 0.645626962184906, 0.648457407951355, 0.6483159065246582, 0.6476082801818848, 0.6450608372688293, 0.6461930274963379, 0.634022057056427, 0.638692319393158, 0.6161901950836182]\n"
     ]
    }
   ],
   "source": [
    "# Print the accuracy values\n",
    "print(\"Training Accuracy:\", history.history['accuracy'])\n",
    "//print(\"Validation Accuracy:\", history.history['val_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2b0f1923",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "model_json = model.to_json()\n",
    "with open(\"emotiondetector.json\",'w') as json_file:\n",
    "    json_file.write(model_json)\n",
    "model.save(\"emotiondetector.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46e2098e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import model_from_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "90703547",
   "metadata": {},
   "outputs": [],
   "source": [
    "json_file = open(\"emotiondetector.json\", \"r\")\n",
    "model_json = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(model_json)\n",
    "model.load_weights(\"emotiondetector.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3932bbe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = ['angry','disgust','fear','happy','neutral','sad','surprise']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a76e7d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import load_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b0ccbacf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ef(image):\n",
    "    img = load_img(image,color_mode='grayscale' )\n",
    "    feature = np.array(img)\n",
    "    feature = feature.reshape(1,48,48,1)\n",
    "    return feature/255.0\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5882c78e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original image is of sad\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "model prediction is  sad\n"
     ]
    }
   ],
   "source": [
    "image = 'images/train/sad/42.jpg'\n",
    "print(\"original image is of sad\")\n",
    "img = ef(image)\n",
    "pred = model.predict(img)\n",
    "pred_label = label[pred.argmax()]\n",
    "print(\"model prediction is \",pred_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8994206c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c83af9e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original image is of sad\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "model prediction is  sad\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x301407df0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 'images/train/sad/127.jpg'\n",
    "print(\"original image is of sad\")\n",
    "img = ef(image)\n",
    "pred = model.predict(img)\n",
    "pred_label = label[pred.argmax()]\n",
    "print(\"model prediction is \",pred_label)\n",
    "plt.imshow(img.reshape(48,48),cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5adb7b7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original image is of fear\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "model prediction is  fear\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x301679960>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 'images/train/fear/1703.jpg'\n",
    "print(\"original image is of fear\")\n",
    "img = ef(image)\n",
    "pred = model.predict(img)\n",
    "pred_label = label[pred.argmax()]\n",
    "print(\"model prediction is \",pred_label)\n",
    "plt.imshow(img.reshape(48,48),cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e5ea96ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original image is of disgust\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "model prediction is  disgust\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x3016c1510>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ufP78+akxdZ2pkl+PKulVpeUjIyNu3CvDDh0d4D1v9bzUdRqyVpWWqz3z2gHUnqlyfm996HtXPW8v7r3WSZLI5z2Gb0IAgGhIQgCAaEhCAIBoSEIAgGhIQgCAaEhCAIBoSEIAgGgKtk9o2rRpqTXwXu27GiugavJV3bzXC6R6dfr6+tx4V1fXpB73ZnhjJNSx1fNSPRTenp85c8Zd29vb68a9c1cjKg4fPuzG16xZ48Yfe+yx1Jjq1VEjEbweCzUGQvXbDA0NuXFvdIfqS1FjC7zXWx07pAdQ7Vno6+X1D+bzMyf0cyHksVWfkPrcGMM3IQBANCQhAEA0JCEAQDQkIQBANCQhAEA0JCEAQDQkIQBANAXbJzRz5szU+nqvHyCfNflmfg/G+fPn3bWq18Drf1JrVa+O17+h5hwpqh/AO3c1L6iystKNFxcXp8a83o2beez9+/e78dra2tTYF7/4RXdtVVWVG/eet5rZE9rL412Hx44dc9f+9Kc/dePe3J17773XXfu5z33OjS9evDg1pvZMzb5RnxvenCS1Vp2bt159XqljK+rc01y5ckX2o43hmxAAIBqSEAAgGpIQACAakhAAIBqSEAAgGpIQACCaW7JE2ytf9cpLzfRt7lWJt1fyqMZIzJ492417ZaInTpxw1548edKN19XVpcbWrl3rrq2pqXHjP/7xj934L37xi0kfe/369W78V7/6VWpMvR4qrkrXvddElWCr533XXXelxlTZuirXP3TokBv/13/919SYKu/u7+934x51DR8/ftyNr1q1KjXmldOb6RaI//7v/3bjXlm810Zg5pd3m/ll2OrzbLIl1mO8z6TQY4/hmxAAIBqSEAAgGpIQACAakhAAIBqSEAAgGpIQACAakhAAIJqC7RMqLS21TCZzw1hJSUnqOtUnpG5trmr2vccuLy9313q9H+qxly5d6q49deqUG0/bSzOzBQsWuGtDe17uueee1Nhv/uZvumt/4zd+Y9KP7Y38MNN9QKoPwhs9oPa0oqLCjXvnrtaqvpTh4WE37o0kUdewGmfi9dN88MEH7lrVJ+T1KN19993u2t/6rd9y4+q97b0moeMWvF4d9XmlhIy2mWzsWjk9g5aWFvvCF75gFRUVNm/ePPvKV75y3YWTJIk1NzdbfX29lZSU2Nq1a+3o0aO5PAwA4A6RUxJqa2uzjRs32r59+6y1tdUuXbpkjY2NE4YXbd261bZt22Y7duyw9vZ2y2aztm7duuDBaQCA209Ofx339ttvT/jv119/3ebNm2cHDhywRx55xJIkse3bt9vmzZvtySefNDOzN954w+rq6mznzp329a9/ferOHABwywv6C8W+vj4zM6uurjYzs46ODuvq6rLGxsbxP5PJZGzNmjW2d+/eGx5jdHTU+vv7J/wAAO4Mk05CSZLYCy+8YA8//LAtW7bMzMy6urrM7PqbZdbV1Y3HrtXS0mJVVVXjPwsXLpzsKQEAbjGTTkLPPvusHT582P7lX/7luti11R5JkqRWgGzatMn6+vrGfzo7Oyd7SgCAW8ykSrSfe+45e+utt2zPnj0TSlGz2ayZffqNaP78+eO/7+7uTh0lkMlk3PJhAMDtK6cklCSJPffcc/bmm2/aO++8Y0uWLJkQX7JkiWWzWWttbbUVK1aY2adzOtra2uzll1/O6cTKyspSk5M3L0XVzYfOG/Jm0Khkqs4tpDekvr7ejaf9daiZ7lNQe6J6fR5++OFJH1v9G+Hv//7vp8bOnTvnru3t7XXjc+fOdeNez5jX22FmNmfOHDfuzQxSe+adl5nZQw895Ma9a02t/bd/+zc3/tprr6XGvJlXZmbLly9340eOHEmNeb1qZnpG02c/+1k37s0rUteZ4vVWebGbofqEvOvYi+XSJ5RTEtq4caPt3LnT/uM//sMqKirGP9iqqqqspKTEioqKrKmpybZs2WINDQ3W0NBgW7ZssdLSUnvqqadyeSgAwB0gpyT0yiuvmNn1kzhff/11+9M//VMzM3vxxRdtZGTENmzYYD09PbZq1SrbvXu3/D95AMCdJ+e/jlOKioqsubnZmpubJ3tOAIA7BDcwBQBEQxICAERDEgIAREMSAgBEU7DzhIqLi1P7bi5evJi6Ts1KUVV6M2fOdONen5By9d3Gb8TrM1KPq2bneH1EagaMt99mn/aCeU6fPp0aU30OqifG63FSvR+f+cxn3Pjo6Kgbn6o+iVzXqz1TfV+LFy92496+qffH7/3e77lxb9aRuoZXr17txt9///3UmNozNS+otbXVjXvHV/1m6lrx3gPqvan61dQ17n02hM4yGj/OlBwFAIBJIAkBAKIhCQEAoiEJAQCiIQkBAKIhCQEAoinYEu2RkZFJlbmq+9uVlpa6ca+E1MwvUb2Ze+t5vPJyVXar9sorp1TjLWbPnu3GVZmoV76qSs9Vaa33vNRrqcpT1fPyXhP1eqlrRZW9e9SeqT2vqqpKjZ09e9Zdq8ri//iP/zg1pp6zKjf2xi2o0vL/+Z//cePHjx9341fPVbuWaoFQz8u7DtU1quKqpcXjlY6r53Q1vgkBAKIhCQEAoiEJAQCiIQkBAKIhCQEAoiEJAQCiIQkBAKIp2D6h6dOnp/Y7eDXoqg9I9dOo3hGvx0Ld2jykn6avr89dq/pOvGN7IyTMzAYHB924Wl9SUpIaU7086tjetaCOrfppenp63Pi5c+dSY0uXLnXXqnPzrlO1J+paUNeh9x5S7w8VP3XqVGpM9ZaoPfPOu7a21l27f/9+N65GPXg9M+q81evl7Yva74GBATeueP1uXv8TfUIAgFsCSQgAEA1JCAAQDUkIABANSQgAEA1JCAAQDUkIABBNwfYJXbx4Ufbd3Egu9ek3oh7Tq5v3egXMPp2R5PHmqah5KKp3xFuvZt+oOS8q7vW8qNk2Ku6du9fHY2b2v//7v268q6vLjXu9I2qGjJrj4s30UddR6Dwhr/fE6/ky09epd279/f3uWnWNe89L9bqpPVWfC16PkvpMCvnMUvtdVlbmxtUsMY/X36Q+C6/GNyEAQDQkIQBANCQhAEA0JCEAQDQkIQBANCQhAEA0BVuifeHChdTyW6+sUJVSqtumq3LjoaGh1JgqS1TlkF75qyqjDimzVnumxl+EPC/12Kq01nP27Fk33t3d7ca9Mmkzs7q6utTY6dOn3bWqJHjx4sWTPi81OkCNcvD2XI1KCSnRVu89VfbulZarsvXQERXe+0+tVbxjT6aN5WaPbeZfS97nAiXaAIBbAkkIABANSQgAEA1JCAAQDUkIABANSQgAEA1JCAAQTcH2Cc2aNSv11u1efbrqv1B9Qiru9UGoXgTF6ydQPRKq3t/r5VG351fHVn0n3vNSt++vqKhw497r3dPT465VfURqX7zrUO2J6rfxxhKo10O9B1Qfkdd7ovZ03rx5btzbs9AePu+81fgKdeyBgQE3HjLWQI1yUPviUX1Eqq/Lu46988rlnPkmBACIhiQEAIiGJAQAiIYkBACIhiQEAIiGJAQAiIYkBACIpmD7hDxe7buqi1c9Fqqm3+vBUPX+au6OV5Ov1qoepZD+jJB5QWZ+X0paL9gYdW7efKczZ864a9Wsoq6uLjeueiw81dXVbtzrWykrK3PXqj4gNd/Gew+oPiG1J15/lLrO1LXiXQuqz2d4eNiNqz31Xi/Vg5RPahaY+szy1nufpepz9mp8EwIAREMSAgBEQxICAERDEgIAREMSAgBEQxICAERDEgIARHPb9QmF9MuY6X4Br+ZfzYgpLy93414fRD77m9SxKysr3Xg2m3XjtbW1qTHVf6FeL6835MSJE5Nea6b7jD7++OPUmLoOFy5c6MYbGhpSY6pPSF1n6vX2ZlepPiH12Gquj0f1N3mzqVQPkpprpfppvB4/9d4M6QFUx1afZ2ru1WRnBjFPCABwSyAJAQCiIQkBAKIhCQEAoiEJAQCiIQkBAKIp2BLt4uLi1JJlr1RT3bJdlUOG3E5elRurW9F7VCmlKjf2ynJVWa0q0a6pqXHj3r6ocmE1GsArPVfXgrrF/smTJ924VwKurqN58+a5ca88XJUqq/JwVY7slVGrY4e0EvT19blre3t73bi356rE+vTp025claZ7j60+F7ySeDP/3NXnQugoB+/18s5bHfdqOX0TeuWVV2z58uVWWVlplZWVtnr1avvJT34yHk+SxJqbm62+vt5KSkps7dq1dvTo0VweAgBwB8kpCS1YsMBeeukl279/v+3fv9++9KUv2RNPPDGeaLZu3Wrbtm2zHTt2WHt7u2WzWVu3bp38P1IAwJ0ppyT0+OOP2x/8wR/YPffcY/fcc4/97d/+rZWXl9u+ffssSRLbvn27bd682Z588klbtmyZvfHGGzY8PGw7d+7M1/kDAG5hky5MuHz5su3atcuGhoZs9erV1tHRYV1dXdbY2Dj+ZzKZjK1Zs8b27t2bepzR0VHr7++f8AMAuDPknISOHDli5eXllslk7JlnnrE333zT7r//fuvq6jIzs7q6ugl/vq6ubjx2Iy0tLVZVVTX+o+6pBQC4feSchO699147dOiQ7du3z77xjW/Y008/be+99954/NrqmCRJ3IqZTZs2WV9f3/hPZ2dnrqcEALhF5VyiXVxcbHfffbeZmT344IPW3t5u3/nOd+yb3/ymmZl1dXXZ/Pnzx/98d3f3dd+OrpbJZIJKlwEAt67gPqEkSWx0dNSWLFli2WzWWltbbcWKFWb2aR9GW1ubvfzyyzkf99KlS6k18F7tu6rJV+MWVEL0ju+NmDDTtzf3bruu+jNU3Ou3Ub04oXGP6tVRlZUlJSWpsbH/WUpz+PBhN66uhfr6+tSYuhZUf8evfvWr1JgaMaH29L777nPjjzzySGpMvR7q33S9a0Wdt+pB8npTPvzwQ3et908GZrrfxjt31TOj+oS8vrDQPiFlsn1CuTxuTknoW9/6lq1fv94WLlxoAwMDtmvXLnvnnXfs7bfftqKiImtqarItW7ZYQ0ODNTQ02JYtW6y0tNSeeuqpXB4GAHCHyCkJnT592r72ta/ZqVOnrKqqypYvX25vv/22rVu3zszMXnzxRRsZGbENGzZYT0+PrVq1ynbv3m0VFRV5OXkAwK0tpyT02muvufGioiJrbm625ubmkHMCANwhuIEpACAakhAAIBqSEAAgGpIQACCagp0n5PF6dULnBal+G0/IbA4ltAfJo/ozVO+V2tOQ+wGqfgPv3BYtWuSunTt3rhs/d+6cG+/o6EiNqZ4WtedeP1t1dbW7VvVtef1NZv5sqpD3h5n/HlGvtTc7yszf0w8++MBdq65Rrx/NzO+ZUe97dS2EfG6E9DepuNejlEufEN+EAADRkIQAANGQhAAA0ZCEAADRkIQAANGQhAAA0RRsifaVK1dSy/y8kmBVQqpKmVXcK5UOLV/1SmtVya8qo/bKW9XIglmzZrlxr6RXUXumbn7rlTJXVVUFPbba83vvvTc1NmfOHHetKv/2SmPVnqjb+6vRAb29vamx2bNnu2srKyvduHeNq7LewcFBN97T05Ma88rpzfSeqfYL73ND7XfIOAb1eaWO7Y2JMPPLw73zokQbAHBLIAkBAKIhCQEAoiEJAQCiIQkBAKIhCQEAoiEJAQCiKdg+oUuXLqX2cYTUzYeOJfB6R1RfSciYidDbvQ8MDKTG1JgI1eegeii8vpba2lp3repz8PZleHjYXatGA9TV1bnxsrKy1FhfX5+7Vo1b8Hqc1Fr1HlDX6cmTJ1Nj6rVWPWfeNa56VtS4hbNnz6bGTpw44a5Ve6LeX95nUsg1fDOPHbI2JO5dC7mMluGbEAAgGpIQACAakhAAIBqSEAAgGpIQACAakhAAIBqSEAAgmoLtExoZGUmtQ/f6WlQfgzd/Rh3bzO9zUGtVTb53bNVroHoovF4Edd6q50XN7fHmEXn9S2Zm77//vhv3nrfqOzl9+rQbV31C8+bNS42pfjR1nXqvibqG1SwjNR/KmxmUz74Tbx6Qme5XO3XqVGrszJkz7lrVw6eelzq3kGN714qa26Ous5DHpk8IAHDLIwkBAKIhCQEAoiEJAQCiIQkBAKIhCQEAoinYEu3Lly+nlgCmjXgYW+dRt01XvPJYVeqsRgd4owdUCah6XqrE26Meu6amxo175crq9VKlzt7roc5ryZIlbty7zsz88la13+pa8HR1dblxVeqsSrQ/97nPpcZUOb7iPe/e3l53rboWPvzww0kfW+2Juk69dgBVRp1LOfO11Hmp61CVaKsRF2ko0QYA3BJIQgCAaEhCAIBoSEIAgGhIQgCAaEhCAIBoSEIAgGgKtk9odHQ0tdZc1d17Zs6c6cYrKircuNdPoPoY1Hl7Nf2qD0j1KIUcW/UKqOc1NDSUGlO9OIsWLZr0sbu7u9216nlVV1e78bKystSY6pNQ/RteXO236ok5efJk0HqPev98/PHHqTHVO6XeXx0dHamxwcFBd616vdR7JGSsQcjnmTq26gMK6VGaquPyTQgAEA1JCAAQDUkIABANSQgAEA1JCAAQDUkIABANSQgAEE3B9gl5fRKq9t1TUlLixr2ZPmZmM2akb5nXN3IzvF4ENdNHzRXxemJCeiDM9J55PQPq9VBx79iqN+Sjjz5y415Pi5k/W0f1P2UymUnHVV+J6qdZsWKFG/fee6H9Nv39/akx9f5RfV/enCXVE6beX6qvy3ve6vXK57yhfPUBmfnnTZ8QAOCWQBICAERDEgIAREMSAgBEQxICAERDEgIAREMSAgBEU7B9Qn19famzf7yZPorXp2Cm5w15tfGqn0YZHR1NjakZL6qPwZvz4j2umd4zNcvImxMze/Zsd63qofD2vLS01F2rnrfqRzt9+nRqLHT+k9fro/qA1J6qmT/e81bX+NmzZ934uXPnUmNeD56Z2bFjx9y410ek+s1Un5Dq+5qqnpkbUdeKJ6SnUj22tydJktz05yHfhAAA0ZCEAADRkIQAANGQhAAA0ZCEAADRkIQAANEUbIn25cuXU8sDBwYGUtepW+SrcuOQckpV3q1uJ6/Kej2q3Nij9qyvr8+Nq/LWmpqa1JgqN1Ylpl6ZqCrRVqXKqhXAKz1X15l6rb1zU2Xrc+bMceOqdNYro1avhyrR9tarMuijR4+6cY/aM/XeVOvzKeSx1fNSJvt5+H82yqGlpcWKioqsqalpwoM3NzdbfX29lZSU2Nq1a4MuHgDA7WvSSai9vd1effVVW758+YTfb9261bZt22Y7duyw9vZ2y2aztm7dOvfbCwDgzjSpJDQ4OGhf/epX7R/+4R8mfPVPksS2b99umzdvtieffNKWLVtmb7zxhg0PD9vOnTun7KQBALeHSSWhjRs32pe//GV77LHHJvy+o6PDurq6rLGxcfx3mUzG1qxZY3v37r3hsUZHR62/v3/CDwDgzpBzYcKuXbvsl7/8pbW3t18XG5vxXldXN+H3dXV1dvz48Rser6Wlxf76r/8619MAANwGcvom1NnZac8//7z98z//s1s5dG1FRpIkqVUamzZtsr6+vvGfzs7OXE4JAHALy+mb0IEDB6y7u9tWrlw5/rvLly/bnj17bMeOHfbBBx+Y2affiObPnz/+Z7q7u6/7djQmk8nIEmEAwO0ppyT06KOP2pEjRyb87s/+7M/svvvus29+85u2dOlSy2az1traaitWrDCzT/sh2tra7OWXX87pxJIkSa2P93piVP+F6uVRvNvNq9p4VbNfXl6eGlPnrW737o16CB07oPo7vMpIdft+b0/M/F4gtd/qf37U7f8rKytTY9ls1l2r+m28a2loaGjSa83818PM7yNS/2brjbcw8/f8k08+cdd2dHS4ce/1Cu3zUe8Bb89j9erE5H1+XyunJFRRUWHLli2b8LuysjKrqakZ/31TU5Nt2bLFGhoarKGhwbZs2WKlpaX21FNP5fJQAIA7wJTfMeHFF1+0kZER27Bhg/X09NiqVats9+7dsjsdAHDnCU5C77zzzoT/LioqsubmZmtubg49NADgNscNTAEA0ZCEAADRkIQAANGQhAAA0RTsPKHy8vLU3hivT0j1UKh+G9Xz4lH1/GVlZW7cOzevz8dM90F4fSlqz9SsIm9ekJnf66P6ZVQfkdfTMjg4GHRsxbtriLrOVG+Wd+4hs6PM9Pwn7zpWe6pmT9XW1qbGDh065K5VfURen5DaM9UHFDJvSK3NZx9QaI+SxzvvJEnkZ9YYvgkBAKIhCQEAoiEJAQCiIQkBAKIhCQEAoiEJAQCiKdgS7dLSUisuLr5hzCujPnPmjHvcnp4eN65KOb0bsYaUcZr5JabeyAIzv1zYzGxkZCQ1pkp2VVw9L29P1fNSpbNeqbM6b1WirR47n7zyV7Xf3mttpkc5eKW13d3d7lp1HXpDK/fu3euuTfs8GOPti2q9uF3LqEOPPdn1uewX34QAANGQhAAA0ZCEAADRkIQAANGQhAAA0ZCEAADRkIQAANEUbJ9QJpNJ7QvweksymYx7XHWL/d7eXjeueiw8Ib0GlZWVQcf26v1V34nqt1GjILxxC6ovq7+/3417PS2qr0T1QKhxC16fUchIEDO/R0n1N6k+IfV6eb126vb8Xq+bmdm7776bGjt58qS7Vr23vWsp9PVQ75F89pR516l634d8Lpj5zyvkvCY8xk3/SQAAphhJCAAQDUkIABANSQgAEA1JCAAQDUkIABANSQgAEE3B9gnNnDkztafHq9kvKytzj7to0SI3rvocTp06lRpTs4q8fhkzs+Hh4dSY6l9SfRBeb5XqFVB9KbNnz3bjXo+T2hPVn+HFVa/CpUuX3LjaU2+96jFS/WZef5TqnVKvl9pT7zpW859+/vOfu/GDBw+mxlRfl3q9vOet+njUnoT004QeO2Rt6Bwk7/hTNQeJb0IAgGhIQgCAaEhCAIBoSEIAgGhIQgCAaEhCAIBoSEIAgGgKtk9o2rRpqX0aXi9P6FwPtX7WrFmpse7ubndtyNwdNcdF9bSox/aoXh7V3+GdezabdddWVFS4ca83RPWVnDt3zo2r+TXeuak+oJA+osHBQXetOm91bt5cnmPHjrlr//M//9ONe7OpVI9fSF9XPvtlVDxkbah8HtvbU+YJAQBuCSQhAEA0JCEAQDQkIQBANCQhAEA0JCEAQDQFW6I9OjqaWubnlVGr29irEmx1q3qvRFuVxqpRD17prSpPVXGvjFqVcfb19blxVcI9MjKSGlOl53V1dW7cK9dXZdBdXV1uXJWZ1tTUpMbUtaDK2r3rVK1VpeeffPKJG/eOv2/fPnetej3Ly8tTY6qNQI1Z8UrLldBSZu89oD5zQsrH813+7Z1byBiVq/FNCAAQDUkIABANSQgAEA1JCAAQDUkIABANSQgAEE3BlWiPlfZ55bXe3XJVubAql1TrvbJEVRKsyle9Mmt1Xor3vEPLPNXz8kpnvbsqm/nl3WZ+KWjIfqtjm/nl+urY6ty811vtyfDwsBtXe+5d4+o6VHsWUtbrrb2Z9fkUckfpmOetTPZ5jcVu5rkVJQW2AydOnLCFCxfGPg0AQKDOzk5bsGCB+2cKLglduXLFTp48aRUVFVZUVGT9/f22cOFC6+zstMrKytind0tgz3LHnuWOPcvdnbJnSZLYwMCA1dfXy799Kri/jps2bdoNM2dlZeVt/aLlA3uWO/Ysd+xZ7u6EPauqqrqpP0dhAgAgGpIQACCagk9CmUzGvv3tb8sbQuLX2LPcsWe5Y89yx55dr+AKEwAAd46C/yYEALh9kYQAANGQhAAA0ZCEAADRkIQAANEUfBL63ve+Z0uWLLFZs2bZypUr7d133419SgVjz5499vjjj1t9fb0VFRXZj370ownxJEmsubnZ6uvrraSkxNauXWtHjx6Nc7IFoKWlxb7whS9YRUWFzZs3z77yla/YBx98MOHPsGfXe+WVV2z58uXjXf6rV6+2n/zkJ+Nx9szX0tJiRUVF1tTUNP479uzXCjoJ/eAHP7CmpibbvHmzHTx40H7nd37H1q9fbx999FHsUysIQ0ND9sADD9iOHTtuGN+6datt27bNduzYYe3t7ZbNZm3dunU2MDDwf3ymhaGtrc02btxo+/bts9bWVrt06ZI1Njba0NDQ+J9hz663YMECe+mll2z//v22f/9++9KXvmRPPPHE+Icme5auvb3dXn31VVu+fPmE37NnV0kK2Be/+MXkmWeemfC7++67L/mrv/qrSGdUuMwsefPNN8f/+8qVK0k2m01eeuml8d+dP38+qaqqSv7+7/8+whkWnu7u7sTMkra2tiRJ2LNczJkzJ/nHf/xH9swxMDCQNDQ0JK2trcmaNWuS559/PkkSrrNrFew3oQsXLtiBAwessbFxwu8bGxtt7969kc7q1tHR0WFdXV0T9i+TydiaNWvYv/+vr6/PzMyqq6vNjD27GZcvX7Zdu3bZ0NCQrV69mj1zbNy40b785S/bY489NuH37NlEBXcX7TFnzpyxy5cvW11d3YTf19XVWVdXV6SzunWM7dGN9u/48eMxTqmgJEliL7zwgj388MO2bNkyM2PPPEeOHLHVq1fb+fPnrby83N588027//77xz802bOJdu3aZb/85S+tvb39uhjX2UQFm4TGXDvVM0kSOekTv8b+3dizzz5rhw8ftp///OfXxdiz691777126NAh6+3ttX//93+3p59+2tra2sbj7NmvdXZ22vPPP2+7d+92p++yZ58q2L+Oq62ttenTp1/3rae7u/u6/4PA9bLZrJkZ+3cDzz33nL311lv2s5/9bMLsKvYsXXFxsd1999324IMPWktLiz3wwAP2ne98hz27gQMHDlh3d7etXLnSZsyYYTNmzLC2tjb7u7/7O5sxY8b4vrBnnyrYJFRcXGwrV6601tbWCb9vbW21hx56KNJZ3TqWLFli2Wx2wv5duHDB2tra7tj9S5LEnn32WfvhD39o//Vf/2VLliyZEGfPbl6SJDY6Osqe3cCjjz5qR44csUOHDo3/PPjgg/bVr37VDh06ZEuXLmXPrhavJkLbtWtXMnPmzOS1115L3nvvvaSpqSkpKytLPvzww9inVhAGBgaSgwcPJgcPHkzMLNm2bVty8ODB5Pjx40mSJMlLL72UVFVVJT/84Q+TI0eOJH/yJ3+SzJ8/P+nv74985nF84xvfSKqqqpJ33nknOXXq1PjP8PDw+J9hz663adOmZM+ePUlHR0dy+PDh5Fvf+lYybdq0ZPfu3UmSsGc34+rquCRhz65W0EkoSZLku9/9brJo0aKkuLg4+fznPz9eTosk+dnPfpaY2XU/Tz/9dJIkn5aCfvvb306y2WySyWSSRx55JDly5Ejck47oRntlZsnrr78+/mfYs+v9+Z//+fh7cO7cucmjjz46noCShD27GdcmIfbs15gnBACIpmD/TQgAcPsjCQEAoiEJAQCiIQkBAKIhCQEAoiEJAQCiIQkBAKIhCQEAoiEJAQCiIQkBAKIhCQEAovl/M1JzEuvaC4kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 'images/train/disgust/11223.jpg'\n",
    "print(\"original image is of disgust\")\n",
    "img = ef(image)\n",
    "pred = model.predict(img)\n",
    "pred_label = label[pred.argmax()]\n",
    "print(\"model prediction is \",pred_label)\n",
    "plt.imshow(img.reshape(48,48),cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1c4befa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original image is of happy\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "model prediction is  happy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x301723850>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 'images/train/happy/156.jpg'\n",
    "print(\"original image is of happy\")\n",
    "img = ef(image)\n",
    "pred = model.predict(img)\n",
    "pred_label = label[pred.argmax()]\n",
    "print(\"model prediction is \",pred_label)\n",
    "plt.imshow(img.reshape(48,48),cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8d6bb2fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original image is of surprise\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "model prediction is  surprise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x3017235b0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 'images/train/surprise/175.jpg'\n",
    "print(\"original image is of surprise\")\n",
    "img = ef(image)\n",
    "pred = model.predict(img)\n",
    "pred_label = label[pred.argmax()]\n",
    "print(\"model prediction is \",pred_label)\n",
    "plt.imshow(img.reshape(48,48),cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b005764f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original image is of surprise\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "model prediction is  sad\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x3020002e0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3b6onTPF6mFQ/Wsi9q7atzoeas7KystSY2m+1b975VtewulbU/eXd29570ycEALghkIQAANGQhAAA0ZCEAADRkIQAANGQhAAA0ZCEAADR5G2f0JgxY1Lr0L0eDFUXr+KqX8Dr9QlZL8jM7KOPPhpWzEwfl1fTr3o/1H6rngBvPSGvh8hM92V549WchPaleOck9DrzxqueFTVnIb0joXPmHbc6LtWP5q2do7atjktd496cqrWM1LWgeoE86v7y7k0z/zPN6/nKpZeNb0IAgGhIQgCAaEhCAIBoSEIAgGhIQgCAaEhCAIBoSEIAgGjytk+osLAwtUbdq09X/QCq30b1C3hrlqg1ZLz9VnHVK6C27R232raaM9Xn4PUqqN6P8vJyN+5RvQrquELmXL13yLpUoesJqfPl9fqonhfF6+Xp6upyx6p7M0To2lMe1WMU0oOkzqVaHy3kOvXOh7q3Lsc3IQBANCQhAEA0JCEAQDQkIQBANCQhAEA0JCEAQDR5W6I9ZsyY1LJJr/xPlfyqEu5r2a80quw2pCxXHVdIGXVoya8qb/UeBx+yrIBZ2PlUS2+EUCXz6ri9fVOtAOp8hLQSqP1W58OLT5w40R2r7gHvuNT5COVdp6GfOSHUZ466v7xryZvTXOabb0IAgGhIQgCAaEhCAIBoSEIAgGhIQgCAaEhCAIBoSEIAgGjytk+ooKAgtSfB69VR9enq0eeKV/MfutxCSH+G4u236itRc6bi3nuH9Cmo8aqvRO13yJyrbYc8vl/NibrO1HXq7VtoX5c3Xp0vtW2vt0r10al4yHGHfuZ47x3ag6Tm1OMdVy7HzDchAEA0JCEAQDQkIQBANCQhAEA0JCEAQDQkIQBANCQhAEA0N2SfkFcbH9pPE9I7EtIjoajejpBth/QKmIX3QYQI6aEI7eXxth/avzGS69+oa8WLh/QYmfnHpbathNybKh6yFljoGkzevIxk/6Davncu6RMCANwQSEIAgGhIQgCAaEhCAIBoSEIAgGhIQgCAaG66Eu2QElEzXYrplSyGlmJ6+5bJZIY91izsseshc2IWVq4cUk4cukRFSJm0ug7V0gHee4cuVxJa1jtS7x1a1u69t7fMw7XE1Zyr8+kJKR9X5zJkmRUz/7i9pTfUshyD9uGafxMAgOuMJAQAiIYkBACIhiQEAIiGJAQAiIYkBACIhiQEAIgmb/uERo0alVrDHvLI9wsXLrhxVVfvxUNr9j1q26rXwKvbDznma4l71H6HLB0Q2i8T0rcS2o8WQh2X6p8K2bY67pA+OyWkF06daxUfyTkN7Z8K4c1blKUcGhoa7HOf+5yVlJTY1KlT7Wtf+5odOHDgqjevr6+3yspKKyoqstraWtu3b18ubwMAuEXklISampps+fLltmPHDmtsbLSPP/7Y6urqrLu7e+B31qxZY2vXrrX169dbc3OzZbNZW7x4sXV1dV33nQcA3Nhy+netN954Y9B/v/zyyzZ16lTbtWuXfelLX7IkSWzdunW2atUqW7p0qZmZbdiwwSoqKmzjxo32rW996/rtOQDghhf0j40dHR1mZlZWVmZmZgcPHrTW1larq6sb+J1MJmOLFi2y7du3D7mNvr4+6+zsHPQCANwahp2EkiSxZ555xh544AGbNWuWmZm1traamVlFRcWg362oqBiIXamhocFKS0sHXlVVVcPdJQDADWbYSejJJ5+0d955x3784x9fFbuy2iNJktQKkJUrV1pHR8fAq6WlZbi7BAC4wQyr1vmpp56yV1991bZt22bTp08f+Hk2mzWzT74RTZs2beDnbW1tV307uiSTychlCgAAN6ecklCSJPbUU0/Z5s2bbevWrVZdXT0oXl1dbdls1hobG23u3Llm9klfTlNTk73wwgs57ViSJKm15iHrCam+k5A+h5Gk1isZyb4TZSTX5QnpUQpdB0n1fnjj1fkKuY5C+0ZC1twKXXsqZN9Dxoas32SmrwUvHtqv5sVD7q1rMdw+oVz2K6cktHz5ctu4caP9y7/8i5WUlAz8nae0tNSKioqsoKDAVqxYYatXr7aamhqrqamx1atXW3FxsT366KO5vBUA4BaQUxJ68cUXzcystrZ20M9ffvll+5M/+RMzM3v22Wett7fXli1bZu3t7bZgwQLbsmWLlZSUXJcdBgDcPHL+5ziloKDA6uvrrb6+frj7BAC4RfAAUwBANCQhAEA0JCEAQDQkIQBANHm7nlB/f39qrbnXLxC6NoeKe/XvoT1E3nhvPSCzkV3HJWS9IEX1rKi+Ey+u5iS03yakfyPkWlFjQ/ujVE9NyHt7/TTqfITc2+o6G8lentD753r14wwl5FoabuxKfBMCAERDEgIAREMSAgBEQxICAERDEgIAREMSAgBEk7cl2t5SDp7QEuyQx9yrbYeUao5kmfRICymp7+vrc+PevKjH7yuqVNm7VtR7q/PpzVloWW7ocgwjNTZUSClzYWGhG1fXqddCEdoC4cVV64badkiLhHd/5FLmzzchAEA0JCEAQDQkIQBANCQhAEA0JCEAQDQkIQBANCQhAEA0N2SfkFcbr+r9Qx8X721f1eyrbXu9DKr/IuRR9KGPsQ9ZEkH1b6jjnjhx4rDfO/S4xo4dmxpT14Lqf/LeW/UgqeNSvSFej4fadiaTceM9PT3D3i/FmzN1HV24cGHY2zYzGzMm/aNUXePq/vPOh5pv1a8TskRMV1dXasw7z1fimxAAIBqSEAAgGpIQACAakhAAIBqSEAAgGpIQACAakhAAIJq87RPq6+uz8+fPDxkrLi5OHaf6AULq/c38uns1VtXke30SqpdA9SKErkETwtt31S+jeke8OfeuEzXWTPfjhPS8jBs3zo1789LR0eGOHcnr0OuNMtPXmXf/qWtc7XfIulVKyL6F9uF512Fob6Ia732e7tixIzWm7uvL8U0IABANSQgAEA1JCAAQDUkIABANSQgAEA1JCAAQTd6WaHd0dKSWB5aUlKSOUyWHqnw19DH4npBSzZBlIMz8Uks1Z2q/Q6jj6uzsdOPTpk0b9rZbWlrc+Pjx4924t6yHWlJELR0Q0gqgzldvb68bD1n2Q7VAeNepOl8q7u3bSN7XoUKXM/Go86W27d1/r7/++rC3ezm+CQEAoiEJAQCiIQkBAKIhCQEAoiEJAQCiIQkBAKIhCQEAosnbPqFjx46lPu6+uro6dZzqeQld0sB7rLrq/VBLA3j7rvocVF2+Nz7mMhCq50Utx/DrX/86NTZhwgR37MmTJ9347t273fjcuXNTY/fee687VvWleH1GXg+Rmb/EhJlZW1ubGz9+/HhqrKqqyh1bXl7uxkN6zkL6iEKXQlHnK2SJCsX7XFH3j+oJU31d77777rBiuRwz34QAANGQhAAA0ZCEAADRkIQAANGQhAAA0ZCEAADRkIQAANHkbZ/QBx98YEVFRUPGamtrR+x9Q3oRQnoJ1LZVb4ji1e2r/Qrl9WCoOUu7Bi7xequ6urrcsTNmzHDjU6ZMGfZ79/X1uWPVcXvne+zYse7YM2fOuHFvPS4zs/vuuy81po5Lrf/krdGk5kTFQ/rZVF9LSHwk1zhT+xX6ubFly5bUmDqua8U3IQBANCQhAEA0JCEAQDQkIQBANCQhAEA0JCEAQDQkIQBANHnbJ/Tee++lrqly/vz51HGqz0f1xISsO6LWC1J9DN6+h/byeONVn0LoWiwhfQ6qFyGTyQzrfc3Muru73bhay8ij1pZS59Pr71BzkrYO1yUhc6722zsf1/LenpA+IHUthArpw1Nz4u276ttSn0lHjhxx47/85S9TY956XUmSyD69S/gmBACIhiQEAIiGJAQAiIYkBACIhiQEAIiGJAQAiCZvS7Tff//91NLho0ePpo5Tj+cPKRFV1KPmlZAyz5jl3+q4L168mBpTj5pXJb9eiWrIMhBmZmfPnnXjaS0E16Knp8eNeyXe6rjU+VAl+d5xqf1WQpb1GMl7N1TI/eXdH2b+cav7R73322+/7ca963Dy5Mmpsf7+/pEp0X7xxRdt9uzZNmHCBJswYYItXLjQXn/99YF4kiRWX19vlZWVVlRUZLW1tbZv375c3gIAcAvJKQlNnz7dnn/+edu5c6ft3LnTvvzlL9sjjzwykGjWrFlja9eutfXr11tzc7Nls1lbvHjxNWdEAMCtJack9PDDD9tDDz1kd999t9199932V3/1VzZ+/HjbsWOHJUli69ats1WrVtnSpUtt1qxZtmHDBuvp6bGNGzeO1P4DAG5gw/5jwMWLF23Tpk3W3d1tCxcutIMHD1pra6vV1dUN/E4mk7FFixbZ9u3bU7fT19dnnZ2dg14AgFtDzklo7969Nn78eMtkMvbEE0/Y5s2b7b777rPW1lYzM6uoqBj0+xUVFQOxoTQ0NFhpaenAq6qqKtddAgDcoHJOQvfcc4/t2bPHduzYYd/+9rftscces/379w/Er6wSSZLErRxZuXKldXR0DLxaWlpy3SUAwA0q5xLtwsJCmzlzppmZzZ8/35qbm+173/uePffcc2Zm1traatOmTRv4/ba2tqu+HV0uk8nIMlwAwM0puE8oSRLr6+uz6upqy2az1tjYaHPnzjWzT2rMm5qa7IUXXsh5u62tral9A963pZqaGne76vH9ild3r+r9Q/ppQh9FP5J9QspIPube62lRVZnqfKjeK2/7aikH9Qh+r/9DbVv1L4X0N6nlLVTfinc+1TWu+pu8+0f1hIX2IHnXsdq2+twIoe6BX/ziF27c6wXylonIZdmNnJLQd77zHVuyZIlVVVVZV1eXbdq0ybZu3WpvvPGGFRQU2IoVK2z16tVWU1NjNTU1tnr1aisuLrZHH300l7cBANwickpCH374oX3jG9+wEydOWGlpqc2ePdveeOMNW7x4sZmZPfvss9bb22vLli2z9vZ2W7BggW3ZssVKSkpGZOcBADe2nJLQD3/4QzdeUFBg9fX1Vl9fH7JPAIBbBA8wBQBEQxICAERDEgIAREMSAgBEk7frCRUUFKT2DWzdujV13KVKPW+7IXGPVzdvpnsovL4V1Uugej96e3uH9b5m4T1KXp+E6v3w9tvM70dQ6+6cP3/ejat9U70nIbw+InUtlJaWunE13ut5UesJqTkL6acJofZL9V6pffOuBXV/qbj3uaH6cS5/ms1QvLXZzPzzNXbs2NRYLp8ZfBMCAERDEgIAREMSAgBEQxICAERDEgIAREMSAgBEk7cl2oWFhamli7/+9a9Tx50+fdrdrldWaKZLMVUZtkeVxnpxVcapyo294wp5tL+ZXpbAK/NUpbEh5eFqTtT5CDkutUSFWhLBi6tSf3VcquzdK2cOXXrDu/9UGXXIvafK6UPjIcelrlPvOlTno7m52Y2fO3fOjY8bNy415h1zLstT8E0IABANSQgAEA1JCAAQDUkIABANSQgAEA1JCAAQDUkIABBN3vYJlZeXp9bXf/DBB6njDhw44G7385//vBtXvSFe/buq2Ve9Pl4vQugSFCG9OmpJBBXv7u5Ojal+AtVD4W1bLTugqN4Q7zH66nyE9CB5x2xmlslk3LjqM/KUlJS4cdWH583ppEmT3LHq/vKuQ7XkgYorIf1sqrfKO1/Hjx93x+7cudONe31AZv615PUX0icEALghkIQAANGQhAAA0ZCEAADRkIQAANGQhAAA0ZCEAADR5G2fUFlZWer6IadOnUod95//+Z/udr/whS+4cdUb4q0NEtpr4PURha4h4+2b6l9S/RmqH8fbd7WeSUdHhxufPHlyaqyiosIdq/Zbnc9sNpsaa29vd8eqPiFvztT5Uj0rIWvjhKyJpXz44YduXK3LU1pamhpT51L1o6m1jLx+GtWHp3jn8+2333bHqmtczam3rpXXY6SuscvxTQgAEA1JCAAQDUkIABANSQgAEA1JCAAQDUkIABBN3pZoZzKZ1EeFe6Wxb731lrvdxx9/3I17jyc388tjQx8Xrx7pHsIr81TllKoM9LXXXnPjR48eTY2pZQlU2e4f/uEfpsaeeuopd6x675BrQS1p0NvbO+xtq3Jhtd/quL2yXbWUg3euzcwaGxtTY83Nze5YtezA3LlzU2OqXF8dV8i9rUrmVXn44cOHU2N79uxxx06dOtWNd3Z2uvHx48enxrxrNJfPMr4JAQCiIQkBAKIhCQEAoiEJAQCiIQkBAKIhCQEAoiEJAQCiyds+oeLi4tR+B6+m/9ChQ+52d+/e7cbr6urcuPeIfu9x7ma618DrJ1B192q5Ba8XSD3uvaury42rXgOvd0T1SNxxxx1ufPPmzamxY8eOuWNVb8gDDzzgxmtra1NjEyZMcMeqXh3vnEybNs0d6z1+38zsvffec+Otra2pMXWd/dM//ZMb37t3b2rsM5/5jDt2+vTpbtxb1kONVX1bIUulqPtHOXLkSGosZCkGM70siNdf5d0/aumZy/FNCAAQDUkIABANSQgAEA1JCAAQDUkIABANSQgAEA1JCAAQTd72CY0ePTq1ht1bq0X1fmzdutWNf/GLX3TjXr+O6iVQvD4htSaJiofst1qL5Stf+Yob9/o3VF+XWg/lwoULqbEDBw64Y9WaP++++64b99bGKSsrc8eePHnSjXvHdd9997ljVY+S13di5q/ro3rK+vr63Pjs2bNTY/fff787tqqqyo3PmDEjNXbu3Dl3rOoT8q5hM7O2trbUmOqFUz2Ap06dSo2pNZZUH5HqOSsqKkqNeetaqf6jy/FNCAAQDUkIABANSQgAEA1JCAAQDUkIABANSQgAEA1JCAAQTd72CXV3d6euSeHVxpeXl7vbVb0f77//vhufNWtWakz1UChev4Bax0WtVeSNV2uOKGnrPl3iraei3tvrvzDze0tKS0vdsR988IEbV70OXo/Tzp073bGqR8nrd1PrN6nj9no/zPx1sWpqatyx3rpVZmZz5sxJjU2aNMkdq/ppvF4f1S+j1gLr6Ohw417PjOrhU+sNZbPZ1NiZM2fcseo6U9eCd29794fqF7sc34QAANGQhAAA0ZCEAADRkIQAANGQhAAA0ZCEAADR5G2Jdn9/vyw7HooqOVSlg//2b//mxj/96U+nxkLKpFXce7S/mS5BzeXR6rmOnTJlihv3HsGvlt5QpbNe+evMmTPdsaqc3yuNNTNrbW1Nje3bt88d++GHH7px71p64IEH3LGqRFs9/t9rYwi9v7ylOU6fPu2O9ZZqMPOv07Nnz7pjVSmzV4Jt5i/XoJbWePPNN93422+/nRqbOHGiO1Ydl1rGxbsOvfOV1l4zlKBvQg0NDVZQUGArVqwY+FmSJFZfX2+VlZVWVFRktbW18oYEANyahp2Empub7aWXXrpqkao1a9bY2rVrbf369dbc3GzZbNYWL14sG7IAALeeYSWhc+fO2de//nX7u7/7u0FdzkmS2Lp162zVqlW2dOlSmzVrlm3YsMF6enps48aN122nAQA3h2EloeXLl9tXv/rVq5Z1PnjwoLW2tlpdXd3AzzKZjC1atMi2b98+5Lb6+vqss7Nz0AsAcGvIuTBh06ZN9qtf/WrIdegv/aG2oqJi0M8rKirs8OHDQ26voaHB/uIv/iLX3QAA3ARy+ibU0tJiTz/9tP3oRz9yqy6urFhKkiS1imnlypXW0dEx8GppaclllwAAN7Ccvgnt2rXL2trabN68eQM/u3jxom3bts3Wr19vBw4cMLNPvhFNmzZt4Hfa2tqu+nZ0SSaTkWW4AICbU05J6MEHH7S9e/cO+tk3v/lN+9SnPmXPPfec3XXXXZbNZq2xsdHmzp1rZp/0tzQ1NdkLL7yQ045lMpnUx4h7PTOqj8Gr5zcz+6//+i83vnv37tSY95h6M/9R82Z+z4s6LrVtb87Uo+bHjx/vxlVviPf4/yNHjgS9t9dvc+W1eiXVt6X6bbwep4ceesgde+7cOTfuHXd3d7c7Vv1dVfXjeJWsarkFNWdeX4rqA1L9Nt68qN4o1S9z8uRJN15WVpYae++999yxR48edePe8hinTp1yx6p+HbWUivd56fUm5tInlFMSKikpuWo9nXHjxtnkyZMHfr5ixQpbvXq11dTUWE1Nja1evdqKi4vt0UcfzeWtAAC3gOv+xIRnn33Went7bdmyZdbe3m4LFiywLVu2yM54AMCtJzgJbd26ddB/FxQUWH19vdXX14duGgBwk+MBpgCAaEhCAIBoSEIAgGhIQgCAaPJ2PaExY8ak1qF7fSmqX0b1C6g+og0bNqTG1qxZ445NksSNe1Qfg+L1Gam1itR7q/WGvH4CtaaP6mHyntyhzrXqsbjUfJ3Gm7e77rrLHdve3u7GvX4b1d/0P//zP25crZN05513psZU35baN68xXa2NE7K2lLqOVL+a6o/yPjd+8pOfuGN7enrcuHcdq88UtZ6Q+rzzrnGvN0p9plyOb0IAgGhIQgCAaEhCAIBoSEIAgGhIQgCAaEhCAIBo8rZEu7+/3/r7+4eMeSWL6jH1qmRRPYp+//79qbGNGze6Y7/5zW+68bNnz6bGVOmrVwZt5pdZqxLrtPNwreO9MtIpU6a4Y9Vxe0siqPLV2267zY2rsl7vvQ8dOuSOVeXG3vlSJb1Tp0514+phwt68qDlT58srw/aW5TAzq6qqcuNeubG671Xpubq//uM//iM1pq5xdZ15JffqXKprRS25MH369NSY1/ah5utyfBMCAERDEgIAREMSAgBEQxICAERDEgIAREMSAgBEQxICAERzQ/YJeT0WqmdF1cWrnhivB+O1115zx959991u/P7770+NdXZ2umPVfntCl4lQvSHe9lUvz+TJk92498h4tV+FhYVu/OOPP3bjXm/JhAkT3LGqN6Srqys1VlFR4Y5VPTHd3d1u3OsF8nrZzPQSFt4yLGq5BHUPePum5kS9t1oe4+TJk6kxdb5UT5l3HavjUksqqH3zejK9z9pcPo/4JgQAiIYkBACIhiQEAIiGJAQAiIYkBACIhiQEAIiGJAQAiCZv+4R6e3tT+0u8/gxVN++tAWOm13nx6t/VWiv//M//7MbLy8tTYzNnznTH9vb2unFvv1VNv4qrnhevN0v18qjz6a3VotY0OXPmjBtXc9rR0ZEaU3Om+tW8fVfX2fHjx9242jdvnRgvZqb3zaPOlzoft99+e2pMXUe//e1v3fiOHTvcuNdnpHqrDh8+7Marq6tTY6rnS603pOLe5+WMGTNSY14/2JX4JgQAiIYkBACIhiQEAIiGJAQAiIYkBACIhiQEAIiGJAQAiCZv+4R6enpSeym8fhq1jotak0StIeOtz6HWp1F9Dq+88kpq7PHHH3fHev0yZv76NKpXR635o9YjUvPi8XpxzPz+D7VGjOpvUr0OXk+Z6kfzzoeZ37+h9lsdt+rHmThxYmpM9aWo/idvzrz+PzOz0tJSN+7dX8eOHXPH/vznP3fjap0y772PHj3qjs1ms268p6cnNaY+77xzaWZ2+vRpN37nnXemxrz+J7WO0eX4JgQAiIYkBACIhiQEAIiGJAQAiIYkBACIhiQEAIgmb0u0M5lM6mPhT5w4kTquqqrK3a4qA1Ul3F6JqSpVVo+599771VdfdccuXbrUjXvHrcopQ5d68ErAVUmvKo319l2V2xcXF7txVcrsHZcqnVXl+t61FHIdmYWVcKsy6ZBy/PPnzw97rJl/LW3fvt0dq64VteTBwYMHU2Mhc2Lmz7k6l6dOnXLj06dPd+PeOaFEGwBwwyMJAQCiIQkBAKIhCQEAoiEJAQCiIQkBAKLJuxLtS09s9sotvac6qycfq9JBVTLsUSXa6r29cmRVvqqeyuyVUatjDnmyuJlfyqy2HUJtWz09XB2XV2atnjw+kiXa3lOXzXTJsFeireY0pBxZ3buKdx2Hfi6o8d57h3ymmPn7NtLH5Z1vb9uXjlndB2ZmBcm1/Nb/R0ePHpW9PgCA/NfS0iJ7kfIuCfX399vx48etpKTECgoKrLOz06qqqqylpUU2AOITzFnumLPcMWe5u1XmLEkS6+rqssrKSvkvDnn3z3GjRo0aMnNOmDDhpj5pI4E5yx1zljvmLHe3wpypp2tcQmECACAakhAAIJq8T0KZTMa++93vug8OxWDMWe6Ys9wxZ7ljzq6Wd4UJAIBbR95/EwIA3LxIQgCAaEhCAIBoSEIAgGhIQgCAaPI+Cf3gBz+w6upqGzt2rM2bN8/eeuut2LuUN7Zt22YPP/ywVVZWWkFBgf3sZz8bFE+SxOrr662ystKKioqstrbW9u3bF2dn80BDQ4N97nOfs5KSEps6dap97WtfswMHDgz6Hebsai+++KLNnj17oMt/4cKF9vrrrw/EmTNfQ0ODFRQU2IoVKwZ+xpz9n7xOQq+88oqtWLHCVq1aZbt377bf/d3ftSVLltiRI0di71pe6O7utjlz5tj69euHjK9Zs8bWrl1r69evt+bmZstms7Z48WL5xO2bVVNTky1fvtx27NhhjY2N9vHHH1tdXZ11d3cP/A5zdrXp06fb888/bzt37rSdO3fal7/8ZXvkkUcGPjSZs3TNzc320ksv2ezZswf9nDm7TJLHPv/5zydPPPHEoJ996lOfSv78z/880h7lLzNLNm/ePPDf/f39STabTZ5//vmBn50/fz4pLS1N/uZv/ibCHuaftra2xMySpqamJEmYs1xMmjQp+fu//3vmzNHV1ZXU1NQkjY2NyaJFi5Knn346SRKusyvl7TehCxcu2K5du6yurm7Qz+vq6mz79u2R9urGcfDgQWttbR00f5lMxhYtWsT8/T8dHR1mZlZWVmZmzNm1uHjxom3atMm6u7tt4cKFzJlj+fLl9tWvftW+8pWvDPo5czZY3j1F+5JTp07ZxYsXraKiYtDPKyoqrLW1NdJe3TguzdFQ83f48OEYu5RXkiSxZ555xh544AGbNWuWmTFnnr1799rChQvt/PnzNn78eNu8ebPdd999Ax+azNlgmzZtsl/96lfW3Nx8VYzrbLC8TUKXFBQUDPrvJEmu+hnSMX9De/LJJ+2dd96xX/ziF1fFmLOr3XPPPbZnzx47e/as/eQnP7HHHnvMmpqaBuLM2f9paWmxp59+2rZs2WJjx45N/T3m7BN5+89x5eXlNnr06Ku+9bS1tV31fxC4WjabNTNj/obw1FNP2auvvmpvvvnmoLWrmLN0hYWFNnPmTJs/f741NDTYnDlz7Hvf+x5zNoRdu3ZZW1ubzZs3z8aMGWNjxoyxpqYm++u//msbM2bMwLwwZ5/I2yRUWFho8+bNs8bGxkE/b2xstC9+8YuR9urGUV1dbdlsdtD8XbhwwZqamm7Z+UuSxJ588kn76U9/aj//+c+turp6UJw5u3ZJklhfXx9zNoQHH3zQ9u7da3v27Bl4zZ8/377+9a/bnj177K677mLOLhevJkLbtGlTcttttyU//OEPk/379ycrVqxIxo0blxw6dCj2ruWFrq6uZPfu3cnu3bsTM0vWrl2b7N69Ozl8+HCSJEny/PPPJ6WlpclPf/rTZO/evckf//EfJ9OmTUs6Ozsj73kc3/72t5PS0tJk69atyYkTJwZePT09A7/DnF1t5cqVybZt25KDBw8m77zzTvKd73wnGTVqVLJly5YkSZiza3F5dVySMGeXy+sklCRJ8v3vfz+54447ksLCwuSzn/3sQDktkuTNN99MzOyq12OPPZYkySeloN/97neTbDabZDKZ5Etf+lKyd+/euDsd0VBzZWbJyy+/PPA7zNnVHn/88YF7cMqUKcmDDz44kICShDm7FlcmIebs/7CeEAAgmrz9mxAA4OZHEgIAREMSAgBEQxICAERDEgIAREMSAgBEQxICAERDEgIAREMSAgBEQxICAERDEgIARPO/b54XMqkEfysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 'images/train/surprise/26.jpg'\n",
    "print(\"original image is of surprise\")\n",
    "img = ef(image)\n",
    "pred = model.predict(img)\n",
    "pred_label = label[pred.argmax()]\n",
    "print(\"model prediction is \",pred_label)\n",
    "plt.imshow(img.reshape(48,48),cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b633b444",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original image is of surprise\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "model prediction is  sad\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x302076290>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 'images/train/surprise/26.jpg'\n",
    "print(\"original image is of surprise\")\n",
    "img = ef(image)\n",
    "pred = model.predict(img)\n",
    "pred_label = label[pred.argmax()]\n",
    "print(\"model prediction is \",pred_label)\n",
    "plt.imshow(img.reshape(48,48),cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9d8b4a1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original image is of surprise\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "model prediction is  surprise\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x3020dc730>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 'images/train/surprise/29.jpg'\n",
    "print(\"original image is of surprise\")\n",
    "img = ef(image)\n",
    "pred = model.predict(img)\n",
    "pred_label = label[pred.argmax()]\n",
    "print(\"model prediction is \",pred_label)\n",
    "plt.imshow(img.reshape(48,48),cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c94d310a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original image is of disgust\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "model prediction is  disgust\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x302136530>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = 'images/train/disgust/388.jpg'\n",
    "print(\"original image is of disgust\")\n",
    "img = ef(image)\n",
    "pred = model.predict(img)\n",
    "pred_label = label[pred.argmax()]\n",
    "print(\"model prediction is \",pred_label)\n",
    "plt.imshow(img.reshape(48,48),cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f97a437f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}