Gender_and_Age_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from tqdm.notebook import tqdm\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import load_img\n",
    "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "BASE_DIR = 'Gender_Dataset/utkface_aligned_cropped/UTKFace'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/23708 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "3df35c148ad74f828cc344d283f83a1e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# labels - age, gender, ethnicity\n",
    "image_paths = []\n",
    "age_labels = []\n",
    "gender_labels = []\n",
    "\n",
    "for filename in tqdm(os.listdir(BASE_DIR)):\n",
    "    image_path = os.path.join(BASE_DIR, filename)\n",
    "    temp = filename.split('_')\n",
    "    age = int(temp[0])\n",
    "    gender = int(temp[1])\n",
    "    image_paths.append(image_path)\n",
    "    age_labels.append(age)\n",
    "    gender_labels.append(gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "image     Gender_Dataset/utkface_aligned_cropped/UTKFace...\nage                                                      16\ngender                                                    1\nName: 1000, dtype: object"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert to dataframe\n",
    "df = pd.DataFrame()\n",
    "df['image'], df['age'], df['gender'] = image_paths, age_labels, gender_labels\n",
    "df.iloc[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# map labels for gender\n",
    "gender_dict = {0:'Male', 1:'Female'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import albumentations as A\n",
    "\n",
    "def extract_features(images):\n",
    "    features = []\n",
    "\n",
    "    # Define the data augmentation transformations\n",
    "    transform = A.Compose([\n",
    "        A.Rotate(limit=10, p=0.5),\n",
    "        A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=10, p=0.5),\n",
    "        A.HorizontalFlip(p=0.5),\n",
    "        A.VerticalFlip(p=0.5),\n",
    "        A.RandomBrightnessContrast(p=0.5),\n",
    "        A.RandomGamma(p=0.5),\n",
    "        A.GaussNoise(p=0.5),\n",
    "        A.Blur(p=0.5),\n",
    "        A.Resize(160, 160)\n",
    "    ])\n",
    "\n",
    "    for image in tqdm(images):\n",
    "        img = load_img(image,grayscale=True)\n",
    "        img = img.resize((160, 160), Image.ANTIALIAS)\n",
    "        img = np.array(img)\n",
    "\n",
    "        # Perform data augmentation on the image\n",
    "        augmented = transform(image=img)\n",
    "        augmented_image = augmented[\"image\"]\n",
    "\n",
    "        features.append(augmented_image)\n",
    "\n",
    "    features = np.array(features)\n",
    "    features = features.reshape(len(features), 160, 160, 1)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/23708 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f10d954c1f8a4904b08e59dcf9f0bd06"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = extract_features(df['image'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(23708, 160, 160, 1)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#Conversion of gender and age into a numpy array\n",
    "y_gender = np.array(df['gender'])\n",
    "y_age = np.array(df['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "input_shape = (160, 160, 1)\n",
    "inputs = Input((input_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 160, 160, 1  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 160, 160, 16  160         ['input_1[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 160, 160, 16  64         ['conv2d[0][0]']                 \n",
      " alization)                     )                                                                 \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 160, 160, 16  0           ['batch_normalization[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 80, 80, 16)   0           ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 80, 80, 32)   4640        ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 80, 80, 32)  128         ['conv2d_1[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 80, 80, 32)   0           ['batch_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 40, 40, 32)  0           ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 40, 40, 64)   18496       ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 40, 40, 64)  256         ['conv2d_2[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)            (None, 40, 40, 64)   0           ['batch_normalization_2[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 20, 20, 64)  0           ['dropout_2[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 20, 20, 128)  73856       ['max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 20, 20, 128)  147584      ['conv2d_3[0][0]']               \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 20, 20, 128)  512        ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " dropout_3 (Dropout)            (None, 20, 20, 128)  0           ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d_3 (MaxPooling2D)  (None, 10, 10, 128)  0          ['dropout_3[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 10, 10, 256)  295168      ['max_pooling2d_3[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 10, 10, 256)  590080      ['conv2d_5[0][0]']               \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 10, 10, 256)  1024       ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " dropout_4 (Dropout)            (None, 10, 10, 256)  0           ['batch_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d_4 (MaxPooling2D)  (None, 5, 5, 256)   0           ['dropout_4[0][0]']              \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 6400)         0           ['max_pooling2d_4[0][0]']        \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 512)          3277312     ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 512)         2048        ['dense[0][0]']                  \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " dropout_5 (Dropout)            (None, 512)          0           ['batch_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 512)          262656      ['dropout_5[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 512)         2048        ['dense_1[0][0]']                \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " dropout_6 (Dropout)            (None, 512)          0           ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " gender_output (Dense)          (None, 1)            513         ['dropout_6[0][0]']              \n",
      "                                                                                                  \n",
      " age_output (Dense)             (None, 1)            513         ['dropout_6[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 4,677,058\n",
      "Trainable params: 4,674,018\n",
      "Non-trainable params: 3,040\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import Input, Conv2D, Dropout, MaxPooling2D, Flatten, Dense, BatchNormalization\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "\n",
    "# Convolutional layers\n",
    "\n",
    "x = Conv2D(16, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=l2(0.01))(inputs)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "x = Conv2D(32, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=l2(0.01))(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "x = Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=l2(0.01))(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "x = Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=l2(0.01))(x)\n",
    "x = Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=l2(0.01))(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "x = Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=l2(0.01))(x)\n",
    "x = Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=l2(0.01))(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "# Flatten layer\n",
    "x = Flatten()(x)\n",
    "\n",
    "# Fully connected layers\n",
    "x = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.4)(x)\n",
    "\n",
    "x = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.4)(x)\n",
    "\n",
    "# Define separate outputs\n",
    "gender_output = Dense(1, activation='sigmoid', name='gender_output')(x)\n",
    "age_output = Dense(1, activation='relu', name='age_output')(x)\n",
    "\n",
    "# Create the model with both outputs\n",
    "model = Model(inputs=inputs, outputs=[gender_output, age_output])\n",
    "\n",
    "# Unfreeze all layers\n",
    "for layer in model.layers:\n",
    "    layer.trainable = True\n",
    "\n",
    "# Recompile the model after unfreezing layers\n",
    "model.compile(loss=['binary_crossentropy', 'mae'], optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
    "\n",
    "# Adjust learning rate dynamically\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=0.0001, mode='auto')  \n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=8, restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import Callback\n",
    "\n",
    "class ConvolutionCallback(Callback):\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        if logs.get('gender_output_accuracy') >= 0.93:\n",
    "            print(\"Reached greater than 93.0% accuracy, so canceling training!\")\n",
    "            self.model.stop_training = True\n",
    "\n",
    "convolutionCallback = ConvolutionCallback()\n",
    "\n",
    "callbacks = [reduce_lr, convolutionCallback, early_stopping]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "593/593 [==============================] - 699s 1s/step - loss: 23.5397 - gender_output_loss: 0.7775 - age_output_loss: 13.0140 - gender_output_accuracy: 0.5510 - age_output_accuracy: 0.0378 - val_loss: 49.7614 - val_gender_output_loss: 0.6828 - val_age_output_loss: 42.6123 - val_gender_output_accuracy: 0.5742 - val_age_output_accuracy: 0.0000e+00 - lr: 0.0010\n",
      "Epoch 2/30\n",
      "593/593 [==============================] - 690s 1s/step - loss: 17.4918 - gender_output_loss: 0.7105 - age_output_loss: 11.0229 - gender_output_accuracy: 0.5554 - age_output_accuracy: 0.0521 - val_loss: 39.4478 - val_gender_output_loss: 0.7769 - val_age_output_loss: 33.8158 - val_gender_output_accuracy: 0.5814 - val_age_output_accuracy: 0.0000e+00 - lr: 0.0010\n",
      "Epoch 3/30\n",
      "593/593 [==============================] - 691s 1s/step - loss: 14.6970 - gender_output_loss: 0.6723 - age_output_loss: 10.0423 - gender_output_accuracy: 0.6081 - age_output_accuracy: 0.0539 - val_loss: 36.1590 - val_gender_output_loss: 0.7067 - val_age_output_loss: 32.1976 - val_gender_output_accuracy: 0.5816 - val_age_output_accuracy: 0.0000e+00 - lr: 0.0010\n",
      "Epoch 4/30\n",
      "593/593 [==============================] - 690s 1s/step - loss: 14.1256 - gender_output_loss: 0.6864 - age_output_loss: 10.0931 - gender_output_accuracy: 0.5771 - age_output_accuracy: 0.0556 - val_loss: 38.5813 - val_gender_output_loss: 0.6742 - val_age_output_loss: 34.8492 - val_gender_output_accuracy: 0.5816 - val_age_output_accuracy: 0.0000e+00 - lr: 0.0010\n",
      "Epoch 5/30\n",
      "593/593 [==============================] - 694s 1s/step - loss: 13.1903 - gender_output_loss: 0.6688 - age_output_loss: 10.0043 - gender_output_accuracy: 0.6034 - age_output_accuracy: 0.0588 - val_loss: 42.4177 - val_gender_output_loss: 0.9180 - val_age_output_loss: 38.6101 - val_gender_output_accuracy: 0.6031 - val_age_output_accuracy: 0.0000e+00 - lr: 0.0010\n",
      "Epoch 6/30\n",
      "593/593 [==============================] - 693s 1s/step - loss: 12.7318 - gender_output_loss: 0.6665 - age_output_loss: 9.5790 - gender_output_accuracy: 0.6037 - age_output_accuracy: 0.0545 - val_loss: 39.4996 - val_gender_output_loss: 0.6672 - val_age_output_loss: 36.5228 - val_gender_output_accuracy: 0.5829 - val_age_output_accuracy: 0.0000e+00 - lr: 0.0010\n",
      "Epoch 7/30\n",
      "593/593 [==============================] - 694s 1s/step - loss: 12.9981 - gender_output_loss: 0.6606 - age_output_loss: 9.3686 - gender_output_accuracy: 0.6149 - age_output_accuracy: 0.0483 - val_loss: 40.2490 - val_gender_output_loss: 0.6755 - val_age_output_loss: 36.6489 - val_gender_output_accuracy: 0.6044 - val_age_output_accuracy: 0.0000e+00 - lr: 0.0010\n",
      "Epoch 8/30\n",
      "593/593 [==============================] - 752s 1s/step - loss: 12.0512 - gender_output_loss: 0.6195 - age_output_loss: 8.5739 - gender_output_accuracy: 0.6623 - age_output_accuracy: 0.0328 - val_loss: 36.7582 - val_gender_output_loss: 0.7024 - val_age_output_loss: 32.6829 - val_gender_output_accuracy: 0.5854 - val_age_output_accuracy: 0.0000e+00 - lr: 0.0010\n",
      "Epoch 9/30\n",
      "593/593 [==============================] - 744s 1s/step - loss: 11.2163 - gender_output_loss: 0.5759 - age_output_loss: 7.7934 - gender_output_accuracy: 0.6999 - age_output_accuracy: 0.0238 - val_loss: 33.9735 - val_gender_output_loss: 0.5971 - val_age_output_loss: 30.9734 - val_gender_output_accuracy: 0.6809 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 10/30\n",
      "593/593 [==============================] - 1246s 2s/step - loss: 10.1288 - gender_output_loss: 0.5510 - age_output_loss: 7.4703 - gender_output_accuracy: 0.7204 - age_output_accuracy: 0.0206 - val_loss: 33.1649 - val_gender_output_loss: 0.5928 - val_age_output_loss: 30.7131 - val_gender_output_accuracy: 0.6803 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 11/30\n",
      "593/593 [==============================] - 1299s 2s/step - loss: 9.5052 - gender_output_loss: 0.5332 - age_output_loss: 7.2677 - gender_output_accuracy: 0.7312 - age_output_accuracy: 0.0183 - val_loss: 30.6153 - val_gender_output_loss: 0.5707 - val_age_output_loss: 28.4726 - val_gender_output_accuracy: 0.7054 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 12/30\n",
      "593/593 [==============================] - 1201s 2s/step - loss: 9.0675 - gender_output_loss: 0.5146 - age_output_loss: 7.0739 - gender_output_accuracy: 0.7412 - age_output_accuracy: 0.0159 - val_loss: 30.8697 - val_gender_output_loss: 0.5750 - val_age_output_loss: 28.8977 - val_gender_output_accuracy: 0.6946 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 13/30\n",
      "593/593 [==============================] - 801s 1s/step - loss: 8.7945 - gender_output_loss: 0.5052 - age_output_loss: 6.9505 - gender_output_accuracy: 0.7512 - age_output_accuracy: 0.0165 - val_loss: 28.7709 - val_gender_output_loss: 0.5629 - val_age_output_loss: 26.9157 - val_gender_output_accuracy: 0.7109 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 14/30\n",
      "593/593 [==============================] - 679s 1s/step - loss: 8.5646 - gender_output_loss: 0.4898 - age_output_loss: 6.8246 - gender_output_accuracy: 0.7588 - age_output_accuracy: 0.0143 - val_loss: 28.2850 - val_gender_output_loss: 0.5561 - val_age_output_loss: 26.5126 - val_gender_output_accuracy: 0.7229 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 15/30\n",
      "593/593 [==============================] - 703s 1s/step - loss: 8.3681 - gender_output_loss: 0.4819 - age_output_loss: 6.6953 - gender_output_accuracy: 0.7587 - age_output_accuracy: 0.0149 - val_loss: 28.7316 - val_gender_output_loss: 0.5483 - val_age_output_loss: 27.0139 - val_gender_output_accuracy: 0.7193 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 16/30\n",
      "593/593 [==============================] - 680s 1s/step - loss: 8.1541 - gender_output_loss: 0.4722 - age_output_loss: 6.5242 - gender_output_accuracy: 0.7705 - age_output_accuracy: 0.0129 - val_loss: 30.4149 - val_gender_output_loss: 0.5461 - val_age_output_loss: 28.7205 - val_gender_output_accuracy: 0.7178 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 17/30\n",
      "593/593 [==============================] - 681s 1s/step - loss: 8.0721 - gender_output_loss: 0.4627 - age_output_loss: 6.4762 - gender_output_accuracy: 0.7771 - age_output_accuracy: 0.0140 - val_loss: 28.5029 - val_gender_output_loss: 0.5394 - val_age_output_loss: 26.8443 - val_gender_output_accuracy: 0.7328 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 18/30\n",
      "593/593 [==============================] - 710s 1s/step - loss: 7.8643 - gender_output_loss: 0.4589 - age_output_loss: 6.2976 - gender_output_accuracy: 0.7757 - age_output_accuracy: 0.0123 - val_loss: 29.5423 - val_gender_output_loss: 0.5382 - val_age_output_loss: 27.9075 - val_gender_output_accuracy: 0.7324 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 19/30\n",
      "593/593 [==============================] - 729s 1s/step - loss: 7.7324 - gender_output_loss: 0.4468 - age_output_loss: 6.1915 - gender_output_accuracy: 0.7835 - age_output_accuracy: 0.0131 - val_loss: 26.4434 - val_gender_output_loss: 0.5572 - val_age_output_loss: 24.7975 - val_gender_output_accuracy: 0.7265 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 20/30\n",
      "593/593 [==============================] - 739s 1s/step - loss: 7.6690 - gender_output_loss: 0.4420 - age_output_loss: 6.1431 - gender_output_accuracy: 0.7867 - age_output_accuracy: 0.0123 - val_loss: 25.5015 - val_gender_output_loss: 0.5397 - val_age_output_loss: 23.8823 - val_gender_output_accuracy: 0.7358 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 21/30\n",
      "593/593 [==============================] - 801s 1s/step - loss: 7.5600 - gender_output_loss: 0.4340 - age_output_loss: 6.0436 - gender_output_accuracy: 0.7905 - age_output_accuracy: 0.0144 - val_loss: 25.3363 - val_gender_output_loss: 0.5476 - val_age_output_loss: 23.7054 - val_gender_output_accuracy: 0.7442 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 22/30\n",
      "593/593 [==============================] - 766s 1s/step - loss: 7.4995 - gender_output_loss: 0.4264 - age_output_loss: 5.9940 - gender_output_accuracy: 0.7946 - age_output_accuracy: 0.0132 - val_loss: 30.1913 - val_gender_output_loss: 0.5174 - val_age_output_loss: 28.5978 - val_gender_output_accuracy: 0.7400 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23/30\n",
      "593/593 [==============================] - 804s 1s/step - loss: 7.3727 - gender_output_loss: 0.4185 - age_output_loss: 5.8799 - gender_output_accuracy: 0.8012 - age_output_accuracy: 0.0143 - val_loss: 23.0870 - val_gender_output_loss: 0.5487 - val_age_output_loss: 21.4660 - val_gender_output_accuracy: 0.7482 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 24/30\n",
      "593/593 [==============================] - 815s 1s/step - loss: 7.2562 - gender_output_loss: 0.4082 - age_output_loss: 5.7700 - gender_output_accuracy: 0.8040 - age_output_accuracy: 0.0142 - val_loss: 23.3818 - val_gender_output_loss: 0.5362 - val_age_output_loss: 21.7645 - val_gender_output_accuracy: 0.7531 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 25/30\n",
      "593/593 [==============================] - 776s 1s/step - loss: 7.2053 - gender_output_loss: 0.4073 - age_output_loss: 5.7158 - gender_output_accuracy: 0.8021 - age_output_accuracy: 0.0119 - val_loss: 28.0733 - val_gender_output_loss: 0.5319 - val_age_output_loss: 26.4602 - val_gender_output_accuracy: 0.7385 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 26/30\n",
      "593/593 [==============================] - 819s 1s/step - loss: 7.1599 - gender_output_loss: 0.3955 - age_output_loss: 5.6815 - gender_output_accuracy: 0.8123 - age_output_accuracy: 0.0142 - val_loss: 28.2118 - val_gender_output_loss: 0.5173 - val_age_output_loss: 26.6121 - val_gender_output_accuracy: 0.7453 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 27/30\n",
      "593/593 [==============================] - 906s 2s/step - loss: 7.0978 - gender_output_loss: 0.3965 - age_output_loss: 5.6169 - gender_output_accuracy: 0.8139 - age_output_accuracy: 0.0138 - val_loss: 22.2572 - val_gender_output_loss: 0.5826 - val_age_output_loss: 20.5863 - val_gender_output_accuracy: 0.7425 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 28/30\n",
      "593/593 [==============================] - 879s 1s/step - loss: 6.9958 - gender_output_loss: 0.3885 - age_output_loss: 5.5169 - gender_output_accuracy: 0.8168 - age_output_accuracy: 0.0134 - val_loss: 26.1018 - val_gender_output_loss: 0.5256 - val_age_output_loss: 24.4847 - val_gender_output_accuracy: 0.7653 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 29/30\n",
      "593/593 [==============================] - 788s 1s/step - loss: 6.9566 - gender_output_loss: 0.3848 - age_output_loss: 5.4774 - gender_output_accuracy: 0.8183 - age_output_accuracy: 0.0123 - val_loss: 20.6724 - val_gender_output_loss: 0.6448 - val_age_output_loss: 18.9272 - val_gender_output_accuracy: 0.7375 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n",
      "Epoch 30/30\n",
      "593/593 [==============================] - 805s 1s/step - loss: 6.9308 - gender_output_loss: 0.3807 - age_output_loss: 5.4430 - gender_output_accuracy: 0.8226 - age_output_accuracy: 0.0129 - val_loss: 21.1049 - val_gender_output_loss: 0.5107 - val_age_output_loss: 19.4824 - val_gender_output_accuracy: 0.7689 - val_age_output_accuracy: 0.0000e+00 - lr: 1.0000e-04\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x=X, y=[y_gender, y_age], batch_size=32, epochs=30, validation_split=0.2, callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results for gender\n",
    "acc = history.history['gender_output_accuracy']\n",
    "val_acc = history.history['val_gender_output_accuracy']\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'b', label='Training Accuracy')\n",
    "plt.plot(epochs, val_acc, 'r', label='Validation Accuracy')\n",
    "plt.title('Accuracy Graph')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "\n",
    "loss = history.history['gender_output_loss']\n",
    "val_loss = history.history['val_gender_output_loss']\n",
    "\n",
    "plt.plot(epochs, loss, 'b', label='Training Loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation Loss')\n",
    "plt.title('Loss Graph')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Zt28fhg8fjueffx7NmjWDj48Prly5UqYxuLu7w9vbG4cPH9Yey83NxbFS/CO3cePGyMnJwaFDh7THkpKScP78eTRp0kR7zN/fH2PHjsWGDRswZcoUfPvtt9rXvLy8MGzYMPz8889YuHAhvvnmG4PjKQsVqzumSRPAzg5ITgauXAFq11Y6IiIi0lP9+vWxYcMG9O7dGyqVCjNnzjS4C6Q0JkyYgPDwcNSrVw+NGjXC4sWLce/evRKtrxITEwNXV1ftc5VKhRYtWqBPnz4YPXo0li1bBldXV7z11luoVq0a+vTpAwCYOHEiQkND0aBBA9y7dw+RkZFo3LgxAOC9995DYGAgmjZtiszMTPzxxx/a18xVxUpC7OyAZs2Ao0dlawiTECIii/PZZ5/h1VdfRYcOHVClShVMnz4dKSkpZR7H9OnTkZCQgKFDh8La2hpjxoxBSEhIiVaX7dy5s85za2tr5OTkYPny5XjjjTfw7LPPIisrC507d8Zff/2l7RrKzc1FWFgYrl+/Djc3N/To0QOff/45ADnXyYwZM3DlyhU4OjqiU6dOWLt2rfH/cCNSCaU7uApJSUmBu7s71Go13NzcjH+DMWPkpGVvvw18+KHxr09EpJCMjAzExsaidu3acHBwUDqcCicvLw+NGzfGgAEDMGfOHKXDMYlHfccM+f2uWC0hAItTiYjIKK5evYpt27ahS5cuyMzMxJdffonY2Fi8/PLLSodmMSpWYSqQn4QcPcriVCIiMpiVlRVWrFiBJ554Ak8++SRiYmKwY8cOs6/DMCcVryWkWTPA2hq4fRu4cQOoXl3piIiIyAL5+/tj3759Sodh0SpeS4ijoxwlA7BLhoiISEEVLwkBWBdCRERkBpiEEBERkSKYhBAREZEiKmYS0rKlXNjgxg0gMVHpaIiIiCqkipmEuLgADRvK/ePHlY2FiIiogqqYSQjALhkionKka9eumDhxovZ5rVq1sHDhwke+R6VSYePGjaW+t7GuUxExCWESQkSkmN69e6NHjx5FvrZ3716oVCqcPHlS7+sePnwYY8aMKW14OmbNmoWWLVs+dDw+Ph6hoaFGvVdhK1asgIeHh0nvoYRSJSEff/wxVCqVTvaZkZGBsLAwVK5cGS4uLujfvz8SzbHugkkIEZHiRo4cie3bt+P69esPvbZ8+XK0adMGzZs31/u6Xl5ecHJyMkaIj+Xj4wN7e/syuVd5Y3AScvjwYSxbtuyhL8ekSZOwefNmrF+/HlFRUbh58yb69etX6kCNrlUr+RgbC9y7p2wsRESmIASQlqbMVsJlMZ599ll4eXlhxYoVOsdTU1Oxfv16jBw5EklJSRg0aBCqVasGJycnNGvWDGvWrHnkdQt3x1y8eBGdO3eGg4MDmjRpgu3btz/0nunTp6NBgwZwcnJCnTp1MHPmTGRnZwOQLRGzZ8/GiRMnoFKpoFKptDEX7o6JiYnB008/DUdHR1SuXBljxoxBamqq9vXhw4ejb9+++OSTT+Dr64vKlSsjLCxMey9DxMXFoU+fPnBxcYGbmxsGDBig0wBw4sQJPPXUU3B1dYWbmxsCAwNx5MgRAHINnN69e6NSpUpwdnZG06ZN8ddffxkciz4MmrY9NTUVgwcPxrfffou5c+dqj6vVanz//fdYvXo1nn76aQAyk23cuDEOHjyI9u3bGydqY/DwAOrUAf77Txan/i9eIqJyIz1dFuIrITUVcHZ+7Gk2NjYYOnQoVqxYgXfeeQcqlQoAsH79euTm5mLQoEFITU1FYGAgpk+fDjc3N/z555945ZVXULduXbRt2/ax98jLy0O/fv3g7e2NQ4cOQa1W67Tga7i6umLFihXw8/NDTEwMRo8eDVdXV0ybNg0DBw7EqVOn8Pfff2PHjh0AAHd394eukZaWhpCQEAQFBeHw4cO4desWRo0ahfHjx+skWpGRkfD19UVkZCQuXbqEgQMHomXLlhg9evRj/56i/j5NAhIVFYWcnByEhYVh4MCB2L17NwBg8ODBaNWqFb766itYW1sjOjoatra2AICwsDBkZWVhz549cHZ2xpkzZ+BSVt8bYYChQ4eKiRMnCiGE6NKli3jjjTeEEELs3LlTABD37t3TOb9GjRris88+K/JaGRkZQq1Wa7dr164JAEKtVhsSmn5eeEEIQIgFC0x/LyIiE3vw4IE4c+aMePDggTyQmir/P06JLTW1xHGfPXtWABCRkZHaY506dRJDhgwp9j29evUSU6ZM0T4v+FskhBA1a9YUn3/+uRBCiK1btwobGxtx48YN7etbtmwRAERERESx91iwYIEIDAzUPn///fdFixYtHjqv4HW++eYbUalSJZFa4O//888/hZWVlUhISBBCCDFs2DBRs2ZNkZOToz3nxRdfFAMHDiw2luXLlwt3d/ciX9u2bZuwtrYWcXFx2mOnT58WAMS///4rhBDC1dVVrFixosj3N2vWTMyaNavYexf00HesALVarffvt94tIWvXrsWxY8dw+PDhh15LSEiAnZ3dQ8Uz3t7eSEhIKPJ64eHhmD17tr5hGEfr1sCvv7IuhIjKJycn2SKh1L1LqFGjRujQoQN++OEHdO3aFZcuXcLevXvxwQcfAAByc3Px0UcfYd26dbhx4waysrKQmZlZ4pqPs2fPwt/fH35+ftpjQUFBD533yy+/YNGiRbh8+TJSU1ORk5MDNze3Ev8dmnu1aNECzgVagZ588knk5eXh/Pnz8Pb2BgA0bdoU1tbW2nN8fX0RExOj170K3tPf3x/+/v7aY02aNIGHhwfOnj2LJ554ApMnT8aoUaOwcuVKBAcH48UXX0TdunUBAK+//jrGjRuHbdu2ITg4GP379zeoDscQetWEXLt2DW+88QZWrVoFBwcHowQwY8YMqNVq7Xbt2jWjXLdEWJxKROWZSiW7RJTY/tetUlIjR47Eb7/9hvv372P58uWoW7cuunTpAgBYsGABvvjiC0yfPh2RkZGIjo5GSEgIsrKyjPZRHThwAIMHD0bPnj3xxx9/4Pjx43jnnXeMeo+CNF0hGiqVCnl5eSa5FyBH9pw+fRq9evXCrl270KRJE0RERAAARo0ahf/++w+vvPIKYmJi0KZNGyxevNhksRSkVxJy9OhR3Lp1C61bt4aNjQ1sbGwQFRWFRYsWwcbGBt7e3sjKykJycrLO+xITE+Hj41PkNe3t7eHm5qazlRlNceqFC8D9+2V3XyIi0jFgwABYWVlh9erV+Omnn/Dqq69q60P27duHPn36YMiQIWjRogXq1KmDCxculPjajRs3xrVr1xAfH689dvDgQZ1z9u/fj5o1a+Kdd95BmzZtUL9+fVy9elXnHDs7O+Tm5j72XidOnEBaWpr22L59+2BlZYWGmkkyjUzz9xX8R/yZM2eQnJyMJppV4wE0aNAAkyZNwrZt29CvXz8sX75c+5q/vz/Gjh2LDRs2YMqUKfj2229NEmtheiUh3bp1Q0xMDKKjo7VbmzZtMHjwYO2+ra0tdu7cqX3P+fPnERcXV2TTl+KqVgWqV5c9mCdOKB0NEVGF5eLigoEDB2LGjBmIj4/H8OHDta/Vr18f27dvx/79+3H27Fm89tprek39EBwcjAYNGmDYsGE4ceIE9u7di3feeUfnnPr16yMuLg5r167F5cuXsWjRIm1LgUatWrUQGxuL6Oho3LlzB5mZmQ/da/DgwXBwcMCwYcNw6tQpREZGYsKECXjllVe0XTGGys3N1fn9jY6OxtmzZxEcHIxmzZph8ODBOHbsGP79918MHToUXbp0QZs2bfDgwQOMHz8eu3fvxtWrV7Fv3z4cPnwYjRs3BgBMnDgRW7duRWxsLI4dO4bIyEjta6amVxLi6uqKgIAAnc3Z2RmVK1dGQEAA3N3dMXLkSEyePBmRkZE4evQoRowYgaCgIPMaGVMQu2SIiMzCyJEjce/ePYSEhOjUb7z77rto3bo1QkJC0LVrV/j4+KBv374lvq6VlRUiIiLw4MEDtG3bFqNGjcKHH36oc85zzz2HSZMmYfz48WjZsiX279+PmTNn6pzTv39/9OjRA0899RS8vLyKHCbs5OSErVu34u7du3jiiSfwwgsvoFu3bvjyyy/1+zCKkJqailatWulsvXv3hkqlwqZNm1CpUiV07twZwcHBqFOnDn755RcAgLW1NZKSkjB06FA0aNAAAwYMQGhoqLYeMzc3F2FhYWjcuDF69OiBBg0aYOnSpaWOtyRUQpRwMHcxunbtipYtW2rHY2dkZGDKlClYs2YNMjMzERISgqVLlxbbHVNYSkoK3N3doVary6ZrZvZsYNYsYOhQ4McfTX8/IiITycjIQGxsLGrXrm20uj2igh71HTPk99ugeUIK0oxB1nBwcMCSJUuwZMmS0l66bLAlhIiISBEVd+0YDU0ScuaMnNiHiIiIygSTED8/WaCalwcYOEabiIiI9MckRKVilwwREZECmIQATEKIqFwp5XgDomIZ+7vFJAQo/0nI+fPAoUNKR0FEJqaZhTOd9W1kIpoZZAtOOV8apR4dUy4EBsrHmBggKwuws1M2HmNKSQE6dADu3gU2bACef17piIjIRKytreHh4YFbt24BkHNWqPScPp2oOHl5ebh9+zacnJxgY2Oc9IFJCADUrAlUqgTcuwecPp0/nXt58O23MgEBgFdeAfbvB8poYSIiKnuaOZk0iQiRMVlZWaFGjRpGS26ZhAD5xak7d8oumfKShGRnA/+bRA4+PkBCAvDcc8C//8oRQURU7qhUKvj6+qJq1arIzs5WOhwqZ+zs7GBlZbxKDiYhGgWTkJEjlY7GONauBa5fB7y95d/VuTNw6RLwwgvAjh3lq9uJiHRYW1sbrd+eyFRYmKpR3opThQAWLJD7r78O+PoCmzcDbm7A3r1AWJg8h4iISCFMQjQ0SciJE0BOjrKxGMO2bbLQ1tkZGDdOHmvUSLaOWFkB330HLF6sbIxERFShMQnRqFcPcHEBHjyQQ1otnaYVZNQoWXSrERoKzJ8v9ydNArZvL/vYiIiIwCQkn5VVfkGqpXfJHDsm61usrWWiUdjkycCwYXKq+gEDgAsXyj5GIiKq8JiEFFRe6kI++UQ+Dhgghx8XplIBX38NtG8PJCfLETPJyWUZIREREZMQHeUhCbl6FVi3Tu6/+Wbx5zk4ABERQPXqsvtp0CAgN7dsYiQiIgKTEF2aJOT4cdlVYYk+/1wmE8HBj5/vxMcH2LQJcHQE/v4bmDatbGIkIiICkxBdjRrJFoL79+V8Gpbm7l056gV4dCtIQa1bAytWyP3PPsvfJyIiMjEmIQXZ2AAtWsh9S+yS+eorIC1N/g3PPFPy9w0YAMycKfdfe01O7U5ERGRiTEIKs9S6kIyM/Hk/pk6Vxaf6mDVLLm6XlSUf4+KMHiIREVFBTEIKs9QkZOVKIDER8PcHBg7U//1WVsBPP8nF7W7dAvr0ka0qREREJsIkpLCCSYilTGuelwd8+qncnzgRsLU17DouLsDvvwNeXkB0NDB8uOUW6BIRkdljElJY06byR/zePTnc1RJs3iyH2bq7A6NHl+5aNWsCGzbIz+DXX4E5c4wTIxERUSFcRbcwe3sgIEAO0z12DKhVS+mIHk8zRfvYsYCra+mv17GjLHIdNUrWigQEAP37F31uZqYclZOUlL8VfH73rmxZmTNHFv4SERH9D38VitK6dX4S0q+f0tE82oEDwL59gJ2dXC3XWEaOlAvgffEFMHSoXGPm3r2HE42S1o00aiSniiciIvofJiFFad0a+P57yyhO1bSCDBkC+PkZ99qffAKcOSMTkGXLij/Pygrw9AQqV5Zbwf3YWDkzqyaZ0XfUDhERlVtMQooSGCgfjx6Vxanm+sN54QKwcaPcnzrV+Ne3sQHWr5eTmAEPJxiazc1NJiJFSUqSs7EePw788w/QqZPx4yQiIovEJKQozZvLFWhv3QLi443fwmAsn30mk6RnnwUaNzbNPdzdgdmzDX9/5cqylebbb2VrCJMQIiL6H46OKYqjY/6Purl2ySQm5k+xXtIp2pWiqVWJiLCcEUdERGRyTEKKY+6Tln35pRyZ0rat+bcuBAQA3brJOUeWLFE6GiIiMhNMQopjzklIWhqwdKncf/NN861ZKeiNN+Tjt99yJlYiIgLAJKR45pyE/PCDHCJbt65c58US9Ool401OllPMExFRhcckpDgtW8rHa9eA27cVDUVHTk7+aJUpU2QBrSWwsgImTJD7ixZZzpT4RERkMkxCiuPqCjRoIPePH1c2loJ++w24cgWoUkWu7WJJRoyQn+vZs3LuESIiqtCYhDyKuXXJCJE/Odn48XIUjyVxc5OJCCCH6xIRUYWmVxLy1VdfoXnz5nBzc4ObmxuCgoKwZcsW7etdu3aFSqXS2caOHWv0oMuMJgk5elTZODR275axODoCYWFKR2OYCRNkIe1ff8nJ1oiIqMLSKwmpXr06Pv74Yxw9ehRHjhzB008/jT59+uD06dPac0aPHo34+HjtNn/+fKMHXWbMrSVE0woyYoTsjrFE9erJIlUAWLxY2ViIiEhRKiFKVyHo6emJBQsWYOTIkejatStatmyJhQsXGny9lJQUuLu7Q61Ww83NrTShld7du3LGT81+pUrKxXLqFNCsmSzwvHBBjjSxVDt2AM88A7i4ANevy1lZiYjIohny+21wTUhubi7Wrl2LtLQ0BAUFaY+vWrUKVapUQUBAAGbMmIH09PRHXiczMxMpKSk6m9nw9ARq1ZL70dFKRiIXkwPkqr6WnIAAcuKypk2B1FQ53JiIiCokvZOQmJgYuLi4wN7eHmPHjkVERASaNGkCAHj55Zfx888/IzIyEjNmzMDKlSsxZMiQR14vPDwc7u7u2s3f39+wv8RUzKFL5vp1YNUquW/uU7SXhEqVP5X74sVAbq6y8RARkSL07o7JyspCXFwc1Go1fv31V3z33XeIiorSJiIF7dq1C926dcOlS5dQt5h/vWdmZiIzM1P7PCUlBf7+/ubRHQMAH34IvPsu8PLL+YlAWRs1Cvj+e6BzZyAqSpkYjC09HfD3l91cGzcCffooHREREZVCmXTH2NnZoV69eggMDER4eDhatGiBL4oZbtmuXTsAwKVLl4q9nr29vXa0jWYzK0q3hOzcKRMQAJg7V5kYTMHJCRg9Wu5zuC4RUYVU6nlC8vLydFoyCor+Xx2Fr69vaW+jHE0Scv68rGEoS2lpwJgxcv///s/8F6rTV1iYnPE1MhI4eVLpaIiIqIzplYTMmDEDe/bswZUrVxATE4MZM2Zg9+7dGDx4MC5fvow5c+bg6NGjuHLlCn7//XcMHToUnTt3RvPmzU0Vv+l5ewN+fnKisBMnyvbe770H/Pef7LYIDy/be5cFf39ZaAvIqdyJiKhC0SsJuXXrFoYOHYqGDRuiW7duOHz4MLZu3YpnnnkGdnZ22LFjB7p3745GjRphypQp6N+/PzZv3myq2MuOEl0yhw4BmqHOX38tZxstjzSr665aBdy5o2wsRERUpmz0Ofl7TW1CEfz9/RFVXoomC2vdGvjjj7JLQrKygJEjgbw8YMgQoGfPsrmvEjp0AAID5Uyw33wDvP220hEREVEZ4doxJREYKB/LKgkJDwdOnwa8vIDPPy+beypFpcpvDVm6FMjOVjYeIiIqM0xCSkLTHXP6NJCRYdp7nTolhwUDcg4NS52eXR8DBsjamxs35CrBRERUITAJKYlq1WSrRG4uEBNjuvvk5spumOxs4Lnn5I9zRWBvD4wbJ/c5XJeIqMJgElISKlV+a4gp/6W+aBHw77+yCHXpUnnfimLsWMDODjh4UH4GRERU7jEJKan+/eXjvHnArFlyyK4x/fcf8M47cv+TT2TrS0Xi7Q289JLcZ2sIEVGFwCSkpEaNyp+xdPZsYPJk4yUiQsjZQx88AJ56St6rItIUqK5bJ+tDiIioXGMSUlIqlWyp0EyqtXChTBaMsfjaDz8Au3YBjo7At99WrG6Yglq3Bjp2BHJygK++UjoaIiIyMSYh+powAVixArCyksnDoEFyXg9D3bwJTJki9+fOBYpZ6K/C0LSGLFtm+pFIRESkKCYhhhg2DFi/HrC1lY99+shVYfUlhFwTRq0G2rbN/wGuyPr2BWrUkLOnrl6tdDRERGRCTEIM1a+fnEXV0RH4+2+gRw+ZTOjj11+BTZtkMvP993Ixt4rOxkYubAfIAlVjFwATEZHZYBJSGt27A9u3yyG1e/cCTz9d8vVPkpKA8ePl/ttvAwEBpovT0owaJZO7kyeB8roUABERMQkptSefBHbvljObHjsGdO5cspEdkycDt24BTZsCM2aYPEyL4ukJDB0q9zlcl4io3GISYgytWsmWkOrVgbNngU6d5Lwfxfn7b+Cnn+QomO+/lzOGkq7XX5ePmzYBsbHKxkJERCbBJMRYGjUC/vkHqFdP/mh27CjXmins/n3gtdfk/sSJQLt2ZRqmxWjSBHjmGVkTMn++0tEQEZEJMAkxppo1ZYtIQAAQHy+7Zg4f1j1nxgwgLg6oXRuYM0eZOC2FZujy11/nL+pHRETlBpMQY/PxkcWUbdsCd+8C3brlF1fu3QssWSL3v/0WcHZWLk5LEBKSn3y8+y6TNiKicoZJiCl4egI7dsgp2O/fl8N3N2zIn4595EiZnNDjvf028NFHcv+994APPlA2HiIiMhqVEOY1EUNKSgrc3d2hVqvh5uamdDilk5EBDBgAbN6cf8zXFzhzBvDwUCwsizRvHvDWW3L//fflIoJERGQ2DPn9ZkuIKTk4AL/9Jqd21/jqKyYghpg+XSYigFxA8P33OZEZEZGFs1E6gHLP1hZYuVKOgnFwkFO8k2GmTZNr9rz5puyWEUImJBV1wT8iIgvHJKQsWFtzXRhjmTpVJh1Tp8pCVSFkQsJEhIjI4rA7hizPlCnAZ5/J/blz5cgZds0QEVkcJiFkmSZNAj7/XO5/9BHwzjtMRIiILAyTELJcEyfmry0THi4ngmMiQkRkMZiEkGV7/XVg0SK5rxnGy0SEiMgiMAkhyzdhAvDll3J//nw5ioaJCBGR2WMSQuVDWFj+lPiffCKH8TIRISIya0xCqPz4v/8Dli6V+59+KkfRMBEhIjJbnCeEypdx4+SEZmPHytEzmZnAmDFA/fqAk5PS0RERUQFcO4bKp2++AV57TfdYjRpAgwZAw4b5W4MG8rgVGwWJiErDkN9vtoRQ+TRmDODuLofwnj8P3L0LxMXJbccO3XMdHGRLiSYpKZigVKqkTPxERBUAW0KoYrhzRyYj588DFy7k71+6BGRnF/++l1+Wa/+wpYSI6JHYEkJUnCpV5Pbkk7rHc3KAK1d0ExPNFh8PrF4tW0Xee0+RsImIyjO2hBAV58cfgeHD5eJ4f/8NdO+udERERGbLkN9vtjETFWfYMGD0aDnM9+WXgWvXlI6IiKhc0SsJ+eqrr9C8eXO4ubnBzc0NQUFB2LJli/b1jIwMhIWFoXLlynBxcUH//v2RmJho9KCJysyiRUCrVkBSEjBgAJCVpXRERETlhl5JSPXq1fHxxx/j6NGjOHLkCJ5++mn06dMHp0+fBgBMmjQJmzdvxvr16xEVFYWbN2+iX79+JgmcqEw4OAC//ipH2hw8KKeEJyIioyh1TYinpycWLFiAF154AV5eXli9ejVeeOEFAMC5c+fQuHFjHDhwAO3bty/y/ZmZmcjMzNQ+T0lJgb+/P2tCyLz8/jvQp4/c/+UX2SpCRERaZVoTkpubi7Vr1yItLQ1BQUE4evQosrOzERwcrD2nUaNGqFGjBg4cOFDsdcLDw+Hu7q7d/P39DQ2JyHSeew6YPl3ujxwpR88QEVGp6J2ExMTEwMXFBfb29hg7diwiIiLQpEkTJCQkwM7ODh4eHjrne3t7IyEhodjrzZgxA2q1WrtdY/Efmau5c4EuXYDUVKB/fyAtTemIiIgsmt7zhDRs2BDR0dFQq9X49ddfMWzYMERFRRkcgL29Pezt7Q1+P1GZsbEB1q6VhaqnT8v1aX76SQ7hJSIivendEmJnZ4d69eohMDAQ4eHhaNGiBb744gv4+PggKysLycnJOucnJibCx8fHWPESKcvHR9aEWFsDP/8s16ghIiKDlHqekLy8PGRmZiIwMBC2trbYuXOn9rXz588jLi4OQUFBpb0Nkfno3Bn46CO5//rrwNGjysZDRGSh9OqOmTFjBkJDQ1GjRg3cv38fq1evxu7du7F161a4u7tj5MiRmDx5Mjw9PeHm5oYJEyYgKCio2JExRBbrzTeB/fuBTZuAF16QiYinp9JRERFZFL2SkFu3bmHo0KGIj4+Hu7s7mjdvjq1bt+KZZ54BAHz++eewsrJC//79kZmZiZCQECxdutQkgRMpSqUCVqwAAgOB//6Ts6tu2sSF7oiI9MC1Y4hK4/hxICgIyMyUXTQzZigdERGRIrh2DFFZa9UK+PJLuf/uu0BkpLLxEBFZECYhRKU1cqRcbTcvD3jpJeDmTcOvlZoKbNsGvP++HIVDRFSO6T1PCBEVolIBS5YAx44BJ08CAwcCu3YBtraPf+/9+8A//wBRUXI7cgTIycl/PScHGDzYdLETESmILSFExuDkJBe6c3OTScXbbxd9nloN/PmnHF3Tti1QqRLQsycwb55cIC8nB6hRA+jYUZ7/6qsyOakIUlOBvn2Bli2B2FiloyGiMsDCVCJj2rBBTumu2e/aFdi7VyYSu3cD0dGy26ag2rXldPBdu8rHWrXkOQMGAL/9JhOV/fuBRo3K9E8pU/fvy2Tsn3/k83r15L63t7JxVQR//w3Mni1HezVsqHQ0ZMEM+f1mEkJkbFOmAJ99JrtjcnKAwv8Tq1dPJhuarUaNoq/z4AHw9NOyhaR2bflYtarp4y9rKSlAaKhMtNzd5RYXJ4t+IyPlczKdli2BEyeAN94AFi5UOhqyYExCiMxBdrZMHjT/qm/YUDfpqFat5Ne6fRto317ORdKunaw1cXIyTdxKSEkBevQADhwAPDyA7dtl0tGxI3Drlvy8/v4bcHBQOtLy6fRpICBA7rdqJeuaiAzEIbpE5sDWFtiyBfjrLzlS5tw5YNky4OWX9UtAAMDLS17L0xM4dAgYMgTIzTVN3GVNrQZCQmQCUqkSsGMH0KYNUL++TDxcXWU31qBBusW6ZDxr1uTvR0fL/yZEZYhJCJEpuLjILgZf39Jfq0EDYONGwM4OiIiQRa2WLjkZ6N5ddjFpEpDAwPzXW7UCfv9d/s0bN8oVi82r0dbyCQGsXi33VSr5fN8+ZWOiCodJCJEl6NRJFg4CwOefA4sXKxpOqWgSkH//lS08u3YBrVs/fF7XrsDatXIq/O+/L37EERnm0CE5CsnZWRZBA7KImqgMMQkhshSDBuWv3jtxomwpsDT37gHPPAMcPgxUriwTkJYtiz//+edlVxYAfPyxLPg1BU2rwNy5QFaWae5hbjStIH37yrocANizR7FwqGJiEkJkSd56Cxg1Sg7hHTRITm5mKe7eBYKDZcxVqsgEpEWLx79v1CggPFzuT5kC/PSTceM6fVoWwA4eDMycCXzxhXGvb45ycvJn5H35ZaBzZ7l/+DCQnq5cXFThMAkhsiQqFbB0qezOSE8Hnn0WuHpV6ageT5OAHDsmi20jI4HmzUv+/unTgUmT5P6rrwJ//FH6mNLTZRdPy5ayG8LaWh7/8EPgzp3SX9+c7dolRx9VrixbpmrXlkXT2dmym4aojDAJIbI0trbA+vXyRzwxUU7ylZysdFTFS0oCunWTKw5XrSoTEM2w0JJSqYBPPgFeeUWODnrxxfwh0Ib46y+gaVPZwpKTAzz3HHDpkkxI1GpgzhzDr20JNF0xAwbI75NKJeuOANaFUJliEkJkidzc5PTvfn7AmTNAv37mWctw545MQKKj5eynkZHyx98QmgLVXr2AjAygd28gJka/a9y4IROYXr2AK1cAf385+mbTJjlT7YIF8rylS4GLFw2L09w9eCBn8wVkV4yGpkuGdSFUhpiEEFmq6tVlIuLiIn/cR482r2GsmgTkxIn8BKRJk9Jd09YWWLcOePJJ2foTElKydWZycmStR6NGco0fa2tg6lSZwPXpk39ecLAcWp2TA8yYUbpYzdWff8pp8mvUADp0yD+uSUL27zfPhJbKJSYhRJasZUv5o2xtLQs2P/hA6Yik27flrLEnTwI+PnLdnMaNjXNtJydg82agWTMgPl7WNCQmFn/+v//KxQInTpSL5LVvDxw9Kls9XFwePn/+fNnq8ttv8ge5vNF0xQwaJP9OjcaNZY3IgwecOZXKDJMQIksXGiq7DwBg1izgxx8VDQe3bskEJCZGTta2e7fxF9+rVEnOqlqrFnD5shxiWni2T7UaGD9eJh3Hj8tp4ZctkxNyPWpUTkCALH4F5Ggcc2pdKq3kZNkSAuh2xQAyIdGs3sy6ECojTEKIyoMxY+QIEkAOad21q+xjSE+XBZ9PPw2cOiXrVXbvNt3KrH5+wLZtstg1Olp2q2RkyKRh7VqZ+CxZIp8PGSKnzx8zRvdf/8X54APZ4nLwoOy+KS82bJBdLU2bypakwlgXQmWMSQhRefHRR3K0Q06OLFSdN092J2Rmmu6eV67IH/pevWRTfq9ect6NatVkAtKggenuDTy8zsyLL8o6kUGDgIQEef+dO4GVK2VdSkn5+uZPj//WW+WnRkLTFfPyy3JETGGaJOSff+RcNEQmxlV0icqTjAxZDFqwlsHeXtZEdOwotw4dZNeEIbKz5Q/UX3/JZv2zZ3Vf9/eXichbbwE1axr8Z+ht927ZJaNJuOztgXfeAaZNk/uGSE2VSU5Cgpwqf+JEY0WrjPh4mRwKIVdlrl374XNycmRXV2qqLCjWZy4XqvAM+f1mEkJU3ty/L4ey7tkjE4bbt3VfV6lk3YMmKenYUY6UKE5CQv6qwNu2ASkp+a9ZW8uRKj17yuSjadOi/4VdFjZuBIYOlUnW4sUygSit776To44qVZK1J5Uqlf6aSlm4UE74FhT06ILbkBD533nxYllTQ1RCTEKISJcQcr6Lf/7J34qa/8LfXzcpyciQLR1//ilHkhTk5SWLYXv1kiNTzOmHOS+vZDUfJZWbK4tYT5+WQ3o184hYorZt5bTsj0suPvwQePdd2bW1bl3ZxWdMaWmyQDoxMf+x4L7mMTVVdlsOGqR0xOUCkxAierzERDlCRJOUHDsmf2wfJTBQJh29egFt2hj3h97cbdkiW3rs7GRxa1HdGObu4kVZH2NtLSdse1R9zN69sjbE21t24SjVslUSv/8up/AvnGikpZX8Gq1bP5xok0GYhBCR/tLS5HohmqTkwAGZZHTvLn98Q0PlXB8VlRCyxWfnTuCll4A1a5SOSH8ffAC8/77savn770efm5Eha4YyM4ELF4zTrWUKd+7IEVLZ2UW/bm8vEynNVrWq7qOzc/5EdfHxFfs7biSG/H7bmDgmIjJ3zs5yWO3TT8vnmlERFam141E069a0bi2H/k6aJLs2LIUQuqNiHsfBQf59e/fKuiJzTULWrpUJSIMG8r+JJrnQJBquro9vxQkMlK0gW7cCw4aVTdykg/8vQ0S6rKyYgBTWsqUsegVkbYh5NSA/2vHjwPnzMrno27dk77GE+UJ++kk+hoUBY8fKYelPPgnUqyfXVipJN1JoqHzcssV0cdIj8f9piIhKYu5c+UO+d6+sRbAUmlaQ3r3lj3NJmHsScvasLLK1sZFdZIbSJCHbtsnhyVTmmIQQEZVE9erA5Mlyf9q04msRzElubn4NS0m6YjSCgmRr2JUrwLVrJgmtVFaulI+hobLrxVDt2snRXffuyTWGqMwxCSEiKqnp0+UQ5QsXgG++UTqax9u7F7h5E3B3z/9Xf0m4usoaGM01zEleHvDzz3L/lVdKdy1ra1mADch5cKjMMQkhIiopNze5SCAgHwsvmmduNF0xL7yg/8yx5tolExUlW2fc3WUXU2mxLkRRTEKIiPQxerQckXHnjpzoylxlZuYvvqdPV4yGuSYhmoLUgQNljU5p9eghH48dk7MDU5liEkJEpA9bW2D+fLn/+efmWTMByGGn9+7Jxfi6dNH//R07ysezZx+e+l8p6en5iZVmtFJpeXvndz1t3Wqca1KJMQkhItLXc8/JloKMDDnFuTnSdMW89JKsfdBX5cpyLSBATmJnDjZulFOt16kj1wgyFnbJKEavJCQ8PBxPPPEEXF1dUbVqVfTt2xfnz5/XOadr165QqVQ629ixY40aNBGRojQTmAFypMbx48rGU9j9+/nDiA3pitEwty4ZTVfMK68Ydzr5nj3lI4fqljm9kpCoqCiEhYXh4MGD2L59O7Kzs9G9e3ekFZqnf/To0YiPj9du8zVNl0RE5cUTT8iFz4QwvwnMNm0CHjyQs50GBhp+HXNKQm7eBLZvl/ulHRVTGIfqKkavJOTvv//G8OHD0bRpU7Ro0QIrVqxAXFwcjhZa/MfJyQk+Pj7ajWvAEFG59NFHcmG7XbvMqym/4DTtpWkx6NRJPkZHAykppQ6rVFavlsNzn3wSqFvXuNcuOFTXnP47VgClqglR/294mqenp87xVatWoUqVKggICMCMGTOQnp5e7DUyMzORkpKisxERWYRatYDXX5f7b75pHk35t2/LbgWg9EvUV6sm6y/y8oD9+0sfW2loumKMVZBaGOtCFGFwEpKXl4eJEyfiySefREBAgPb4yy+/jJ9//hmRkZGYMWMGVq5ciSFDhhR7nfDwcLi7u2s3f39/Q0MiIip7b78NeHoCZ84Aixcr3y2zfr2cKTUwEGjYsPTXM4cumRMngJgY2er04oumuYdmqO7Ro0BiomnuQQ8xOAkJCwvDqVOnsHbtWp3jY8aMQUhICJo1a4bBgwfjp59+QkREBC5fvlzkdWbMmAG1Wq3drpnrcDcioqJUqgTMnCn3J08GunUDDh1SLh59VswtCXNIQjStIM89Jz9vU+BQXUUYlISMHz8ef/zxByIjI1G9evVHntuuXTsAwKVLl4p83d7eHm5ubjobEZFFGT9edsfY2QGRkUD79nJV17NnyzaOK1eAfftkHcjAgca5pqYu5PBhWexa1nJygFWr5L6pumI02CVT5vRKQoQQGD9+PCIiIrBr1y7Url37se+Jjo4GAPj6+hoUIBGR2bOxkROYXbgADB8uF3+LiAACAoCRI8tuQjNNy3TXrrKewxjq1pUTnmVlKTNyZPt22T1SpUp+l4mpaJKQrVtll5ap3L8PvPGGnIK+gtMrCQkLC8PPP/+M1atXw9XVFQkJCUhISMCD/2XHly9fxpw5c3D06FFcuXIFv//+O4YOHYrOnTujefPmJvkDiIjMRs2awPLlwMmTQN++sqDzhx/kUNkpU4CkJNPe39hdMYBsVVGyS0bTFTNokJyt1pTatQM8PORQXVN2qS1YACxaBLz2munuYSmEHgAUuS1fvlwIIURcXJzo3Lmz8PT0FPb29qJevXrizTffFGq1usT3UKvVAoBe7yEiMksHDgjRpYsQslxVCDc3IebMEeL+fePf6+RJeQ9bWyHu3jXutZcskdcODjbudR9HrRbCwUHe+/DhsrnngAHyfu++a5rrp6cLUaVK/nfi3DnT3EcBhvx+690dU9Q2fPhwAIC/vz+ioqKQlJSEjIwMXLx4EfPnz2edBxFVTO3byxqRv/8GWraUc23MnCm7OL78UnZxGMuaNfKxZ0/jF29q6kIOHACys4177Uf59Vc5NX7jxqWbdE0fpq4LWbVKLn6osWmTae5jIbh2DBGRKalUQEiIHPq5Zo1MQG7dAiZMABo1kj9KeXmlu4cQpumK0WjaVCY2aWllO0V9wblBjDlN+6OYcqiuEMDChXJfM7UFkxAiIjI5Kyu5mNzZs8BXXwE+PkBsLDBkCNCqFfDbb8Dly4aNQDlwALh6FXBxAZ591jSxa1pDyqou5MoVWbipUgGDB5fNPQH536VVK7lv7KG627cDp0/L/06aIuIDByr0vCRMQoiIypKtLTB2LHDpkpz23d1dFrK+8AJQrx7g5CQnPwsIkC0oI0YA77wDLFkiV5H991/g+nXd2Vk1rSDPPy/fbwplXZyqGZb71FNAWU9iqVnQzthdMp9/Lh9ffVW2LgUGytaRzZuNex8LohJC6en9dKWkpMDd3R1qtZq1JERU/t29C8ybB2zYANy4UfKWEJVKTrDl5yeHBqemyh9NUw1j/fff/IXe7tyRrSOmIoTsqrpwAVixAhg2zHT3Ksq+fUDHjjIZvHVLri1TWmfPAk2ayP9uFy/Kbrk5c4D33pOtV+UgETHk95tJCBGRuRACUKvlirE3bshHzVbweXz8w+vU+PgAcXGmG8aanZ1fF3LyJNCsmWnuA8jhse3by1adhATA1dV09ypKTg7g5QUkJ8s1c4KCSn/NsWOBZcvk0O2ICHksJgZo3hxwcJCJnbNz6e+jIEN+v21MHBMREZWUSiXnqfDwkP9qLk5enlyoTpOcJCQAHTqYdh4NW1t5j+3bZZeMKZMQTUFqv35ln4AAcvK57t2Bdetk61Jpk5CkpPy/aeLE/OMBAUDt2rI2aNs22Z1WwbAmhIjI0lhZya6YVq1kU/6oUY9OWoylLOpCsrLyizZfecV093kcYw7VXbZMdrO1apX/GQIy6ezTR+5v3Fj6+1ggJiFERFQymhEye/eabrXgv/6SdTK+vnIxQKVoamuOHJF1IYbKypJzwgDApEkPDzXWJCF//PFwF1sFwCSEiIhKpm1buUhffLwcTmwKmm6LIUOMUxBqKGMN1V23Tn5evr5FLyqoKYC9e1cWxFYwTEKIiKhkHB1lIgKYpksmKUm2CACmXzG3JErbJSNE/rDcsDCZwBVmY5M/t0sFnLiMSQgREZWcKetCfvlFjsJp1Sp/RlEllXZV3b17gWPH5OiXRy1WV7AuxLwGrJockxAiIiq5gnUhxqbpilGyILWg9u3lSKW7d+U8KfrStIIMHQpUqVL8ed27A/b2cpTMqVMGhWqpmIQQEVHJdeggR+f895+cudVYLlyQ84NYWwODBhnvuqVhYwM884zc17dL5vLl/O6VgsNyi+Likn+fCtYlwySEiIhKzs0tv2DTmK0hK1fKx5AQWRRqLgytC1m0SHat9OghVwF+HE2XDJMQIiKiRzB2XUheXn4SYg4FqQUZMlRXrQZ++EHuT5pUsvf07i2H7x45YtwWJjPHJISIiPRj7LqQvXvlKsBubsBzzxnnmsbi66v/UN3vvpNr+TRtmt/N8jje3rIGBQB+/13/OC0UkxAiItJPx47y8fRpueZJaWlaQV58UQ4DNjf6dMnk5MiuGEDWghSenOxR+vaVjxWoS4ZJCBER6cfLK3+a+H/+Kd21HjyQE3oB5tcVo6HPUN2ICLmQYJUqwODB+t1HUxcSGSm7dCoAJiFERKQ/Y9WFbNoE3L8P1KqV38JibgoO1T18+NHnaobljhunf6tOw4Zyy84G/v7boFAtDZMQIiLSn7HqQgpO025lpj9JJR2qe+gQcOCAXHH4//7PsHtVsAXtzPS/OBERmTVNEnLsmGzJMERCQn6xp7lMUFacktSFLFwoHwcNMnyYsaYu5K+/5OJ35RyTECIi0p+/P1C7thxeu3//48+/fVvWOixaBIwZAwQFAQ0ayPe3by/3zVnBobq3bz/8+rVrwPr1cr+kw3KL0q6dHCmTkgJERRl+HQtho3QARERkoTp1klON79kjJxkD5NDU06fl9OMxMfLx1CkgMbHoazg6AtOmlV3MhvL1BVq2BKKjZevNkCG6r3/5pSxa7dpVnmcoKys5Z8h338l6mZIO8bVQTEKIiMgwnTvLmo7Vq/OTjtjYos9VqYA6deTCdM2a5T/Wry9rKCxBaKhMQv76SzcJSU0FvvlG7pemFUSjT5/8JGTxYv2G+VoYJiFERGQYzQiZK1fkpuHjk59oaJKNJk0AZ2clojSe0FAgPDx/qK61tTz+449AcjJQrx7w7LOlv0+3bvKzun5d1twEBpb+mmaKSQgRERmmfn05JPXcufyEIyDg0SvGWrKgIMDdPX+obvv2sqbliy/k62+8YZwRPo6OsntrwwbZGlKOkxAWphIRkeEmTgS+/hoYP17WQ5TXBAQoeqjun38CFy/KeUSGDzfevSrIgnZMQoiIiEqqZ0/5qElCNJOTjR4NuLgY7z69esnunpMni6+zKQeYhBAREZVUwaG6O3bIYcfW1sCECca9T+XK+XOxlOPWECYhREREJaUZqitE/gRrL7wg500xtgrQJcMkhIiISB+a2VMTEuSjMYblFkWThOzdCyQlmeYeCmMSQkREpA9NEgLIETPt2pnmPrVry+HNubmyALYcYhJCRESkj6AgORoGMF0riEY575JhEkJERKQPGxtg7Vrgk0+A/v1Ney/NgnZbtwIZGaa9lwL0SkLCw8PxxBNPwNXVFVWrVkXfvn1x/vx5nXMyMjIQFhaGypUrw8XFBf3790dicWsGEBERWaKQEGDKFONMTvYorVsD1asDaWnAzp2mvZcC9Pr0oqKiEBYWhoMHD2L79u3Izs5G9+7dkZaWpj1n0qRJ2Lx5M9avX4+oqCjcvHkT/fr1M3rgRERE5Z5KBTz3nNzfuFHRUExBJYQQhr759u3bqFq1KqKiotC5c2eo1Wp4eXlh9erVeOGFFwAA586dQ+PGjXHgwAG0b9/+sddMSUmBu7s71Go13NzcDA2NiIiofNi2Tba8eHsDN2+avvXFQIb8fpfqL1Gr1QAAT09PAMDRo0eRnZ2N4OBg7TmNGjVCjRo1cODAgSKvkZmZiZSUFJ2NiIiI/qdrV8DNDUhMBA4dUjoaozI4CcnLy8PEiRPx5JNPIiAgAACQkJAAOzs7eGiqhv/H29sbCZrx1IWEh4fD3d1du/mbYsIXIiIiS2Vnlz9dfDkbJWNwEhIWFoZTp05h7dq1pQpgxowZUKvV2u3atWuluh4REVG5U06H6toY8qbx48fjjz/+wJ49e1C9enXtcR8fH2RlZSE5OVmnNSQxMRE+Pj5FXsve3h729vaGhEFERFQxhIYCtrbAuXPA+fNAw4ZKR2QUerWECCEwfvx4REREYNeuXahdu7bO64GBgbC1tcXOAsOIzp8/j7i4OAQFBRknYiIioorG3R146im5X5rWkLg44IsvgK+/Nk5cpaRXS0hYWBhWr16NTZs2wdXVVVvn4e7uDkdHR7i7u2PkyJGYPHkyPD094ebmhgkTJiAoKKhEI2OIiIioGH36yJEymzYB06aV/H0XLwK//QZs2AAcPiyP1awJvPaaHAKsIL2G6KqKCXb58uUYPnw4ADlZ2ZQpU7BmzRpkZmYiJCQES5cuLbY7pjAO0SUiIirC9etytV6VCoiPl0N2iyIEcOpUfuIRE5P/mkoFdOwoZ3odN04WvRqJIb/fpZonxBSYhBARERWjTRvg6FHg22+BUaPyjwsBHDmSn3hcvJj/mo2N7Mrp319OA19c8lJKhvx+G1SYSkRERAro21cmIZs2ASNGAPv35yceBUeX2tvLCc769QN69wb+N5+XuWFLCBERkaWIiQGaN5fdKJUqyQnMNJydgV69ZOLRsyfg6lqmobElhIiIqDwLCADq1AH++08mIB4ecm2Z/v2BZ54BHB2VjlAvTEKIiIgshUoFrFwJ/P478PTTckp3IxaXljUmIURERJakQwe5lQPmuRQfERERlXtMQoiIiEgRTEKIiIhIEUxCiIiISBFMQoiIiEgRTEKIiIhIEUxCiIiISBFMQoiIiEgRTEKIiIhIEUxCiIiISBFMQoiIiEgRTEKIiIhIEUxCiIiISBFMQoiIiEgRTEKIiIhIEUxCiIiISBFMQoiIiEgRTEKIiIhIEUxCiIiISBFMQoiIiEgRTEKIiIhIEUxCiIiISBFMQoiIiEgRTEKIiIhIEUxCiIiISBFMQoiIiEgRTEKIiIhIEUxCiIiISBFMQoiIiEgReiche/bsQe/eveHn5weVSoWNGzfqvD58+HCoVCqdrUePHsaKl4iIiMoJvZOQtLQ0tGjRAkuWLCn2nB49eiA+Pl67rVmzplRBEhERUfljo+8bQkNDERoa+shz7O3t4ePjY3BQREREVP6ZpCZk9+7dqFq1Kho2bIhx48YhKSmp2HMzMzORkpKisxEREVH5Z/QkpEePHvjpp5+wc+dOzJs3D1FRUQgNDUVubm6R54eHh8Pd3V27+fv7GzskIiIiMkMqIYQw+M0qFSIiItC3b99iz/nvv/9Qt25d7NixA926dXvo9czMTGRmZmqfp6SkwN/fH2q1Gm5uboaGRkRERGUoJSUF7u7uev1+m3yIbp06dVClShVcunSpyNft7e3h5uamsxEREVH5Z/Ik5Pr160hKSoKvr6+pb0VEREQWRO/RMampqTqtGrGxsYiOjoanpyc8PT0xe/Zs9O/fHz4+Prh8+TKmTZuGevXqISQkxKiBExERkWXTOwk5cuQInnrqKe3zyZMnAwCGDRuGr776CidPnsSPP/6I5ORk+Pn5oXv37pgzZw7s7e2NFzURERFZvFIVppqCIYUtREREpCyzLEwlIiIiKgqTECIiIlIEkxAiIiJSBJMQIiIiUgSTECIiIlIEkxAiIiJSBJMQIiIiUgSTECIiIlIEkxAiIiJSRIVKQq5fB27fVjoKIiIiAipQEnLiBNCuHdCnD/DggdLREBERUYVJQuzsgPR04MABYNgwIC9P6YiIiIgqtgqThDRuDEREALa2wPr1wNtvKx0RERFRxVZhkhAA6NoV+O47uT9vHvDtt4qGQ0REVKFVqCQEAIYOBWbNkvvjxgFbtyoaDhERUYVV4ZIQAHjvPZmM5OYCL74InDypdEREREQVT4VMQlQq2RXTtStw/z7Qqxdw86bSUREREVUsFTIJAeRomQ0bgEaN5Pwhzz4LpKYqHRUREVHFUWGTEACoVAn480/Ayws4fhx46SUgJ0fpqIiIiCqGCp2EAECdOsDmzYCDg0xIJk4EhFA6KiIiovKvwichgJxJ9eefZa3IkiXAwoVKR0RERFT+MQn5n/79gQUL5P6UKXJiMyIiIjIdJiEFTJ4s5w4RAhg8GPj3X6UjIiIiKr+YhBSgUgGLFgE9e8pF7nr3BmJjlY6KiIiofGISUoiNDbB2LdCyJXDrlpxD5N49paMiIiIqf5iEFMHVFfjjD6BaNeDsWVkvkpWldFRERETlC5OQYlSrJofsurgAkZHAmDEcuktERGRMTEIeoUULYN06wNoa+PFHYO5cpSMiIiIqP5iEPEZoqJw7BJAL302cyBoRIiIiY2ASUgKvvQbMmCH3v/gCqFdPJiac4p2IiMhwTEJK6KOPgG3bgKZNgbt3gfHjZXfN1q1KR0ZERGSZmITo4ZlngOho4KuvgCpVgDNngB495DDec+eUjo6IiMiyMAnRk40NMHYscPGinN7d1hb46y8gIAB44w3ZSkJERESPxyTEQB4ewCefAKdPA336ALm5crbVevWAxYuB7GylIyQiIjJveiche/bsQe/eveHn5weVSoWNGzfqvC6EwHvvvQdfX184OjoiODgYFy9eNFa8Zqd+fWDjRmDHDqBZMzly5vXXgebNgS1blI6OiIjIfOmdhKSlpaFFixZYohm3Wsj8+fOxaNEifP311zh06BCcnZ0REhKCjIyMUgdrzrp1A44fB5YtA7y8ZI1Iz56yZuTMGaWjIyIiMj8qIQyfB1SlUiEiIgJ9+/YFIFtB/Pz8MGXKFEydOhUAoFar4e3tjRUrVuCll1567DVTUlLg7u4OtVoNNzc3Q0NTlFoNfPghsHCh7JaxtpZ1JK+9JrttMjKAzEz5WHC/8GPB/exswMlJTimv2dzcdJ8XPOboKBfkIyIiKguG/H7bGDOA2NhYJCQkIDg4WHvM3d0d7dq1w4EDB4pMQjIzM5GZmal9npKSYsyQFOHuDsyfL6d6nzYNiIiQ84oU03hkElZWuolJ9epA585Aly7AE08A9vZlFwsREVFRjJqEJCQkAAC8vb11jnt7e2tfKyw8PByzZ882Zhhmo149YMMGufbM228D588DDg75m719yR4dHOSonPR04P79/C0l5eHnqany3nl5skVGrZbPz5yR85wA8nodOsiEpEsXoF07eYyIiKgsGTUJMcSMGTMwefJk7fOUlBT4+/srGJHxPfUUcOBA2dwrLw9IS9NNUlJS5GrAUVFyu30b2LVLboBMdtq3z09KgoJkdw4REZEpGTUJ8fHxAQAkJibC19dXezwxMREtW7Ys8j329vawZ9+A0RTshvHzyz/erZuc5VUI3YRk924gMTH/OQDY2QFt2+YnJR06AM7Oivw5RERUjhk1CalduzZ8fHywc+dObdKRkpKCQ4cOYdy4cca8FRlIpQKaNJHbuHEyKblwQTcpuXkT+OcfuX34oewKqlkTqFVLPhber15dnkNERKQPvX86UlNTcenSJe3z2NhYREdHw9PTEzVq1MDEiRMxd+5c1K9fH7Vr18bMmTPh5+enHUFD5kWlAho2lNuYMTIpuXw5PyGJigKuXZPHLl8u+hrW1kC1akUnKJp9O7sy/KOIiMgi6D1Ed/fu3XjqqaceOj5s2DCsWLECQgi8//77+Oabb5CcnIyOHTti6dKlaNCgQYmuXx6G6JYnQgA3bgD//QdcvQpcuSIfNftxcUBW1qOv4eIiE5xJk2SrCRERlT+G/H6Xap4QU2ASYlny8mRNSeHkpOB+ero818YGGDwYePNNuRoxERGVH0xCyOwIAWzdCsybJ7t3NHr1AqZPBzp25KRqRETlgSG/31zAjkxKpZJT10dGAocOAS+8II/9+aecPK1DBzmZW16e0pESEVFZYxJCZaZtW2D9ejlp22uvyflJDh4E+vUDGjcGvvtOTlFPREQVA5MQKnP16wNffy1rRt55B/DwkMOER48GatcGPv4YSE5WOkoiIjI1JiGkGG9vYO5cOcLms8/kyJmEBGDGDMDfH5g6Fbh+XekoiYjIVJiEkOJcXeXw3f/+A376CQgIkGvgfPopUKcOMHw4EBOjdJRERGRsTELIbNjaAq+8Apw8KQtXu3QBsrOBH38EmjeXBa47d8oRN0REZPmYhJDZUamAnj3lkN6DB4EXX5Rr4mzdCgQHA61bA6tWyQSFiIgsF5MQMmvt2gHr1gEXL8oF+JycgOhoYMgQ2VXz6adylWAiIrI8TELIItSpAyxeLItY586VRa3Xr8viVX9/YNo0FrESEVkaJiFkUSpXlsN6r1yR84o0aiRbQhYskMN7hw4FTpxQOkoiIioJJiFkkRwcgJEjgdOngc2bZRFrTg6wciXQsiUQEgJs384iViIic8a1Y6jcOHxY1oisX58/DXzjxnJ9mpYt5da8uVzVl4iIjIsL2BEBiI0FFi4Evv8eSEvTfU2lAurVkwlJq1b5yYmPDxfSIyIqDSYhRAXcuwfs2CFrRKKj5XbjRtHnVq2an5BotgYNAGvrsoqWiMiyMQkheoxbt3STkuho4Ny5olfxdXSUrSY1auRvNWvm7/v6AjY2ZfwHEBGZKSYhRAZ48AA4dUo3MTlx4uGunMKsrYFq1XQTk8Ibv8JEVFEY8vvNf8dRhefoCDzxhNw08vKAy5flejZxcQ9v167JGVs1z4vj5ibnMSm4Va+u+9zJyfR/IxGROWJLCJEBcnOBxMSiE5SrV+Xj3bslu5anZ9HJiY+PfK1yZfno6sriWSIyX+yOITIjqalyFtdr1/K3ws/v3y/59WxsZDJSMDF53H7lyoCzM5MXIjI9dscQmREXFzmja6NGxZ+jVusmJQUTlVu3ZGtKUhKQkSEnY7t1S276sLPTTUpKsl+5snwfEZEpMQkhUpC7u9wCAh593oMH+QlJwcfi9pOS5JaVJbeEBLnpw8VFJiNVquQnJgX3i3rOVhci0geTECIL4OgoR+JUq1by9wgBpKfnJyQFk5PCzwvu37snC3NTU+V29WrJ72lvLxMTHx85hNnXF/Dze/ixalUObyYiJiFE5ZZKJVsmnJ3lcOGSyssDkpN1E5Y7dx7/PCsLyMyUE8IVNylcwdi8vYtOVCpXlsmMg4N8LGor+JqtLVtfiCwVkxAi0mFllV/cWr9+yd4jhJxXRZOQxMfL7ebNh/cTEuToIk0X0fHjpY+5YHLi5ZXfauTnp/tYrZpsheFMuETmgUkIEZWaSiVrSFxc5ORtj5KbKxOVohKUmzdld1BmpizGzczU3TTHcnJ0r6l5PSVFFu6ePl38/a2tZXdRwcREs+/jA3h46G729qX7bIioeByiS0QWJy/v4cQkM1MW8CYmymTmxo2HHxMSip6i/1EcHGTxcMHEpPBzzTEnp6K7jIo7xm4kKk84RJeIKgQrK1ms6+io3/s0k8wVl6QkJsph08nJ8lEImeRkZMjXjM3OLj8hcXSUM+xqRkwV3C/uWMHntrbGj4/I1JiEEFGFYW0tu178/IA2bR59bl6enExOk5AkJxe9FXwtPf3hLqTCW0GaIdT6TFpXHFvbh1tcCra8POqYo6NszalUKX8r/JyjmcgU+LUiIiqClVV+K4OxCJE/iqjwlp4ua1rUat2tqGMFX9MstJidLbfUVOPFW5CLi25SUnjTJC2a7qmCxxwc2PVERWMSQkRURlSq/FYIY8nJkcmIphWmYI2MZv9xx9LTZUvOvXsPb5pWGs28Mdeu6R+jnV3xSYqmlsbB4dGbo2PRx+3sZCuQlZXRPlIqQ0xCiIgsWME1hUwhJ6foBKXwMU2XVMHXkpNlt1ZWlmFLDujDyio/ISm8Peq4prbIyenhx6KOFXzN2VnW5bi5cdi3oZiEEBFRsWxs5Cy4Varo/14hZOvJo5KU5OT84t/C24MHxb+WlaV7r7y8/NeU4OLycCFxcZumoNjRUSZCmiRJs19ws7Ut3wkOkxAiIjIJlQpwdZWbPrP2lkTBYdrZ2TIp0dTFlHTLypKJzoMHsktK81hw/1HHUlPzi4013VWPmy3YEJpWnsLJyeNaeh71uq8v8Pbbxo9VX0xCiIjI4hg6TNvYMjNLVkRcVFFxZmb+CKmCW26u7j1M0crTsGE5TUJmzZqF2bNn6xxr2LAhzp07Z+xbERERKcreXi4FULWq8a6Zl5ffUlN4K3i8JC1Axb1uqhoifZmkJaRp06bYsWNH/k04wJyIiKhErKyMP4rKXJkkO7CxsYGPj0+Jzs3MzERmgRl8UlJSTBESERERmRmTjKy+ePEi/Pz8UKdOHQwePBhxcXHFnhseHg53d3ft5u/vb4qQiIiIyMwYfQG7LVu2IDU1FQ0bNkR8fDxmz56NGzdu4NSpU3B1dX3o/KJaQvz9/bmAHRERkQUxZAE7k6+im5ycjJo1a+Kzzz7DyJEjH3s+V9ElIiKyPIb8fpt8olsPDw80aNAAly5dMvWtiIiIyIKYPAlJTU3F5cuX4evra+pbERERkQUxehIydepUREVF4cqVK9i/fz+ef/55WFtbY9CgQca+FREREVkwow/RvX79OgYNGoSkpCR4eXmhY8eOOHjwILy8vIx9KyIiIrJgRk9C1q5da+xLEhERUTlk8poQIiIioqIwCSEiIiJFMAkhIiIiRTAJISIiIkWY3fK2mglcuZAdERGR5dD8buszEbvZJSH3798HAC5kR0REZIHu378Pd3f3Ep1r8rVj9JWXl4ebN2/C1dUVKpXKqNfWLI537do1rkujB35u+uNnZhh+bobh52YYfm76e9RnJoTA/fv34efnByurklV7mF1LiJWVFapXr27Se7i5ufELZwB+bvrjZ2YYfm6G4edmGH5u+ivuMytpC4gGC1OJiIhIEUxCiIiISBEVKgmxt7fH+++/D3t7e6VDsSj83PTHz8ww/NwMw8/NMPzc9Gfsz8zsClOJiIioYqhQLSFERERkPpiEEBERkSKYhBAREZEimIQQERGRIpiEEBERkSIqTBKyZMkS1KpVCw4ODmjXrh3+/fdfpUMya7NmzYJKpdLZGjVqpHRYZmfPnj3o3bs3/Pz8oFKpsHHjRp3XhRB477334OvrC0dHRwQHB+PixYvKBGtGHve5DR8+/KHvX48ePZQJ1kyEh4fjiSeegKurK6pWrYq+ffvi/PnzOudkZGQgLCwMlStXhouLC/r374/ExESFIjYPJfncunbt+tD3bezYsQpFbB6++uorNG/eXDszalBQELZs2aJ93VjftQqRhPzyyy+YPHky3n//fRw7dgwtWrRASEgIbt26pXRoZq1p06aIj4/Xbv/884/SIZmdtLQ0tGjRAkuWLCny9fnz52PRokX4+uuvcejQITg7OyMkJAQZGRllHKl5edznBgA9evTQ+f6tWbOmDCM0P1FRUQgLC8PBgwexfft2ZGdno3v37khLS9OeM2nSJGzevBnr169HVFQUbt68iX79+ikYtfJK8rkBwOjRo3W+b/Pnz1coYvNQvXp1fPzxxzh69CiOHDmCp59+Gn369MHp06cBGPG7JiqAtm3birCwMO3z3Nxc4efnJ8LDwxWMyry9//77okWLFkqHYVEAiIiICO3zvLw84ePjIxYsWKA9lpycLOzt7cWaNWsUiNA8Ff7chBBi2LBhok+fPorEYylu3bolAIioqCghhPxu2draivXr12vPOXv2rAAgDhw4oFSYZqfw5yaEEF26dBFvvPGGckFZiEqVKonvvvvOqN+1ct8SkpWVhaNHjyI4OFh7zMrKCsHBwThw4ICCkZm/ixcvws/PD3Xq1MHgwYMRFxendEgWJTY2FgkJCTrfPXd3d7Rr147fvRLYvXs3qlatioYNG2LcuHFISkpSOiSzolarAQCenp4AgKNHjyI7O1vn+9aoUSPUqFGD37cCCn9uGqtWrUKVKlUQEBCAGTNmID09XYnwzFJubi7Wrl2LtLQ0BAUFGfW7Znar6BrbnTt3kJubC29vb53j3t7eOHfunEJRmb927dphxYoVaNiwIeLj4zF79mx06tQJp06dgqurq9LhWYSEhAQAKPK7p3mNitajRw/069cPtWvXxuXLl/H2228jNDQUBw4cgLW1tdLhKS4vLw8TJ07Ek08+iYCAAADy+2ZnZwcPDw+dc/l9y1fU5wYAL7/8MmrWrAk/Pz+cPHkS06dPx/nz57FhwwYFo1VeTEwMgoKCkJGRARcXF0RERKBJkyaIjo422net3CchZJjQ0FDtfvPmzdGuXTvUrFkT69atw8iRIxWMjCqCl156SbvfrFkzNG/eHHXr1sXu3bvRrVs3BSMzD2FhYTh16hTrtPRU3Oc2ZswY7X6zZs3g6+uLbt264fLly6hbt25Zh2k2GjZsiOjoaKjVavz6668YNmwYoqKijHqPct8dU6VKFVhbWz9UtZuYmAgfHx+ForI8Hh4eaNCgAS5duqR0KBZD8/3id6/06tSpgypVqvD7B2D8+PH4448/EBkZierVq2uP+/j4ICsrC8nJyTrn8/smFfe5FaVdu3YAUOG/b3Z2dqhXrx4CAwMRHh6OFi1a4IsvvjDqd63cJyF2dnYIDAzEzp07tcfy8vKwc+dOBAUFKRiZZUlNTcXly5fh6+urdCgWo3bt2vDx8dH57qWkpODQoUP87unp+vXrSEpKqtDfPyEExo8fj4iICOzatQu1a9fWeT0wMBC2trY637fz588jLi6uQn/fHve5FSU6OhoAKvT3rSh5eXnIzMw07nfNuLWz5mnt2rXC3t5erFixQpw5c0aMGTNGeHh4iISEBKVDM1tTpkwRu3fvFrGxsWLfvn0iODhYVKlSRdy6dUvp0MzK/fv3xfHjx8Xx48cFAPHZZ5+J48ePi6tXrwohhPj444+Fh4eH2LRpkzh58qTo06ePqF27tnjw4IHCkSvrUZ/b/fv3xdSpU8WBAwdEbGys2LFjh2jdurWoX7++yMjIUDp0xYwbN064u7uL3bt3i/j4eO2Wnp6uPWfs2LGiRo0aYteuXeLIkSMiKChIBAUFKRi18h73uV26dEl88MEH4siRIyI2NlZs2rRJ1KlTR3Tu3FnhyJX11ltviaioKBEbGytOnjwp3nrrLaFSqcS2bduEEMb7rlWIJEQIIRYvXixq1Kgh7OzsRNu2bcXBgweVDsmsDRw4UPj6+go7OztRrVo1MXDgQHHp0iWlwzI7kZGRAsBD27Bhw4QQcpjuzJkzhbe3t7C3txfdunUT58+fVzZoM/Cozy09PV10795deHl5CVtbW1GzZk0xevToCv+PhqI+LwBi+fLl2nMePHgg/u///k9UqlRJODk5ieeff17Ex8crF7QZeNznFhcXJzp37iw8PT2Fvb29qFevnnjzzTeFWq1WNnCFvfrqq6JmzZrCzs5OeHl5iW7dumkTECGM911TCSGEgS0zRERERAYr9zUhREREZJ6YhBAREZEimIQQERGRIpiEEBERkSKYhBAREZEimIQQERGRIpiEEBERkSKYhBAREZEimIQQERGRIpiEEBERkSKYhBAREZEi/h+BgmP7DSgd6AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot results for age\n",
    "loss = history.history['age_output_loss']\n",
    "val_loss = history.history['val_age_output_loss']\n",
    "epochs = range(len(loss))\n",
    "\n",
    "plt.plot(epochs, loss, 'b', label='Training Loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation Loss')\n",
    "plt.title('Loss Graph')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model\n",
    "model.save(\"Gender_Age_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 584ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 27ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 26ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 24ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 24ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 10ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 33ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 26ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 27ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 25ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 24ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 24ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 30ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 21ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
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      "1/1 [==============================] - 0s 31ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "model = load_model('Gender_Age_model.h5')\n",
    "\n",
    "def predict_gender_age(model, X, y_gender, y_age, gender_dict):\n",
    "    male_indices = [i for i, gender in enumerate(y_gender) if gender == 1]\n",
    "    female_indices = [i for i, gender in enumerate(y_gender) if gender == 0]\n",
    "    num_images_per_gender = min(len(male_indices), len(female_indices), 50)\n",
    "    male_samples = random.sample(male_indices, num_images_per_gender)\n",
    "    female_samples = random.sample(female_indices, num_images_per_gender)\n",
    "\n",
    "    samples = male_samples + female_samples\n",
    "    num_images = len(samples)\n",
    "    age_classes = len(set(y_age))\n",
    "    age_samples = [random.choice(range(age_classes)) for _ in range(num_images)]\n",
    "\n",
    "    fig, axs = plt.subplots(5, 5, figsize=(20, 20))\n",
    "    fig.subplots_adjust(hspace=0.5)\n",
    "\n",
    "    for i, ax in enumerate(axs.flat):\n",
    "        sample_index = samples[i]\n",
    "        image = X[sample_index]\n",
    "\n",
    "        if image.ndim == 3 and image.shape[2] == 3:\n",
    "            # RGB image\n",
    "            grayscale_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n",
    "        else:\n",
    "            # Grayscale image\n",
    "            grayscale_image = image\n",
    "\n",
    "        ax.imshow(grayscale_image, cmap='gray')\n",
    "        ax.axis('off')\n",
    "\n",
    "        original_gender = gender_dict[y_gender[sample_index]]\n",
    "        original_age = y_age[sample_index]\n",
    "\n",
    "        if grayscale_image.ndim == 2:\n",
    "            # Expand dimensions for grayscale image\n",
    "            grayscale_image = np.expand_dims(grayscale_image, axis=-1)\n",
    "\n",
    "        pred = model.predict(np.expand_dims(grayscale_image, axis=0))\n",
    "        predicted_gender = gender_dict[np.argmax(pred[0])]\n",
    "        predicted_age = np.argmax(pred[1])\n",
    "\n",
    "        ax.set_title(f'Original: Gender-{original_gender}, Age-{original_age}\\nPredicted: Gender-{predicted_gender}, Age-{predicted_age}')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Example usage:\n",
    "# Assuming you have a trained model named 'model', the input images 'X', gender labels 'y_gender',\n",
    "# age labels 'y_age', and the gender dictionary 'gender_dict'\n",
    "predict_gender_age(model, X, y_gender, y_age, gender_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def predict(image_index):\n",
    "  print(\"Original Gender:\", gender_dict[y_gender[image_index]], \"Original Age:\", y_age[image_index])\n",
    "  # predict from model\n",
    "  pred = model.predict(X[image_index].reshape(1, 160, 160, 1))\n",
    "  pred_gender = gender_dict[round(pred[0][0][0])]\n",
    "  pred_age = round(pred[1][0][0])\n",
    "  print(\"Predicted Gender:\", pred_gender, \"Predicted Age:\", pred_age)\n",
    "  plt.axis('off')\n",
    "  plt.imshow(X[image_index].reshape(160, 160));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001E150A19E10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 142ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "pred = [array([[0.6055843]], dtype=float32), array([[0.]], dtype=float32)]\n",
      "Predicted Gender: Female Predicted Age: 0\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "model = load_model('Gender_Age_model.h5')\n",
    "gender_dict = {0: 'Male', 1: 'Female'}\n",
    "def predict(img_path):\n",
    "  # Load the image using OpenCV\n",
    "  img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "  # Resize the image to the desired shape (160x160)\n",
    "  img = cv2.resize(img, (160, 160))\n",
    "\n",
    "  # Reshape the image for model input\n",
    "  img = img.reshape(1, 160, 160, 1)\n",
    "\n",
    "  # Predict using the model\n",
    "  pred = model.predict(img)\n",
    "  print(f'pred = {pred}')\n",
    "  pred_gender = gender_dict[round(pred[0][0][0])]\n",
    "  pred_age = round(pred[1][0][0])\n",
    "  print(\"Predicted Gender:\", pred_gender, \"Predicted Age:\", pred_age)\n",
    "  plt.axis('off')\n",
    "  plt.imshow(img.reshape(160, 160))\n",
    "\n",
    "img_path = './2.jpg'\n",
    "predict(img_path)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 492ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 69ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 43ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 50ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 43ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
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      "1/1 [==============================] - 0s 42ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "1/1 [==============================] - 0s 42ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
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     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "# Load the pre-trained model\n",
    "model = load_model('Gender_Age_model.h5')\n",
    "# Define the gender labels\n",
    "gender_dict = {0: 'Male', 1: 'Female'}\n",
    "# Load the face cascade classifier\n",
    "face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
    "# Open the webcam\n",
    "video_capture = cv2.VideoCapture(0)\n",
    "while True:\n",
    "    # Read the video frame\n",
    "    ret, frame = video_capture.read()\n",
    "\n",
    "    # Convert the frame to grayscale\n",
    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    # Detect faces in the frame\n",
    "    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))\n",
    "\n",
    "    # Process each detected face\n",
    "    for (x, y, w, h) in faces:\n",
    "        # Extract the face region\n",
    "        face = gray[y:y+h, x:x+w]\n",
    "\n",
    "        # Resize the face image to the required input size of the model\n",
    "        resized_face = cv2.resize(face, (160, 160))\n",
    "\n",
    "        normalized_face = np.expand_dims(resized_face, axis=-1)\n",
    "        normalized_face = np.expand_dims(normalized_face, axis=0)\n",
    "\n",
    "        pred = model.predict(normalized_face)\n",
    "        pred_gender = gender_dict[round(pred[0][0][0])]\n",
    "        pred_age = int(round(pred[1][0][0]))\n",
    "\n",
    "        # Draw bounding box and predicted gender label on the frame\n",
    "        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)\n",
    "        cv2.putText(frame, pred_gender, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, ((y+h)/(x+w)), (0, 255, 0), 2)\n",
    "        cv2.putText(frame, str(pred_age), (x+w-10, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\n",
    "\n",
    "    # Display the resulting frame\n",
    "    cv2.imshow('Webcam', frame)\n",
    "\n",
    "    # Exit loop if 'q' is pressed\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "# Release the video capture and close the window\n",
    "video_capture.release()\n",
    "cv2.destroyAllWindows()"
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