Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b734ea1d",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed45467f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06792a7c2082478aba1d6c4eb3ff30fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/23708 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BASE_DIR = 'UTKFace/'\n",
    "# Reading all the images present in Base_Dir\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": 3,
   "id": "fe283ddd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "df['image'], df['age'], df['gender'] = image_paths, age_labels, gender_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27471753",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_dict = {0:'Male', 1:'Female'}\n",
    "df = df.astype({'age':'float32', 'gender': 'int32'})\n",
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(df, test_size=0.85, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7829e5a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras import utils\n",
    "from keras.preprocessing import image\n",
    "x_train = []\n",
    "for file in train.image:\n",
    "    img = keras.utils.load_img(\"\"+file, grayscale=True)\n",
    "    img = img.resize((128,128), Image.ANTIALIAS)\n",
    "    img = np.array(img)\n",
    "    x_train.append(img)\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "\n",
    "x_train = x_train.reshape(len(x_train), 128,128,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a0b33c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train.shape\n",
    "x_train = x_train/255\n",
    "y_gender = np.array(train.gender)\n",
    "y_age = np.array(train.age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0b75631e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.initializers import random_uniform, glorot_uniform, constant, identity\n",
    "from tensorflow.keras.layers import Dropout, Input, Add, Dense, Activation, BatchNormalization, Flatten, Conv2D, MaxPooling2D, GlobalMaxPooling2D\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "input_size = (128,128,1)\n",
    "inputs = Input((input_size))\n",
    "X = Conv2D(64, (3, 3), activation='relu', kernel_initializer = glorot_uniform(seed=0))(inputs)\n",
    "X = BatchNormalization(axis = 3)(X)\n",
    "X = MaxPooling2D((3, 3))(X)\n",
    "\n",
    "X = Conv2D(128, (3, 3), activation='relu')(X)\n",
    "X = MaxPooling2D((2, 2), strides=(2, 2))(X)\n",
    "\n",
    "X = Conv2D(256, (3, 3), activation='relu')(X)\n",
    "X = MaxPooling2D((2, 2))(X)\n",
    "\n",
    "X = Flatten()(X)\n",
    "\n",
    "dense_1 = Dense(256, activation='relu')(X)\n",
    "dense_2 = Dense(256, activation='relu' )(X)\n",
    "dense_3 = Dense(128, activation='relu' )(dense_2)\n",
    "dropout_1 = Dropout(0.4)(dense_1)\n",
    "dropout_2 = Dropout(0.4)(dense_3)\n",
    "output_1 = Dense(1,activation='sigmoid', name='gender_output')(dropout_1)\n",
    "output_2 = Dense(1, activation='relu', name='age_output')(dropout_2)\n",
    "\n",
    "model = Model(inputs=[inputs], outputs=[output_1,output_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ba4abaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "320/320 [==============================] - 217s 655ms/step - loss: 16.7963 - gender_output_loss: 0.6776 - age_output_loss: 16.1188 - gender_output_accuracy: 0.6578 - age_output_accuracy: 0.0484 - val_loss: 26.1215 - val_gender_output_loss: 0.5976 - val_age_output_loss: 25.5239 - val_gender_output_accuracy: 0.7809 - val_age_output_accuracy: 0.0562\n",
      "Epoch 2/10\n",
      "320/320 [==============================] - 197s 614ms/step - loss: 12.9054 - gender_output_loss: 0.5121 - age_output_loss: 12.3933 - gender_output_accuracy: 0.7559 - age_output_accuracy: 0.0247 - val_loss: 13.8784 - val_gender_output_loss: 0.5170 - val_age_output_loss: 13.3614 - val_gender_output_accuracy: 0.7893 - val_age_output_accuracy: 0.0337\n",
      "Epoch 3/10\n",
      "320/320 [==============================] - 192s 601ms/step - loss: 11.2964 - gender_output_loss: 0.4434 - age_output_loss: 10.8529 - gender_output_accuracy: 0.7972 - age_output_accuracy: 0.0113 - val_loss: 11.3588 - val_gender_output_loss: 0.4012 - val_age_output_loss: 10.9577 - val_gender_output_accuracy: 0.8118 - val_age_output_accuracy: 0.0197\n",
      "Epoch 4/10\n",
      "320/320 [==============================] - 189s 592ms/step - loss: 10.3400 - gender_output_loss: 0.4115 - age_output_loss: 9.9286 - gender_output_accuracy: 0.8175 - age_output_accuracy: 0.0063 - val_loss: 10.8006 - val_gender_output_loss: 0.3871 - val_age_output_loss: 10.4135 - val_gender_output_accuracy: 0.8427 - val_age_output_accuracy: 0.0197\n",
      "Epoch 5/10\n",
      "320/320 [==============================] - 135s 423ms/step - loss: 9.6249 - gender_output_loss: 0.3514 - age_output_loss: 9.2735 - gender_output_accuracy: 0.8363 - age_output_accuracy: 0.0078 - val_loss: 11.4630 - val_gender_output_loss: 0.3790 - val_age_output_loss: 11.0839 - val_gender_output_accuracy: 0.8202 - val_age_output_accuracy: 0.0140\n",
      "Epoch 6/10\n",
      "320/320 [==============================] - 138s 431ms/step - loss: 9.3474 - gender_output_loss: 0.3457 - age_output_loss: 9.0017 - gender_output_accuracy: 0.8453 - age_output_accuracy: 0.0053 - val_loss: 11.4494 - val_gender_output_loss: 0.3888 - val_age_output_loss: 11.0606 - val_gender_output_accuracy: 0.8287 - val_age_output_accuracy: 0.0056\n",
      "Epoch 7/10\n",
      "320/320 [==============================] - 131s 410ms/step - loss: 9.0447 - gender_output_loss: 0.3208 - age_output_loss: 8.7239 - gender_output_accuracy: 0.8525 - age_output_accuracy: 0.0053 - val_loss: 10.8631 - val_gender_output_loss: 0.3588 - val_age_output_loss: 10.5044 - val_gender_output_accuracy: 0.8371 - val_age_output_accuracy: 0.0028\n",
      "Epoch 8/10\n",
      "320/320 [==============================] - 132s 414ms/step - loss: 8.3975 - gender_output_loss: 0.3018 - age_output_loss: 8.0958 - gender_output_accuracy: 0.8609 - age_output_accuracy: 0.0059 - val_loss: 9.5459 - val_gender_output_loss: 0.3531 - val_age_output_loss: 9.1928 - val_gender_output_accuracy: 0.8399 - val_age_output_accuracy: 0.0056\n",
      "Epoch 9/10\n",
      "320/320 [==============================] - 125s 391ms/step - loss: 8.1591 - gender_output_loss: 0.2908 - age_output_loss: 7.8683 - gender_output_accuracy: 0.8681 - age_output_accuracy: 0.0063 - val_loss: 9.7983 - val_gender_output_loss: 0.3327 - val_age_output_loss: 9.4657 - val_gender_output_accuracy: 0.8399 - val_age_output_accuracy: 0.0084\n",
      "Epoch 10/10\n",
      "320/320 [==============================] - 126s 393ms/step - loss: 7.8277 - gender_output_loss: 0.2722 - age_output_loss: 7.5555 - gender_output_accuracy: 0.8756 - age_output_accuracy: 0.0053 - val_loss: 8.5628 - val_gender_output_loss: 0.3138 - val_age_output_loss: 8.2490 - val_gender_output_accuracy: 0.8764 - val_age_output_accuracy: 0.0056\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=['binary_crossentropy','mae'], optimizer='adam', metrics=['accuracy'])\n",
    "history = model.fit(x=x_train, y=[y_gender, y_age], batch_size = 10, epochs=10, validation_split= 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b67daf74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender Accuracy: : 82.67%\n",
      "Age Accuracy: : 1.71%\n"
     ]
    }
   ],
   "source": [
    "val_gen_acc = np.mean(history.history['val_gender_output_accuracy']) \n",
    "print(\"%s: %.2f%%\" % ('Gender Accuracy: ',(val_gen_acc*100)))\n",
    "val_age_acc = np.mean(history.history['val_age_output_accuracy']) \n",
    "print(\"%s: %.2f%%\" % ('Age Accuracy: ',(val_age_acc*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9ddc415",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Gender Losses\n",
    "plt.plot(history.history['gender_output_loss'])\n",
    "plt.plot(history.history['val_gender_output_loss'])\n",
    "plt.title('Gender loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plotting the Age Losses\n",
    "plt.plot(history.history['age_output_loss'])\n",
    "plt.plot(history.history['val_age_output_loss'])\n",
    "plt.title('Age loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "819eaadf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}