Harassment_Detection_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vaRVZ8rZq9JK"
   },
   "source": [
    "Basic CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 128
    },
    "id": "PJbUA074rGbw",
    "outputId": "09963cf0-2c0b-4bd9-f3be-a33c2a0212ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.12.0\n",
      "WARNING:tensorflow:From C:\\Users\\sudha\\AppData\\Local\\Temp\\ipykernel_17004\\1596216831.py:12: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.config.list_physical_devices('GPU')` instead.\n",
      "Is using GPU? False\n"
     ]
    }
   ],
   "source": [
    "\n",
    "%matplotlib inline\n",
    "\n",
    "if not os.path.isdir('models'):\n",
    "    os.mkdir('models')\n",
    "    \n",
    "print('TensorFlow version:', tf.__version__)\n",
    "print('Is using GPU?', tf.test.is_gpu_available())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "2E-wZ1d0rIEl",
    "outputId": "ae504672-3f2b-4d3b-a926-3c8205c07fab"
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, InputLayer, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D\n",
    "from keras.preprocessing import image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import keras.utils as image\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "dwXGoxZerIHr",
    "outputId": "69081f03-be33-4d58-c3d6-d717c3570979"
   },
   "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>image</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>images/harassment_yes\\Screenshot 2023-07-22 01...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>images/harassment_yes\\Screenshot 2023-07-22 01...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>images/harassment_yes\\Screenshot 2023-07-22 01...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>images/harassment_yes\\Screenshot 2023-07-22 01...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>images/harassment_yes\\Screenshot 2023-07-22 01...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               image  class\n",
       "0  images/harassment_yes\\Screenshot 2023-07-22 01...      1\n",
       "1  images/harassment_yes\\Screenshot 2023-07-22 01...      1\n",
       "2  images/harassment_yes\\Screenshot 2023-07-22 01...      1\n",
       "3  images/harassment_yes\\Screenshot 2023-07-22 01...      1\n",
       "4  images/harassment_yes\\Screenshot 2023-07-22 01...      1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('harrasment_as_csv.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "nD7oYva8rIKe",
    "outputId": "f6484064-e060-49d5-c2df-1bb5bfdc9ecb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:07<00:00, 136.43it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1000, 224, 224, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating an empty list\n",
    "train_image = []\n",
    "\n",
    "# for loop to read and store frames\n",
    "for i in tqdm(range(train.shape[0])):\n",
    "    # loading the image and keeping the target size as (224,224,3)\n",
    "    img = image.load_img(train['image'][i], target_size=(224,224,3))\n",
    "    # converting it to array\n",
    "    img = image.img_to_array(img)\n",
    "    # normalizing the pixel value\n",
    "    img = img/255.0\n",
    "    # appending the image to the train_image list\n",
    "    train_image.append(img)\n",
    "    \n",
    "# converting the list to numpy array\n",
    "x = np.array(train_image)\n",
    "\n",
    "# shape of the array\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "24pOkVogrkYb"
   },
   "outputs": [],
   "source": [
    "# separating the target\n",
    "y = train['class']\n",
    "\n",
    "# creating the training and validation set\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42, test_size=0.2, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "Z0yAyqFkrkW9"
   },
   "outputs": [],
   "source": [
    "# creating dummies of target variable for train and validation set\n",
    "y_train = pd.get_dummies(y_train)\n",
    "y_test = pd.get_dummies(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "mm6kcUOb7ISS",
    "outputId": "9f59aeb0-4020-4653-f765-7c6e995081ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 224, 224, 32)      896       \n",
      "                                                                 \n",
      " batch_normalization (Batch  (None, 224, 224, 32)      128       \n",
      " Normalization)                                                  \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 222, 222, 32)      9248      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 111, 111, 32)      0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 111, 111, 32)      0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 111, 111, 64)      18496     \n",
      "                                                                 \n",
      " batch_normalization_1 (Bat  (None, 111, 111, 64)      256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 109, 109, 64)      36928     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 54, 54, 64)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 54, 54, 64)        0         \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 54, 54, 128)       73856     \n",
      "                                                                 \n",
      " batch_normalization_2 (Bat  (None, 54, 54, 128)       512       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 52, 52, 128)       147584    \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPoolin  (None, 26, 26, 128)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 26, 26, 128)       0         \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 26, 26, 256)       295168    \n",
      "                                                                 \n",
      " batch_normalization_3 (Bat  (None, 26, 26, 256)       1024      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 24, 24, 256)       590080    \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPoolin  (None, 12, 12, 256)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 12, 12, 256)       0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 36864)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 512)               18874880  \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 512)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 256)               131328    \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 2)                 514       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 20180898 (76.98 MB)\n",
      "Trainable params: 20179938 (76.98 MB)\n",
      "Non-trainable params: 960 (3.75 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
    "from tensorflow.keras.layers import Dropout, Flatten, Input, Dense\n",
    "\n",
    "def create_model():\n",
    "    \n",
    "    def add_conv_block(model, num_filters):\n",
    "        \n",
    "        model.add(Conv2D(num_filters, 3, activation='relu', padding='same'))\n",
    "        model.add(BatchNormalization())\n",
    "        model.add(Conv2D(num_filters, 3, activation='relu', padding='valid'))\n",
    "        model.add(MaxPooling2D(pool_size=2))\n",
    "        model.add(Dropout(0.2))\n",
    "\n",
    "        return model\n",
    "    \n",
    "    model = tf.keras.models.Sequential()\n",
    "    model.add(Input(shape=(224, 224, 3)))\n",
    "    \n",
    "    model = add_conv_block(model, 32)\n",
    "    model = add_conv_block(model, 64)\n",
    "    model = add_conv_block(model, 128)\n",
    "    model = add_conv_block(model, 256)\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(512, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(256, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(2, activation='softmax'))\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "model = create_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "3HvY1Ysy8MVc",
    "outputId": "243d0a42-42c7-49eb-f970-ed268ac0bbac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "7/7 [==============================] - 81s 11s/step - loss: 19.8910 - accuracy: 0.4950 - val_loss: 2.0888 - val_accuracy: 0.5000\n",
      "Epoch 2/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 5.5332 - accuracy: 0.5275 - val_loss: 0.7179 - val_accuracy: 0.4350\n",
      "Epoch 3/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 2.3302 - accuracy: 0.4863 - val_loss: 1.6707 - val_accuracy: 0.5000\n",
      "Epoch 4/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 1.0927 - accuracy: 0.4963 - val_loss: 0.9155 - val_accuracy: 0.4950\n",
      "Epoch 5/200\n",
      "7/7 [==============================] - 74s 10s/step - loss: 0.8393 - accuracy: 0.4650 - val_loss: 0.6908 - val_accuracy: 0.4950\n",
      "Epoch 6/200\n",
      "7/7 [==============================] - 74s 10s/step - loss: 0.7188 - accuracy: 0.4950 - val_loss: 0.6926 - val_accuracy: 0.5000\n",
      "Epoch 7/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.6956 - accuracy: 0.5088 - val_loss: 0.6896 - val_accuracy: 0.5100\n",
      "Epoch 8/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.6974 - accuracy: 0.5163 - val_loss: 0.6934 - val_accuracy: 0.5000\n",
      "Epoch 9/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.6765 - accuracy: 0.5412 - val_loss: 0.6899 - val_accuracy: 0.4900\n",
      "Epoch 10/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.6742 - accuracy: 0.5800 - val_loss: 0.6939 - val_accuracy: 0.4900\n",
      "Epoch 11/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.6912 - accuracy: 0.5763 - val_loss: 0.6870 - val_accuracy: 0.5150\n",
      "Epoch 12/200\n",
      "7/7 [==============================] - 74s 11s/step - loss: 0.6563 - accuracy: 0.6212 - val_loss: 0.6898 - val_accuracy: 0.5000\n",
      "Epoch 13/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.6436 - accuracy: 0.6237 - val_loss: 0.6804 - val_accuracy: 0.6200\n",
      "Epoch 14/200\n",
      "7/7 [==============================] - 82s 12s/step - loss: 0.6407 - accuracy: 0.6463 - val_loss: 0.6944 - val_accuracy: 0.4900\n",
      "Epoch 15/200\n",
      "7/7 [==============================] - 80s 11s/step - loss: 0.5977 - accuracy: 0.6787 - val_loss: 0.6897 - val_accuracy: 0.5000\n",
      "Epoch 16/200\n",
      "7/7 [==============================] - 80s 11s/step - loss: 0.6030 - accuracy: 0.6750 - val_loss: 0.7126 - val_accuracy: 0.4950\n",
      "Epoch 17/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.5555 - accuracy: 0.7100 - val_loss: 0.7248 - val_accuracy: 0.4900\n",
      "Epoch 18/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.5141 - accuracy: 0.7362 - val_loss: 0.7598 - val_accuracy: 0.5000\n",
      "Epoch 19/200\n",
      "7/7 [==============================] - 79s 11s/step - loss: 0.4893 - accuracy: 0.7550 - val_loss: 0.7286 - val_accuracy: 0.5000\n",
      "Epoch 20/200\n",
      "7/7 [==============================] - 80s 11s/step - loss: 0.4331 - accuracy: 0.7925 - val_loss: 0.7762 - val_accuracy: 0.5000\n",
      "Epoch 21/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.4583 - accuracy: 0.7750 - val_loss: 0.7437 - val_accuracy: 0.5150\n",
      "Epoch 22/200\n",
      "7/7 [==============================] - 80s 11s/step - loss: 0.4448 - accuracy: 0.8037 - val_loss: 0.8115 - val_accuracy: 0.5000\n",
      "Epoch 23/200\n",
      "7/7 [==============================] - 86s 12s/step - loss: 0.4422 - accuracy: 0.7875 - val_loss: 0.7424 - val_accuracy: 0.5050\n",
      "Epoch 24/200\n",
      "7/7 [==============================] - 87s 12s/step - loss: 0.3935 - accuracy: 0.8288 - val_loss: 0.7631 - val_accuracy: 0.5000\n",
      "Epoch 25/200\n",
      "7/7 [==============================] - 84s 12s/step - loss: 0.3752 - accuracy: 0.8175 - val_loss: 0.7293 - val_accuracy: 0.4950\n",
      "Epoch 26/200\n",
      "7/7 [==============================] - 80s 11s/step - loss: 0.3458 - accuracy: 0.8338 - val_loss: 0.7291 - val_accuracy: 0.5000\n",
      "Epoch 27/200\n",
      "7/7 [==============================] - 79s 11s/step - loss: 0.3401 - accuracy: 0.8612 - val_loss: 0.7101 - val_accuracy: 0.5200\n",
      "Epoch 28/200\n",
      "7/7 [==============================] - 82s 12s/step - loss: 0.3293 - accuracy: 0.8500 - val_loss: 0.6955 - val_accuracy: 0.5200\n",
      "Epoch 29/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.2999 - accuracy: 0.8750 - val_loss: 0.7650 - val_accuracy: 0.4800\n",
      "Epoch 30/200\n",
      "7/7 [==============================] - 81s 12s/step - loss: 0.2798 - accuracy: 0.8875 - val_loss: 0.9070 - val_accuracy: 0.4900\n",
      "Epoch 31/200\n",
      "7/7 [==============================] - 80s 11s/step - loss: 0.2644 - accuracy: 0.8888 - val_loss: 0.7487 - val_accuracy: 0.4950\n",
      "Epoch 32/200\n",
      "7/7 [==============================] - 81s 11s/step - loss: 0.2537 - accuracy: 0.9013 - val_loss: 0.8233 - val_accuracy: 0.5000\n",
      "Epoch 33/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.2489 - accuracy: 0.8963 - val_loss: 0.8416 - val_accuracy: 0.4950\n",
      "Epoch 34/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.2703 - accuracy: 0.8963 - val_loss: 0.7085 - val_accuracy: 0.5000\n",
      "Epoch 35/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.2615 - accuracy: 0.8913 - val_loss: 0.7264 - val_accuracy: 0.5450\n",
      "Epoch 36/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.2594 - accuracy: 0.9013 - val_loss: 0.6581 - val_accuracy: 0.6350\n",
      "Epoch 37/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.2340 - accuracy: 0.8963 - val_loss: 0.7377 - val_accuracy: 0.5300\n",
      "Epoch 38/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.2611 - accuracy: 0.9013 - val_loss: 0.7127 - val_accuracy: 0.5450\n",
      "Epoch 39/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.2238 - accuracy: 0.9050 - val_loss: 0.8293 - val_accuracy: 0.5200\n",
      "Epoch 40/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.1847 - accuracy: 0.9337 - val_loss: 0.9447 - val_accuracy: 0.5250\n",
      "Epoch 41/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.1880 - accuracy: 0.9137 - val_loss: 0.8043 - val_accuracy: 0.5450\n",
      "Epoch 42/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.1236 - accuracy: 0.9513 - val_loss: 0.9949 - val_accuracy: 0.5350\n",
      "Epoch 43/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.2091 - accuracy: 0.9350 - val_loss: 0.9753 - val_accuracy: 0.5100\n",
      "Epoch 44/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.2237 - accuracy: 0.9075 - val_loss: 0.8393 - val_accuracy: 0.5000\n",
      "Epoch 45/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.1613 - accuracy: 0.9350 - val_loss: 1.3333 - val_accuracy: 0.5150\n",
      "Epoch 46/200\n",
      "7/7 [==============================] - 74s 10s/step - loss: 0.1525 - accuracy: 0.9488 - val_loss: 1.1767 - val_accuracy: 0.5500\n",
      "Epoch 47/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.1388 - accuracy: 0.9475 - val_loss: 1.0045 - val_accuracy: 0.5650\n",
      "Epoch 48/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.1491 - accuracy: 0.9400 - val_loss: 1.2527 - val_accuracy: 0.6150\n",
      "Epoch 49/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.1247 - accuracy: 0.9463 - val_loss: 0.7242 - val_accuracy: 0.6800\n",
      "Epoch 50/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.1144 - accuracy: 0.9488 - val_loss: 1.3012 - val_accuracy: 0.6150\n",
      "Epoch 51/200\n",
      "7/7 [==============================] - 79s 11s/step - loss: 0.1628 - accuracy: 0.9450 - val_loss: 0.9247 - val_accuracy: 0.6200\n",
      "Epoch 52/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.1020 - accuracy: 0.9575 - val_loss: 1.0049 - val_accuracy: 0.6150\n",
      "Epoch 53/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0947 - accuracy: 0.9550 - val_loss: 1.2526 - val_accuracy: 0.6050\n",
      "Epoch 54/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.1128 - accuracy: 0.9475 - val_loss: 1.0044 - val_accuracy: 0.5950\n",
      "Epoch 55/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.1043 - accuracy: 0.9675 - val_loss: 0.7835 - val_accuracy: 0.6800\n",
      "Epoch 56/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0691 - accuracy: 0.9737 - val_loss: 0.8554 - val_accuracy: 0.6500\n",
      "Epoch 57/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0694 - accuracy: 0.9787 - val_loss: 0.7683 - val_accuracy: 0.6800\n",
      "Epoch 58/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0980 - accuracy: 0.9638 - val_loss: 0.8707 - val_accuracy: 0.6650\n",
      "Epoch 59/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.1005 - accuracy: 0.9563 - val_loss: 0.6379 - val_accuracy: 0.7200\n",
      "Epoch 60/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0560 - accuracy: 0.9800 - val_loss: 0.7843 - val_accuracy: 0.7050\n",
      "Epoch 61/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0441 - accuracy: 0.9800 - val_loss: 0.7802 - val_accuracy: 0.7000\n",
      "Epoch 62/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0548 - accuracy: 0.9750 - val_loss: 0.7637 - val_accuracy: 0.6950\n",
      "Epoch 63/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0427 - accuracy: 0.9850 - val_loss: 0.9995 - val_accuracy: 0.6600\n",
      "Epoch 64/200\n",
      "7/7 [==============================] - 82s 12s/step - loss: 0.0416 - accuracy: 0.9825 - val_loss: 1.3616 - val_accuracy: 0.6350\n",
      "Epoch 65/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0460 - accuracy: 0.9787 - val_loss: 1.7633 - val_accuracy: 0.6150\n",
      "Epoch 66/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0939 - accuracy: 0.9700 - val_loss: 1.2243 - val_accuracy: 0.6900\n",
      "Epoch 67/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.1126 - accuracy: 0.9600 - val_loss: 0.9216 - val_accuracy: 0.7100\n",
      "Epoch 68/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.1274 - accuracy: 0.9563 - val_loss: 1.0995 - val_accuracy: 0.6500\n",
      "Epoch 69/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.1390 - accuracy: 0.9513 - val_loss: 1.2023 - val_accuracy: 0.6700\n",
      "Epoch 70/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.1023 - accuracy: 0.9613 - val_loss: 1.0857 - val_accuracy: 0.6800\n",
      "Epoch 71/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.1096 - accuracy: 0.9675 - val_loss: 1.0852 - val_accuracy: 0.6550\n",
      "Epoch 72/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0523 - accuracy: 0.9750 - val_loss: 1.1069 - val_accuracy: 0.6550\n",
      "Epoch 73/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0453 - accuracy: 0.9775 - val_loss: 0.9627 - val_accuracy: 0.7250\n",
      "Epoch 74/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0495 - accuracy: 0.9812 - val_loss: 1.4127 - val_accuracy: 0.7100\n",
      "Epoch 75/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0374 - accuracy: 0.9850 - val_loss: 0.9376 - val_accuracy: 0.7550\n",
      "Epoch 76/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0322 - accuracy: 0.9887 - val_loss: 1.3178 - val_accuracy: 0.7550\n",
      "Epoch 77/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0261 - accuracy: 0.9900 - val_loss: 1.5770 - val_accuracy: 0.7450\n",
      "Epoch 78/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0209 - accuracy: 0.9962 - val_loss: 1.2645 - val_accuracy: 0.7750\n",
      "Epoch 79/200\n",
      "7/7 [==============================] - 79s 11s/step - loss: 0.0357 - accuracy: 0.9837 - val_loss: 1.2818 - val_accuracy: 0.7500\n",
      "Epoch 80/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0475 - accuracy: 0.9837 - val_loss: 1.0880 - val_accuracy: 0.7150\n",
      "Epoch 81/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0263 - accuracy: 0.9912 - val_loss: 0.9543 - val_accuracy: 0.7550\n",
      "Epoch 82/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0453 - accuracy: 0.9850 - val_loss: 0.9920 - val_accuracy: 0.7750\n",
      "Epoch 83/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0232 - accuracy: 0.9912 - val_loss: 0.8212 - val_accuracy: 0.8200\n",
      "Epoch 84/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0272 - accuracy: 0.9912 - val_loss: 0.8065 - val_accuracy: 0.8150\n",
      "Epoch 85/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0797 - accuracy: 0.9825 - val_loss: 0.5986 - val_accuracy: 0.8550\n",
      "Epoch 86/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0436 - accuracy: 0.9825 - val_loss: 1.5196 - val_accuracy: 0.7800\n",
      "Epoch 87/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0548 - accuracy: 0.9825 - val_loss: 0.9101 - val_accuracy: 0.7950\n",
      "Epoch 88/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0324 - accuracy: 0.9862 - val_loss: 0.9806 - val_accuracy: 0.7800\n",
      "Epoch 89/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0484 - accuracy: 0.9850 - val_loss: 1.1446 - val_accuracy: 0.7600\n",
      "Epoch 90/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0334 - accuracy: 0.9862 - val_loss: 0.9204 - val_accuracy: 0.8000\n",
      "Epoch 91/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0350 - accuracy: 0.9850 - val_loss: 0.8369 - val_accuracy: 0.8100\n",
      "Epoch 92/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0251 - accuracy: 0.9900 - val_loss: 1.1811 - val_accuracy: 0.8100\n",
      "Epoch 93/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0406 - accuracy: 0.9862 - val_loss: 1.8633 - val_accuracy: 0.7400\n",
      "Epoch 94/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0309 - accuracy: 0.9825 - val_loss: 1.5486 - val_accuracy: 0.7800\n",
      "Epoch 95/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0261 - accuracy: 0.9862 - val_loss: 1.5212 - val_accuracy: 0.7650\n",
      "Epoch 96/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0244 - accuracy: 0.9887 - val_loss: 1.1129 - val_accuracy: 0.8150\n",
      "Epoch 97/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0119 - accuracy: 0.9950 - val_loss: 0.9590 - val_accuracy: 0.8350\n",
      "Epoch 98/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0232 - accuracy: 0.9925 - val_loss: 0.9836 - val_accuracy: 0.8350\n",
      "Epoch 99/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0229 - accuracy: 0.9912 - val_loss: 1.0063 - val_accuracy: 0.8450\n",
      "Epoch 100/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0184 - accuracy: 0.9900 - val_loss: 1.0146 - val_accuracy: 0.8300\n",
      "Epoch 101/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0190 - accuracy: 0.9925 - val_loss: 1.1339 - val_accuracy: 0.8350\n",
      "Epoch 102/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0452 - accuracy: 0.9887 - val_loss: 1.5598 - val_accuracy: 0.8350\n",
      "Epoch 103/200\n",
      "7/7 [==============================] - 79s 11s/step - loss: 0.0177 - accuracy: 0.9937 - val_loss: 1.5447 - val_accuracy: 0.8300\n",
      "Epoch 104/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0418 - accuracy: 0.9912 - val_loss: 1.4428 - val_accuracy: 0.8400\n",
      "Epoch 105/200\n",
      "7/7 [==============================] - 79s 11s/step - loss: 0.0478 - accuracy: 0.9812 - val_loss: 1.3927 - val_accuracy: 0.8000\n",
      "Epoch 106/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0419 - accuracy: 0.9850 - val_loss: 1.5938 - val_accuracy: 0.8100\n",
      "Epoch 107/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0841 - accuracy: 0.9750 - val_loss: 1.4860 - val_accuracy: 0.7850\n",
      "Epoch 108/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.1143 - accuracy: 0.9688 - val_loss: 1.9564 - val_accuracy: 0.7900\n",
      "Epoch 109/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0537 - accuracy: 0.9825 - val_loss: 0.9267 - val_accuracy: 0.8350\n",
      "Epoch 110/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0861 - accuracy: 0.9750 - val_loss: 1.5227 - val_accuracy: 0.7750\n",
      "Epoch 111/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0521 - accuracy: 0.9837 - val_loss: 6.2298 - val_accuracy: 0.5250\n",
      "Epoch 112/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0454 - accuracy: 0.9875 - val_loss: 4.7240 - val_accuracy: 0.5650\n",
      "Epoch 113/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0647 - accuracy: 0.9800 - val_loss: 3.4818 - val_accuracy: 0.6250\n",
      "Epoch 114/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0955 - accuracy: 0.9750 - val_loss: 4.3737 - val_accuracy: 0.6150\n",
      "Epoch 115/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0603 - accuracy: 0.9825 - val_loss: 1.9821 - val_accuracy: 0.6850\n",
      "Epoch 116/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0460 - accuracy: 0.9837 - val_loss: 1.9311 - val_accuracy: 0.7600\n",
      "Epoch 117/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0659 - accuracy: 0.9712 - val_loss: 1.5867 - val_accuracy: 0.7950\n",
      "Epoch 118/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0971 - accuracy: 0.9700 - val_loss: 1.6008 - val_accuracy: 0.8150\n",
      "Epoch 119/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0627 - accuracy: 0.9775 - val_loss: 1.9827 - val_accuracy: 0.8000\n",
      "Epoch 120/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0728 - accuracy: 0.9775 - val_loss: 1.8994 - val_accuracy: 0.7950\n",
      "Epoch 121/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0423 - accuracy: 0.9862 - val_loss: 1.5895 - val_accuracy: 0.7450\n",
      "Epoch 122/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0389 - accuracy: 0.9887 - val_loss: 1.2629 - val_accuracy: 0.8050\n",
      "Epoch 123/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0274 - accuracy: 0.9900 - val_loss: 1.0782 - val_accuracy: 0.8350\n",
      "Epoch 124/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0398 - accuracy: 0.9875 - val_loss: 1.0845 - val_accuracy: 0.8300\n",
      "Epoch 125/200\n",
      "7/7 [==============================] - 80s 11s/step - loss: 0.0219 - accuracy: 0.9912 - val_loss: 1.1640 - val_accuracy: 0.8050\n",
      "Epoch 126/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0331 - accuracy: 0.9862 - val_loss: 1.1734 - val_accuracy: 0.8200\n",
      "Epoch 127/200\n",
      "7/7 [==============================] - 75s 10s/step - loss: 0.0299 - accuracy: 0.9912 - val_loss: 1.2118 - val_accuracy: 0.8100\n",
      "Epoch 128/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0139 - accuracy: 0.9950 - val_loss: 0.9571 - val_accuracy: 0.8350\n",
      "Epoch 129/200\n",
      "7/7 [==============================] - 80s 11s/step - loss: 0.0113 - accuracy: 0.9962 - val_loss: 0.9697 - val_accuracy: 0.8550\n",
      "Epoch 130/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0129 - accuracy: 0.9950 - val_loss: 0.9653 - val_accuracy: 0.8700\n",
      "Epoch 131/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0094 - accuracy: 0.9962 - val_loss: 0.8470 - val_accuracy: 0.8850\n",
      "Epoch 132/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.8634 - val_accuracy: 0.8650\n",
      "Epoch 133/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0251 - accuracy: 0.9937 - val_loss: 1.2554 - val_accuracy: 0.8100\n",
      "Epoch 134/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0199 - accuracy: 0.9925 - val_loss: 1.2201 - val_accuracy: 0.8250\n",
      "Epoch 135/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0086 - accuracy: 0.9962 - val_loss: 1.1210 - val_accuracy: 0.8300\n",
      "Epoch 136/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0301 - accuracy: 0.9862 - val_loss: 1.0830 - val_accuracy: 0.8350\n",
      "Epoch 137/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0210 - accuracy: 0.9962 - val_loss: 0.8656 - val_accuracy: 0.8450\n",
      "Epoch 138/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0096 - accuracy: 0.9987 - val_loss: 0.7251 - val_accuracy: 0.8500\n",
      "Epoch 139/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0068 - accuracy: 0.9975 - val_loss: 0.7825 - val_accuracy: 0.8450\n",
      "Epoch 140/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0119 - accuracy: 0.9950 - val_loss: 0.8700 - val_accuracy: 0.8450\n",
      "Epoch 141/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0175 - accuracy: 0.9925 - val_loss: 1.2941 - val_accuracy: 0.8250\n",
      "Epoch 142/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0259 - accuracy: 0.9950 - val_loss: 1.1377 - val_accuracy: 0.8500\n",
      "Epoch 143/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0090 - accuracy: 0.9975 - val_loss: 1.1699 - val_accuracy: 0.8650\n",
      "Epoch 144/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0084 - accuracy: 0.9987 - val_loss: 1.2367 - val_accuracy: 0.8450\n",
      "Epoch 145/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 1.4465 - val_accuracy: 0.8350\n",
      "Epoch 146/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0096 - accuracy: 0.9987 - val_loss: 1.3509 - val_accuracy: 0.8450\n",
      "Epoch 147/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0150 - accuracy: 0.9900 - val_loss: 1.1651 - val_accuracy: 0.8400\n",
      "Epoch 148/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0067 - accuracy: 0.9975 - val_loss: 1.2133 - val_accuracy: 0.8550\n",
      "Epoch 149/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 1.1751 - val_accuracy: 0.8650\n",
      "Epoch 150/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0038 - accuracy: 0.9987 - val_loss: 1.1376 - val_accuracy: 0.8650\n",
      "Epoch 151/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0103 - accuracy: 0.9950 - val_loss: 1.0033 - val_accuracy: 0.8550\n",
      "Epoch 152/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0052 - accuracy: 0.9987 - val_loss: 1.0416 - val_accuracy: 0.8500\n",
      "Epoch 153/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0051 - accuracy: 0.9987 - val_loss: 0.8862 - val_accuracy: 0.8550\n",
      "Epoch 154/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.8518 - val_accuracy: 0.8600\n",
      "Epoch 155/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0094 - accuracy: 0.9962 - val_loss: 0.9460 - val_accuracy: 0.8500\n",
      "Epoch 156/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0071 - accuracy: 0.9975 - val_loss: 1.2457 - val_accuracy: 0.8150\n",
      "Epoch 157/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0036 - accuracy: 0.9987 - val_loss: 1.3814 - val_accuracy: 0.8100\n",
      "Epoch 158/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0069 - accuracy: 0.9975 - val_loss: 0.8533 - val_accuracy: 0.8600\n",
      "Epoch 159/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0046 - accuracy: 0.9975 - val_loss: 0.9442 - val_accuracy: 0.8600\n",
      "Epoch 160/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0094 - accuracy: 0.9937 - val_loss: 1.2214 - val_accuracy: 0.8250\n",
      "Epoch 161/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0033 - accuracy: 0.9975 - val_loss: 1.4873 - val_accuracy: 0.8200\n",
      "Epoch 162/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 1.6914 - val_accuracy: 0.8250\n",
      "Epoch 163/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0048 - accuracy: 0.9975 - val_loss: 1.7409 - val_accuracy: 0.8200\n",
      "Epoch 164/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 1.5276 - val_accuracy: 0.8250\n",
      "Epoch 165/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0086 - accuracy: 0.9987 - val_loss: 1.5411 - val_accuracy: 0.8150\n",
      "Epoch 166/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0155 - accuracy: 0.9925 - val_loss: 1.1309 - val_accuracy: 0.8500\n",
      "Epoch 167/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0059 - accuracy: 0.9962 - val_loss: 1.0371 - val_accuracy: 0.8600\n",
      "Epoch 168/200\n",
      "7/7 [==============================] - 79s 11s/step - loss: 0.0073 - accuracy: 0.9975 - val_loss: 0.9602 - val_accuracy: 0.8600\n",
      "Epoch 169/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.8822 - val_accuracy: 0.8750\n",
      "Epoch 170/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0076 - accuracy: 0.9962 - val_loss: 0.9674 - val_accuracy: 0.8600\n",
      "Epoch 171/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0047 - accuracy: 0.9975 - val_loss: 0.8628 - val_accuracy: 0.8800\n",
      "Epoch 172/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0667 - accuracy: 0.9825 - val_loss: 1.1001 - val_accuracy: 0.8450\n",
      "Epoch 173/200\n",
      "7/7 [==============================] - 74s 10s/step - loss: 0.0480 - accuracy: 0.9862 - val_loss: 2.3037 - val_accuracy: 0.7550\n",
      "Epoch 174/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0489 - accuracy: 0.9825 - val_loss: 1.4956 - val_accuracy: 0.8500\n",
      "Epoch 175/200\n",
      "7/7 [==============================] - 81s 12s/step - loss: 0.0313 - accuracy: 0.9900 - val_loss: 2.2210 - val_accuracy: 0.7750\n",
      "Epoch 176/200\n",
      "7/7 [==============================] - 81s 11s/step - loss: 0.0212 - accuracy: 0.9925 - val_loss: 2.2508 - val_accuracy: 0.7850\n",
      "Epoch 177/200\n",
      "7/7 [==============================] - 80s 11s/step - loss: 0.0412 - accuracy: 0.9887 - val_loss: 3.1090 - val_accuracy: 0.7700\n",
      "Epoch 178/200\n",
      "7/7 [==============================] - 81s 11s/step - loss: 0.0251 - accuracy: 0.9887 - val_loss: 3.5064 - val_accuracy: 0.7550\n",
      "Epoch 179/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0292 - accuracy: 0.9937 - val_loss: 3.7469 - val_accuracy: 0.7200\n",
      "Epoch 180/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0260 - accuracy: 0.9912 - val_loss: 3.9814 - val_accuracy: 0.7300\n",
      "Epoch 181/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0312 - accuracy: 0.9862 - val_loss: 2.7725 - val_accuracy: 0.7600\n",
      "Epoch 182/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0282 - accuracy: 0.9912 - val_loss: 2.1171 - val_accuracy: 0.8000\n",
      "Epoch 183/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0140 - accuracy: 0.9975 - val_loss: 1.7607 - val_accuracy: 0.8450\n",
      "Epoch 184/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0381 - accuracy: 0.9887 - val_loss: 1.6198 - val_accuracy: 0.8150\n",
      "Epoch 185/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0265 - accuracy: 0.9937 - val_loss: 1.2051 - val_accuracy: 0.8250\n",
      "Epoch 186/200\n",
      "7/7 [==============================] - 74s 11s/step - loss: 0.0169 - accuracy: 0.9925 - val_loss: 1.1019 - val_accuracy: 0.8300\n",
      "Epoch 187/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0267 - accuracy: 0.9912 - val_loss: 0.9408 - val_accuracy: 0.8550\n",
      "Epoch 188/200\n",
      "7/7 [==============================] - 75s 11s/step - loss: 0.0256 - accuracy: 0.9937 - val_loss: 0.9656 - val_accuracy: 0.8550\n",
      "Epoch 189/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0261 - accuracy: 0.9925 - val_loss: 1.2578 - val_accuracy: 0.8500\n",
      "Epoch 190/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0148 - accuracy: 0.9950 - val_loss: 1.6441 - val_accuracy: 0.8250\n",
      "Epoch 191/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0084 - accuracy: 0.9975 - val_loss: 1.9439 - val_accuracy: 0.8100\n",
      "Epoch 192/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0081 - accuracy: 0.9962 - val_loss: 1.7446 - val_accuracy: 0.8200\n",
      "Epoch 193/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0101 - accuracy: 0.9950 - val_loss: 1.6138 - val_accuracy: 0.8300\n",
      "Epoch 194/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0062 - accuracy: 0.9975 - val_loss: 1.8972 - val_accuracy: 0.8200\n",
      "Epoch 195/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0031 - accuracy: 0.9987 - val_loss: 2.0123 - val_accuracy: 0.8150\n",
      "Epoch 196/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0085 - accuracy: 0.9975 - val_loss: 1.7832 - val_accuracy: 0.8250\n",
      "Epoch 197/200\n",
      "7/7 [==============================] - 77s 11s/step - loss: 0.0142 - accuracy: 0.9962 - val_loss: 1.6395 - val_accuracy: 0.8300\n",
      "Epoch 198/200\n",
      "7/7 [==============================] - 78s 11s/step - loss: 0.0065 - accuracy: 0.9987 - val_loss: 1.6714 - val_accuracy: 0.8300\n",
      "Epoch 199/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 1.6931 - val_accuracy: 0.8400\n",
      "Epoch 200/200\n",
      "7/7 [==============================] - 76s 11s/step - loss: 0.0081 - accuracy: 0.9950 - val_loss: 1.4641 - val_accuracy: 0.8400\n",
      "CPU times: total: 23h 34min 24s\n",
      "Wall time: 4h 15min 46s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "h = model.fit(\n",
    "    x_train, y_train,\n",
    "    validation_data=(x_test, y_test),\n",
    "    epochs=200, batch_size=128\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 3s 359ms/step - loss: 1.4641 - accuracy: 0.8400\n",
      "Test Accuracy: 83.99999737739563\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(x_test, y_test)\n",
    "print(f\"Test Accuracy: {scores[1]*100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sudha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\engine\\training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "#saving our model\n",
    "model.save('harrasment_cnn_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "Child_Labour_Final.ipynb",
   "provenance": []
  },
  "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}