2022_UB_Master_Colab_introduction_age.ipynb.txt

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    "colab": {
      "name": "2022_UB-Master_Colab_introduction_age.ipynb",
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      "toc_visible": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dG2MLbQvZy9h"
      },
      "source": [
        "# **The Problem: Automatic Apparent Age Estimation**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XwmfFS5oPbhn"
      },
      "source": [
        "# Auxiliary and introductory material\n",
        "\n",
        "Getting Started with TensorFlow in Google Colaboratory\n",
        "Intro to Google Colab:\n",
        "https://www.youtube.com/watch?v=inN8seMm7UI\n",
        "\n",
        "Installing Tensorflow (CPU or GPU):\n",
        "https://www.youtube.com/watch?v=PitcORQSjNM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtnpcwuJon27"
      },
      "source": [
        "# Pre-requisites:\n",
        "Installing tensorflow-gpu (GPU) and OpenCv.\n",
        "Check GPU usage instructions [here](https://research.google.com/colaboratory/faq.html#gpu-availability)\n",
        "\n",
        "# IMPORTANT:\n",
        "**1:** THE CODE WAS TESTED ON TENSORFLOW VERSION 2.4.0\n",
        "\n",
        "**2:** Sometimes the code downloads data/models from our server. It may happen that you get a \"file not found\" error due to some instability of the server. In this case, please keep trying! If the error persist, please contact me."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GnRxTFRGorml",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "576357d6-892e-4737-8303-ec3ec425aa97"
      },
      "source": [
        "!pip install tensorflow-gpu==2.4.0\n",
        "!pip install opencv-python\n",
        "!pip install h5py\n",
        "\n",
        "# to enable Colab-GPU version:\n",
        "# 1) Runtime -> reset runtime\n",
        "# 2) Runtime -> Change runtime type"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMULLE13o-Qo"
      },
      "source": [
        "# Downloading and decompressing the Appa-Real Age Dataset [(source)](http://chalearnlap.cvc.uab.es/challenge/13/track/13/description/)\n",
        "\n",
        "- As default, RGB images (cropped faces) are in the range of [0, 255], and labels are in the range of ~0.9 to ~90 (years old).\n",
        "- The data is divided in train, validation and test set. \n",
        "- Matadata is also provided\n",
        "  - gender: male / female \n",
        "  - ethnicity: asian / afroamerican / caucasian\n",
        "  - facial expression: neutral / slightlyhappy / happy / other\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8DZFR8GpIfL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb0be239-f759-4c8b-b75e-6ee9be2ad040"
      },
      "source": [
        "# downloading the data\n",
        "!wget https://data.chalearnlap.cvc.uab.cat/Colab_2021/app_data.zip\n",
        "\n",
        "# decompressing the data\n",
        "from zipfile import ZipFile\n",
        "\n",
        "with ZipFile('app_data.zip','r') as zip:\n",
        "   zip.extractall()\n",
        "   print('Data decompressed successfully')\n",
        "\n",
        "# removing the .zip file after extraction to clean space\n",
        "!rm app_data.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-01 10:27:37--  https://data.chalearnlap.cvc.uab.cat/Colab_2021/app_data.zip\n",
            "Resolving data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)... 158.109.8.102\n",
            "Connecting to data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)|158.109.8.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 799021037 (762M) [application/zip]\n",
            "Saving to: ‘app_data.zip’\n",
            "\n",
            "app_data.zip        100%[===================>] 762.00M  3.70MB/s    in 2m 15s  \n",
            "\n",
            "2022-03-01 10:29:53 (5.62 MB/s) - ‘app_data.zip’ saved [799021037/799021037]\n",
            "\n",
            "Data decompressed successfully\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a18X0kFGXUnt"
      },
      "source": [
        "# Loading the train/validation data, and re-scaling the labels to [0..1]\n",
        "- X_[train,valid,test] = Face images\n",
        "- Y_[train,valid,test] = Ground truth \n",
        "- M_[train,valid,test] = Metadata (gender, ethnicicy, facial expression)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9GRhB1gzXlYc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4aff0c1-eeaa-4331-90c6-4a87121e61cf"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "# loading the train data\n",
        "X_train = np.load('./data/data_train.npy')\n",
        "Y_train = np.load('./data/labels_train.npy')\n",
        "M_train = np.load('./data/meta_data_train.npy')\n",
        "\n",
        "# loading the validation data\n",
        "X_valid = np.load('./data/data_valid.npy')\n",
        "Y_valid = np.load('./data/labels_valid.npy')\n",
        "M_valid = np.load('./data/meta_data_valid.npy')\n",
        "\n",
        "# loading the test data\n",
        "X_test = np.load('./data/data_test.npy')\n",
        "Y_test = np.load('./data/labels_test.npy')\n",
        "M_test = np.load('./data/meta_data_test.npy')\n",
        "\n",
        "# train labels are real numbers, ranging from ~0.9 to ~89 (years old);\n",
        "# we will re-scale the labels to [0,1] by using a normalization factor of 100,\n",
        "# assuming there is no sample with age > 100.\n",
        "Y_train = Y_train/100\n",
        "Y_valid = Y_valid/100\n",
        "# Y_test = Y_test/100 # -> we don't normalize the test labels as we will evaluate \n",
        "                      # them using the raw data, i.e., the apparent age values\n",
        "\n",
        "print('Train data size and shape', X_train.shape)\n",
        "print('Train labels size and shape', Y_train.shape)\n",
        "print('Train metadata size and shape', M_train.shape)\n",
        "print('----')\n",
        "print('Valid data size and shape', X_valid.shape)\n",
        "print('Valid labels size and shape', Y_valid.shape)\n",
        "print('Valid metadata size and shape', M_valid.shape)\n",
        "print('----')\n",
        "print('Test data size and shape', X_test.shape)\n",
        "print('Test labels size and shape', Y_test.shape)\n",
        "print('Test metadata size and shape', M_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data size and shape (4065, 224, 224, 3)\n",
            "Train labels size and shape (4065,)\n",
            "Train metadata size and shape (4065, 3)\n",
            "----\n",
            "Valid data size and shape (1482, 224, 224, 3)\n",
            "Valid labels size and shape (1482,)\n",
            "Valid metadata size and shape (1482, 3)\n",
            "----\n",
            "Test data size and shape (1978, 224, 224, 3)\n",
            "Test labels size and shape (1978,)\n",
            "Test metadata size and shape (1978, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HVnh9fvUvoJ"
      },
      "source": [
        "# Visualizing some training samples\n",
        "Next, we multiply the normalized age labels by 100 to show the original age values on top of each sample."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxnI7M8s-JXf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "fa3ce997-b041-44bd-bccd-612e9afbc15c"
      },
      "source": [
        "import cv2\n",
        "import random\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(20, 20))\n",
        "for i, ax in enumerate(axes):\n",
        "    idx = random.randint(0, len(X_train))\n",
        "    ax.imshow(cv2.cvtColor(X_train[idx,:,:,:], cv2.COLOR_BGR2RGB))\n",
        "    ax.set_title(Y_train[idx]*100)\n",
        "    ax.set(xlabel=[M_train[idx][0],M_train[idx][1],M_train[idx][2]])\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDzps0ccD2pR"
      },
      "source": [
        "# Visualizing the age distribution of Train data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMCqsJtBD8KG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "7e5c2cc9-cf59-4f6b-c7b6-324bd36a6c6b"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 4))\n",
        "fig.suptitle('Age distribution', fontsize=14, fontweight='bold')\n",
        "\n",
        "# labels are multiplied by 100 to show the original values\n",
        "ax1.hist(Y_train*100, bins = 50)\n",
        "ax1.set_title('Y_train labels')\n",
        "ax1.set(xlabel='Apparent age', ylabel='Num. of samples')\n",
        "ax1.set_xlim([0, 100])\n",
        "\n",
        "ax2.hist(Y_valid*100, bins = 50)\n",
        "ax2.set_title('Y_valid labels')\n",
        "ax2.set(xlabel='Apparent age', ylabel='Num. of samples')\n",
        "ax2.set_xlim([0, 100])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 100.0)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JA6aqKknN9LgkK2mGlbL11lvPe1y6P+fXS1oM7CGUJGkwXD82FLR9vKFtvwbYqmO/Ldu2B6iqVVW1vKqWL1mypKfBSpIWB3sIJUkaDCcA+wAHtY/Hd7S/OclRwDOAWzuGlmoIuWqppEFiQShJ0gJLciSwC7BZktXAgTSF4DFJ9geuAvZsdz+R5pYTl9LcdmK/BQ9YkrRoWRBKkrTAqmqvSV56wQT7FvCm3kYkSRpVziGUJEmSpBFlQShJkiRJI8qCUJIkSZJGVM8KwiQbJjkzyU+TXJjkQ237tknOSHJpkqOTbNC2P6jdvrR9fVmvYpMkSZIk9baH8E7g+VW1A7AjsGuSnYFPAAdX1WOBm4H92/33B25u2w9u95MkSZIk9UjPVhltV0X7dbu5fvtXwPOBV7ftRwAfBA4FVrTPAb4OfDZJ2veRJEla1Ca7PyF4j0JJvdPTOYRJ1k1yHnAD8H3gMuCWqlrb7rIaWNo+XwpcDdC+fivwyAnec2WSs5OcvWbNml6GL0mSJEmLWk8Lwqq6u6p2BLYEdgIePw/vuaqqllfV8iVLlsw5RkmSJEkaVQuyymhV3QKcAjwT2CTJ2FDVLYFr2ufXAFsBtK8/HLhpIeKTJEmSpFHUy1VGlyTZpH3+YODPgItpCsM92t32AY5vn5/QbtO+/h/OH5QkSZKk3unZojLAFsARSdalKTyPqapvJ7kIOCrJR4GfAIe1+x8GfCnJpcCvgFf1MDZJkiRJGnm9XGX0fOApE7RfTjOfcHz774BX9CoeSZIkSdL9LcgcQkmSJEnS4LEglCRJkqQRZUEoSZIkSSPKglCSJEmSRpQFoSRJkiSNqF7edkKSJGkkLTvgO/0OQZK6Yg+hJEmSJI0oC0JJkiRJGlEWhJIkSZI0oiwIJUmSJGlEWRBKkiRJ0oiyIJQkSZKkEWVBKEmSJEkjyoJQkiRJkkaUN6bXnE12890rD3rxAkciSZIkaSYsCHWvyQo7SZIkSYuTQ0YlSZIkaURZEEqSJEnSiLIglCRJkqQRZUEoSZIkSSPKRWXUM1MtUuMKpJI0sSTvAP4KKOACYD9gC+Ao4JHAOcDeVfX7vgUpSVo07CGUJGlAJFkKvBVYXlVPBNYFXgV8Aji4qh4L3Azs378oJUmLiQWhJEmDZT3gwUnWAx4CXAs8H/h6+/oRwEv7FJskaZHpWUGYZKskpyS5KMmFSd7Wtn8wyTVJzmv/XtRxzPuTXJrkkiQv7FVskiQNoqq6BvgH4Jc0heCtNENEb6mqte1uq4GlEx2fZGWSs5OcvWbNmoUIWZI05Ho5h3At8K6qOjfJxsA5Sb7fvnZwVf1D585JtqcZFvME4FHAvyf5o6q6u4cxSpI0MJI8AlgBbAvcAnwN2LXb46tqFbAKYPny5dWLGCVJi0vPegir6tqqOrd9fjtwMZP8otlaARxVVXdW1RXApcBOvYpPkqQB9KfAFVW1pqruAo4DngVs0g4hBdgSuKZfAUqSFpcFmUOYZBnwFOCMtunNSc5P8oX211BoisWrOw6bcEiMw2EkSYvYL4GdkzwkSYAXABcBpwB7tPvsAxzfp/gkSYtMzwvCJBsBxwJvr6rbgEOBxwA70syP+ORM3q+qVlXV8qpavmTJknmPV5KkfqmqM2gWjzmX5pYT69AMAX0f8M4kl9LceuKwvgUpSVpUenofwiTr0xSDX6mq4wCq6vqO1/8F+Ha7eQ2wVcfhDomRJI2cqjoQOHBc8+U4jUKS1AO9XGU0NL9gXlxVn+po36Jjt5cBP2ufnwC8KsmDkmwLbAec2av4JEmSJGnU9bKH8FnA3sAFSc5r2z4A7JVkR6CAK4E3AlTVhUmOoZkrsRZ4kyuMSpIkSVLv9KwgrKofAZngpROnOOZjwMd6FZMkSZIk6T4LssqoJEmSJGnwWBBKkiRJ0oiyIJQkSZKkEWVBKEmSJEkjyoJQkiRJkkbUtAVhkmcleWj7/LVJPpVkm96HJknSYDNHSpKGXTc9hIcCdyTZAXgXcBnwxZ5GJUnScDBHSpKGWjcF4dqqKmAF8Nmq+hywcW/DkiRpKJgjJUlDrZsb09+e5P3A3sBzkqwDrN/bsNRLyw74Tr9DkKTFwhwpSRpq3fQQvhK4E3h9VV0HbAn8355GJUnScDBHSpKG2rQ9hFV1XZJjge3aphuBb/Q0KkmShoA5cvFw9IykUTVtQZjkDcBKYFPgMcBS4J+AF/Q2NC1mUyXeKw968QJGIkmzZ46UJA27buYQvgnYCTgDoKp+keQPehqVJEnDwRypBTHZD6n+iCpprrqZQ3hnVf1+bCPJekD1LiRJkoaGOVKSNNS6KQh/kOQDwIOT/BnwNeBbvQ1LkqShYI6UJA21bgrCA4A1wAXAG4ETgb/tZVCSJA0Jc6Qkaah1s8roPcC/tH+SJKlljpQkDbtJC8IkFzDFPIiqenJPIpIkacCZIyVJi8VUPYS7L1gUkiQNF3OkJGlRmLQgrKqrxp4n+UOaZbULOKuqrluA2CRJGkjmSEnSYjHtojJJ/go4E3g5sAdwepLX9zowSZIGnTlSkjTsurkx/XuAp1TVTQBJHgn8GPhCLwOTJGkImCMlSUOtm9tO3ATc3rF9e9smSdKoM0dKkoZaNz2ElwJnJDmeZn7ECuD8JO8EqKpPTXRQkq2ALwKbt8etqqpDkmwKHA0sA64E9qyqm5MEOAR4EXAHsG9VnTuHzyZJUq/NKkdKkjQouikIL2v/xhzfPm48zXFrgXdV1blJNgbOSfJ9YF/g5Ko6KMkBNDf1fR+wG7Bd+/cM4ND2UZKkQTXbHClJ0kDo5sb0H5rNG1fVtcC17fPbk1wMLKX59XSXdrcjgFNpCsIVwBerqmgm5W+SZIv2fSRJGjizzZGSJA2KaQvCJMuBvwG26dx/JjfdTbIMeApwBrB5R5F3Hc2QUmiKxas7Dlvdtt2vIEyyElgJsPXWW3cbgiRJ824+cqQkSf3UzZDRr9CsonYBcM9MT5BkI+BY4O1VdVszVbBRVZWkZvJ+VbUKWAWwfPnyGR0rSdI8m1OOlCSp37opCNdU1QmzefMk69MUg1+pquPa5uvHhoIm2QK4oW2/Btiq4/At2zZJkgbVrHOkJEmDoJuC8MAknwdOBu4ca+wo8CbUrhp6GHDxuFXWTgD2AQ5qH4/vaH9zkqNoFpO51fmDs7fsgO/0OwRJGgWzypFTSbIJ8HngiTQrl74euIQJVuieddSSJLW6KQj3Ax4PrM99w2EKmC7ZPQvYG7ggyXlt2wdoCsFjkuwPXAXs2b52Is0tJy6lue3Efl1+BkmS+mW2OXIqhwAnVdUeSTYAHkKTPydaoVsz4I+lkvRA3RSET6+qx830javqR0AmefkFE+xfwJtmeh5JkvpoVjlyMkkeDjyX5hZNVNXvgd8nmWyFbkmS5qSbgvDHSbavqot6Ho0kScNlvnPktsAa4F+T7ACcA7yNyVfovh9X4h49U/V6XnnQixcwEknDap0u9tkZOC/JJUnOT3JBkvN7HZgkSUNgvnPkesBTgUOr6inAb2iGh96rHVEz4SrbVbWqqpZX1fIlS5bMIQxJ0qjopodw155HIUnScJrvHLkaWF1VZ7TbX6cpCCdboVuSpDmZtiCsqqsAkvwBsGHPI9LIc/iLpGEx3zmyqq5LcnWSx1XVJTRz7i9q/yZaoVuSpDmZtiBM8hLgk8CjaH6R3Aa4GHhCb0OTJGmw9ShHvgX4SrvC6OU0K5muw8QrdEuSNCfdDBn9CM0ciX+vqqckeR7w2t6GJUnSUJj3HFlV5wHLJ3jpASt0S5I0V90sKnNXVd0ErJNknao6hYkTlSRJo8YcKUkaat30EN6SZCPgNJohLDfQrHomSdKoM0dKkoZaNwXhCuC3wDuA1wAPBz7cy6AkSRoS5sgBNNXiZJKk++tmyCgAVbUW+C/gSuC2XgUkSdKwMUdKkoZVNwXhacCGSZYC3wP2Bg7vZVCSJA0Jc6Qkaah1UxCmqu4AXg78Y1W9Am85IUkSmCMlSUOuq4IwyTNp5kaMDcpft3chSZI0NMyRkqSh1k1B+Dbg/cA3qurCJI8GTultWJIkDQVzpCRpqE27ymhVnUYzR2Js+3Lgrb0MSpKkYWCOlCQNu65XGZUkSZIkLS4WhJIkSZI0oiYtCJN8on18xcKFI0nS4DNHSpIWi6l6CF+UJDST5SVJ0n3MkZKkRWGqRWVOAm4GNkpyGxCgxh6r6mELEJ8kSYPIHClJWhQmLQir6j3Ae5IcX1UrFjAmaV4tO+A7k7525UEvXsBIJC0W5kgtVuZMafR0c9uJFUk2B57eNp1RVWt6G5Y0sckSlUlKUj+YIyVJw27aVUbbCfNnAq8A9gTOTLJHrwOTJGnQmSMlScNu2h5C4G+Bp1fVDQBJlgD/Dnx9qoOSfAHYHbihqp7Ytn0QeAMw9uvpB6rqxPa19wP7A3cDb62q787400iStLBmlSMlSRoU3dyHcJ2xRNe6qcvjDgd2naD94Krasf0bKwa3B14FPKE95h+TrNvFOSRJ6qfZ5khJkgZCNz2EJyX5LnBku/1K4MTpDqqq05Is6zKOFcBRVXUncEWSS4GdgP/q8nhJkvphVjlSkqRB0c2iMu9J8nLg2W3Tqqr6xhzO+eYkrwPOBt5VVTcDS4HTO/ZZ3bY9QJKVwEqArbfeeg5hSJI0Nz3IkZIkLahuegipquOA4+bhfIcCH6G5V9NHgE8Cr5/JG1TVKmAVwPLly2seYpIkadbmMUdKkrTgFnSeQ1VdX1V3V9U9wL/QDAsFuAbYqmPXLds2SZIkSVKPLGhBmGSLjs2XAT9rn58AvCrJg5JsC2xHs4y3JEmSJKlHuhoyOhtJjgR2ATZLsho4ENglyY40Q0avBN4IUFUXJjkGuAhYC7ypqu7uVWxafCa7Yb0kSZKkyc2qIEzywar64FT7VNVeEzQfNsX+HwM+Npt4JEkaFN3kSEmSBsVsewjPmdcoJElaPMyRGghTjZ658qAXL2AkkgbZrOYQVtW35jsQSZIWA3OkJGmYTNtD2C7y8hZgWef+VfWS3oUlSdLgM0dKkoZdN0NGv0kz9+9bwD29DUeSpKFijpQkDbVuCsLfVdVneh6JJEnDxxwpSRpq3RSEhyQ5EPgecOdYY1Wd27OoJEkaDj3JkUnWBc4Grqmq3duhqUcBj6RZtGbvqvr9XM6h0ebtmiSN6aYgfBKwN/B87hsOU+22JEmjrFc58m3AxcDD2u1PAAdX1VFJ/gnYHzh0jueQJKmrgvAVwKP9JVKSpAeY9xyZZEvgxTT35n1nktAUmK9udzkC+CAWhJKkedDNbSd+BmzS60AkSRpCvciRnwbey309jo8Ebqmqte32amDpPJ9TkjSiuukh3AT4eZKzuP/8CJfUliSNunnNkUl2B26oqnOS7DKL41cCKwG23nrre9u9QbkkaTLdFIQH9jwKSZKG03znyGcBL0nyImBDmjmEhwCbJFmv7SXcErhmooOrahWwCmD58uU1z7FJkhahaQvCqvrBQgQiSdKwme8cWVXvB94P0PYQvruqXpPka8AeNCuN7gMcP5/nlSSNrmnnECa5Pclt7d/vktyd5LaFCE6SpEG2gDnyfTQLzFxKM6fwsB6cQ5I0grrpIdx47Hm70tkKYOdeBiVJ0jDoZY6sqlOBU9vnlwM7zcf7SpLUqZtVRu9VjW8CL+xRPJIkDSVzpCRpGE3bQ5jk5R2b6wDLgd/1LCJ1bapV4yRJvWeOlCQNu25WGf2LjudrgStphsRIkjTqzJGSpKHWzRzC/RYiEEmSho05sn8cJSNJ82PSgjDJ/5niuKqqj/QgnjmZLDl4011J0nwaxhwpSdJEpuoh/M0EbQ8F9qdZ8tpkJ0kaVeZISdKiMGlBWFWfHHueZGPgbcB+NDfF/eRkx0mStNiZIyVJi8WUcwiTbAq8E3gNcATw1Kq6eSECkxaCw4wlzZY5UuqNqeaHmp+l+TfVHML/C7wcWAU8qap+vWBRSZI0wMyRkqTFYqoewncBdwJ/C/xNkrH20EyYf9hUb5zkC8DuwA1V9cS2bVPgaGAZzdLce1bVzWne/BDgRcAdwL5Vde4sP5MkSb02pxwpDaPZrOxqj540+NaZ7IWqWqeqHlxVG1fVw7TNx2YAAA5nSURBVDr+Nu4y0R0O7Dqu7QDg5KraDji53QbYDdiu/VsJHDrTDyJJ0kKZhxwpSdJAmLQgnKuqOg341bjmFTTzLGgfX9rR/sVqnA5skmSLXsUmSZIkSephQTiJzavq2vb5dcDm7fOlwNUd+61u2x4gycokZyc5e82aNb2LVJIkSZIWuYUuCO9VVQXULI5bVVXLq2r5kiVLehCZJEmSJI2GKW870QPXJ9miqq5th4Te0LZfA2zVsd+WbZvUFy55LUmSpFGw0AXhCcA+wEHt4/Ed7W9OchTwDODWjqGlkiRJGkL+wCoNvp4VhEmOBHYBNkuyGjiQphA8Jsn+wFXAnu3uJ9LccuJSmttO7NeruCRJkiRJjZ4VhFW11yQvvWCCfQt4U69ikSRJkiQ9UN8WlZEkSZIk9ddCzyGUJElDYrL5X879kqTFY+QLwkGf7DxVfJIkSZI0FyNfEEqSJGnh+aO3NBicQyhJkiRJI8qCUJIkSZJGlENGpRka9HmnkiRJUrcsCCVJGmHO45Kk0eaQUUmSJEkaUfYQSouMQ1olSZLULXsIJUmSJGlE2UMoLZDJeu7stZMkqTuOgpHmnz2EkiRJkjSi7CGU5tFCrtbnyoCSJE3PXkVpavYQSpI0IJJsleSUJBcluTDJ29r2TZN8P8kv2sdH9DtWSdLiYEEoSdLgWAu8q6q2B3YG3pRke+AA4OSq2g44ud2WJGnOLAglSRoQVXVtVZ3bPr8duBhYCqwAjmh3OwJ4aX8ilCQtNs4hlCRpACVZBjwFOAPYvKqubV+6Dth8kmNWAisBtt56657F5pwsDaL5nFvvd1yjZCQKQhff0CAz6UgaL8lGwLHA26vqtiT3vlZVlaQmOq6qVgGrAJYvXz7hPpIkdXLIqCRJAyTJ+jTF4Feq6ri2+fokW7SvbwHc0K/4JEmLiwWhJEkDIk1X4GHAxVX1qY6XTgD2aZ/vAxy/0LFJkhankRgyKknSkHgWsDdwQZLz2rYPAAcBxyTZH7gK2LNP8UmLymymFc33VI/J3s9pI1oofSkIk1wJ3A7cDaytquVJNgWOBpYBVwJ7VtXN/YhPkqR+qKofAZnk5RcsZCySpNHQzyGjz6uqHatqebvtPZYkSZIkaQEN0pDRFcAu7fMjgFOB9/UrGHD1Ry0+fqclSZLUqV89hAV8L8k57T2ToMt7LEmSJEmS5ke/egifXVXXJPkD4PtJft754lT3WFqom+5Ko8beQ0mSpNHTl4Kwqq5pH29I8g1gJ9p7LFXVtVPdY8mb7mqUzGb1M0kaRP7oJEmDacGHjCZ5aJKNx54Dfw78DO+xJEmSJEkLqh89hJsD32juvct6wFer6qQkZ+E9liRJUgdHSkhSby14QVhVlwM7TNB+E0N0jyVvIipJkiRp2A3SbSckDRnnBEmSdB87DDSMLAglTcshW5IkSYuTBeGA8B/ckiRJkhZav25ML0mSJEnqMwtCSZIkSRpRFoSSJEmSNKKcQyhJkiT1kGtFaJDZQyhJkiRJI8qCUJIkSZJGlENG55k36pYkSZI0LOwhlCRJkqQRZUEoSZIkSSPKglCSJEmSRpRzCBeQSw5Lkha72eQ686Mk9Y8FoaSemM0CSy7KJEmStLAsCCUtOHsDJEma2nz/SOqPrpqMcwglSZIkaUTZQyhJkiQNkYXs7ZvsXPYqLh4WhJKGwmyHmZqwJEmSJueQUUmSJEkaURaEkiRJkjSiHDIqaSS52pokSQ1X/x5tFoSSFjWTnCRJ0uQGriBMsitwCLAu8PmqOqjPIUnSnNgbqflgfpTUjWH9IdRc2T8DVRAmWRf4HPBnwGrgrCQnVNVF/Y1M0iiZTTI1WamXzI+SpF4ZqIIQ2Am4tKouB0hyFLACMOFJWpQsPtUl86OkkTWfudKeyAcatIJwKXB1x/Zq4Bl9ikWSuraQQ3Tm+1yjmgCHjPlRktQTg1YQTivJSmBlu3lnkp/1M54hsxlwY7+DGCJer5nxes3cQFyzfKLfEXTtcf0OYNCZI+dkIP57HCJer5lZdNerx7ljztdrNvENUT4cb075cdAKwmuArTq2t2zb7lVVq4BVAEnOrqrlCxfecPN6zYzXa2a8XjPnNZuZJGf3O4Y+mjY/gjlyLrxeM+P1mhmv18x4vWZmrvlx0G5MfxawXZJtk2wAvAo4oc8xSZLUb+ZHSVJPDFQPYVWtTfJm4Ls0y2p/oaou7HNYkiT1lflRktQrA1UQAlTVicCJXe6+qpexLEJer5nxes2M12vmvGYzM9LXa4b5EUb8es2C12tmvF4z4/WaGa/XzMzpeqWq5isQSZIkSdIQGbQ5hJIkSZKkBTK0BWGSXZNckuTSJAf0O55Bk2SrJKckuSjJhUne1rZvmuT7SX7RPj6i37EOkiTrJvlJkm+329smOaP9nh3dLuYgIMkmSb6e5OdJLk7yTL9fk0vyjva/xZ8lOTLJhn6/7pPkC0lu6LxNwmTfpzQ+016385M8tX+RDx7z49TMj7Njfuye+XFmzI/T63WOHMqCMMm6wOeA3YDtgb2SbN/fqAbOWuBdVbU9sDPwpvYaHQCcXFXbASe327rP24CLO7Y/ARxcVY8Fbgb270tUg+kQ4KSqejywA8118/s1gSRLgbcCy6vqiTSLgrwKv1+dDgd2Hdc22fdpN2C79m8lcOgCxTjwzI9dMT/Ojvmxe+bHLpkfu3Y4PcyRQ1kQAjsBl1bV5VX1e+AoYEWfYxooVXVtVZ3bPr+d5v+MltJcpyPa3Y4AXtqfCAdPki2BFwOfb7cDPB/4eruL16uV5OHAc4HDAKrq91V1C36/prIe8OAk6wEPAa7F79e9quo04Ffjmif7Pq0AvliN04FNkmyxMJEOPPPjNMyPM2d+7J75cVbMj9PodY4c1oJwKXB1x/bqtk0TSLIMeApwBrB5VV3bvnQdsHmfwhpEnwbeC9zTbj8SuKWq1rbbfs/usy2wBvjXdgjR55M8FL9fE6qqa4B/AH5Jk+huBc7B79d0Jvs+mQMm57WZAfNj18yP3TM/zoD5cU7mLUcOa0GoLiXZCDgWeHtV3db5WjVLzLrMLJBkd+CGqjqn37EMifWApwKHVtVTgN8wbviL36/7tOP6V9D8Q+FRwEN54NAPTcHvk+ab+bE75scZMz/OgPlxfsz1OzWsBeE1wFYd21u2beqQZH2aZPeVqjqubb5+rNu4fbyhX/ENmGcBL0lyJc0Qq+fTzAHYpB3CAH7POq0GVlfVGe3212kSoN+vif0pcEVVramqu4DjaL5zfr+mNtn3yRwwOa9NF8yPM2J+nBnz48yYH2dv3nLksBaEZwHbtSsQbUAz+fSEPsc0UNrx/YcBF1fVpzpeOgHYp32+D3D8Qsc2iKrq/VW1ZVUto/k+/UdVvQY4Bdij3c3r1aqq64CrkzyubXoBcBF+vybzS2DnJA9p/9scu15+v6Y22ffpBOB17UpqOwO3dgybGXXmx2mYH2fG/Dgz5scZMz/O3rzlyKG9MX2SF9GMaV8X+EJVfazPIQ2UJM8GfghcwH1j/j9AM0/iGGBr4Cpgz6oaP0l1pCXZBXh3Ve2e5NE0v4huCvwEeG1V3dnP+AZFkh1pFhjYALgc2I/mRya/XxNI8iHglTQrHP4E+CuaMf1+v4AkRwK7AJsB1wMHAt9kgu9T+4+Gz9IMK7oD2K+qzu5H3IPI/Dg18+PsmR+7Y36cGfPj9HqdI4e2IJQkSZIkzc2wDhmVJEmSJM2RBaEkSZIkjSgLQkmSJEkaURaEkiRJkjSiLAglSZIkaURZEEqzkOSlSSrJ4/sdy0SSLEvy6n7HIUkaLeZHafhYEEqzsxfwo/axp5KsN4vDlgEmPEnSQjM/SkPGglCaoSQbAc8G9gde1dG+S5LTknwnySVJ/inJOu1rv05ycJILk5ycZEnb/oYkZyX5aZJjkzykbT+8Pf4M4O+TPCbJSUnOSfLDsV9e2/0+k+THSS5PskcbzkHAc5Kcl+Qd4+NvYzg3yQVJVnS89ndt7D9KcmSSd7ftE55fkqQx5kfzo4aTBaE0cyuAk6rqv4Gbkjyt47WdgLcA2wOPAV7etj8UOLuqngD8ADiwbT+uqp5eVTsAF9Mk0TFbAn9SVe8EVgFvqaqnAe8G/rFjvy1oEvDuNIkO4ADgh1W1Y1UdPC7+3wEvq6qnAs8DPpnG04G/BHYAdgOWdxwz1fklSQLzo/lRQ2k2Xe3SqNsLOKR9flS7fU67fWZVXQ6Q5EiaRPR14B7g6HafLwPHtc+fmOSjwCbARsB3O87ztaq6u/3F9U+AryUZe+1BHft9s6ruAS5KsnkX8Qf4eJLntnEtBTYHngUcX1W/A36X5Fvt55ju/JIkgflx/PmloWBBKM1Akk2B5wNPSlLAukAleU+7S407ZPz2+PbDgZdW1U+T7Avs0rHPb9rHdYBbqmrHSd7rzs4Qp/sMwGuAJcDTququJFcCG06x/3TnlySNOPOjNLwcMirNzB7Al6pqm6paVlVbAVcAz2lf3ynJtu3ciFfSTKyH5r+1sfkLr+5o3xi4Nsn6NInoAarqNuCKJK8AaIev7DBNnLe37z2RhwM3tMnuecA2bft/An+RZMP2V8/d53B+SdJoMT+aHzWkLAilmdkL+Ma4tmO5bzW1s4DP0sx3uKJj39/QJMOf0fyC+uG2/e+AM2iSzc+nOO9rgP2T/BS4kGaexlTOB+5uJ+O/Y9xrXwGWJ7kAeN3YeavqLOCE9th/Ay4Abp3l+SVJo8X8aH7UkErVZD32kmYiyS7Au6tq9wle+3VVbbTwUc1Mko2q6tftam6nASur6tx+xyVJGl7mR2mwOYdQUqdVSbanmTNxhMlOkiTA/KhFzB5CSZIkSRpRziGUJEmSpBFlQShJkiRJI8qCUJIkSZJGlAWhJEmSJI0oC0JJkiRJGlEWhJIkSZI0ov5/5YVmIpPeUWgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uShj-2u-9HCD"
      },
      "source": [
        "# Visualizing the distributions of metadata (Train data)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SCU4nm5-qY2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "b662e978-6da6-41f8-90e4-6a92ad7b4b02"
      },
      "source": [
        "gender = []\n",
        "etnhicity = []\n",
        "emotion = []\n",
        "for sample in M_train:\n",
        "  gender.append(sample[0])\n",
        "  etnhicity.append(sample[1])\n",
        "  emotion.append(sample[2])\n",
        "\n",
        "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 4))\n",
        "fig.suptitle('Metadata distribution', fontsize=14, fontweight='bold')\n",
        "\n",
        "ax1.hist(gender)\n",
        "ax2.hist(etnhicity)\n",
        "ax3.hist(emotion)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1404.,    0.,    0., 1784.,    0.,    0.,  712.,    0.,    0.,\n",
              "         165.]),\n",
              " array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8, 2.1, 2.4, 2.7, 3. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNx5EZps5WN2"
      },
      "source": [
        "# Visualizing the age distribution per Ethnicity\n",
        "- First, we define a function to visualize the age distribution per ethnicity. Then, we visualize the distributions of train / validation / test sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHNdQeyT8hSa"
      },
      "source": [
        "def compute_hist_per_ethnicity(y_data, metadata, set):\n",
        "  \n",
        "  vec_as = []\n",
        "  vec_af = []\n",
        "  vec_ca = []\n",
        "  for i in range(0,len(y_data)):\n",
        "    if(metadata[i][1] == 'asian'):\n",
        "      vec_as.append(y_data[i])\n",
        "    if(metadata[i][1] == 'afroamerican'):\n",
        "      vec_af.append(y_data[i])\n",
        "    if(metadata[i][1] == 'caucasian'):\n",
        "      vec_ca.append(y_data[i])\n",
        "\n",
        "  fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 3))\n",
        "  fig.suptitle(['Age distribution per Ethnicity ', set], fontsize=14, fontweight='bold')\n",
        "\n",
        "  ax1.hist(vec_as, bins=50)\n",
        "  ax1.set_xlim([0, 100])\n",
        "  ax1.set(xlabel='Asian', ylabel='Num. of samples')\n",
        "\n",
        "  ax2.hist(vec_af, bins=50)\n",
        "  ax2.set_xlim([0, 100])\n",
        "  ax2.set(xlabel='Afroamerican', ylabel='Num. of samples')\n",
        "\n",
        "  ax3.hist(vec_ca, bins=50) \n",
        "  ax3.set_xlim([0, 100])\n",
        "  ax3.set(xlabel='Caucasian', ylabel='Num. of samples')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nqiYZMF9m4S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "outputId": "e03dce7f-ccea-4457-dc8f-44d8f1e7df15"
      },
      "source": [
        "# train set\n",
        "compute_hist_per_ethnicity(Y_train*100, M_train, 'Train set')\n",
        "\n",
        "# validation set\n",
        "compute_hist_per_ethnicity(Y_valid*100, M_valid, 'Validation set')\n",
        "\n",
        "# test set\n",
        "# note, we do not multiply 'Y_test' by 100 because it was not normalized \n",
        "# to be in the range of [0,1] as the train and validation sets.\n",
        "compute_hist_per_ethnicity(Y_test, M_test, 'Test set')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JkiRJgBVESZIkSVJjBVGSJEmSBFhBlCRJkiQ1W4w6gEG6wx3uUKtXrx51GJIG6JRTTvlZVa0adRyLYW6SVh5zk6Rxtdj8tKIqiKtXr2bt2rWjDkPSACX54ahjWCxzk7TymJskjavF5ie7mEqSJEmSACuIkiRJkqTGCqIkzUOS3ZKc3vP4VZKXjjouSZKkQVhR1yBK0rBV1QXAHgBJNgcuAY4aaVCSJEkDYguiJC3c3sBFVbXsB6uQJEkCWxCXldUHf/EPr9cd8vgRRiKpeQbw8eneSHIQcBDAzjvvvJQxLUvmN0mj1JuDwDykTZstiJK0AEm2Ap4IfGq696tqTVVNVNXEqlXL+lZpkiRpE2ILoiQtzGOBU6vqslEHIknayNZAaXFsQZSkhdmfGbqXSpIkLVdWECVpnpJsDTwKOHLUsUiSJA2SXUwlaZ6q6mrg9qOOQ5IkadCG1oKY5ANJLk9ydk/Z65Nc0nOD6cfNsOw+SS5IcmGSg4cVoyRJkiRpo2F2MT0c2Gea8ndU1R7tcczUN9uNp99NNwDE7sD+SXYfYpySJEmSJIZYQayqE4GfL2DRBwAXVtXFVXUtcASw70CDkyRJkiTdxCiuQXxhkmcDa4GXV9WVU97fAfhxz/R64IFLFZwkSdK4S/L3wPOBAs4CngtsT/fD+u2BU4BntR/bNU/eKkObsqUexfQ9wF2BPYBLgbctdoVJDkqyNsnaDRs2LHZ1kiRJYy3JDsCLgYmquiewOfAM4C10l/LcDbgSOHB0UUparpa0glhVl1XV76vqBuC/6LqTTnUJsFPP9I6tbKZ1rqmqiaqaWLVq1WADliRJGk9bALdIsgVwS7of3h8JfLq9/yHgSSOKTdIytqQVxCTb90w+GTh7mtlOBnZNskuSreh+ETt6KeKTJEkad1V1CfDvwI/oKoa/pOtS+ouqur7Ntp7ush1Jmpdh3ubi48D/ArslWZ/kQODfkpyV5EzgEcDft3nvnOQYgJbYXggcB5wHfLKqzhlWnJIkSctJktvSDeC3C3BnYGumHzl+umW9NEfSrIY2SE1V7T9N8ftnmPcnwON6po8BbnILDEmSJPGnwA+qagNAkiOBhwDbJtmi/dg+7SU6VbUGWAMwMTFRSxeypOViFKOYSpIkaeF+BDwoyS2B3wJ7040OfzywH91Ips8BPjeyCMfI1BFJJc1uqUcxlSRJ0iJU1Ul0g9GcSneLi83oWgVfCbwsyYV0t7qYtueWJM3GFkRJkqRlpqpeB7xuSvHFTD9CvCT1zRZESZIkSRJgBVGS5i3Jtkk+neT8JOclefCoY5IkSRoEu5hK0vwdChxbVfu1+7XectQBSZIkDYIVREmahyS3AR4OHABQVdcC144yJkmSpEGxi6kkzc8uwAbgg0lOS3JYkq1HHZQkSdIgWEGUpPnZAtgTeE9V3Re4Gjh46kxJDkqyNsnaDRs2LHWMkiRJC2IFUZLmZz2wvt2HDLp7ke05daaqWlNVE1U1sWrVqiUNUJIkaaGsIErSPFTVT4EfJ9mtFe0NnDvCkCRJkgbGQWokaf5eBHy0jWB6MfDcEccjSZI0EFYQJWmequp0YGLUcUiSJA2aXUwlSZIkSYAVREmSJElSYwVRkiRJkgQMsYKY5ANJLk9ydk/ZW5Ocn+TMJEcl2XaGZdclOSvJ6UnWDitGSZIkSdJGw2xBPBzYZ0rZV4B7VtW9ge8Br5pl+UdU1R5V5UAQkiRJkrQE5qwgJnlIkq3b679M8vYkd5lruao6Efj5lLIvV9X1bfI7wI4LiFmSBmKh+U2SBsEcJGkc9dOC+B7gN0nuA7wcuAj48AC2/TzgSzO8V8CXk5yS5KDZVpLkoCRrk6zdsGHDAMKStAkZVn6TpH6YgySNnX4qiNdXVQH7Au+qqncD2yxmo0leA1wPfHSGWR5aVXsCjwX+LsnDZ1pXVa2pqomqmli1atViwpK06Rl4fpOkeTAHSRo7W/Qxz1VJXgU8C3hYks2ALRe6wSQHAE8A9m5J8Saq6pL2fHmSo4AHACcudJuSNIOB5jdJmidzkKSx008L4tOBa4DnVdVP6a4bfOtCNpZkH+AfgSdW1W9mmGfrJNtMvgYeDZw93byStEgDy2+StADmIEljZ84KYktYnwFu1op+Bhw113JJPg78L7BbkvVJDgTeRdd14ivtFhbvbfPeOckxbdHtgG8lOQP4LvDFqjp2nvslSXNaaH6TpEEwB0kaR3N2MU3yV8BBwO2AuwI7AO8F9p5tuaraf5ri988w70+Ax7XXFwP3mSuuTd3qg794o+l1hzx+RJFIy9dC85skDYI5SNI46ucaxL+juwbwJICq+n6SOw41KklaGuY3SaNkDhqAqT+aS1qcfiqI11TVtUkASLIF3W0oJGm5W1B+S7IOuAr4Pd0ohBPDDFLSiuU5lqSx008F8RtJXg3cIsmjgL8FPj/csCRpSSwmvz2iqn42vNAkbQI8x5I0dvoZxfRgYANwFvDXwDHAa4cZlCQtEfObpFEyB0kaO3O2IFbVDcB/tYckrRiLyG8FfDlJAe+rqjUDD07Siuc5lqRxNGMFMclZzNIPvqruPZSIJGnIBpDfHlpVl7TBJL6S5PyqOnHKNg6iG52QnXfeebEhrxi9g0k4+rI2VYM4x0qyLXAYcM+2rucBFwCfAFYD64CnVdWVi49Y0qZkthbEJyxZFJK0tBaV36rqkvZ8eZKj6EYhPHHKPGuANQATExMOOiGp1yDOsQ4Fjq2q/ZJsBdwSeDXwtao6JMnBdF1YXzmAbUnahMx4DWJV/XDyAVxDd2/Ce9ONuPXDpQpQkgZtMfktydZJtpl8DTwaOHvYMUtaORZ7jpXkNsDDafeXrqprq+oXwL7Ah9psHwKeNIz4Ja1scw5Sk+T5wHeBpwD7Ad9J8rxhByZJw7bA/LYd8K0kZ7Rlv1hVxw43Ukkr0SLOsXahG9zmg0lOS3JY+8Fqu6q6tM3zU7p8JUnz0s9tLv4BuG9VXQGQ5PbAt4EPDDMwSVoC885vVXUx3a/9krRYCz3H2gLYE3hRVZ2U5FC67qR/UFXVBtK6Ea+PXpje66cneR21Vqp+bnNxBd0NoSdd1cokabkzv0kapYXmoPXA+qo6qU1/mq7CeFmS7QHa8+VTF6yqNVU1UVUTq1atWlTwklamfloQLwROSvI5ulGy9gXOTPIygKp6+xDjk6RhMr9JGqUF5aCq+mmSHyfZraouAPYGzm2P5wCHtOfPLcE+SFph+qkgXtQekyaTzTaDD0eSlpT5TdIoLSYHvQj4aBvB9GLguXQ9wz6Z5EDgh8DTBhirpE3EnBXEqnrDUgQiSUvN/CZplBaTg6rqdGBimrf2XnhEktRHBTHJBPAa4C698/dzE1dJGmfmN0mjZA6SNI766WL6UbpRts4CbpjPypN8gO5msJdX1T1b2e2ATwCrgXXA06rqymmWfQ7w2jb55qr60NR5JGmRFpzfJGkAzEGSxk4/FcQNVXX0Atd/OPAu4MM9ZQcDX6uqQ5Ic3KZf2btQq0S+jq7rRAGnJDl6uoqkJC3CYvKbJC2WOUjS2Omngvi6JIcBXwOumSysqiPnWrCqTkyyekrxvsBe7fWHgBOYUkEEHgN8pap+DpDkK8A+wMf7iFeS+rXg/CZJA2AOkjR2+qkgPhe4O7AlG7s/FLDQ5LVdVV3aXv8U2G6aeXYAftwzvb6VSdIgDTq/SdJ8mIMkjZ1+Koj3r6rdhrHxqqoktZh1JDkIOAhg5513HkhckjYZQ8tvktQHc9AytvrgL95oet0hjx9RJNJgbdbHPN9OsvsAt3lZku0B2vPl08xzCbBTz/SOrewmqmpNVU1U1cSqVasGGKakTcCg85skzYc5SNLY6acF8UHA6Ul+QNc/PnSNfwsdgvlo4DnAIe35c9PMcxzwL0lu26YfDbxqgduTpJkMOr9J0nyYgxZgasudpMHqp4K4z0JXnuTjdAPS3CHJerqRSQ8BPpnkQOCHwNPavBPAC6rq+VX18yRvAk5uq3rj5IA1kjRAC85vkjQA5iBJY2fOCmJV/RAgyR2Bm89n5VW1/wxv7T3NvGuB5/dMfwD4wHy2J0nzsZj8lmRzYC1wSVU9YQjhSVrhFpODJGlY5rwGMckTk3wf+AHwDbqb239pyHFJ0tAtMr+9BDhvSKFJ2gR4jiVpHPUzSM2b6PrIf6+qdqFr/fvOUKOSpKWxoPyWZEfg8cBhww1P0grnOZaksdNPBfG6qroC2CzJZlV1PDAx5LgkaSksNL+9E/hHNt63TJIWwnMsSWOnn0FqfpHkVsCJwEeTXA5cPdywJGlJzDu/JXkCcHlVnZJkr1nm8x6tkubiOZaksdNPC+K+wG+AvweOBS4C/myYQUnSEllIfnsI8MQk64AjgEcm+cjUmbxHq6Q+eI4laez0U0EEoKquB/6X7gLqXw0rIElaavPJb1X1qqrasapWA88Avl5Vfzn0ICWtWJ5jSRon/XQxPRF4WLtp/Zfp7k34dOCZwwxMkpaA+U3SKJmD5rD64C+OOgRpk9NPC2Kq6jfAU4D/rKqnAvcYbliStCQWld+q6gTvgShpETzHkjR2+qogJnkw3a9Zkz/jbD68kCRpyZjfJI2SOUjS2OmngvgS4FXAUVV1TpI/Ao4fbliStCTMb5JGyRwkaezMeQ1iVZ1I10d+cvpi4MXDDEqSloL5TdIomYMkjaO+RzGVJEmSJK1sVhAlSZIkScAsFcQkb2nPT126cCRp+MxvkkbJHLTpWn3wF2/0kMbRbC2Ij0sSuounJWklMb9JGiVzkKSxNdsgNccCVwK3SvIrIEBNPlfVrReywSS7AZ/oKfoj4J+r6p098+wFfA74QSs6sqreuJDtSdI0hpLfJKlP5iBJY2vGFsSq+oeq2hb4YlXduqq26X1e6Aar6oKq2qOq9gDuB/wGOGqaWb85OZ+VQ0mDNKz8Jkn9GEQOSrJ5ktOSfKFN75LkpCQXJvlEkq2GuhOSVqw5B6mpqn2TbJfkCe2xaoDb3xu4qKp+OMB1SlJfhpzfJGlWi8xBLwHO65l+C/COqrobXevkgYOMVdKmY84KYruA+rvAU4GnAd9Nst+Atv8M4OMzvPfgJGck+VKSewxoe5L0B0POb5I0q4XmoCQ7Ao8HDmvTAR4JfLrN8iHgScOIWdLKN9s1iJNeC9y/qi4HaL9ufZWNSWhBWteHJzL9BdqnAnepql8neRzwWWDXGdZzEHAQwM4777yYkCRteoaS3ySpTwvNQe8E/hHYpk3fHvhFVV3fptcDOww+XM1m6qik6w55/IgikRann/sgbjaZuJor+lxuLo8FTq2qy6a+UVW/qqpft9fHAFsmucN0K6mqNVU1UVUTq1bZO0zSvAwrv0lSP+adg5I8Abi8qk5ZyAaTHJRkbZK1GzZsWMgqJK1w/bQgHpvkODZ2BX06cMwAtr0/M3QvTXIn4LKqqiQPoEuWVwxgm5LUa975LcnNgROBm9Hl0E9X1euGGqWklWoh51gPAZ7YeljdHLg1cCiwbZItWivijsAl0y1cVWuANQATExO1+F2QtNLMWUGsqn9I8hTgoa1oTVVNN+po35JsDTwK+Oueshe07b0X2A/4myTXA78FnlFVJjFJA7XA/HYN8MjWBX5L4FtJvlRV3xlqsJJWnIXkoKp6Fe3ynHZbsFdU1TOTfIru/OkI4Dl0twvTCE3tciotF/20IFJVRwJHDmqjVXU1XX/53rL39rx+F/CuQW1PkmYy3/zWfqz6dZvcsj38AUvSggzwHOuVwBFJ3gycBrx/AOuUtAnqq4Koxen9BWk+FyzP55enhW5D0vwl2Rw4Bbgb8O6qOmnEIUnaBFXVCcAJ7fXFwANGGY+klcEKoiTNU1X9HtgjybbAUUnuWVVn986zUkdYnu7HqHH7gWrc4pEkaTlxtD5JWqCq+gVwPLDPNO85wrIkSVp2FlRBTPL6AcchSWNhrvyWZFVrOSTJLegG3Dp/CUKTtAnwHEvSqC20i+mC7r0jScvAXPlte+BD7TrEzYBPVtUXhh+WpE2E51iSRmpBFcSq+vygA5GkcTBXfquqM4H7LlE4kjYxnmNJGrU5K4hJdgFeBKzunb+qnji8sCRp+MxvkkbJHCRpHPXTgvhZuivwVu8AABQFSURBVHvpfB64YbjhSNKSMr9JGqWxyUFTb63lCMDLh8dOg9ZPBfF3VfUfQ49Ekpae+U3SKJmDJI2dfiqIhyZ5HfBl4JrJwqo6dWhRSdLSML9JGiVzkG7E1kCNg34qiPcCngU8ko3dH6pNawzNlVy8ibT0B+Y3SaNkDpI0dvqpID4V+KOqunbYwUjSEjO/SRolc9AmbuqP+tI42KyPec4Gth12IJI0AuY3SaNkDpI0dvppQdwWOD/Jydy4f7xDMEta7sxvkkbJHCRp7PRTQXzd0KOQpNEwv0kaJXPQFHa5lEZvzgpiVX1jGBtOsg64Cvg9cH1VTUx5P8ChwOOA3wAHOKqXpEEaVn6TpH6YgySNozkriEmuohtRC2ArYEvg6qq69QC2/4iq+tkM7z0W2LU9Hgi8pz1L0kAMOb9J0qzMQZLGUT8tiNtMvm6tevsCDxpmUM2+wIerqoDvJNk2yfZVdekSbFvSJmCE+U2SzEGSxlI/o5j+QXU+CzxmANsu4MtJTkly0DTv7wD8uGd6fSuTpIEbcH6TpHkxB0kaF/10MX1Kz+RmwATwuwFs+6FVdUmSOwJfSXJ+VZ0435W0yuVBADvvvPMAwhquuW5ivxTblNQZYn6TpDmZgySNo35GMf2zntfXA+voukAsSlVd0p4vT3IU8ACgt4J4CbBTz/SOrWzqetYAawAmJiZq6vuSNIt557ckOwEfBraj6wmxpqoOHVaAkla0oZxjDcN0PzYvxY/ckpZeP9cgPnfQG02yNbBZVV3VXj8aeOOU2Y4GXpjkCLrBaX7p9YeSBmmB+e164OVVdWqSbYBTknylqs4dcHiSVrhhnGNpZbFirlGYsYKY5J9nWa6q6k2L2O52wFHd9dhsAXysqo5N8oK28vcCx9Dd4uJCuttcmEQlDcRi8lv7oerS9vqqJOfRXR9tBVFSX4Z8jiVJizJbC+LV05RtDRwI3B5YcPKqqouB+0xT/t6e1wX83UK3IUmzGEh+S7IauC9w0jTv9XV9dO+vw3P9KjyfeRdrKbY11/XRM8WwlJ/DUlvJ+6YbGdo5liQt1owVxKp62+Tr1o3qJXSteEcAb5tpOUkad4PIb0luBXwGeGlV/WqabXh9tKRpeY4laZzNeg1iktsBLwOeCXwI2LOqrlyKwCRpmBaT35JsSVc5/GhVHTm8KCWtVJ5jSRpXs12D+FbgKXS/gN+rqn69ZFFJ0hAtJr+1m1m/Hzivqt4+pBAlrWCeY0kaZ7O1IL4cuAZ4LfCaNqAMQOguEbz1kGOTpGFZTH57CPAs4Kwkp7eyV1fVMcMKVtKK4zmWFsz7W2vYZrsGcbOlDESSlspi8ltVfYvuJE6SFmSx51gz3Y+1dVv9BLCa7p6KT7PbqqT5mvM+iMvR1F9WxnkkuOUUqyRJGgvT3o8VOAD4WlUdkuRg4GDglSOMUyPQz70TPf/UbGwllCRJWkaq6tKqOrW9vgqYvB/rvnQD3tCenzSaCCUtZ1YQJUmSlqkp92PdrqoubW/9lK4LqiTNy4rsYipJkrTSTb0fa89gN1RVJbnJPViTHAQcBLDzzjvPuG4HQpE2XbYgSpIkLTMz3I/1siTbt/e3By6fulxVramqiaqaWLVq1dIFLGnZsIIoSZK0jMxyP9ajgee0188BPrfUsUla/uxiKkmStLxMez9W4BDgk0kOBH4IPG1E8UlaxqwgSpIkLSNz3I9176WMRcvDXNeU9nPN6UJuheHtNJYnK4iSJEmaN0/+pZXJaxAlSZIkSYAtiGNnqYeVnm17U38JHMYvhf76KEmSNP48Z9t0LHkLYpKdkhyf5Nwk5yR5yTTz7JXkl0lOb49/Xuo4JUmSJGlTM4oWxOuBl1fVqUm2AU5J8pWqOnfKfN+sqieMID5JkiRJ2iQteQtiVV1aVae211cB5wE7LHUckrRQST6Q5PIkZ486FkmSpEEa6TWISVYD9wVOmubtByc5A/gJ8IqqOmcJQ5Ok2RwOvAv48IjjkKSx5TVr0vI0slFMk9wK+Azw0qr61ZS3TwXuUlX3Af4f8NlZ1nNQkrVJ1m7YsGF4AUtSU1UnAj8fdRySJEmDNpIWxCRb0lUOP1pVR059v7fCWFXHJPnPJHeoqp9NM+8aYA3AxMREDTFsSZIkSdhCvJIteQUxSYD3A+dV1dtnmOdOwGVVVUkeQNfSecUShilJi5LkIOAggJ133vlG7811O5uF3u6md7m5/lEvdN65yod1q56l/kx6DfKzlCRp3I2iBfEhwLOAs5Kc3speDewMUFXvBfYD/ibJ9cBvgWdUla2DkpYNezdIkqTlaMkriFX1LSBzzPMuugEgBmI+N4MfxjZWqoX+ar4pflaSJOmmPCeQxs/IBqmRpOUqyceB/wV2S7I+yYGjjkmSJGkQRnqbC0lajqpq/1HHIEnSOLE1eOWwBVGSJEmSBFhBlCRJkiQ1VhAlSZIkSYAVREmSJElS4yA1kiRJWjQHKZFWBlsQJUmSJEmALYiSJElaArYwaup3YN0hjx/KMlocK4h92hS/nItJ5IP6JzDbeuZzDAa1nmEY59gkSZK0abGCKEmSJGnJbYoNMMuB1yBKkiRJkgBbECVJkiQtU7ZCDp4tiJIkSZIkwAqiJEmSJKkZSRfTJPsAhwKbA4dV1SFT3r8Z8GHgfsAVwNOrat1SxylJ05krh0nSqJiftJz1Mwr+MG6XMt06N+Wuqkvegphkc+DdwGOB3YH9k+w+ZbYDgSur6m7AO4C3LG2UkjS9PnOYJC0585OkQRhFC+IDgAur6mKAJEcA+wLn9syzL/D69vrTwLuSpKpqKQOVpGn0k8MkaRTMT9KAzNVSObWFcSW1Qo7iGsQdgB/3TK9vZdPOU1XXA78Ebr8k0UnS7PrJYZI0CuYnSYuWpW6US7IfsE9VPb9NPwt4YFW9sGees9s869v0RW2en02zvoOAg9rkPYGzh7wLw3YH4Cb7ucy4D+NjJezHblW1zaiDmNRPDmvl5qbxsxL2AVbGfqyEfRir3AR9n2OttNwEK+P75D6Mh5WwD7DI/DSKLqaXADv1TO/YyqabZ32SLYDb0A1WcxNVtQZYA5BkbVVNDDziJeQ+jIeVsA+wMvYjydpRxzBFPznM3DSGVsI+wMrYj5WyD6OOYRpz5qeVlptgZeyH+zAeVsI+wOLz0yi6mJ4M7JpklyRbAc8Ajp4yz9HAc9rr/YCve/2hpDHRTw6TpFEwP0latCVvQayq65O8EDiObgjmD1TVOUneCKytqqOB9wP/neRC4Od0CU6SRm6mHDbisCTJ/CRpIEZyH8SqOgY4ZkrZP/e8/h3w1AWses0iQxsH7sN4WAn7ACtjP8ZuH6bLYXMYu31YAPdhfKyE/XAfhmSe+Wks92EBVsJ+uA/jYSXsAyxyP5Z8kBpJkiRJ0ngaxTWIkiRJkqQxtCIqiEn2SXJBkguTHDzqePqRZKckxyc5N8k5SV7Sym+X5CtJvt+ebzvqWOeSZPMkpyX5QpveJclJ7Xh8ol0oP9aSbJvk00nOT3Jekgcvt2OR5O/bd+nsJB9PcvNxPxZJPpDk8nZrm8myaT/3dP6j7cuZSfYcXeT9WY65CcxP48TcNDrmp/Fjbhov5qfRWIrctOwriEk2B94NPBbYHdg/ye6jjaov1wMvr6rdgQcBf9fiPhj4WlXtCnytTY+7lwDn9Uy/BXhHVd0NuBI4cCRRzc+hwLFVdXfgPnT7s2yORZIdgBcDE1V1T7rBCZ7B+B+Lw4F9ppTN9Lk/Fti1PQ4C3rNEMS7IMs5NYH4aJ+am0Tkc89O4MTeNF/PTaBzOsHNTVS3rB/Bg4Lie6VcBrxp1XAvYj88BjwIuALZvZdsDF4w6tjni3rF9ER8JfAEI3Q1Gt5ju+Izjg+4+mz+gXZPbU75sjgWwA/Bj4HZ0g099AXjMcjgWwGrg7Lk+d+B9wP7TzTeOj5WSm1rs5qfRxG9uGn385qcxfpibRroP5qfRxj7U3LTsWxDZeHAnrW9ly0aS1cB9gZOA7arq0vbWT4HtRhRWv94J/CNwQ5u+PfCLqrq+TS+H47ELsAH4YOvucViSrVlGx6KqLgH+HfgRcCnwS+AUlt+xgJk/9+X2t77c4p2W+WmkzE3jx/w0JsxNI2d+Gi8DzU0roYK4rCW5FfAZ4KVV9ave96qr6o/tMLNJngBcXlWnjDqWRdoC2BN4T1XdF7iaKV0ilsGxuC2wL13CvjOwNTftfrDsjPvnvtKZn0bO3DTGxv2zX8nMTWPB/DSmBvG5r4QK4iXATj3TO7aysZdkS7oE99GqOrIVX5Zk+/b+9sDlo4qvDw8BnphkHXAEXVeJQ4Ftk0zeY3M5HI/1wPqqOqlNf5ou6S2nY/GnwA+qakNVXQccSXd8ltuxgJk/9+X2t77c4r0R89NYMDeNH/PTiJmbxob5abwMNDethAriycCubcShreguLj16xDHNKUmA9wPnVdXbe946GnhOe/0cuv71Y6mqXlVVO1bVarrP/etV9UzgeGC/NttY7wNAVf0U+HGS3VrR3sC5LKNjQdc94kFJbtm+W5P7sKyORTPT53408Ow2IteDgF/2dKcYR8syN4H5aVyYm8aS+WmEzE3jw/w0dgabm0Z9keUgHsDjgO8BFwGvGXU8fcb8ULrm3zOB09vjcXT90L8GfB/4KnC7Ucfa5/7sBXyhvf4j4LvAhcCngJuNOr4+4t8DWNuOx2eB2y63YwG8ATgfOBv4b+Bm434sgI/T9fu/ju7XyANn+tzpLuJ/d/s7P4tu1LGR78Mc+7fsclOL2/w0Jg9z00jjNj+N2cPcNF4P89PIYh56bkpbWJIkSZK0iVsJXUwlSZIkSQNgBVGSJEmSBFhBlCRJkiQ1VhAlSZIkSYAVREmSJElSYwVRYyHJk5JUkrvPMd8xSbZdqrgkLT9T80mSVUlOSnJakoeNOr6pknx71DFIGq0kd0pyRJKLkpzSznf+eESxHJZk91FsW+PB21xoLCT5BHBnuhvGvm7U8UhavqbmkyTPAP60qp4/zbybV9XvlzzIbttbVNX1o9i2pPHRbtL+beBDVfXeVnYf4NZV9c2RBqdNki2IGrkkt6K7+e2BwDNa2fZJTkxyepKzJ3/1T7IuyR3a68+2X9nOSXJQz/p+neT/JjkjyXeSbDeC3ZI0AlPzSZI9gH8D9m355BYtR7wtyRnAg5O8rOWZs5O8tGdds+WYt7byryZ5QJITklyc5Iltns3bPCcnOTPJX7fyvZJ8M8nRwLmT6+tZ9yuTnNXy1yGt7K/aes5I8pkkt2zlhyf5jyTfbtveb8gfr6TheARw3WTlEKCqzgBOS/K1JKe2vLAvQJLVSc6enDfJK5K8vr2+W8tLZ7Tl7prkVjOsZ+skX2zznp3k6a38hCQT7fV7kqxt+e4NPdtcl+QNPeuctQeYlhcriBoH+wLHVtX3gCuS3A/4C+C4qtoDuA9w+jTLPa+q7gdMAC9OcvtWvjXwnaq6D3Ai8FdD3wNJ4+JG+QTYHPhn4BNVtUdV/ZYuR5zUcsRvgecCDwQeBPxVkvu2dc2WY75eVfcArgLeDDwKeDLwxjbPgcAvq+r+wP3bendp7+0JvKSqbtR9LMljW/wPbLH9W3vryKq6fys7r6170vZ0FeInAIcs7COTNGL3BE6Zpvx3wJOrak+6SuTbWmvjbD4KvLvliz8BLp1lPfsAP6mq+1TVPYFjp1nfa6pqArg38H+S3LvnvZ+1db4HeEW/O6vxZwVR42B/4Ij2+og2fTLw3PaL2L2q6qpplntxawH4DrATsGsrvxb4Qnt9CrB6OGFLGkPT5ZOpfg98pr1+KHBUVV1dVb8GjgQmr1OcLcdMnkidBXyjqq5rr1e38kcDz05yOnAScPue5b9bVT+YJq4/BT5YVb8BqKqft/J7tlbHs4BnAvfoWeazVXVDVZ0L2FtCWlkC/EuSM4GvAjswy995km2AHarqKICq+l3LJzOt5yzgUUnekuRhVfXLaVb7tCSnAqfR5Z7eaxOPbM+ea60wW4w6AG3aktwOeCRwryRF92t/Af8APBx4PHB4krdX1Yd7ltuL7mTqwVX1myQnADdvb19XGy+u/T1+z6VNwiz55Jwps/5urusO55FjbgCuAaiqG5JM5psAL6qq46ZZ79Xz3LXDgSdV1RlJDgD26nnvmt7Vz3O9ksbDOcB0XcSfCawC7ldV1yVZR5eHrufGjTw3n2bZOddTVd9LsifwOODNSb5WVZO9IGi9Hl4B3L+qrkxy+JRtTeYfz7VWGFsQNWr7Af9dVXepqtVVtRPwA7rK4WVV9V/AYXRdsnrdBriynbjdna5rmKRN20z5ZKdZlvkm8KQkt0yyNV030W+y+BxzHPA3SbYESPLHbf2z+Qpdz4nJawxv18q3AS5t63rmPOOQNP6+DtxsyrXO9wbuAlzeKnWPaNMAlwF3THL7JDej62JO6221PsmT2jpu1vLJbaZbT5I7A7+pqo8Ab+Wm51q3pvtB65fpxnN47DB2XuPH2r5GbX/gLVPKPkP3i/nVSa4Dfg08e8o8xwIvSHIecAFdFzBJm7aZ8slbgPdPt0BVndp+Ff9uKzqsqk5Lci6LyzGH0XW5OrVd67MBeNJsC1TVsekG1Vmb5FrgGODVwD/RdVPd0J63mWcsksZYVVWSJwPvTPJKumsG1wGvB/6jdS9fC5zf5r8uyRvp8tYlk+XNs4D3tfevA55Kd13i56euB7gX8NYkN7R5/2ZKXGckOa3N/2Pgfwa86xpT3uZCkiRJkgTYxVSSJEmS1FhBlCRJkiQBVhAlSZIkSY0VREmSJEkSYAVRkiRJktRYQZQkSZIkAVYQJUmSJEmNFURJkiRJEgD/P9TQMC+UAjWYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x216 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IOc3kPMYzTCI"
      },
      "source": [
        "# Preprocessing the data (face images)\n",
        "- Later, we will define our model based on ResNet50 (our backbone). Originally,\n",
        "ResNet50 uses a regularization that changes the range of the input images. Thus,\n",
        "to be aligned with the ResNet50 input, we preprocess our input images using the respective 'preprocess_input' function. Later, if you decide to use another model as backbone (rather than ResNet), you may skip the following preprocessing stage."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42bGw2eCzZ2r"
      },
      "source": [
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "\n",
        "# train\n",
        "for i in range(0,X_train.shape[0]):\n",
        "  x = X_train[i,:,:,:]\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  X_train[i,] = preprocess_input(x)\n",
        "\n",
        "# validation\n",
        "for i in range(0,X_valid.shape[0]):\n",
        "  x = X_valid[i,:,:,:]\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  X_valid[i,] = preprocess_input(x)  \n",
        "\n",
        "# test\n",
        "for i in range(0,X_test.shape[0]):\n",
        "  x = X_test[i,:,:,:]\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  X_test[i,] = preprocess_input(x)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGjZNPq_r9I7"
      },
      "source": [
        "# Downloading the ResNet50 model pre-trained on Faces\n",
        "We are using ResNet50 pre-trained on Faces (source [here](https://github.com/ox-vgg/vgg_face2))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQwDAOner6rD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8de4e6ed-fe06-4f93-82a3-63b32ff50094"
      },
      "source": [
        "# downloading the data\n",
        "!wget https://data.chalearnlap.cvc.uab.cat/Colab_2021/model.zip\n",
        "\n",
        "# decompressing the data\n",
        "with ZipFile('model.zip','r') as zip:\n",
        "   zip.extractall()\n",
        "   print('Model decompressed successfully')\n",
        "\n",
        "# removing the .zip file after extraction  to clean space\n",
        "!rm model.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-01 10:30:19--  https://data.chalearnlap.cvc.uab.cat/Colab_2021/model.zip\n",
            "Resolving data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)... 158.109.8.102\n",
            "Connecting to data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)|158.109.8.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 107893665 (103M) [application/zip]\n",
            "Saving to: ‘model.zip’\n",
            "\n",
            "model.zip           100%[===================>] 102.89M  14.8MB/s    in 21s     \n",
            "\n",
            "2022-03-01 10:30:40 (4.98 MB/s) - ‘model.zip’ saved [107893665/107893665]\n",
            "\n",
            "Model decompressed successfully\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a29243uAmxUS"
      },
      "source": [
        "# Loading the pre-trained model\n",
        "- You can see the data (e.g., we have downloaded) and structure of Colab by clicking on 'Files', on the left side <-- of this interface.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J--yWIXom0uf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d1e9b46-e392-406c-d550-0ce9771fb2c9"
      },
      "source": [
        "import h5py\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# loading the pretrained model\n",
        "model = tf.keras.models.load_model('./model/weights.h5')\n",
        "\n",
        "# print the model summary\n",
        "print(model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "base_input (InputLayer)         [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1/7x7_s2 (Conv2D)           (None, 112, 112, 64) 9408        base_input[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1/7x7_s2/bn (BatchNormaliza (None, 112, 112, 64) 256         conv1/7x7_s2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 112, 112, 64) 0           conv1/7x7_s2/bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 55, 55, 64)   0           activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_reduce (Conv2D)     (None, 55, 55, 64)   4096        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 55, 55, 64)   0           conv2_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 55, 55, 64)   0           conv2_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_proj (Conv2D)       (None, 55, 55, 256)  16384       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_proj/bn (BatchNorma (None, 55, 55, 256)  1024        conv2_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 55, 55, 256)  0           conv2_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv2_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 55, 55, 256)  0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_reduce (Conv2D)     (None, 55, 55, 64)   16384       activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 55, 55, 64)   0           conv2_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 55, 55, 64)   0           conv2_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 55, 55, 256)  0           conv2_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 55, 55, 256)  0           add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_reduce (Conv2D)     (None, 55, 55, 64)   16384       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 55, 55, 64)   0           conv2_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 55, 55, 64)   0           conv2_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 55, 55, 256)  0           conv2_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 55, 55, 256)  0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_reduce (Conv2D)     (None, 28, 28, 128)  32768       activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 28, 28, 128)  0           conv3_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 28, 28, 128)  0           conv3_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_proj (Conv2D)       (None, 28, 28, 512)  131072      activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_proj/bn (BatchNorma (None, 28, 28, 512)  2048        conv3_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 28, 28, 512)  0           conv3_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv3_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 28, 28, 128)  0           conv3_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 28, 28, 128)  0           conv3_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 28, 28, 512)  0           conv3_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 28, 28, 128)  0           conv3_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 28, 28, 128)  0           conv3_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 28, 28, 512)  0           conv3_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_4_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 28, 28, 128)  0           conv3_4_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_4_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 28, 28, 128)  0           conv3_4_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_4_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 28, 28, 512)  0           conv3_4_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_reduce (Conv2D)     (None, 14, 14, 256)  131072      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 14, 14, 256)  0           conv4_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 14, 14, 256)  0           conv4_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_proj (Conv2D)       (None, 14, 14, 1024) 524288      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_proj/bn (BatchNorma (None, 14, 14, 1024) 4096        conv4_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 14, 14, 1024) 0           conv4_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv4_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 14, 14, 256)  0           conv4_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 14, 14, 256)  0           conv4_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 14, 14, 1024) 0           conv4_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 14, 14, 256)  0           conv4_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 14, 14, 256)  0           conv4_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 14, 14, 1024) 0           conv4_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_4_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 14, 14, 256)  0           conv4_4_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_4_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 14, 14, 256)  0           conv4_4_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_4_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 14, 14, 1024) 0           conv4_4_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_5_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 14, 14, 256)  0           conv4_5_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_5_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 14, 14, 256)  0           conv4_5_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_5_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, 14, 14, 1024) 0           conv4_5_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_6_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 14, 14, 256)  0           conv4_6_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_6_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 14, 14, 256)  0           conv4_6_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_6_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, 14, 14, 1024) 0           conv4_6_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_reduce (Conv2D)     (None, 7, 7, 512)    524288      activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 7, 7, 512)    0           conv5_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 7, 7, 512)    0           conv5_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_proj (Conv2D)       (None, 7, 7, 2048)   2097152     activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_proj/bn (BatchNorma (None, 7, 7, 2048)   8192        conv5_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, 7, 7, 2048)   0           conv5_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv5_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_reduce (Conv2D)     (None, 7, 7, 512)    1048576     activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 7, 7, 512)    0           conv5_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 7, 7, 512)    0           conv5_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, 7, 7, 2048)   0           conv5_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_reduce (Conv2D)     (None, 7, 7, 512)    1048576     activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 7, 7, 512)    0           conv5_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 7, 7, 512)    0           conv5_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, 7, 7, 2048)   0           conv5_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_49[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 2048)         0           avg_pool[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dim_proj (Dense)                (None, 512)          1049088     flatten_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "classifier_low_dim (Dense)      (None, 8631)         4419072     dim_proj[0][0]                   \n",
            "==================================================================================================\n",
            "Total params: 29,029,312\n",
            "Trainable params: 28,976,192\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LrDpADCzzfjU"
      },
      "source": [
        "# Adapting the model to our needs\n",
        "- In summary, we will ignore the last layer 'classifier_low_dim' and will include a few other layers on top of our backbone. Here, we also define the activation function we are going to use as output of the last FC layer (Sigmoid, in the case)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ABLExjBziRn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12179d4f-1b95-4ced-9017-a770b54cbdb8"
      },
      "source": [
        "# Using the FC layer before the 'classifier_low_dim' layer as feature vector\n",
        "fc_512 = model.get_layer('dim_proj').output\n",
        "\n",
        "# adding a dropout layer to minimize overfiting problems\n",
        "dp_layer = Dropout(0.5)(fc_512)\n",
        "\n",
        "# adding a few hidden FC layers to learn hidden representations\n",
        "fc_128 = Dense(128, activation='relu', name='f_128')(fc_512)\n",
        "fc_32 = Dense(32, activation='relu', name='f_32')(fc_128)\n",
        "\n",
        "# Includint an additional FC layer with sigmoid activation, used to regress\n",
        "# the apparent age\n",
        "output = Dense(1, activation='sigmoid', name='predict')(fc_32)\n",
        "\n",
        "# building and pringing the final model\n",
        "model = Model(inputs=model.get_layer('base_input').output,outputs=output)\n",
        "print(model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "base_input (InputLayer)         [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1/7x7_s2 (Conv2D)           (None, 112, 112, 64) 9408        base_input[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1/7x7_s2/bn (BatchNormaliza (None, 112, 112, 64) 256         conv1/7x7_s2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 112, 112, 64) 0           conv1/7x7_s2/bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 55, 55, 64)   0           activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_reduce (Conv2D)     (None, 55, 55, 64)   4096        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 55, 55, 64)   0           conv2_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 55, 55, 64)   0           conv2_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_proj (Conv2D)       (None, 55, 55, 256)  16384       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_proj/bn (BatchNorma (None, 55, 55, 256)  1024        conv2_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 55, 55, 256)  0           conv2_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv2_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 55, 55, 256)  0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_reduce (Conv2D)     (None, 55, 55, 64)   16384       activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 55, 55, 64)   0           conv2_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 55, 55, 64)   0           conv2_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 55, 55, 256)  0           conv2_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 55, 55, 256)  0           add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_reduce (Conv2D)     (None, 55, 55, 64)   16384       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 55, 55, 64)   0           conv2_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 55, 55, 64)   0           conv2_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 55, 55, 256)  0           conv2_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 55, 55, 256)  0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_reduce (Conv2D)     (None, 28, 28, 128)  32768       activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 28, 28, 128)  0           conv3_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 28, 28, 128)  0           conv3_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_proj (Conv2D)       (None, 28, 28, 512)  131072      activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_proj/bn (BatchNorma (None, 28, 28, 512)  2048        conv3_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 28, 28, 512)  0           conv3_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv3_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 28, 28, 128)  0           conv3_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 28, 28, 128)  0           conv3_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 28, 28, 512)  0           conv3_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 28, 28, 128)  0           conv3_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 28, 28, 128)  0           conv3_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 28, 28, 512)  0           conv3_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_4_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 28, 28, 128)  0           conv3_4_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_4_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 28, 28, 128)  0           conv3_4_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_4_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 28, 28, 512)  0           conv3_4_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_reduce (Conv2D)     (None, 14, 14, 256)  131072      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 14, 14, 256)  0           conv4_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 14, 14, 256)  0           conv4_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_proj (Conv2D)       (None, 14, 14, 1024) 524288      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_proj/bn (BatchNorma (None, 14, 14, 1024) 4096        conv4_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 14, 14, 1024) 0           conv4_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv4_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 14, 14, 256)  0           conv4_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 14, 14, 256)  0           conv4_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 14, 14, 1024) 0           conv4_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 14, 14, 256)  0           conv4_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 14, 14, 256)  0           conv4_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 14, 14, 1024) 0           conv4_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_4_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 14, 14, 256)  0           conv4_4_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_4_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 14, 14, 256)  0           conv4_4_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_4_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 14, 14, 1024) 0           conv4_4_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_5_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 14, 14, 256)  0           conv4_5_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_5_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 14, 14, 256)  0           conv4_5_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_5_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, 14, 14, 1024) 0           conv4_5_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_6_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 14, 14, 256)  0           conv4_6_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_6_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 14, 14, 256)  0           conv4_6_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_6_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, 14, 14, 1024) 0           conv4_6_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_reduce (Conv2D)     (None, 7, 7, 512)    524288      activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 7, 7, 512)    0           conv5_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 7, 7, 512)    0           conv5_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_proj (Conv2D)       (None, 7, 7, 2048)   2097152     activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_proj/bn (BatchNorma (None, 7, 7, 2048)   8192        conv5_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, 7, 7, 2048)   0           conv5_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv5_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_reduce (Conv2D)     (None, 7, 7, 512)    1048576     activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 7, 7, 512)    0           conv5_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 7, 7, 512)    0           conv5_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, 7, 7, 2048)   0           conv5_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_reduce (Conv2D)     (None, 7, 7, 512)    1048576     activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 7, 7, 512)    0           conv5_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 7, 7, 512)    0           conv5_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, 7, 7, 2048)   0           conv5_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_49[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 2048)         0           avg_pool[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dim_proj (Dense)                (None, 512)          1049088     flatten_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "f_128 (Dense)                   (None, 128)          65664       dim_proj[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "f_32 (Dense)                    (None, 32)           4128        f_128[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "predict (Dense)                 (None, 1)            33          f_32[0][0]                       \n",
            "==================================================================================================\n",
            "Total params: 24,680,065\n",
            "Trainable params: 24,626,945\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dq_-Zh3Bt5Gx"
      },
      "source": [
        "# Freezing the first layers to allow the fine-tuning of the last FC layers (only)\n",
        "- Next, we set some layer to be trainable or not, and print if layers are set to trainable = True or False.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pfL6NhouTe-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12bd4973-1380-409f-a4b0-e321f073ac5a"
      },
      "source": [
        "counter = 0\n",
        "for layer in model.layers:\n",
        "  if counter <= 174: \n",
        "    layer.trainable = False\n",
        "  else:\n",
        "    layer.trainable = True\n",
        "  print(counter, layer.name, layer.trainable)\n",
        "  counter +=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 base_input False\n",
            "1 conv1/7x7_s2 False\n",
            "2 conv1/7x7_s2/bn False\n",
            "3 activation_1 False\n",
            "4 max_pooling2d_1 False\n",
            "5 conv2_1_1x1_reduce False\n",
            "6 conv2_1_1x1_reduce/bn False\n",
            "7 activation_2 False\n",
            "8 conv2_1_3x3 False\n",
            "9 conv2_1_3x3/bn False\n",
            "10 activation_3 False\n",
            "11 conv2_1_1x1_increase False\n",
            "12 conv2_1_1x1_proj False\n",
            "13 conv2_1_1x1_increase/bn False\n",
            "14 conv2_1_1x1_proj/bn False\n",
            "15 add_1 False\n",
            "16 activation_4 False\n",
            "17 conv2_2_1x1_reduce False\n",
            "18 conv2_2_1x1_reduce/bn False\n",
            "19 activation_5 False\n",
            "20 conv2_2_3x3 False\n",
            "21 conv2_2_3x3/bn False\n",
            "22 activation_6 False\n",
            "23 conv2_2_1x1_increase False\n",
            "24 conv2_2_1x1_increase/bn False\n",
            "25 add_2 False\n",
            "26 activation_7 False\n",
            "27 conv2_3_1x1_reduce False\n",
            "28 conv2_3_1x1_reduce/bn False\n",
            "29 activation_8 False\n",
            "30 conv2_3_3x3 False\n",
            "31 conv2_3_3x3/bn False\n",
            "32 activation_9 False\n",
            "33 conv2_3_1x1_increase False\n",
            "34 conv2_3_1x1_increase/bn False\n",
            "35 add_3 False\n",
            "36 activation_10 False\n",
            "37 conv3_1_1x1_reduce False\n",
            "38 conv3_1_1x1_reduce/bn False\n",
            "39 activation_11 False\n",
            "40 conv3_1_3x3 False\n",
            "41 conv3_1_3x3/bn False\n",
            "42 activation_12 False\n",
            "43 conv3_1_1x1_increase False\n",
            "44 conv3_1_1x1_proj False\n",
            "45 conv3_1_1x1_increase/bn False\n",
            "46 conv3_1_1x1_proj/bn False\n",
            "47 add_4 False\n",
            "48 activation_13 False\n",
            "49 conv3_2_1x1_reduce False\n",
            "50 conv3_2_1x1_reduce/bn False\n",
            "51 activation_14 False\n",
            "52 conv3_2_3x3 False\n",
            "53 conv3_2_3x3/bn False\n",
            "54 activation_15 False\n",
            "55 conv3_2_1x1_increase False\n",
            "56 conv3_2_1x1_increase/bn False\n",
            "57 add_5 False\n",
            "58 activation_16 False\n",
            "59 conv3_3_1x1_reduce False\n",
            "60 conv3_3_1x1_reduce/bn False\n",
            "61 activation_17 False\n",
            "62 conv3_3_3x3 False\n",
            "63 conv3_3_3x3/bn False\n",
            "64 activation_18 False\n",
            "65 conv3_3_1x1_increase False\n",
            "66 conv3_3_1x1_increase/bn False\n",
            "67 add_6 False\n",
            "68 activation_19 False\n",
            "69 conv3_4_1x1_reduce False\n",
            "70 conv3_4_1x1_reduce/bn False\n",
            "71 activation_20 False\n",
            "72 conv3_4_3x3 False\n",
            "73 conv3_4_3x3/bn False\n",
            "74 activation_21 False\n",
            "75 conv3_4_1x1_increase False\n",
            "76 conv3_4_1x1_increase/bn False\n",
            "77 add_7 False\n",
            "78 activation_22 False\n",
            "79 conv4_1_1x1_reduce False\n",
            "80 conv4_1_1x1_reduce/bn False\n",
            "81 activation_23 False\n",
            "82 conv4_1_3x3 False\n",
            "83 conv4_1_3x3/bn False\n",
            "84 activation_24 False\n",
            "85 conv4_1_1x1_increase False\n",
            "86 conv4_1_1x1_proj False\n",
            "87 conv4_1_1x1_increase/bn False\n",
            "88 conv4_1_1x1_proj/bn False\n",
            "89 add_8 False\n",
            "90 activation_25 False\n",
            "91 conv4_2_1x1_reduce False\n",
            "92 conv4_2_1x1_reduce/bn False\n",
            "93 activation_26 False\n",
            "94 conv4_2_3x3 False\n",
            "95 conv4_2_3x3/bn False\n",
            "96 activation_27 False\n",
            "97 conv4_2_1x1_increase False\n",
            "98 conv4_2_1x1_increase/bn False\n",
            "99 add_9 False\n",
            "100 activation_28 False\n",
            "101 conv4_3_1x1_reduce False\n",
            "102 conv4_3_1x1_reduce/bn False\n",
            "103 activation_29 False\n",
            "104 conv4_3_3x3 False\n",
            "105 conv4_3_3x3/bn False\n",
            "106 activation_30 False\n",
            "107 conv4_3_1x1_increase False\n",
            "108 conv4_3_1x1_increase/bn False\n",
            "109 add_10 False\n",
            "110 activation_31 False\n",
            "111 conv4_4_1x1_reduce False\n",
            "112 conv4_4_1x1_reduce/bn False\n",
            "113 activation_32 False\n",
            "114 conv4_4_3x3 False\n",
            "115 conv4_4_3x3/bn False\n",
            "116 activation_33 False\n",
            "117 conv4_4_1x1_increase False\n",
            "118 conv4_4_1x1_increase/bn False\n",
            "119 add_11 False\n",
            "120 activation_34 False\n",
            "121 conv4_5_1x1_reduce False\n",
            "122 conv4_5_1x1_reduce/bn False\n",
            "123 activation_35 False\n",
            "124 conv4_5_3x3 False\n",
            "125 conv4_5_3x3/bn False\n",
            "126 activation_36 False\n",
            "127 conv4_5_1x1_increase False\n",
            "128 conv4_5_1x1_increase/bn False\n",
            "129 add_12 False\n",
            "130 activation_37 False\n",
            "131 conv4_6_1x1_reduce False\n",
            "132 conv4_6_1x1_reduce/bn False\n",
            "133 activation_38 False\n",
            "134 conv4_6_3x3 False\n",
            "135 conv4_6_3x3/bn False\n",
            "136 activation_39 False\n",
            "137 conv4_6_1x1_increase False\n",
            "138 conv4_6_1x1_increase/bn False\n",
            "139 add_13 False\n",
            "140 activation_40 False\n",
            "141 conv5_1_1x1_reduce False\n",
            "142 conv5_1_1x1_reduce/bn False\n",
            "143 activation_41 False\n",
            "144 conv5_1_3x3 False\n",
            "145 conv5_1_3x3/bn False\n",
            "146 activation_42 False\n",
            "147 conv5_1_1x1_increase False\n",
            "148 conv5_1_1x1_proj False\n",
            "149 conv5_1_1x1_increase/bn False\n",
            "150 conv5_1_1x1_proj/bn False\n",
            "151 add_14 False\n",
            "152 activation_43 False\n",
            "153 conv5_2_1x1_reduce False\n",
            "154 conv5_2_1x1_reduce/bn False\n",
            "155 activation_44 False\n",
            "156 conv5_2_3x3 False\n",
            "157 conv5_2_3x3/bn False\n",
            "158 activation_45 False\n",
            "159 conv5_2_1x1_increase False\n",
            "160 conv5_2_1x1_increase/bn False\n",
            "161 add_15 False\n",
            "162 activation_46 False\n",
            "163 conv5_3_1x1_reduce False\n",
            "164 conv5_3_1x1_reduce/bn False\n",
            "165 activation_47 False\n",
            "166 conv5_3_3x3 False\n",
            "167 conv5_3_3x3/bn False\n",
            "168 activation_48 False\n",
            "169 conv5_3_1x1_increase False\n",
            "170 conv5_3_1x1_increase/bn False\n",
            "171 add_16 False\n",
            "172 activation_49 False\n",
            "173 avg_pool False\n",
            "174 flatten_1 False\n",
            "175 dim_proj True\n",
            "176 f_128 True\n",
            "177 f_32 True\n",
            "178 predict True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HM5L7P0EvVYn"
      },
      "source": [
        "# Printing the MODEL (summary) we have just defined"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UqipjTHzM6np",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4190fa45-9018-41f8-ec60-3545ef1c0753"
      },
      "source": [
        "print(model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "base_input (InputLayer)         [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1/7x7_s2 (Conv2D)           (None, 112, 112, 64) 9408        base_input[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1/7x7_s2/bn (BatchNormaliza (None, 112, 112, 64) 256         conv1/7x7_s2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 112, 112, 64) 0           conv1/7x7_s2/bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 55, 55, 64)   0           activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_reduce (Conv2D)     (None, 55, 55, 64)   4096        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 55, 55, 64)   0           conv2_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 55, 55, 64)   0           conv2_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_proj (Conv2D)       (None, 55, 55, 256)  16384       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_proj/bn (BatchNorma (None, 55, 55, 256)  1024        conv2_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 55, 55, 256)  0           conv2_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv2_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 55, 55, 256)  0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_reduce (Conv2D)     (None, 55, 55, 64)   16384       activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 55, 55, 64)   0           conv2_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 55, 55, 64)   0           conv2_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 55, 55, 256)  0           conv2_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 55, 55, 256)  0           add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_reduce (Conv2D)     (None, 55, 55, 64)   16384       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 55, 55, 64)   0           conv2_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 55, 55, 64)   0           conv2_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 55, 55, 256)  0           conv2_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 55, 55, 256)  0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_reduce (Conv2D)     (None, 28, 28, 128)  32768       activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 28, 28, 128)  0           conv3_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 28, 28, 128)  0           conv3_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_proj (Conv2D)       (None, 28, 28, 512)  131072      activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_proj/bn (BatchNorma (None, 28, 28, 512)  2048        conv3_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 28, 28, 512)  0           conv3_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv3_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 28, 28, 128)  0           conv3_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 28, 28, 128)  0           conv3_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 28, 28, 512)  0           conv3_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 28, 28, 128)  0           conv3_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 28, 28, 128)  0           conv3_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 28, 28, 512)  0           conv3_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_4_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 28, 28, 128)  0           conv3_4_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_4_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 28, 28, 128)  0           conv3_4_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_4_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 28, 28, 512)  0           conv3_4_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_reduce (Conv2D)     (None, 14, 14, 256)  131072      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 14, 14, 256)  0           conv4_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 14, 14, 256)  0           conv4_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_proj (Conv2D)       (None, 14, 14, 1024) 524288      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_proj/bn (BatchNorma (None, 14, 14, 1024) 4096        conv4_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 14, 14, 1024) 0           conv4_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv4_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 14, 14, 256)  0           conv4_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 14, 14, 256)  0           conv4_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 14, 14, 1024) 0           conv4_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 14, 14, 256)  0           conv4_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 14, 14, 256)  0           conv4_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 14, 14, 1024) 0           conv4_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_4_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 14, 14, 256)  0           conv4_4_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_4_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 14, 14, 256)  0           conv4_4_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_4_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 14, 14, 1024) 0           conv4_4_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_5_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 14, 14, 256)  0           conv4_5_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_5_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 14, 14, 256)  0           conv4_5_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_5_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, 14, 14, 1024) 0           conv4_5_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_6_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 14, 14, 256)  0           conv4_6_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_6_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 14, 14, 256)  0           conv4_6_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_6_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, 14, 14, 1024) 0           conv4_6_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_reduce (Conv2D)     (None, 7, 7, 512)    524288      activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 7, 7, 512)    0           conv5_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 7, 7, 512)    0           conv5_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_proj (Conv2D)       (None, 7, 7, 2048)   2097152     activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_proj/bn (BatchNorma (None, 7, 7, 2048)   8192        conv5_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, 7, 7, 2048)   0           conv5_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv5_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_reduce (Conv2D)     (None, 7, 7, 512)    1048576     activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 7, 7, 512)    0           conv5_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 7, 7, 512)    0           conv5_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, 7, 7, 2048)   0           conv5_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_reduce (Conv2D)     (None, 7, 7, 512)    1048576     activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 7, 7, 512)    0           conv5_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 7, 7, 512)    0           conv5_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, 7, 7, 2048)   0           conv5_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_49[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 2048)         0           avg_pool[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dim_proj (Dense)                (None, 512)          1049088     flatten_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "f_128 (Dense)                   (None, 128)          65664       dim_proj[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "f_32 (Dense)                    (None, 32)           4128        f_128[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "predict (Dense)                 (None, 1)            33          f_32[0][0]                       \n",
            "==================================================================================================\n",
            "Total params: 24,680,065\n",
            "Trainable params: 1,118,913\n",
            "Non-trainable params: 23,561,152\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwG95hLURSGj"
      },
      "source": [
        "# IMPORTANT: Mounting your google drive to save your results\n",
        "- Colab gives you LIMITED GPU access. Thus, it may kill your process (of training) if you pass a limited amount of training hours. To allow you to save your model while training, you can mount your google drive, as detailed next. This way, if the process is killed, you can (in a new session) load your checkpoints (trained model, from your google drive) and, for example, continue training or make predictions with the model you obtained (even if trained for a few epochs).\n",
        "- In the following examples, the **the beset model (based on validation loss) is saved in my google drive inside a \"/temp/\" directory. You will need to addapt this path to your case.** \n",
        "- To save time, and to allow you to quickly 'play' and run the notebook, we have pre-trained some models, which are loaded (or not) based on some boolean variables (later, you will need to change/adapt these codes to achive the goals of this course)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7sByyqKRae5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6e70781-2e4b-46a4-f092-45af1a53ed56"
      },
      "source": [
        "#--------------------------\n",
        "MOUNT_GOOGLE_DRIVE = True\n",
        "#--------------------------\n",
        "\n",
        "if(MOUNT_GOOGLE_DRIVE==True):\n",
        "  from google.colab import drive\n",
        "  drive.mount('/content/gdrive')\n",
        "  # Note, the default path will be: '/content/gdrive/MyDrive/'\n",
        "  # In my case, the final path will be: '/content/gdrive/MyDrive/temp/' as I\n",
        "  # created a '/temp/' folder in my google drive for this purpose."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n65k0Gc4vXRS"
      },
      "source": [
        "# Training the Model / or downloading a model already trained\n",
        "- As default, the code below will load a pre-trained model, obtained using the same code if LOAD_BEST_MODEL_ST1 is set to False.\n",
        "- Later, you can set LOAD_BEST_MODEL_ST1 to False to perfom the training.\n",
        "  - The code below uses Early stopping (es) with patience = 5 (that is, the training will stop if no improvement on valid_loss is observed on the last 5 epochs).\n",
        "  - It uses the Mean Squared Error (MSE) as loss function ('loss=tf.keras.losses.MeanSquaredError()'). The code also evaluates the Mean Absolute Error (MAE) during training ('metrics=['mae']'). Learning rate is set to 'learning_rate=1e-5', batch size = 32, and the model will be trained for 50 epochs (if Colab allows it based to the time budget)\n",
        "  - The model callback (mc) is set to save the best model based on valid_loss (that is, if validation loss decreases from one epoch to another, a new model is saved on the path you specify).\n",
        "  - Other hyperparameters you can play with are: defining another optimizer, loss function, learning rate, batch size, num of epochs.\n",
        "\n",
        "- Note: in case you want to save your model, stop training, and resume training, check the end of this file **\"II) illustrating how to train + save + stop training + RESUME TRAINING\"** where we provide a more detailed example about this procedure. Recommendation: first train your model for a few epochs to avoid the need of resume training. This way, you will get used with the code and the general pipeline. Later, you can play with that."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71Oh2HLcz2dg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88397546-09fc-44e8-cf6c-6a5eb137602c"
      },
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "# load a model and train history (defined and trained\n",
        "# as below, trained for 38 epochs)\n",
        "#--------------------------\n",
        "LOAD_BEST_MODEL_ST1 = True # (training only the last FC layers)\n",
        "#--------------------------\n",
        "\n",
        "\n",
        "if(LOAD_BEST_MODEL_ST1==True):\n",
        "  # downloading the trained model\n",
        "  !wget https://data.chalearnlap.cvc.uab.cat/Colab_2021/best_model_st1.zip\n",
        "  # decompressing the data\n",
        "  with ZipFile('best_model_st1.zip','r') as zip:\n",
        "    zip.extractall()\n",
        "    print('Model decompressed successfully')\n",
        "  # removing the .zip file after extraction  to clean space\n",
        "  !rm best_model_st1.zip\n",
        "  \n",
        "else:\n",
        "  # defining the early stop criteria\n",
        "  es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)\n",
        "  # saving the best model based on val_loss\n",
        "  mc = ModelCheckpoint('/content/gdrive/MyDrive/temp/best_model.h5', monitor='val_loss', mode='min', save_best_only=True)\n",
        "\n",
        "  # defining the optimizer\n",
        "  model.compile(tf.keras.optimizers.Adam(learning_rate=1e-5),loss=tf.keras.losses.MeanSquaredError(),metrics=['mae'])\n",
        "\n",
        "  # training the model\n",
        "  history = model.fit(X_train, Y_train, validation_data=(X_valid, Y_valid), batch_size=32, epochs=50, shuffle=True, verbose=1, callbacks=[es,mc])\n",
        "\n",
        "  # saving training history (for future visualization)\n",
        "  with open('/content/gdrive/MyDrive/temp/train_history.pkl', 'wb') as handle:\n",
        "    pickle.dump(history.history, handle, protocol=pickle.HIGHEST_PROTOCOL)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-01 10:32:25--  https://data.chalearnlap.cvc.uab.cat/Colab_2021/best_model_st1.zip\n",
            "Resolving data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)... 158.109.8.102\n",
            "Connecting to data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)|158.109.8.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 100009889 (95M) [application/zip]\n",
            "Saving to: ‘best_model_st1.zip’\n",
            "\n",
            "best_model_st1.zip  100%[===================>]  95.38M  5.61MB/s    in 28s     \n",
            "\n",
            "2022-03-01 10:32:54 (3.46 MB/s) - ‘best_model_st1.zip’ saved [100009889/100009889]\n",
            "\n",
            "Model decompressed successfully\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8U1hPksD9BYt"
      },
      "source": [
        "# Visualizing the train history\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xRKbcPYNOFRx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "4d0655e6-b0c2-4ece-ba5d-05dcd398bb56"
      },
      "source": [
        "import pickle\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "# here, it loads the history of the model we have already trained, or loads the \n",
        "# history of the model you defined and trained\n",
        "if(LOAD_BEST_MODEL_ST1==True):\n",
        "  train_hist = pickle.load(open(\"train_history.pkl\",\"rb\"))\n",
        "else:\n",
        "  train_hist = pickle.load(open(\"/content/gdrive/MyDrive/temp/train_history.pkl\",\"rb\"))\n",
        "\n",
        "# we plot both, the LOSS and MAE\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 4))\n",
        "fig.suptitle('Training history (stage 1)', fontsize=14, fontweight='bold')\n",
        "\n",
        "ax1.plot(train_hist['loss'])\n",
        "ax1.plot(train_hist['val_loss'])\n",
        "ax1.set(xlabel='epoch', ylabel='LOSS')\n",
        "ax1.legend(['train', 'valid'], loc='upper right')\n",
        "\n",
        "ax2.plot(train_hist['mae'])\n",
        "ax2.plot(train_hist['val_mae'])\n",
        "ax2.set(xlabel='epoch', ylabel='MAE')\n",
        "ax2.legend(['train', 'valid'], loc='upper right')  \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7044e51fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KDGOQjFz4k90"
      },
      "source": [
        "# Loading the saved model and Making predictions on the Test set\n",
        "- Next, we load the trained model and make predictions on the Test set\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4OKbe-3GBMB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7026058-4733-42d5-82ec-043b6530822b"
      },
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "#--------------------------\n",
        "ENABLE_EVALUATION_ST1 = True\n",
        "#--------------------------\n",
        "\n",
        "# loading the saved model\n",
        "if(LOAD_BEST_MODEL_ST1==True):\n",
        "  saved_model = load_model('best_model.h5')\n",
        "else:\n",
        "  saved_model = load_model('/content/gdrive/MyDrive/temp/best_model.h5')\n",
        "\n",
        "if(ENABLE_EVALUATION_ST1==True):\n",
        "  # predict on the test data\n",
        "  predictions_st1 = saved_model.predict(X_test, batch_size=32, verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "62/62 [==============================] - 24s 195ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I3cP34WQI2pc"
      },
      "source": [
        "# Evaluating the model on the Test set, using the Mean Absolute Error (MAE) as metric.\n",
        "- Note, as the train/validation labels were re-scaled to be in the range of [0,1], the predictions will be in the same range [0,1]. \n",
        "- To evaluate the model on the test set (which was not normalized), we re-scale the predictions back using the normalization factor = 100 (previously defined), in order to have the Mean Absolute Error with respect to the original apparent age labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqdlJy5HGppZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45f7460b-87d3-40ed-e653-1259faf6f11d"
      },
      "source": [
        "if(ENABLE_EVALUATION_ST1==True):\n",
        "  # re-scaling the output predictions (from [0,1] to age range) using the\n",
        "  # the normalization factor mentioned before\n",
        "  predictions_st1_f = predictions_st1*100\n",
        "\n",
        "  # evaluating on test data\n",
        "  error = []\n",
        "  for i in range(0,len(Y_test)):\n",
        "    error.append(abs(np.subtract(predictions_st1_f[i][0],Y_test[i])))\n",
        "\n",
        "  print('MAE = %.8f' %(np.mean(error)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE = 16.98603802\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPSOjBkSFpf3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e500344-bdb1-4636-f805-385191609f63"
      },
      "source": [
        "if(ENABLE_EVALUATION_ST1==True):\n",
        "  # printing some predictions\n",
        "  for i in range(0,10):\n",
        "    print('predicted age = %.3f - Ground truth = %.3f' %(predictions_st1_f[i], Y_test[i]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted age = 45.214 - Ground truth = 23.205\n",
            "predicted age = 59.144 - Ground truth = 70.737\n",
            "predicted age = 29.385 - Ground truth = 55.368\n",
            "predicted age = 22.383 - Ground truth = 24.278\n",
            "predicted age = 10.737 - Ground truth = 25.231\n",
            "predicted age = 65.890 - Ground truth = 35.179\n",
            "predicted age = 48.566 - Ground truth = 26.923\n",
            "predicted age = 16.006 - Ground truth = 48.189\n",
            "predicted age = 5.572 - Ground truth = 18.676\n",
            "predicted age = 31.697 - Ground truth = 57.378\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VITEsCYfHi6q"
      },
      "source": [
        "---\n",
        "# Performing a 2nd Stage of training, where ALL Layers are set to \"trainable\"\n",
        "- Up to here, we have just trained the last FC layers of our model. Now, we will load the model we have trained (referred to it as 1st stage), set all layers to TRAINABLE, and train the whole model. Training will take more time, but we expect to get better results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0vM0ZCzH6_L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f45dfed7-53a4-44ca-e213-9ce268f0c46a"
      },
      "source": [
        "# setting all layers of the model to trainable\n",
        "saved_model.trainable = True\n",
        "\n",
        "counter = 0\n",
        "for layer in saved_model.layers:\n",
        "  print(counter, layer.name, layer.trainable)\n",
        "  counter +=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 base_input True\n",
            "1 conv1/7x7_s2 True\n",
            "2 conv1/7x7_s2/bn True\n",
            "3 activation_1 True\n",
            "4 max_pooling2d_1 True\n",
            "5 conv2_1_1x1_reduce True\n",
            "6 conv2_1_1x1_reduce/bn True\n",
            "7 activation_2 True\n",
            "8 conv2_1_3x3 True\n",
            "9 conv2_1_3x3/bn True\n",
            "10 activation_3 True\n",
            "11 conv2_1_1x1_increase True\n",
            "12 conv2_1_1x1_proj True\n",
            "13 conv2_1_1x1_increase/bn True\n",
            "14 conv2_1_1x1_proj/bn True\n",
            "15 add_1 True\n",
            "16 activation_4 True\n",
            "17 conv2_2_1x1_reduce True\n",
            "18 conv2_2_1x1_reduce/bn True\n",
            "19 activation_5 True\n",
            "20 conv2_2_3x3 True\n",
            "21 conv2_2_3x3/bn True\n",
            "22 activation_6 True\n",
            "23 conv2_2_1x1_increase True\n",
            "24 conv2_2_1x1_increase/bn True\n",
            "25 add_2 True\n",
            "26 activation_7 True\n",
            "27 conv2_3_1x1_reduce True\n",
            "28 conv2_3_1x1_reduce/bn True\n",
            "29 activation_8 True\n",
            "30 conv2_3_3x3 True\n",
            "31 conv2_3_3x3/bn True\n",
            "32 activation_9 True\n",
            "33 conv2_3_1x1_increase True\n",
            "34 conv2_3_1x1_increase/bn True\n",
            "35 add_3 True\n",
            "36 activation_10 True\n",
            "37 conv3_1_1x1_reduce True\n",
            "38 conv3_1_1x1_reduce/bn True\n",
            "39 activation_11 True\n",
            "40 conv3_1_3x3 True\n",
            "41 conv3_1_3x3/bn True\n",
            "42 activation_12 True\n",
            "43 conv3_1_1x1_increase True\n",
            "44 conv3_1_1x1_proj True\n",
            "45 conv3_1_1x1_increase/bn True\n",
            "46 conv3_1_1x1_proj/bn True\n",
            "47 add_4 True\n",
            "48 activation_13 True\n",
            "49 conv3_2_1x1_reduce True\n",
            "50 conv3_2_1x1_reduce/bn True\n",
            "51 activation_14 True\n",
            "52 conv3_2_3x3 True\n",
            "53 conv3_2_3x3/bn True\n",
            "54 activation_15 True\n",
            "55 conv3_2_1x1_increase True\n",
            "56 conv3_2_1x1_increase/bn True\n",
            "57 add_5 True\n",
            "58 activation_16 True\n",
            "59 conv3_3_1x1_reduce True\n",
            "60 conv3_3_1x1_reduce/bn True\n",
            "61 activation_17 True\n",
            "62 conv3_3_3x3 True\n",
            "63 conv3_3_3x3/bn True\n",
            "64 activation_18 True\n",
            "65 conv3_3_1x1_increase True\n",
            "66 conv3_3_1x1_increase/bn True\n",
            "67 add_6 True\n",
            "68 activation_19 True\n",
            "69 conv3_4_1x1_reduce True\n",
            "70 conv3_4_1x1_reduce/bn True\n",
            "71 activation_20 True\n",
            "72 conv3_4_3x3 True\n",
            "73 conv3_4_3x3/bn True\n",
            "74 activation_21 True\n",
            "75 conv3_4_1x1_increase True\n",
            "76 conv3_4_1x1_increase/bn True\n",
            "77 add_7 True\n",
            "78 activation_22 True\n",
            "79 conv4_1_1x1_reduce True\n",
            "80 conv4_1_1x1_reduce/bn True\n",
            "81 activation_23 True\n",
            "82 conv4_1_3x3 True\n",
            "83 conv4_1_3x3/bn True\n",
            "84 activation_24 True\n",
            "85 conv4_1_1x1_increase True\n",
            "86 conv4_1_1x1_proj True\n",
            "87 conv4_1_1x1_increase/bn True\n",
            "88 conv4_1_1x1_proj/bn True\n",
            "89 add_8 True\n",
            "90 activation_25 True\n",
            "91 conv4_2_1x1_reduce True\n",
            "92 conv4_2_1x1_reduce/bn True\n",
            "93 activation_26 True\n",
            "94 conv4_2_3x3 True\n",
            "95 conv4_2_3x3/bn True\n",
            "96 activation_27 True\n",
            "97 conv4_2_1x1_increase True\n",
            "98 conv4_2_1x1_increase/bn True\n",
            "99 add_9 True\n",
            "100 activation_28 True\n",
            "101 conv4_3_1x1_reduce True\n",
            "102 conv4_3_1x1_reduce/bn True\n",
            "103 activation_29 True\n",
            "104 conv4_3_3x3 True\n",
            "105 conv4_3_3x3/bn True\n",
            "106 activation_30 True\n",
            "107 conv4_3_1x1_increase True\n",
            "108 conv4_3_1x1_increase/bn True\n",
            "109 add_10 True\n",
            "110 activation_31 True\n",
            "111 conv4_4_1x1_reduce True\n",
            "112 conv4_4_1x1_reduce/bn True\n",
            "113 activation_32 True\n",
            "114 conv4_4_3x3 True\n",
            "115 conv4_4_3x3/bn True\n",
            "116 activation_33 True\n",
            "117 conv4_4_1x1_increase True\n",
            "118 conv4_4_1x1_increase/bn True\n",
            "119 add_11 True\n",
            "120 activation_34 True\n",
            "121 conv4_5_1x1_reduce True\n",
            "122 conv4_5_1x1_reduce/bn True\n",
            "123 activation_35 True\n",
            "124 conv4_5_3x3 True\n",
            "125 conv4_5_3x3/bn True\n",
            "126 activation_36 True\n",
            "127 conv4_5_1x1_increase True\n",
            "128 conv4_5_1x1_increase/bn True\n",
            "129 add_12 True\n",
            "130 activation_37 True\n",
            "131 conv4_6_1x1_reduce True\n",
            "132 conv4_6_1x1_reduce/bn True\n",
            "133 activation_38 True\n",
            "134 conv4_6_3x3 True\n",
            "135 conv4_6_3x3/bn True\n",
            "136 activation_39 True\n",
            "137 conv4_6_1x1_increase True\n",
            "138 conv4_6_1x1_increase/bn True\n",
            "139 add_13 True\n",
            "140 activation_40 True\n",
            "141 conv5_1_1x1_reduce True\n",
            "142 conv5_1_1x1_reduce/bn True\n",
            "143 activation_41 True\n",
            "144 conv5_1_3x3 True\n",
            "145 conv5_1_3x3/bn True\n",
            "146 activation_42 True\n",
            "147 conv5_1_1x1_increase True\n",
            "148 conv5_1_1x1_proj True\n",
            "149 conv5_1_1x1_increase/bn True\n",
            "150 conv5_1_1x1_proj/bn True\n",
            "151 add_14 True\n",
            "152 activation_43 True\n",
            "153 conv5_2_1x1_reduce True\n",
            "154 conv5_2_1x1_reduce/bn True\n",
            "155 activation_44 True\n",
            "156 conv5_2_3x3 True\n",
            "157 conv5_2_3x3/bn True\n",
            "158 activation_45 True\n",
            "159 conv5_2_1x1_increase True\n",
            "160 conv5_2_1x1_increase/bn True\n",
            "161 add_15 True\n",
            "162 activation_46 True\n",
            "163 conv5_3_1x1_reduce True\n",
            "164 conv5_3_1x1_reduce/bn True\n",
            "165 activation_47 True\n",
            "166 conv5_3_3x3 True\n",
            "167 conv5_3_3x3/bn True\n",
            "168 activation_48 True\n",
            "169 conv5_3_1x1_increase True\n",
            "170 conv5_3_1x1_increase/bn True\n",
            "171 add_16 True\n",
            "172 activation_49 True\n",
            "173 avg_pool True\n",
            "174 flatten_1 True\n",
            "175 dim_proj True\n",
            "176 f_128 True\n",
            "177 f_32 True\n",
            "178 predict True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRrI2Vn9P_bw"
      },
      "source": [
        "# Training the WHOLE Model (2nd Stage)\n",
        "\n",
        "- As default, the code below will load a pre-trained model, obtained using the same code if LOAD_BEST_MODEL_ST2 is set to False.\n",
        "- Later, you can set LOAD_BEST_MODEL_ST2 to False to perfom the training.\n",
        "  - As before, the code below uses Early stopping (es) with patience = 5 (that is, the training will stop if no improvement on valid_loss is observed on the last 5 epochs).\n",
        "  - It uses the Mean Squared Error (MSE) as loss function ('loss=tf.keras.losses.MeanSquaredError()'). The code also evaluates the Mean Absolute Error (MAE) during training ('metrics=['mae']'). Learning rate is set to 'learning_rate=1e-5', batch size = 16, and the model will be trained for 12 epochs (if Colab allows it based to the time budget). Note, if you increase the batch size too much, data may not fit the GPU capacity (as the number of parameters to train increased compared to the 1st stage). This is why we reduced it from 32 to 16.\n",
        "  - The model callback (mc) is set to save the best model based on valid_loss (that is, if validation loss decreases from one epoch to another, a new model is saved on the path you specify).\n",
        "  - Other hyperparameters you can play with are: defining another optimizer, loss function, learning rate, batch size, num of epochs.\n",
        "- WARNING: at this stage, training take more time, and colab may close before you finish training due to time constraints. Thus, you will need to define a good strategy! In case you want to save your model, stop training, and resume training, check the end of this file **\"II) illustrating how to train + save + stop training + RESUME TRAINING\"** where we provide a more detailed example about this procedure.\n",
        "- WARNING: if you save your model and resume training, the train history will be lost. To monitore the training history, you may need to save the train history in another way (e.g., you can copy and paste the logs into a text file before resuming the training)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yq9lGU2p1b47",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c110919d-6cd3-4849-a521-1366f70cfe75"
      },
      "source": [
        "# Loding a pretrained model or train\n",
        "#--------------------------\n",
        "LOAD_BEST_MODEL_ST2 = True\n",
        "#--------------------------\n",
        "\n",
        "if(LOAD_BEST_MODEL_ST2==True):\n",
        "  # downloading the trained model\n",
        "  !wget https://data.chalearnlap.cvc.uab.cat/Colab_2021/best_model_st2.zip\n",
        "  # decompressing the data\n",
        "  with ZipFile('best_model_st2.zip','r') as zip:\n",
        "    zip.extractall()\n",
        "    print('Model decompressed successfully')\n",
        "  # removing the .zip file after extraction  to clean space\n",
        "  !rm best_model_st2.zip\n",
        "\n",
        "else:\n",
        "  \n",
        "  # training all layers (2nd stage), given the model saved on stage 1\n",
        "  saved_model.compile(tf.keras.optimizers.Adam(learning_rate=1e-5),loss=tf.keras.losses.MeanSquaredError(),metrics=['mae'])\n",
        "\n",
        "  # defining the early stop criteria\n",
        "  es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)\n",
        "  # saving the best model (2nd stage) based on val_loss with a different name\n",
        "  mc = ModelCheckpoint('/content/gdrive/MyDrive/temp/best_model_2nd_stage.h5', monitor='val_loss', mode='min', save_best_only=True)\n",
        "\n",
        "  history = saved_model.fit(X_train, Y_train, validation_data=(X_valid, Y_valid), batch_size=16, epochs=12, shuffle=True, verbose=1, callbacks=[es,mc])\n",
        "\n",
        "  # saving training history\n",
        "  with open('/content/gdrive/MyDrive/temp/train_history_2nd_stage.pkl', 'wb') as handle:\n",
        "    pickle.dump(history.history, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-01 10:33:24--  https://data.chalearnlap.cvc.uab.cat/Colab_2021/best_model_st2.zip\n",
            "Resolving data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)... 158.109.8.102\n",
            "Connecting to data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)|158.109.8.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 271392330 (259M) [application/zip]\n",
            "Saving to: ‘best_model_st2.zip’\n",
            "\n",
            "best_model_st2.zip  100%[===================>] 258.82M  6.94MB/s    in 41s     \n",
            "\n",
            "2022-03-01 10:34:06 (6.29 MB/s) - ‘best_model_st2.zip’ saved [271392330/271392330]\n",
            "\n",
            "Model decompressed successfully\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n5hRW7Ryl39L"
      },
      "source": [
        "# Visualizing the Training history of both stages (1st stage and 2nd stage)\n",
        "- Next, we show the train history of the models we have already trained for this course.\n",
        "- The curves of both stages are concatenated. You can observe a peak around epoch 42 (dashed line), which indicates the end of the first stage of training and the start of the 2nd stage, and how the 2nd stage improves the performance with respect to validation loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fv-M1AhXuJJz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "0cc6e498-a165-4a2a-91a4-3558e1bbaa32"
      },
      "source": [
        "if(LOAD_BEST_MODEL_ST1==True and LOAD_BEST_MODEL_ST2==True):\n",
        "  train_hist = pickle.load(open(\"train_history.pkl\",\"rb\"))\n",
        "  train_hist_2nd = pickle.load(open(\"train_history_2nd_stage.pkl\",\"rb\"))\n",
        "\n",
        "  fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 4))\n",
        "  fig.suptitle('Training history (Stage 1 and Stage 2)', fontsize=14, fontweight='bold')\n",
        "\n",
        "  ax1.plot(train_hist['loss']+train_hist_2nd['loss'])\n",
        "  ax1.plot(train_hist['val_loss']+train_hist_2nd['val_loss'])\n",
        "  ax1.axvline(42, 0, 1, ls='--', color='r')\n",
        "  ax1.set(xlabel='epoch', ylabel='Loss')\n",
        "  ax1.legend(['train', 'valid'], loc='upper right')\n",
        "\n",
        "  ax2.plot(train_hist['mae']+train_hist_2nd['mae'])\n",
        "  ax2.plot(train_hist['val_mae']+train_hist_2nd['val_mae'])\n",
        "  ax2.axvline(42, 0, 1, ls='--', color='r')\n",
        "  ax2.set(xlabel='epoch', ylabel='MAE')\n",
        "  ax2.legend(['train', 'valid'], loc='upper right')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6wDuxn8mJ8y"
      },
      "source": [
        "# Evaluating the model lerned at the 2nd stage on the test set\n",
        "- Here, you can observe a decrease in Mean Absolute Error (MAE) compared to the predictions obtained with the model trained at the 1st stage (from MAE = 16.98603930 to MAE = 11.14126198)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGyp-rzFmRhA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f553e13-3495-4583-ea20-a4d77bb9db94"
      },
      "source": [
        "#--------------------------\n",
        "ENABLE_EVALUATION_ST2 = True\n",
        "#--------------------------\n",
        "\n",
        "# loading the saved model\n",
        "if(LOAD_BEST_MODEL_ST2 == True):\n",
        "  saved_model_2nd = load_model('best_model_2nd_stage.h5')\n",
        "else:\n",
        "  saved_model_2nd = load_model('/content/gdrive/MyDrive/temp/best_model_2nd_stage.h5')\n",
        "\n",
        "\n",
        "if(ENABLE_EVALUATION_ST2==True):\n",
        "  # predict on the test data\n",
        "  predictions_2nd = saved_model_2nd.predict(X_test, batch_size=32, verbose=1)\n",
        "\n",
        "  # re-scaling the output predictions (from [0,1] to age range) using the\n",
        "  # the normalization factor mentioned before\n",
        "  predictions_2nd_f = predictions_2nd*100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "62/62 [==============================] - 12s 179ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSAb3Fl00Gwu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83e1342e-e1dc-4bbd-f0e0-dea49c77de60"
      },
      "source": [
        "if(ENABLE_EVALUATION_ST2==True):\n",
        "  # evaluating on test data\n",
        "  error = []\n",
        "  for i in range(0,len(Y_test)):\n",
        "    error.append(abs(np.subtract(predictions_2nd_f[i][0],Y_test[i])))\n",
        "\n",
        "  print('MAE = %.8f' %(np.mean(error)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE = 11.14126488\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niWEjb2W0q6P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de69c6cb-ff31-449c-cb85-de78fd7d75a1"
      },
      "source": [
        "if(ENABLE_EVALUATION_ST2==True):\n",
        "  # printing some predictions\n",
        "  for i in range(0,10):\n",
        "    print('predicted age = %.3f - Ground truth = %.3f' %(predictions_2nd_f[i], Y_test[i]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted age = 45.627 - Ground truth = 23.205\n",
            "predicted age = 53.100 - Ground truth = 70.737\n",
            "predicted age = 39.646 - Ground truth = 55.368\n",
            "predicted age = 21.656 - Ground truth = 24.278\n",
            "predicted age = 24.649 - Ground truth = 25.231\n",
            "predicted age = 27.901 - Ground truth = 35.179\n",
            "predicted age = 13.701 - Ground truth = 26.923\n",
            "predicted age = 24.841 - Ground truth = 48.189\n",
            "predicted age = 20.201 - Ground truth = 18.676\n",
            "predicted age = 45.143 - Ground truth = 57.378\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kR_nPmpJ_Gln"
      },
      "source": [
        "---\n",
        "# Saving the predicted values (on Test set) to be uploaded on Codalab Competition\n",
        "- Next, we show how you can save your predictions (on the test set) so that they can be submitted to our Codalab Competition.\n",
        "- Note, the .csv file MUST be named **'predictions.csv'**, othwerwise you will get an error on Codalab.\n",
        "- The generated zip file can be submitted on codalab ( <- you can download in from the left part of this web interface).\n",
        "- You can reuse and adapt this code to generate a submission file for the different results you will obtain during the course. At the end of this page we provide additional details about how to make a submission and how to name your .zip file so that we can easily compare all submissions given the different stages.\n",
        "\n",
        "> To enter the competition you need to create an account on Codalab, and submit your predictions to our challenge: https://codalab.lisn.upsaclay.fr/competitions/2321?secret_key=b66c95cb-997c-4fc9-af4e-987721abfa6c "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1zWzVF2DwHl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "518c780a-c94d-4825-fadc-ef8b8ee15699"
      },
      "source": [
        "import csv\n",
        "\n",
        "if(ENABLE_EVALUATION_ST2==True):\n",
        "  # saving the predictions as a csv file\n",
        "  with open('predictions.csv', 'w') as csvFile:\n",
        "    writer = csv.writer(csvFile)\n",
        "    writer.writerows(predictions_2nd_f)\n",
        "  csvFile.close()\n",
        "\n",
        "  # compressing the csv file (to be submitted to codalab as prediction)\n",
        "  ! zip predictions.zip predictions.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: predictions.csv (deflated 55%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BXQZXojAxGJc"
      },
      "source": [
        "---\n",
        "---\n",
        "# Accuracy is not enough! We also need to evaluate how biased is our model!\n",
        "- Next, we define different different functions, used to compute a bias score given different attributes.\n",
        "  - Age bias\n",
        "  - Gender bias\n",
        "  - Ethnicity bias\n",
        "  - Facial Expression bias\n",
        "- In a nutshell, given a particular attribute, we compute the MAE for different groups. For the case of age, detailed next, we will have 4 groups base on different age ranges. Then, we will have $MAE_1$, $MAE_2$, $MAE_3$ and $MAE_4$. Then, we compute the Absolute Difference among all. That is,\n",
        "  - $D_{1,2} = |MAE_1-MAE_2|$\n",
        "  - $D_{1,3} = |MAE_1-MAE_3|$\n",
        "  - $D_{1,4} = |MAE_1-MAE_4|$\n",
        "  - $D_{2,3} = |MAE_2-MAE_3|$\n",
        "  - $D_{2,4} = |MAE_2-MAE_4|$\n",
        "  - $D_{3,4} = |MAE_3-MAE_4|$\n",
        "\n",
        "- The final score is obtained by the average of the absolute differentes. In the case of age:\n",
        "  - $B_a = (D_{1,2} + D_{1,3} + D_{1,4} + D_{2,3} + D_{2,4} + D_{3,4})/6$\n",
        "\n",
        "- To minimize your bias score, given a particular attribute, you will need to minimize the Absolute Difference among the different groups being evaluated.\n",
        "- The big challenge here is to minimize ALL bias scores (i.e., age, gender, ethnicity and face expression)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TlPY4wbeUr_7"
      },
      "source": [
        "---\n",
        "# Age Bias ($B_a$) \n",
        "\n",
        "- Evaluates (on the TEST set) how accurate the model is with respect to different age ranges.\n",
        "  - group 1: age < 20\n",
        "  - group 2: 20 <= age < 40\n",
        "  - group 3: 40 <= age < 60\n",
        "  - group 4: 60 <= age\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umMOd4OpWP5y"
      },
      "source": [
        "def age_bias(predictions, gt):\n",
        "  error_g1 = []\n",
        "  error_g2 = []\n",
        "  error_g3 = []\n",
        "  error_g4 = []\n",
        "  for i in range(0,len(gt)):\n",
        "    if(gt[i]<20):\n",
        "      error_g1.append(abs(predictions[i]-gt[i]))\n",
        "    if(gt[i]>=20 and gt[i]<40):\n",
        "      error_g2.append(abs(predictions[i]-gt[i]))\n",
        "    if(gt[i]>=40 and gt[i]<60):\n",
        "      error_g3.append(abs(predictions[i]-gt[i]))\n",
        "    if(gt[i]>=60):\n",
        "      error_g4.append(abs(predictions[i]-gt[i]))\n",
        "\n",
        "  print('=============================')\n",
        "  print('Age analysis:')\n",
        "  print('Size group 1 = %d, MAE = %f' %(len(error_g1), np.mean(error_g1)))\n",
        "  print('Size group 2 = %d, MAE = %f' %(len(error_g2), np.mean(error_g2)))\n",
        "  print('Size group 3 = %d, MAE = %f' %(len(error_g3), np.mean(error_g3)))\n",
        "  print('Size group 4 = %d, MAE = %f' %(len(error_g4), np.mean(error_g4)))\n",
        "\n",
        "  age_bias = (abs(np.mean(error_g1)-np.mean(error_g2)) +\n",
        "            abs(np.mean(error_g1)-np.mean(error_g3)) +\n",
        "            abs(np.mean(error_g1)-np.mean(error_g4)) +\n",
        "            abs(np.mean(error_g2)-np.mean(error_g3)) +\n",
        "            abs(np.mean(error_g2)-np.mean(error_g4)) +\n",
        "            abs(np.mean(error_g3)-np.mean(error_g4)))/6\n",
        "\n",
        "  print('---------')\n",
        "  print('Age bias (Ba) = ', age_bias)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALWjE5J8edq7"
      },
      "source": [
        "# Gender Bias ($B_g$) \n",
        "- Evaluates (on the test set) how accurate the model is with respect to different gender.\n",
        "  - group 1: male\n",
        "  - group 2: female\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c8ua8JzFfFZK"
      },
      "source": [
        "def gender_bias(predictions, gt, metadata):\n",
        "  error_m = []\n",
        "  error_f = []\n",
        "  for i in range(0,len(gt)):\n",
        "    if(metadata[i][0] == 'female'):\n",
        "      error_f.append(abs(predictions[i]-gt[i]))\n",
        "    else:\n",
        "      error_m.append(abs(predictions[i]-gt[i]))\n",
        "\n",
        "  print('=============================')\n",
        "  print('Gender analysis:')\n",
        "  print('Size group female = %d, MAE = %f' %(len(error_f), np.mean(error_f)))\n",
        "  print('Size group male = %d, MAE = %f' %(len(error_m), np.mean(error_m)))\n",
        "\n",
        "  gender_bias = abs(np.mean(error_f)-np.mean(error_m))\n",
        "\n",
        "  print('---------')\n",
        "  print('Gender bias (Bg) = ', gender_bias)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8rIPUIrjkDG"
      },
      "source": [
        "# Ethnicity Bias ($B_e$)\n",
        "- Evaluates (on the test set) how accurate the model is with respect to different ethnicity categories.\n",
        "  - group 1: asian\n",
        "  - group 2: afroamerican\n",
        "  - group 3: caucasian\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8BnG-wK2j8nA"
      },
      "source": [
        "def ethnicity_bias(predictions, gt, metadata):\n",
        "  error_as = []\n",
        "  error_af = []\n",
        "  error_ca = []\n",
        "  for i in range(0,len(gt)):\n",
        "    if(metadata[i][1] == 'asian'):\n",
        "      error_as.append(abs(predictions[i]-gt[i]))\n",
        "    if(metadata[i][1] == 'afroamerican'):\n",
        "      error_af.append(abs(predictions[i]-gt[i]))\n",
        "    if(metadata[i][1] == 'caucasian'):\n",
        "      error_ca.append(abs(predictions[i]-gt[i]))\n",
        "\n",
        "  print('=============================')\n",
        "  print('Ethnicity Analysis:')\n",
        "  print('Size group asian = %d, MAE = %f' %(len(error_as), np.mean(error_as)))\n",
        "  print('Size group afroamerican = %d, MAE = %f' %(len(error_af), np.mean(error_af)))\n",
        "  print('Size group caucasian = %d, MAE = %f' %(len(error_ca), np.mean(error_ca)))\n",
        "  \n",
        "  ethnicity_bias = (abs(np.mean(error_as)-np.mean(error_af)) +\n",
        "                   abs(np.mean(error_as)-np.mean(error_ca)) +\n",
        "                   abs(np.mean(error_af)-np.mean(error_ca)))/3\n",
        "\n",
        "  print('---------')\n",
        "  print('Ethnicity bias (Be) = ', ethnicity_bias)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ohPkAMvxm9Ed"
      },
      "source": [
        "# Face expression bias ($B_f$)\n",
        "- Evaluates (on the test set) how accurate the model is with respect to different face expression categories.\n",
        "  - group 1: neutral\n",
        "  - group 2: slightlyhappy\n",
        "  - group 3: happy\n",
        "  - group 4: other\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dJOGVMGm_Fe"
      },
      "source": [
        "def face_expression_bias(predictions, gt, metadata):\n",
        "  error_h = []\n",
        "  error_s = []\n",
        "  error_n = []\n",
        "  error_o = []\n",
        "  for i in range(0,len(gt)):\n",
        "    if(metadata[i][2]=='happy'):\n",
        "      error_h.append(abs(predictions[i]-gt[i]))\n",
        "    if(metadata[i][2]=='slightlyhappy'):\n",
        "      error_s.append(abs(predictions[i]-gt[i]))\n",
        "    if(metadata[i][2]=='neutral'):\n",
        "      error_n.append(abs(predictions[i]-gt[i]))\n",
        "    if(metadata[i][2]=='other'):\n",
        "      error_o.append(abs(predictions[i]-gt[i]))\n",
        "\n",
        "  print('=============================')\n",
        "  print('Face experession Analysis:')\n",
        "  print('Size group happy = %d, MAE = %f' %(len(error_h), np.mean(error_h)))\n",
        "  print('Size group slightlyhappy = %d, MAE = %f' %(len(error_s), np.mean(error_s)))\n",
        "  print('Size group neutral = %d, MAE = %f' %(len(error_n), np.mean(error_n)))\n",
        "  print('Size group other = %d, MAE = %f' %(len(error_o), np.mean(error_o)))\n",
        "\n",
        "  face_bias = (abs(np.mean(error_h)-np.mean(error_s)) +\n",
        "              abs(np.mean(error_h)-np.mean(error_n)) +\n",
        "              abs(np.mean(error_h)-np.mean(error_o)) +\n",
        "              abs(np.mean(error_s)-np.mean(error_n)) +\n",
        "              abs(np.mean(error_s)-np.mean(error_o)) +\n",
        "              abs(np.mean(error_n)-np.mean(error_o)))/6\n",
        "\n",
        "  print('---------')\n",
        "  print('Face Expression bias (Bf) = ', face_bias)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IRpi-2MFyfFT"
      },
      "source": [
        "---\n",
        "# Computing the Age, Gender and Ethnicity Biases on the Test set\n",
        "- Next, we compute the different bias scores using the model we obtained after the 2nd stage of training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9K2FDPqbyj96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d576972-9cdd-4aa4-cc15-1b17c26b6156"
      },
      "source": [
        "if(ENABLE_EVALUATION_ST2==True):\n",
        "  # computing the age bias (model_stage_2)\n",
        "  age_bias(predictions_2nd_f,Y_test)\n",
        "\n",
        "  # computing the gender bias (model_stage_2)\n",
        "  gender_bias(predictions_2nd_f,Y_test,M_test)\n",
        "\n",
        "  # computing the ethnicity bias (model_stage_2)\n",
        "  ethnicity_bias(predictions_2nd_f,Y_test,M_test)\n",
        "\n",
        "  # computing the face bias (model_stage_2)\n",
        "  face_expression_bias(predictions_2nd_f,Y_test,M_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=============================\n",
            "Age analysis:\n",
            "Size group 1 = 369, MAE = 11.291870\n",
            "Size group 2 = 1044, MAE = 8.094766\n",
            "Size group 3 = 390, MAE = 12.658481\n",
            "Size group 4 = 175, MAE = 25.617014\n",
            "---------\n",
            "Age bias (Ba) =  8.988892873128256\n",
            "=============================\n",
            "Gender analysis:\n",
            "Size group female = 1020, MAE = 10.837108\n",
            "Size group male = 958, MAE = 11.465107\n",
            "---------\n",
            "Gender bias (Bg) =  0.6279993\n",
            "=============================\n",
            "Ethnicity Analysis:\n",
            "Size group asian = 129, MAE = 10.345757\n",
            "Size group afroamerican = 56, MAE = 7.636685\n",
            "Size group caucasian = 1793, MAE = 11.307957\n",
            "---------\n",
            "Ethnicity bias (Be) =  2.447514533996582\n",
            "=============================\n",
            "Face experession Analysis:\n",
            "Size group happy = 589, MAE = 11.054638\n",
            "Size group slightlyhappy = 505, MAE = 11.176367\n",
            "Size group neutral = 756, MAE = 10.947430\n",
            "Size group other = 128, MAE = 12.546231\n",
            "---------\n",
            "Face Expression bias (Bf) =  0.8196889559427897\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4lqbRPFK62iL"
      },
      "source": [
        "---\n",
        "---\n",
        "# Strategies to improve Accuracy (i.e., to reduce the Error): \n",
        "- We will comment about two different strategies: 1) data augmentation and 2) custom loss with weighted samples.\n",
        "\n",
        "# 1) Data augmentation\n",
        "- Next, we will augment the train set of people having age >= 60, as this group got the highest MAE compared to the other groups (regarding the age attribute only). \n",
        "- Different data augmentation strategies can be used. In this example, we  consider horizontal flip, changing the brighness, gaussian blur and translation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iM8PQrOcCB7n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "outputId": "66521924-333c-4379-eabc-709a7b690db1"
      },
      "source": [
        "# loading the train data again (original face images, before preprocessing):\n",
        "X_train = np.load('./data/data_train.npy')\n",
        "\n",
        "# Randomly selecting a random image from train set\n",
        "x = X_train[random.randint(0, len(X_train))]\n",
        "\n",
        "# flip horizontaly\n",
        "x_flipped = cv2.flip(x, 1)\n",
        "\n",
        "# change brightness\n",
        "x_brigth = cv2.cvtColor(x,cv2.COLOR_RGB2HSV)\n",
        "x_brigth[:,:,2] = x_brigth[:,:,2]*.5+np.random.uniform()\n",
        "x_brigth = cv2.cvtColor(x_brigth,cv2.COLOR_HSV2RGB)\n",
        "\n",
        "# gaussian blur (here you can also play with the function parameters)\n",
        "x_blur = cv2.GaussianBlur(x,(5,5),1.0)\n",
        "\n",
        "# translation (randomly translation from -25 to +25 in x and y)\n",
        "rows, cols ,c= x.shape\n",
        "M = np.float32([[1, 0, random.randint(-25, 25)], [0, 1, random.randint(-25, 25)]])\n",
        "x_translate = cv2.warpAffine(x, M, (cols, rows))\n",
        "\n",
        "# Visualizing the augmented data\n",
        "fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1, 5, figsize=(15, 15))\n",
        "ax1.imshow(cv2.cvtColor(x, cv2.COLOR_BGR2RGB)) # original image\n",
        "ax2.imshow(cv2.cvtColor(x_flipped, cv2.COLOR_BGR2RGB)) # flip horizontaly\n",
        "ax3.imshow(cv2.cvtColor(x_brigth, cv2.COLOR_BGR2RGB)) # change brightness\n",
        "ax4.imshow(cv2.cvtColor(x_blur, cv2.COLOR_BGR2RGB)) # gaussian blur\n",
        "ax5.imshow(cv2.cvtColor(x_translate, cv2.COLOR_BGR2RGB)) # translation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f704401d450>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VY2uBi2L-ro-"
      },
      "source": [
        "# Applying the data augmentation described above\n",
        "- Note, for each augmented image, we replicate its original label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMcDuU4xFvNv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1ce65e2-4cfe-49e5-91eb-53643578aaf2"
      },
      "source": [
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "# aux variables\n",
        "X_train_augmented = []\n",
        "Y_train_augmented = []\n",
        "\n",
        "# loading the train data and labels\n",
        "X_train = np.load('./data/data_train.npy')\n",
        "Y_train = np.load('./data/labels_train.npy')\n",
        "Y_train = Y_train/100\n",
        "print('Train set before augmentation = ', np.array(X_train).shape)\n",
        "\n",
        "# augmenting the data\n",
        "for i in range(0,len(X_train)):\n",
        "  # check if image is in the group 'age >= 60'\n",
        "\n",
        "  if Y_train[i]*100>=60: # here labels are multiplied by 100 as they were normalized to be between [0,1]\n",
        "    # flip\n",
        "    X_train_augmented.append(cv2.flip(X_train[i], 1))\n",
        "    Y_train_augmented.append(Y_train[i]) \n",
        "\n",
        "    # changing brightness\n",
        "    x_aux = cv2.cvtColor(X_train[i],cv2.COLOR_RGB2HSV)\n",
        "    x_aux[:,:,2] = x_aux[:,:,2]*.5+np.random.uniform()\n",
        "    X_train_augmented.append(cv2.cvtColor(x_aux,cv2.COLOR_HSV2RGB))\n",
        "    Y_train_augmented.append(Y_train[i]) \n",
        "\n",
        "    # blur\n",
        "    X_train_augmented.append(cv2.GaussianBlur(X_train[i],(5,5),1.0))\n",
        "    Y_train_augmented.append(Y_train[i])\n",
        "   \n",
        "    # translation\n",
        "    rows, cols ,c= X_train[i].shape\n",
        "    M = np.float32([[1, 0, random.randint(-25, 25)], [0, 1, random.randint(-25, 25)]])\n",
        "    X_train_augmented.append(cv2.warpAffine(X_train[i], M, (cols, rows)))\n",
        "    Y_train_augmented.append(Y_train[i])\n",
        "\n",
        "# adding the augmented images to the train set\n",
        "X_train = np.concatenate((X_train, X_train_augmented))\n",
        "Y_train = np.concatenate((Y_train, Y_train_augmented))\n",
        "print('Train set after augmentation = ', np.array(X_train).shape)\n",
        "\n",
        "\n",
        "# post-processing the train data with respect to ResNet-50 Inputs.\n",
        "for i in range(0,X_train.shape[0]):\n",
        "  x = X_train[i,:,:,:]\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  X_train[i,] = preprocess_input(x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set before augmentation =  (4065, 224, 224, 3)\n",
            "Train set after augmentation =  (4657, 224, 224, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JiTXleG_Ik4r"
      },
      "source": [
        "---\n",
        "# Training the 2nd stage again, from the model obtained at the 1st stage, but now using the augmented data\n",
        "- As before, we provide a model already trained (using the augmented data), which will be loaded as default if the code below is not changed. \n",
        "- You can change the boolean variable LOAD_BEST_MODEL_ST2_AUGMENTATION from True to False in order to train the model again, given your redefined strategy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJdYBDYiJp-q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "212f1bd0-85a4-4ba3-aeda-983193b3f300"
      },
      "source": [
        "#--------------------------\n",
        "LOAD_BEST_MODEL_ST2_AUGMENTATION = True\n",
        "#--------------------------\n",
        "\n",
        "if(LOAD_BEST_MODEL_ST2_AUGMENTATION==True):\n",
        "  # downloading the trained model\n",
        "  !wget https://data.chalearnlap.cvc.uab.cat/Colab_2021/best_model_st2_aug.zip\n",
        "  # decompressing the data\n",
        "  with ZipFile('best_model_st2_aug.zip','r') as zip:\n",
        "    zip.extractall()\n",
        "    print('Model decompressed successfully')\n",
        "  # removing the .zip file after extraction  to clean space\n",
        "  !rm best_model_st2_aug.zip\n",
        "\n",
        "else:\n",
        "  # loading the saved model (best model learned at stage 1)\n",
        "  saved_model = load_model('best_model.h5')\n",
        "  \n",
        "  # setting all layers to traineble\n",
        "  saved_model.trainable = True\n",
        "\n",
        "  # training all layers (2nd stage), given the model saved on stage 1\n",
        "  saved_model.compile(tf.keras.optimizers.Adam(learning_rate=1e-5),loss=tf.keras.losses.MeanSquaredError(),metrics=['mae'])\n",
        "\n",
        "  # defining the early stop criteria\n",
        "  es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)\n",
        "  \n",
        "  # saving the best model (2nd stage) based on val_loss with a different name\n",
        "  mc = ModelCheckpoint('/content/gdrive/MyDrive/temp/best_model_2nd_stage_augmentation.h5', monitor='val_loss', mode='min', save_best_only=True)\n",
        "\n",
        "  history = saved_model.fit(X_train, Y_train, validation_data=(X_valid, Y_valid), batch_size=16, epochs=12, shuffle=True, verbose=1, callbacks=[es,mc])\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-01 10:34:34--  https://data.chalearnlap.cvc.uab.cat/Colab_2021/best_model_st2_aug.zip\n",
            "Resolving data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)... 158.109.8.102\n",
            "Connecting to data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)|158.109.8.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 271383554 (259M) [application/zip]\n",
            "Saving to: ‘best_model_st2_aug.zip’\n",
            "\n",
            "best_model_st2_aug. 100%[===================>] 258.81M  11.3MB/s    in 27s     \n",
            "\n",
            "2022-03-01 10:35:02 (9.72 MB/s) - ‘best_model_st2_aug.zip’ saved [271383554/271383554]\n",
            "\n",
            "Model decompressed successfully\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_UY-ci6Yc7VR"
      },
      "source": [
        "# Train History of the provided pre-trained model\n",
        "\n",
        "Epoch 1/12\n",
        "292/292 [==============================] - 3358s 11s/step - loss: 0.2493 - mae: 0.2113 - val_loss: 0.2226 - val_mae: 0.1681\n",
        "\n",
        "Epoch 2/12\n",
        "292/292 [==============================] - 3390s 12s/step - loss: 0.2098 - mae: 0.1414 - val_loss: 0.2137 - val_mae: 0.1467\n",
        "\n",
        "Epoch 3/12\n",
        "292/292 [==============================] - 3310s 11s/step - loss: 0.1978 - mae: 0.1089 - val_loss: 0.2094 - val_mae: 0.1385\n",
        "\n",
        "Epoch 4/12\n",
        "292/292 [==============================] - 3284s 11s/step - loss: 0.1926 - mae: 0.0935 - val_loss: 0.2046 - val_mae: 0.1280\n",
        "\n",
        "Epoch 5/12\n",
        "292/292 [==============================] - 3142s 11s/step - loss: 0.1896 - mae: 0.0838 - val_loss: 0.2022 - val_mae: 0.1219\n",
        "\n",
        "Epoch 6/12\n",
        "292/292 [==============================] - 3072s 11s/step - loss: 0.1872 - mae: 0.0762 - val_loss: 0.1999 - val_mae: 0.1166\n",
        "\n",
        "Epoch 7/12\n",
        "292/292 [==============================] - 3006s 10s/step - loss: 0.1849 - mae: 0.0684 - val_loss: 0.1977 - val_mae: 0.1116\n",
        "\n",
        "Epoch 8/12\n",
        "292/292 [==============================] - 3029s 10s/step - loss: 0.1834 - mae: 0.0631 - val_loss: 0.1964 - val_mae: 0.1093\n",
        "\n",
        "Epoch 9/12\n",
        "292/292 [==============================] - 3043s 10s/step - loss: 0.1821 - mae: 0.0578 - val_loss: 0.1953 - val_mae: 0.1065\n",
        "\n",
        "Epoch 10/12\n",
        "292/292 [==============================] - 2591s 9s/step - loss: 0.1817 - mae: 0.0592 - val_loss: 0.1938 - val_mae: 0.1054\n",
        "\n",
        "Epoch 11/12\n",
        "292/292 [==============================] - 2541s 9s/step - loss: 0.1807 - mae: 0.0575 - val_loss: 0.1904 - val_mae: 0.0978\n",
        "\n",
        "Epoch 12/12\n",
        "292/292 [==============================] - 2421s 8s/step - loss: 0.1788 - mae: 0.0542 - val_loss: 0.1870 - val_mae: 0.0906"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yDrT0WwDAMnb"
      },
      "source": [
        "# Evaluating the model (with data augmentation) on Test set\n",
        "- Here, you can observe a decrease in Mean Absolute Error (MAE) compared to the predictions obtained by the previously trained models:\n",
        "  - Model trained at the 1st stage: MAE = 16.98603930\n",
        "  - Model trained at the 2nd stage (without data augmentation): MAE = 11.14126198\n",
        "  - Model trained at the 2nd stage (with data augmentation): MAE = 10.28505150"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_8LL1RJAQCp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23615c54-d47c-4377-8c7f-f5253630ca28"
      },
      "source": [
        "# loading the saved model\n",
        "if(LOAD_BEST_MODEL_ST2_AUGMENTATION==True):\n",
        "  saved_model_2nd_augm = load_model('best_model_2nd_stage_augmentation.h5')\n",
        "else:\n",
        "  saved_model_2nd_augm = load_model('/content/gdrive/MyDrive/temp/best_model_2nd_stage_augmentation.h5')\n",
        "\n",
        "\n",
        "#--------------------------\n",
        "ENABLE_EVALUATION_ST2_AUGMENTED = True\n",
        "#--------------------------\n",
        "\n",
        "if(ENABLE_EVALUATION_ST2_AUGMENTED==True):\n",
        "  # predict on the test data\n",
        "  predictions_2nd_augmented = saved_model_2nd_augm.predict(X_test, batch_size=32, verbose=1)\n",
        "  # re-scaling the output predictions (from [0,1] to age range) using the\n",
        "  # the normalization factor mentioned before\n",
        "  predictions_2nd_augmented_f = predictions_2nd_augmented*100\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "62/62 [==============================] - 12s 179ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1HljLcyBqI1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3686764c-6a08-43e7-9e49-1a1cc73e625f"
      },
      "source": [
        "if(ENABLE_EVALUATION_ST2_AUGMENTED==True):\n",
        "  # evaluating on test data\n",
        "  error = []\n",
        "  for i in range(0,len(Y_test)):\n",
        "    error.append(abs(np.subtract(predictions_2nd_augmented_f[i][0],Y_test[i])))\n",
        "\n",
        "  print('MAE = %.8f' %(np.mean(error)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE = 10.28504985\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PWowpmtwB8px"
      },
      "source": [
        "# Computing the Age Bias (augmented data)\n",
        "- Next, if we compare the age bias obtained by the two models (without/with) data augmentation, we can observed the model with augmented data was able to recude the age bias (from 8.988896687825521 to 5.779014587402344). In particular, the MAE for group 4 reduced from 25.617018 to 19.334185, suggesting that our data augmentation had a positive impact in training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7F9LfW1ZCBrI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "830ef629-cb7c-4602-b01a-d0fd6a8b0b0e"
      },
      "source": [
        "if(ENABLE_EVALUATION_ST2==True):\n",
        "  # computing the age bias (model_stage_2) - previous model without data augmentation\n",
        "  age_bias(predictions_2nd_f,Y_test)\n",
        "\n",
        "if(ENABLE_EVALUATION_ST2_AUGMENTED==True):  \n",
        "  # computing the age bias (model_stage_2) - with data augmentation\n",
        "  age_bias(predictions_2nd_augmented_f,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=============================\n",
            "Age analysis:\n",
            "Size group 1 = 369, MAE = 11.291870\n",
            "Size group 2 = 1044, MAE = 8.094766\n",
            "Size group 3 = 390, MAE = 12.658481\n",
            "Size group 4 = 175, MAE = 25.617014\n",
            "---------\n",
            "Age bias (Ba) =  8.988892873128256\n",
            "=============================\n",
            "Age analysis:\n",
            "Size group 1 = 369, MAE = 11.260434\n",
            "Size group 2 = 1044, MAE = 7.909566\n",
            "Size group 3 = 390, MAE = 11.660664\n",
            "Size group 4 = 175, MAE = 19.334188\n",
            "---------\n",
            "Age bias (Ba) =  5.779015858968099\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjkJNXGilNfo"
      },
      "source": [
        "---\n",
        "---\n",
        "# Strategies to improve Accuracy (i.e., to reduce the Error):\n",
        "# 2) Custom Loss: sample weights to deal with inbalanced categories\n",
        "- Next, we will created a \"customized loss\", which gives more weight to people having less samples in train data. For this, **we will consider the age range only**. This way, we believe the model will be able to generalize a little bit better to those particular groups."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RGjU8Xetxl3W"
      },
      "source": [
        "# Load the Train data again (to remove the augmented data) and generate the weigths \n",
        "- First, we will generate a weight for each age group (for g =1 to 4);\n",
        "- The formula used to calculate the weight for each group $j$ is:\n",
        "\n",
        "  $w_j=n_{samples} / (n_{classes} * n_{samples,j}),$\n",
        "\n",
        "  Where\n",
        "\n",
        "    - $w_j$ is the weight for each group $j$,\n",
        "    - $n_{samples}$ is the number of samples in the train set,\n",
        "    - $n_{classes}$ is the number of classes (4 in our case, as we divided the ages in 4 groups),\n",
        "    - $n_{samples,j}$ is the number of samples of class (group) $j$.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svMMpvU9xcJR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35314142-5999-4079-a06c-f197cdfb2bf9"
      },
      "source": [
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "# loading the train data again (original face images, before preprocessing):\n",
        "X_train = np.load('./data/data_train.npy')\n",
        "Y_train = np.load('./data/labels_train.npy')\n",
        "Y_train = Y_train/100 # normalizing the age values to be between [0,1]\n",
        "\n",
        "# preprocessing the train data with respect to ResNet-50 Inputs.\n",
        "for i in range(0,X_train.shape[0]):\n",
        "  x = X_train[i,:,:,:]\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  X_train[i,] = preprocess_input(x)\n",
        "\n",
        "# counting the number of samples per group in the train data (age attribute only)\n",
        "g1 = g2 = g3 = g4 = 0\n",
        "for i in range(0,Y_train.shape[0]):\n",
        "    if(Y_train[i]*100<20):\n",
        "      g1 +=1\n",
        "    if(Y_train[i]*100>=20 and Y_train[i]*100<40):\n",
        "      g2 +=1\n",
        "    if(Y_train[i]*100>=40 and Y_train[i]*100<60):\n",
        "      g3 +=1\n",
        "    if(Y_train[i]*100>=60):\n",
        "      g4 +=1\n",
        "print('group(s) size = ', [g1, g2, g3, g4])\n",
        "\n",
        "# generating the weights for each group using the equation defined above\n",
        "w = sum(np.array([g1, g2, g3, g4]))/(4*np.array([g1, g2, g3, g4]))\n",
        "print('weights per group = ', w)\n",
        "\n",
        "# creating a vector with same size as Y_train, that will link a particular label to its weight\n",
        "sample_weights = []\n",
        "for i in range(0,Y_train.shape[0]):\n",
        "    if(Y_train[i]*100<20):\n",
        "      sample_weights.append(w[0])\n",
        "    if(Y_train[i]*100>=20 and Y_train[i]*100<40):\n",
        "      sample_weights.append(w[1])\n",
        "    if(Y_train[i]*100>=40 and Y_train[i]*100<60):\n",
        "      sample_weights.append(w[2])\n",
        "    if(Y_train[i]*100>=60):\n",
        "      sample_weights.append(w[3])\n",
        "sample_weights = np.array(sample_weights)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "group(s) size =  [796, 2389, 732, 148]\n",
            "weights per group =  [1.27669598 0.42538719 1.38831967 6.86655405]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5gTtMXGmx5jA"
      },
      "source": [
        "# I) Using the SAMPLE WEIGHTS to train our model and,\n",
        "- Next, you will see the code we used to train our model (2nd stage) from the model we obtained at the 1st stage, using the customized loss option with sample weights.\n",
        "- As default, the code will load the model already trained. \n",
        "- You can change the boolean variable 'LOAD_BEST_MODEL_ST2_WEIGHTED_LOSS' to False to train your model.\n",
        "- Note, now we include other variables ('RESUME_TRAINING' and 'RESUME_FROM_EPOCH') to allow us resuming training, as well as to inform from what epoch we want to resume the trainind, detailed below.\n",
        "\n",
        "# II) illustrating how to train + save + stop training + RESUME TRAINING\n",
        "- **Imagine** you set 'LOAD_BEST_MODEL_ST2_WEIGHTED_LOSS = False', 'NUM_EPOCHS = 12' and 'RESUME_TRAINING = False' to train your model the first time.\n",
        "- Due to Colab limitations, your process stoped the training at the middle of epoch 10, and you saved the best model based on validation loss on epoch 9.\n",
        "- In the above example, you can resume training from epoch 9 by setting the following parameters:\n",
        "  - 'RESUME_TRAINING = True'\n",
        "  - 'RESUME_FROM_EPOCH = 9'\n",
        "\n",
        "- IMPORTANT: to resume training, you will need to monitor the epoch number where your model stopped before resuming the training, and change the defined variables properly. \n",
        "  - Note that the fit function is adapted to receive the sample weights ('sample_weight=sample_weights').\n",
        "  - Also note that the fit function changes if you are training from epoch 0 (initial_epoch=0) or resume training (initial_epoch=RESUME_FROM_EPOCH). \n",
        "  - Finally, note that when you are resuming training, you load your 'best_model_2nd_stage_weighted.h5' instead of the model trained at stage 1 ('best_model.h5').\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjEx3B3lx8Mu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc038ac4-299d-4680-fc7e-06a1cad294aa"
      },
      "source": [
        "#--------------------------\n",
        "LOAD_BEST_MODEL_ST2_WEIGHTED_LOSS = True\n",
        "NUM_EPOCHS = 12\n",
        "#--------------------------\n",
        "RESUME_TRAINING = False\n",
        "RESUME_FROM_EPOCH = 9\n",
        "#--------------------------\n",
        "\n",
        "if(LOAD_BEST_MODEL_ST2_WEIGHTED_LOSS==True):\n",
        "  # downloading the trained model\n",
        "  !wget https://data.chalearnlap.cvc.uab.cat/Colab_2021/best_model_weighted.zip\n",
        "  # decompressing the data\n",
        "  with ZipFile('best_model_weighted.zip','r') as zip:\n",
        "    zip.extractall()\n",
        "    print('Model decompressed successfully')\n",
        "  # removing the .zip file after extraction  to clean space\n",
        "  !rm best_model_weighted.zip\n",
        "\n",
        "else:\n",
        "  # loading the saved model (best model learned at stage 1)\n",
        "  if(RESUME_TRAINING == False):\n",
        "    saved_model = load_model('best_model.h5') # load model from stage 1\n",
        "  else:\n",
        "    # resume training (stage 2)\n",
        "    saved_model = load_model('/content/gdrive/MyDrive/temp/best_model_2nd_stage_weighted.h5')\n",
        "  \n",
        "  # setting all layers to traineble\n",
        "  saved_model.trainable = True\n",
        "\n",
        "  #=================================================\n",
        "  # training all layers (2nd stage), given the model saved on stage 1\n",
        "  saved_model.compile(tf.keras.optimizers.Adam(learning_rate=1e-5),loss=tf.keras.losses.MeanSquaredError(),metrics=['mae'])\n",
        "  #=================================================\n",
        "\n",
        "  # defining the early stop criteria\n",
        "  es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)\n",
        "  mc = ModelCheckpoint('/content/gdrive/MyDrive/temp/best_model_2nd_stage_weighted.h5', monitor='val_loss', mode='min', save_best_only=True)\n",
        "\n",
        "  if(RESUME_TRAINING == False):  \n",
        "    history = saved_model.fit(X_train, Y_train, sample_weight=sample_weights, validation_data=(X_valid, Y_valid), batch_size=16, epochs=NUM_EPOCHS, initial_epoch=0, shuffle=True, verbose=1, callbacks=[es,mc])\n",
        "  else:\n",
        "    history = saved_model.fit(X_train, Y_train, sample_weight=sample_weights, validation_data=(X_valid, Y_valid), batch_size=16, epochs=NUM_EPOCHS, initial_epoch=RESUME_FROM_EPOCH, shuffle=True, verbose=1, callbacks=[es,mc])\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-01 10:35:25--  https://data.chalearnlap.cvc.uab.cat/Colab_2021/best_model_weighted.zip\n",
            "Resolving data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)... 158.109.8.102\n",
            "Connecting to data.chalearnlap.cvc.uab.cat (data.chalearnlap.cvc.uab.cat)|158.109.8.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 271396890 (259M) [application/zip]\n",
            "Saving to: ‘best_model_weighted.zip’\n",
            "\n",
            "best_model_weighted 100%[===================>] 258.82M  14.4MB/s    in 39s     \n",
            "\n",
            "2022-03-01 10:36:05 (6.58 MB/s) - ‘best_model_weighted.zip’ saved [271396890/271396890]\n",
            "\n",
            "Model decompressed successfully\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D7kqieNu7zDS"
      },
      "source": [
        "# Making predictions on the Test set and Evaluating\n",
        "- Note that in this case, the model obtained MAE = 12.45038828, which is not the best score compared to the ones obtained before. However, are the evaluated biases better? Let's see!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYq1tCet70bN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3111c657-af7c-4de6-9152-c34edb81075c"
      },
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "if(LOAD_BEST_MODEL_ST2_WEIGHTED_LOSS==True):\n",
        "  saved_model_2nd_weighted = load_model('best_model_2nd_stage_weighted.h5')\n",
        "else:\n",
        "  saved_model_2nd_weighted = load_model('/content/gdrive/MyDrive/temp/best_model_2nd_stage_weighted.h5')\n",
        "\n",
        "#--------------------------\n",
        "ENABLE_EVALUATION_WEIGHTED = True\n",
        "#--------------------------\n",
        "\n",
        "if(ENABLE_EVALUATION_WEIGHTED==True):\n",
        "  # predict on the test data\n",
        "  predictions_2nd_weighted = saved_model_2nd_weighted.predict(X_test, batch_size=32, verbose=1)\n",
        "  # re-scaling the output predictions (from [0,1] to age range) using the\n",
        "  # the normalization factor mentioned before\n",
        "  predictions_2nd_weighted_f = predictions_2nd_weighted*100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "62/62 [==============================] - 12s 182ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCyMYVPgILxq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5a3f622-8de6-42e2-9c68-243288f54588"
      },
      "source": [
        "if(ENABLE_EVALUATION_WEIGHTED==True):\n",
        "  # evaluating on test data\n",
        "  error = []\n",
        "  for i in range(0,len(Y_test)):\n",
        "    error.append(abs(np.subtract(predictions_2nd_weighted_f[i][0],Y_test[i])))\n",
        "\n",
        "  print('MAE = %.8f' %(np.mean(error)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE = 12.45039125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RD-J3swDIjQW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c011877a-a70c-45a9-8e05-5cfc1ba17d98"
      },
      "source": [
        "# printing some predictions\n",
        "for i in range(0,20):\n",
        "  print('predicted age = %.3f - Ground truth = %.3f' %(predictions_2nd_weighted_f[i], Y_test[i]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted age = 46.333 - Ground truth = 23.205\n",
            "predicted age = 56.602 - Ground truth = 70.737\n",
            "predicted age = 42.311 - Ground truth = 55.368\n",
            "predicted age = 19.425 - Ground truth = 24.278\n",
            "predicted age = 16.771 - Ground truth = 25.231\n",
            "predicted age = 19.715 - Ground truth = 35.179\n",
            "predicted age = 10.790 - Ground truth = 26.923\n",
            "predicted age = 29.985 - Ground truth = 48.189\n",
            "predicted age = 9.490 - Ground truth = 18.676\n",
            "predicted age = 41.011 - Ground truth = 57.378\n",
            "predicted age = 46.650 - Ground truth = 52.150\n",
            "predicted age = 2.258 - Ground truth = 30.289\n",
            "predicted age = 1.889 - Ground truth = 30.256\n",
            "predicted age = 38.848 - Ground truth = 26.526\n",
            "predicted age = 64.035 - Ground truth = 33.462\n",
            "predicted age = 54.715 - Ground truth = 69.459\n",
            "predicted age = 5.798 - Ground truth = 18.763\n",
            "predicted age = 32.781 - Ground truth = 33.500\n",
            "predicted age = 20.832 - Ground truth = 42.763\n",
            "predicted age = 38.073 - Ground truth = 33.056\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NpJCmvGQXDIS"
      },
      "source": [
        "# Comparing the 2nd stage of training: \n",
        "case a) without augmentation and custom loss **VS.** case b) without augmentation but with custom loss.\n",
        "- Age bias:\n",
        "  - case a: 8.988896687825521\n",
        "  - case b: 3.965182622273763\n",
        "- Gender bias:\n",
        "  - case a: 0.6280031\n",
        "  - case b: 0.54932594\n",
        "- Ethnicity bias:\n",
        "  - case a: 2.447519620259603\n",
        "  - case b: 2.094111124674479\n",
        "- Face Expression bias:\n",
        "  - case a: 0.8196892738342285\n",
        "  - case b: 1.220861275990804\n",
        "\n",
        "As it can be observed, the model with custom loss and weighted samples obtained overall smaller bias scores on all evaluated attributes (except for face expression), even if the weigts were defined based on age attribute only."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjrDG9AHLoPf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca7130e8-a745-4ace-ca85-9067cae44bb3"
      },
      "source": [
        "if(ENABLE_EVALUATION_ST2==True and ENABLE_EVALUATION_WEIGHTED==True):\n",
        "  age_bias(predictions_2nd_f,Y_test)\n",
        "  age_bias(predictions_2nd_weighted_f,Y_test)\n",
        "\n",
        "  gender_bias(predictions_2nd_f,Y_test,M_test)\n",
        "  gender_bias(predictions_2nd_weighted_f,Y_test,M_test)\n",
        "\n",
        "  ethnicity_bias(predictions_2nd_f,Y_test,M_test)\n",
        "  ethnicity_bias(predictions_2nd_weighted_f,Y_test,M_test)\n",
        "\n",
        "  face_expression_bias(predictions_2nd_f,Y_test,M_test)\n",
        "  face_expression_bias(predictions_2nd_weighted_f,Y_test,M_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=============================\n",
            "Age analysis:\n",
            "Size group 1 = 369, MAE = 11.291870\n",
            "Size group 2 = 1044, MAE = 8.094766\n",
            "Size group 3 = 390, MAE = 12.658481\n",
            "Size group 4 = 175, MAE = 25.617014\n",
            "---------\n",
            "Age bias (Ba) =  8.988892873128256\n",
            "=============================\n",
            "Age analysis:\n",
            "Size group 1 = 369, MAE = 12.382669\n",
            "Size group 2 = 1044, MAE = 11.588708\n",
            "Size group 3 = 390, MAE = 11.744704\n",
            "Size group 4 = 175, MAE = 19.306417\n",
            "---------\n",
            "Age bias (Ba) =  3.965182622273763\n",
            "=============================\n",
            "Gender analysis:\n",
            "Size group female = 1020, MAE = 10.837108\n",
            "Size group male = 958, MAE = 11.465107\n",
            "---------\n",
            "Gender bias (Bg) =  0.6279993\n",
            "=============================\n",
            "Gender analysis:\n",
            "Size group female = 1020, MAE = 12.716443\n",
            "Size group male = 958, MAE = 12.167121\n",
            "---------\n",
            "Gender bias (Bg) =  0.5493221\n",
            "=============================\n",
            "Ethnicity Analysis:\n",
            "Size group asian = 129, MAE = 10.345757\n",
            "Size group afroamerican = 56, MAE = 7.636685\n",
            "Size group caucasian = 1793, MAE = 11.307957\n",
            "---------\n",
            "Ethnicity bias (Be) =  2.447514533996582\n",
            "=============================\n",
            "Ethnicity Analysis:\n",
            "Size group asian = 129, MAE = 11.386210\n",
            "Size group afroamerican = 56, MAE = 9.478601\n",
            "Size group caucasian = 1793, MAE = 12.619771\n",
            "---------\n",
            "Ethnicity bias (Be) =  2.0941136678059897\n",
            "=============================\n",
            "Face experession Analysis:\n",
            "Size group happy = 589, MAE = 11.054638\n",
            "Size group slightlyhappy = 505, MAE = 11.176367\n",
            "Size group neutral = 756, MAE = 10.947430\n",
            "Size group other = 128, MAE = 12.546231\n",
            "---------\n",
            "Face Expression bias (Bf) =  0.8196889559427897\n",
            "=============================\n",
            "Face experession Analysis:\n",
            "Size group happy = 589, MAE = 11.887617\n",
            "Size group slightlyhappy = 505, MAE = 12.333583\n",
            "Size group neutral = 756, MAE = 12.667597\n",
            "Size group other = 128, MAE = 14.218004\n",
            "---------\n",
            "Face Expression bias (Bf) =  1.2208625475565593\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-sTrrsB4oR6"
      },
      "source": [
        "---\n",
        "---\n",
        "# Practical Exercises \n",
        "Next, we define a serie of practical exercises (Task 1 and 2, and an optional extra exercise).  **Your goal is to maximize accuracy (i.e., reduce the Mean Absolute Error) and minimize the evaluated bias scores on the different attributes**. Task 1 and 2 have some restrictions so that you can compare the results when following different strategies. Note, you can edit and improve the starting kit on each task, but you are free to start from strach and create a new solution. At the end, you will be evaluated based on a set of items (detailed in the practical classes) and **creativity**. \n",
        "\n",
        "- IMPORTANT: we will use **Codalab** to motivate the students, as they can submit their results on the platform, compete with each other and improve their solutions, but the ranking shown in the leaderboard will not be considered in the evaluation. This is to justify that more creative solutions will be priefered even if they don't provide the best results.\n",
        "- Note: you will be requested to share with the lectors (Sergio and Julio) your final **Colab file** (with a clean code and well documented) and a **Report document** where you describe your experiments and solution, compare and discuss the obtained results in a progressive and clear way. Please, check the class material associated to the practical sessions for more details. \n",
        "\n",
        "---\n",
        "- **Task 1 (with data augmentation):** For this task, you should define your model (e.g., a generic backbone with some small changes to solve the problem at hand, like including/removing some layers, etc), play with the different hyperparameters (e.g., number of epochs, learning rate, batch size, etc), regularizers (e.g., dropout layer), loos function (e.g., MSE, MAE, etc). You can also play with the training strategy (e.g., training using different stages - or not - freezing different layers during training - or not, etc). Then, **you will be requested to perform some data augmentation** to achieve your goal. Note, you could simply expand the idea of the starting kit to cover other attributes (e.g., age > 40 or for \"happy\" expression). However, we expect more creative solutions, where different approaches are employed (e.g., new transformations, covering different attributes, etc). Then, you should submit your solution to codalab and receive real-time feedback, and improve it based on your results.\n",
        "\n",
        "- **Task 2 (custom loos, without data augmentation):** For this task, you should fix the model employed in Task 1, but you can also play with the different hyperparameters, regularizers, loos function and training strategy. Then, **you will be requested to use a custom loss (e.g., sample weights)** to achieve your goal, **without any data augmentation** method. This way, you will be able to compare the different solutions (Task 1 vs. Task 2). Then, you should submit your solution to codalab and receive real-time feedback, and improve it based on your results.\n",
        "\n",
        "- **Extra (optional):** For this task, you can exploit your creativity as much as you can. You are free to employ any strategy, data augmentation, custom loss, etc, all together in order to achieve your goal. Then, you will be able to compare the obtained results for Task 1 vs. Task 2 vs. the extra (optional) task. Then, you can also submit your solution to codalab and receive real-time feedback, and improve it based on your results.\n",
        "---\n",
        "\n",
        "\n",
        "**Codalab Competition link:** https://codalab.lisn.upsaclay.fr/competitions/2321?secret_key=b66c95cb-997c-4fc9-af4e-987721abfa6c \n",
        "\n"
      ]
    }
  ]
}