KinDeNet.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Leonard2310/KinDeNet/blob/main/KinDeNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RZn-koTr-HG2"
      },
      "source": [
        "# COLLEGAMENTO AL DRIVE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62Am2pEa-Ma9",
        "outputId": "fea6b0c0-7b0c-4747-ae6f-8d23f116a885"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive                                                  # Importa il modulo Drive di Google Colab\n",
        "\n",
        "drive.mount('/content/drive')                                                   # Indirizzo base del drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6te5rqdazrLW"
      },
      "source": [
        "# INSTALLAZIONE PACKAGE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NijgCVT-z5k7"
      },
      "outputs": [],
      "source": [
        "!pip install Keras-Applications==1.0.8                                          # Installazione dell'ultima versione di Keras-Applications\n",
        "!pip install Keras-Preprocessing==1.1.2                                         # Installazione dell'ultima versione di Keras-Preprocessing\n",
        "!pip install keras_vggface==0.6                                                 # Installazione dell'ultima versione di VGGFace\n",
        "!pip install keras-tqdm==2.0.1                                                  # Installazione dell'ultima versione di keras-tqdm\n",
        "!pip install imbalanced-learn                                                   # Installazione di Imbalanced-Learn\n",
        "\n",
        "# MODIFICA NECESSARIA PER RISOLVERE INCONGRUENZA NELLE LIBRERIE\n",
        "filename = \"/usr/local/lib/python3.10/dist-packages/keras_vggface/models.py\"\n",
        "text = open(filename).read()\n",
        "open(filename, \"w+\").write(text.replace('keras.engine.topology', 'tensorflow.keras.utils'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jhc1Q6H8AKEi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b813af3-7635-4c2f-a57f-ef5f3a91c54f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Package                       Version\n",
            "----------------------------- ---------------------\n",
            "absl-py                       1.4.0\n",
            "alabaster                     0.7.13\n",
            "albumentations                1.2.1\n",
            "altair                        4.2.2\n",
            "anyio                         3.6.2\n",
            "appdirs                       1.4.4\n",
            "argon2-cffi                   21.3.0\n",
            "argon2-cffi-bindings          21.2.0\n",
            "array-record                  0.2.0\n",
            "arviz                         0.15.1\n",
            "astropy                       5.2.2\n",
            "astunparse                    1.6.3\n",
            "attrs                         23.1.0\n",
            "audioread                     3.0.0\n",
            "autograd                      1.5\n",
            "Babel                         2.12.1\n",
            "backcall                      0.2.0\n",
            "beautifulsoup4                4.11.2\n",
            "bleach                        6.0.0\n",
            "blis                          0.7.9\n",
            "blosc2                        2.0.0\n",
            "bokeh                         2.4.3\n",
            "branca                        0.6.0\n",
            "build                         0.10.0\n",
            "CacheControl                  0.12.11\n",
            "cached-property               1.5.2\n",
            "cachetools                    5.3.0\n",
            "catalogue                     2.0.8\n",
            "certifi                       2022.12.7\n",
            "cffi                          1.15.1\n",
            "chardet                       4.0.0\n",
            "charset-normalizer            2.0.12\n",
            "chex                          0.1.7\n",
            "click                         8.1.3\n",
            "cloudpickle                   2.2.1\n",
            "cmake                         3.25.2\n",
            "cmdstanpy                     1.1.0\n",
            "colorcet                      3.0.1\n",
            "colorlover                    0.3.0\n",
            "community                     1.0.0b1\n",
            "confection                    0.0.4\n",
            "cons                          0.4.5\n",
            "contextlib2                   0.6.0.post1\n",
            "contourpy                     1.0.7\n",
            "convertdate                   2.4.0\n",
            "cryptography                  40.0.2\n",
            "cufflinks                     0.17.3\n",
            "cupy-cuda11x                  11.0.0\n",
            "cvxopt                        1.3.0\n",
            "cvxpy                         1.3.1\n",
            "cycler                        0.11.0\n",
            "cymem                         2.0.7\n",
            "Cython                        0.29.34\n",
            "dask                          2022.12.1\n",
            "datascience                   0.17.6\n",
            "db-dtypes                     1.1.1\n",
            "dbus-python                   1.2.16\n",
            "debugpy                       1.6.6\n",
            "decorator                     4.4.2\n",
            "defusedxml                    0.7.1\n",
            "distributed                   2022.12.1\n",
            "dlib                          19.24.1\n",
            "dm-tree                       0.1.8\n",
            "docutils                      0.16\n",
            "dopamine-rl                   4.0.6\n",
            "duckdb                        0.7.1\n",
            "earthengine-api               0.1.350\n",
            "easydict                      1.10\n",
            "ecos                          2.0.12\n",
            "editdistance                  0.6.2\n",
            "en-core-web-sm                3.5.0\n",
            "entrypoints                   0.4\n",
            "ephem                         4.1.4\n",
            "et-xmlfile                    1.1.0\n",
            "etils                         1.2.0\n",
            "etuples                       0.3.8\n",
            "exceptiongroup                1.1.1\n",
            "fastai                        2.7.12\n",
            "fastcore                      1.5.29\n",
            "fastdownload                  0.0.7\n",
            "fastjsonschema                2.16.3\n",
            "fastprogress                  1.0.3\n",
            "fastrlock                     0.8.1\n",
            "filelock                      3.12.0\n",
            "firebase-admin                5.3.0\n",
            "Flask                         2.2.4\n",
            "flatbuffers                   23.3.3\n",
            "flax                          0.6.9\n",
            "folium                        0.14.0\n",
            "fonttools                     4.39.3\n",
            "frozendict                    2.3.7\n",
            "fsspec                        2023.4.0\n",
            "future                        0.18.3\n",
            "gast                          0.4.0\n",
            "GDAL                          3.3.2\n",
            "gdown                         4.6.6\n",
            "gensim                        4.3.1\n",
            "geographiclib                 2.0\n",
            "geopy                         2.3.0\n",
            "gin-config                    0.5.0\n",
            "glob2                         0.7\n",
            "google                        2.0.3\n",
            "google-api-core               2.11.0\n",
            "google-api-python-client      2.84.0\n",
            "google-auth                   2.17.3\n",
            "google-auth-httplib2          0.1.0\n",
            "google-auth-oauthlib          1.0.0\n",
            "google-cloud-bigquery         3.9.0\n",
            "google-cloud-bigquery-storage 2.19.1\n",
            "google-cloud-core             2.3.2\n",
            "google-cloud-datastore        2.15.1\n",
            "google-cloud-firestore        2.11.0\n",
            "google-cloud-language         2.9.1\n",
            "google-cloud-storage          2.8.0\n",
            "google-cloud-translate        3.11.1\n",
            "google-colab                  1.0.0\n",
            "google-crc32c                 1.5.0\n",
            "google-pasta                  0.2.0\n",
            "google-resumable-media        2.5.0\n",
            "googleapis-common-protos      1.59.0\n",
            "googledrivedownloader         0.4\n",
            "graphviz                      0.20.1\n",
            "greenlet                      2.0.2\n",
            "grpcio                        1.54.0\n",
            "grpcio-status                 1.48.2\n",
            "gspread                       3.4.2\n",
            "gspread-dataframe             3.0.8\n",
            "gym                           0.25.2\n",
            "gym-notices                   0.0.8\n",
            "h5netcdf                      1.1.0\n",
            "h5py                          3.8.0\n",
            "holidays                      0.25\n",
            "holoviews                     1.15.4\n",
            "html5lib                      1.1\n",
            "httpimport                    1.3.0\n",
            "httplib2                      0.21.0\n",
            "humanize                      4.6.0\n",
            "hyperopt                      0.2.7\n",
            "idna                          3.4\n",
            "imageio                       2.25.1\n",
            "imageio-ffmpeg                0.4.8\n",
            "imagesize                     1.4.1\n",
            "imbalanced-learn              0.10.1\n",
            "imgaug                        0.4.0\n",
            "importlib-resources           5.12.0\n",
            "imutils                       0.5.4\n",
            "inflect                       6.0.4\n",
            "iniconfig                     2.0.0\n",
            "intel-openmp                  2023.1.0\n",
            "ipykernel                     5.5.6\n",
            "ipython                       7.34.0\n",
            "ipython-genutils              0.2.0\n",
            "ipython-sql                   0.4.1\n",
            "ipywidgets                    7.7.1\n",
            "itsdangerous                  2.1.2\n",
            "jax                           0.4.10\n",
            "jaxlib                        0.4.10+cuda11.cudnn86\n",
            "jieba                         0.42.1\n",
            "Jinja2                        3.1.2\n",
            "joblib                        1.2.0\n",
            "jsonpickle                    3.0.1\n",
            "jsonschema                    4.3.3\n",
            "jupyter-client                6.1.12\n",
            "jupyter-console               6.1.0\n",
            "jupyter_core                  5.3.0\n",
            "jupyter-server                1.24.0\n",
            "jupyterlab-pygments           0.2.2\n",
            "jupyterlab-widgets            3.0.7\n",
            "kaggle                        1.5.13\n",
            "keras                         2.12.0\n",
            "Keras-Applications            1.0.8\n",
            "Keras-Preprocessing           1.1.2\n",
            "keras-tqdm                    2.0.1\n",
            "keras-vggface                 0.6\n",
            "kiwisolver                    1.4.4\n",
            "korean-lunar-calendar         0.3.1\n",
            "langcodes                     3.3.0\n",
            "lazy_loader                   0.2\n",
            "libclang                      16.0.0\n",
            "librosa                       0.10.0.post2\n",
            "lightgbm                      3.3.5\n",
            "lit                           16.0.5\n",
            "llvmlite                      0.39.1\n",
            "locket                        1.0.0\n",
            "logical-unification           0.4.5\n",
            "LunarCalendar                 0.0.9\n",
            "lxml                          4.9.2\n",
            "Markdown                      3.4.3\n",
            "markdown-it-py                2.2.0\n",
            "MarkupSafe                    2.1.2\n",
            "matplotlib                    3.7.1\n",
            "matplotlib-inline             0.1.6\n",
            "matplotlib-venn               0.11.9\n",
            "mdurl                         0.1.2\n",
            "miniKanren                    1.0.3\n",
            "missingno                     0.5.2\n",
            "mistune                       0.8.4\n",
            "mizani                        0.8.1\n",
            "mkl                           2019.0\n",
            "ml-dtypes                     0.1.0\n",
            "mlxtend                       0.14.0\n",
            "more-itertools                9.1.0\n",
            "moviepy                       1.0.3\n",
            "mpmath                        1.3.0\n",
            "msgpack                       1.0.5\n",
            "multipledispatch              0.6.0\n",
            "multitasking                  0.0.11\n",
            "murmurhash                    1.0.9\n",
            "music21                       8.1.0\n",
            "natsort                       8.3.1\n",
            "nbclient                      0.7.4\n",
            "nbconvert                     6.5.4\n",
            "nbformat                      5.8.0\n",
            "nest-asyncio                  1.5.6\n",
            "networkx                      3.1\n",
            "nibabel                       3.0.2\n",
            "nltk                          3.8.1\n",
            "notebook                      6.4.8\n",
            "numba                         0.56.4\n",
            "numexpr                       2.8.4\n",
            "numpy                         1.22.4\n",
            "oauth2client                  4.1.3\n",
            "oauthlib                      3.2.2\n",
            "opencv-contrib-python         4.7.0.72\n",
            "opencv-python                 4.7.0.72\n",
            "opencv-python-headless        4.7.0.72\n",
            "openpyxl                      3.0.10\n",
            "opt-einsum                    3.3.0\n",
            "optax                         0.1.5\n",
            "orbax-checkpoint              0.2.1\n",
            "osqp                          0.6.2.post8\n",
            "packaging                     23.1\n",
            "palettable                    3.3.3\n",
            "pandas                        1.5.3\n",
            "pandas-datareader             0.10.0\n",
            "pandas-gbq                    0.17.9\n",
            "pandocfilters                 1.5.0\n",
            "panel                         0.14.4\n",
            "param                         1.13.0\n",
            "parso                         0.8.3\n",
            "partd                         1.4.0\n",
            "pathlib                       1.0.1\n",
            "pathy                         0.10.1\n",
            "patsy                         0.5.3\n",
            "pexpect                       4.8.0\n",
            "pickleshare                   0.7.5\n",
            "Pillow                        8.4.0\n",
            "pip                           23.1.2\n",
            "pip-tools                     6.13.0\n",
            "platformdirs                  3.3.0\n",
            "plotly                        5.13.1\n",
            "plotnine                      0.10.1\n",
            "pluggy                        1.0.0\n",
            "polars                        0.17.3\n",
            "pooch                         1.6.0\n",
            "portpicker                    1.3.9\n",
            "prefetch-generator            1.0.3\n",
            "preshed                       3.0.8\n",
            "prettytable                   0.7.2\n",
            "proglog                       0.1.10\n",
            "progressbar2                  4.2.0\n",
            "prometheus-client             0.16.0\n",
            "promise                       2.3\n",
            "prompt-toolkit                3.0.38\n",
            "prophet                       1.1.3\n",
            "proto-plus                    1.22.2\n",
            "protobuf                      3.20.3\n",
            "psutil                        5.9.5\n",
            "psycopg2                      2.9.6\n",
            "ptyprocess                    0.7.0\n",
            "py-cpuinfo                    9.0.0\n",
            "py4j                          0.10.9.7\n",
            "pyarrow                       9.0.0\n",
            "pyasn1                        0.5.0\n",
            "pyasn1-modules                0.3.0\n",
            "pycocotools                   2.0.6\n",
            "pycparser                     2.21\n",
            "pyct                          0.5.0\n",
            "pydantic                      1.10.7\n",
            "pydata-google-auth            1.7.0\n",
            "pydot                         1.4.2\n",
            "pydot-ng                      2.0.0\n",
            "pydotplus                     2.0.2\n",
            "PyDrive                       1.3.1\n",
            "pyerfa                        2.0.0.3\n",
            "pygame                        2.3.0\n",
            "Pygments                      2.14.0\n",
            "PyGObject                     3.36.0\n",
            "pymc                          5.1.2\n",
            "PyMeeus                       0.5.12\n",
            "pymystem3                     0.2.0\n",
            "PyOpenGL                      3.1.6\n",
            "pyparsing                     3.0.9\n",
            "pyproject_hooks               1.0.0\n",
            "pyrsistent                    0.19.3\n",
            "PySocks                       1.7.1\n",
            "pytensor                      2.10.1\n",
            "pytest                        7.2.2\n",
            "python-apt                    0.0.0\n",
            "python-dateutil               2.8.2\n",
            "python-louvain                0.16\n",
            "python-slugify                8.0.1\n",
            "python-utils                  3.5.2\n",
            "pytz                          2022.7.1\n",
            "pytz-deprecation-shim         0.1.0.post0\n",
            "pyviz-comms                   2.2.1\n",
            "PyWavelets                    1.4.1\n",
            "PyYAML                        6.0\n",
            "pyzmq                         23.2.1\n",
            "qdldl                         0.1.7\n",
            "qudida                        0.0.4\n",
            "regex                         2022.10.31\n",
            "requests                      2.27.1\n",
            "requests-oauthlib             1.3.1\n",
            "requests-unixsocket           0.2.0\n",
            "requirements-parser           0.5.0\n",
            "rich                          13.3.4\n",
            "rpy2                          3.5.5\n",
            "rsa                           4.9\n",
            "scikit-image                  0.19.3\n",
            "scikit-learn                  1.2.2\n",
            "scipy                         1.10.1\n",
            "scs                           3.2.3\n",
            "seaborn                       0.12.2\n",
            "Send2Trash                    1.8.0\n",
            "setuptools                    67.7.2\n",
            "shapely                       2.0.1\n",
            "six                           1.16.0\n",
            "sklearn-pandas                2.2.0\n",
            "smart-open                    6.3.0\n",
            "sniffio                       1.3.0\n",
            "snowballstemmer               2.2.0\n",
            "sortedcontainers              2.4.0\n",
            "soundfile                     0.12.1\n",
            "soupsieve                     2.4.1\n",
            "soxr                          0.3.5\n",
            "spacy                         3.5.2\n",
            "spacy-legacy                  3.0.12\n",
            "spacy-loggers                 1.0.4\n",
            "Sphinx                        3.5.4\n",
            "sphinxcontrib-applehelp       1.0.4\n",
            "sphinxcontrib-devhelp         1.0.2\n",
            "sphinxcontrib-htmlhelp        2.0.1\n",
            "sphinxcontrib-jsmath          1.0.1\n",
            "sphinxcontrib-qthelp          1.0.3\n",
            "sphinxcontrib-serializinghtml 1.1.5\n",
            "SQLAlchemy                    2.0.10\n",
            "sqlparse                      0.4.4\n",
            "srsly                         2.4.6\n",
            "statsmodels                   0.13.5\n",
            "sympy                         1.11.1\n",
            "tables                        3.8.0\n",
            "tabulate                      0.8.10\n",
            "tblib                         1.7.0\n",
            "tenacity                      8.2.2\n",
            "tensorboard                   2.12.2\n",
            "tensorboard-data-server       0.7.0\n",
            "tensorboard-plugin-wit        1.8.1\n",
            "tensorflow                    2.12.0\n",
            "tensorflow-datasets           4.9.2\n",
            "tensorflow-estimator          2.12.0\n",
            "tensorflow-gcs-config         2.12.0\n",
            "tensorflow-hub                0.13.0\n",
            "tensorflow-io-gcs-filesystem  0.32.0\n",
            "tensorflow-metadata           1.13.1\n",
            "tensorflow-probability        0.20.1\n",
            "tensorstore                   0.1.36\n",
            "termcolor                     2.3.0\n",
            "terminado                     0.17.1\n",
            "text-unidecode                1.3\n",
            "textblob                      0.17.1\n",
            "tf-slim                       1.1.0\n",
            "thinc                         8.1.9\n",
            "threadpoolctl                 3.1.0\n",
            "tifffile                      2023.4.12\n",
            "tinycss2                      1.2.1\n",
            "toml                          0.10.2\n",
            "tomli                         2.0.1\n",
            "toolz                         0.12.0\n",
            "torch                         2.0.1+cu118\n",
            "torchaudio                    2.0.2+cu118\n",
            "torchdata                     0.6.1\n",
            "torchsummary                  1.5.1\n",
            "torchtext                     0.15.2\n",
            "torchvision                   0.15.2+cu118\n",
            "tornado                       6.3.1\n",
            "tqdm                          4.65.0\n",
            "traitlets                     5.7.1\n",
            "triton                        2.0.0\n",
            "tweepy                        4.13.0\n",
            "typer                         0.7.0\n",
            "types-setuptools              67.8.0.0\n",
            "typing_extensions             4.5.0\n",
            "tzdata                        2023.3\n",
            "tzlocal                       4.3\n",
            "uritemplate                   4.1.1\n",
            "urllib3                       1.26.15\n",
            "vega-datasets                 0.9.0\n",
            "wasabi                        1.1.1\n",
            "wcwidth                       0.2.6\n",
            "webcolors                     1.13\n",
            "webencodings                  0.5.1\n",
            "websocket-client              1.5.1\n",
            "Werkzeug                      2.3.0\n",
            "wheel                         0.40.0\n",
            "widgetsnbextension            3.6.4\n",
            "wordcloud                     1.8.2.2\n",
            "wrapt                         1.14.1\n",
            "xarray                        2022.12.0\n",
            "xarray-einstats               0.5.1\n",
            "xgboost                       1.7.5\n",
            "xlrd                          2.0.1\n",
            "yellowbrick                   1.5\n",
            "yfinance                      0.2.18\n",
            "zict                          3.0.0\n",
            "zipp                          3.15.0\n"
          ]
        }
      ],
      "source": [
        "!pip list"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LKEp7AOU8Glq"
      },
      "source": [
        "# IMPORT LIBRERIE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FppSbBU294dG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7fa8a9c-c223-4fe1-e2af-cb5ae8391cdb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Don't know how to reset  #, please run `%reset?` for details\n",
            "Don't know how to reset  reset, please run `%reset?` for details\n",
            "Don't know how to reset  di, please run `%reset?` for details\n",
            "Don't know how to reset  tutte, please run `%reset?` for details\n",
            "Don't know how to reset  le, please run `%reset?` for details\n",
            "Don't know how to reset  variabili, please run `%reset?` for details\n",
            "Flushing input history\n",
            "Don't know how to reset  maniera, please run `%reset?` for details\n",
            "Don't know how to reset  forzata, please run `%reset?` for details\n"
          ]
        }
      ],
      "source": [
        "%reset -f                                                                       # Reset di tutte le variabili in maniera forzata"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UyXFAEfC7zmf"
      },
      "outputs": [],
      "source": [
        "import numpy as np                                                              # Importa Numpy\n",
        "import pandas as pd                                                             # Importa Pandas\n",
        "import random as rnd                                                            # Importa random\n",
        "import tensorflow as tf                                                         # Importa tensorflow\n",
        "import seaborn as sns                                                           # Importa seaborn\n",
        "import os                                                                       # Importa os\n",
        "import sklearn                                                                  # Importa Scikit-Learn\n",
        "import scipy                                                                    # Importa Scipy\n",
        "import csv                                                                      # Importa CSV\n",
        "import logging                                                                  # Importa logger\n",
        "import gc                                                                       # Importa il Garbage Collector\n",
        "import skimage.io as io                                                         # Importa il modulo Input/ouput di SK-Image\n",
        "import matplotlib.pyplot as plt                                                 # Importa il modulo pyplot di MatPlotLib\n",
        "import matplotlib.patches as mpatches                                           # Importa il modulo patches di MatPlotLib\n",
        "\n",
        "from skimage.transform import resize                                            # Importa il modulo resize da SK-Image\n",
        "from matplotlib.collections import PatchCollection                              # Importa PatchCollection dal modulo collections di MatPlotLib\n",
        "from PIL import Image                                                           # Importa il modulo Image da PIL\n",
        "from tensorflow import keras                                                    # Importa il modulo Keras di TensorFlow\n",
        "from glob import glob                                                           # Importa il modulo glob di Glob\n",
        "from IPython.core.display import mimetypes                                      # Importa il modulo mimetypes dal modulo display del modulo core di IPython\n",
        "from keras_vggface.vggface import VGGFace                                       # Importa il modulo VGGFace dal modulo vggface di keras_vggface\n",
        "from keras.models import Model                                                  # Importa il modulo Model dal modulo models di keras di tensorflow\n",
        "from keras.layers import Input, Lambda, Dense                                   # Importa i moduli Input, Lambda e Dense dal modulo layers di keras di tensorflow\n",
        "from keras.optimizers import Adam                                               # Importa il modulo Adam dal modulo optimizers di keras di tensorflow\n",
        "from keras_tqdm import TQDMNotebookCallback                                     # Importa il modulo TQDMNotebookCallback da Keras-Taqaddum\n",
        "from keras.models import Model, Sequential                                      # Importa i moduli Model e Sequential da keras.models\n",
        "from keras.layers import Input, Flatten, Dense, Dropout                         # Importa i moduli Input, Flatten, Dense e Dropout da keras.layers\n",
        "from keras.applications import ResNet50                                         # Importa il modulo ResNet50 da keras.applications\n",
        "from keras.callbacks import ModelCheckpoint                                     # Importa il modulo ModelCheckpoint da keras.callbacks\n",
        "from keras.callbacks import ReduceLROnPlateau                                   # Importa il modulo ReduceLROnPlateau da keras.callbacks\n",
        "from keras.callbacks import EarlyStopping                                       # Importa il modulo EarlyStopping da keras.callbacks\n",
        "from keras.callbacks import CSVLogger                                           # Importa il modulo CSVLogger da keras.callbacks\n",
        "from sklearn.model_selection import train_test_split                            # Importa il modulo train_test_split da sklearn.model_selection\n",
        "from sklearn.decomposition import PCA, IncrementalPCA                           # Importa il modulo PCA e IncrementalPCA da sklearn.decomposition\n",
        "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, OneHotEncoder     # Importa i moduli MinMaxScaler, LabelEncoder e OneHotEncoder da sklearn.preprocessing\n",
        "from keras.utils import to_categorical                                          # Importa il modulo to_categorical di Keras.util\n",
        "from tqdm import tqdm                                                           # Importa il modulo tqdm da Taqaddum\n",
        "from sklearn.metrics import confusion_matrix                                    # Importa il modulo confusion_matrix di Metrics di SK-Learn\n",
        "from imblearn.over_sampling import SMOTE                                        # Importa il modulo SMOTE di Over_Sampling di Imblearn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3PrVLTP797VV"
      },
      "source": [
        "Alcuni valori vengono calcolati più e più volte, per riutilizzare i valori calcolati secondo necessità ottimizzando l'esecuzione, implementiamo il modulo lru_cache di functools. Essa consente alla funzione di memorizzare valori già calcolati e di riutilizzarli quando necessario."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S91ydo419zbz"
      },
      "outputs": [],
      "source": [
        "from functools import lru_cache                                                 # Importa il modulo lru_cache da functools"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5wLyJTN-ATa"
      },
      "source": [
        "Configuriamo la memoria della GPU in modo che non sia preallocata e sarà in grado di crescere quando ne avrà bisogno. Ciò comporterà un minore utilizzo della memoria dato che le opzioni predefinite prevedono l'utilizzo della totalità della memoria."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jHbQ3AE892Ko"
      },
      "outputs": [],
      "source": [
        "config = tf.compat.v1.ConfigProto()                                             # Crea una configurazione per la sessione che sta per essere eseguita\n",
        "config.gpu_options.allow_growth = True                                          # Configura la sessione per allocare memoria solo se necessario.\n",
        "sess = tf.compat.v1.Session(config = config)                                    # Imposta la sessione con la configurazione scelta"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7u7HzEk8agAz"
      },
      "source": [
        "# PATH"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tiq1Saj4aj8y"
      },
      "outputs": [],
      "source": [
        "# PATH DEL PROGETTO SU GOOGLE DRIVE\n",
        "project_path = '/content/drive/MyDrive/ESM/KinDeNet/'\n",
        "\n",
        "# PATH DEL LOG\n",
        "log_path = project_path + 'app.log'\n",
        "\n",
        "# PATH DEL DATASET E DEI BATCH\n",
        "dataset_path = project_path + 'Dataset_2019/'\n",
        "batch_path = dataset_path + 'FIDs'\n",
        "\n",
        "# PATH DEL DATASET SPLITTATO IN TRAINING - VALIDATION - TEST (SIA CON PER LA TRIPLET LOSS CHE SENZA)\n",
        "dataset_splitted_path = project_path + 'Dataset_Splitted/'\n",
        "\n",
        "training_path = dataset_splitted_path + 'Training'\n",
        "validation_path = dataset_splitted_path + 'Validation'\n",
        "test_path = dataset_splitted_path + 'Test'\n",
        "\n",
        "trainingTL_path = dataset_splitted_path + 'Training_TL'\n",
        "validationTL_path = dataset_splitted_path + 'Validation_TL'\n",
        "testTL_path = dataset_splitted_path + 'Test_TL'\n",
        "\n",
        "# PATH DEI CSV UTILI PER LA TRIPLET LOSS (ANCHOR-POSITIVE-NEGATIVE) PER IL TRAINING E VALIDATION\n",
        "anchorTR_path = trainingTL_path + '/anchor.csv'\n",
        "positiveTR_path = trainingTL_path + '/positive.csv'\n",
        "negativeTR_path = trainingTL_path + '/negative.csv'\n",
        "\n",
        "anchorV_path = validationTL_path + '/anchor.csv'\n",
        "positiveV_path = validationTL_path + '/positive.csv'\n",
        "negativeV_path = validationTL_path + '/negative.csv'\n",
        "\n",
        "# PATH DEI PESI SALVATI\n",
        "ResWeights_path = project_path + 'Weights/'\n",
        "\n",
        "# PATH DELLE FEATURE E DELLE LABELS\n",
        "features_labels_pathTR = project_path + 'FeaturesAndLabels/TRAINING/'\n",
        "features_labels_pathV = project_path + 'FeaturesAndLabels/VALIDATION/'\n",
        "features_labels_pathT = project_path + 'FeaturesAndLabels/TEST/'\n",
        "\n",
        "# PATH DEL MODELLO SALVATO DELLA RETE SIAMESE PER LA TRIPLET LOSS\n",
        "siameseRES_model_path = project_path + '/Models/siameseRES_model.h5'\n",
        "\n",
        "# PATH LOCALI PER OTTIMIZZAZIONE DELLA VELOCITÀ DI ESECUZIONE\n",
        "local_batch_path = '/content/FIDs'\n",
        "\n",
        "local_anchorTR_path = '/content/anchorTR.csv'\n",
        "local_positiveTR_path = '/content/positiveTR.csv'\n",
        "local_negativeTR_path = '/content/negativeTR.csv'\n",
        "\n",
        "local_anchorV_path = '/content/anchorV.csv'\n",
        "local_positiveV_path = '/content/positiveV.csv'\n",
        "local_negativeV_path = '/content/negativeV.csv'\n",
        "\n",
        "local_ResWeights_path = '/content/ResWeights.hdf5'\n",
        "local_CSVLogger_path = '/content/CSVLogger.csv'\n",
        "\n",
        "local_img1TR_path = '/content/img1TR.csv'\n",
        "local_img2TR_path = '/content/img2TR.csv'\n",
        "local_labelTR_path = '/content/labelTR.csv'\n",
        "\n",
        "local_img1V_path = '/content/img1V.csv'\n",
        "local_img2V_path = '/content/img2V.csv'\n",
        "local_labelV_path = '/content/labelV.csv'\n",
        "\n",
        "local_img1T_path = '/content/img1T.csv'\n",
        "local_img2T_path = '/content/img2T.csv'\n",
        "local_labelT_path = '/content/labelT.csv'\n",
        "\n",
        "local_CSVLoggerClassifier_path = '/content/CSVLoggerClassifier.csv'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LvJTmHVg8re0"
      },
      "source": [
        "# VARIABILI INIZIALI GLOBALI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iN5OXoEO8uRh"
      },
      "outputs": [],
      "source": [
        "input_dimension = 224                                                           # Dimensione di input per le immagini quadrate in ingresso\n",
        "input_channels = 3                                                              # Canali di input per le immagini in ingresso\n",
        "\n",
        "num_classes = 12                                                                # Numero totale di classi: 11 gradi di parentela + 'na'\n",
        "\n",
        "feature_dimension = 3840                                                        # Dimensione dei vettori di features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "527dXfmqyLo9"
      },
      "source": [
        "# SET DEL LOGGER"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qWK2r7JuyQH-"
      },
      "outputs": [],
      "source": [
        "log_file = open(log_path, 'w')                                                  # Apertura del file di log (in modalità scrittura)\n",
        "\n",
        "# CONFIGURAZIONE DEL LOGGER\n",
        "logging.basicConfig(filename = log_path,\n",
        "                    level = logging.INFO,\n",
        "                    format ='%(asctime)s %(levelname)s: %(message)s',\n",
        "                    force = True)\n",
        "logger = logging.getLogger(__name__)\n",
        "\n",
        "logger.info(\"Logger Avviato\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pGqDC-PstkvA"
      },
      "source": [
        "# DATASET IN LOCALE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QjSjesGQxvqx"
      },
      "outputs": [],
      "source": [
        "# UNZIP DEL DATASET IN LOCALE (permette una maggiore velocità di accesso alle immagini rispetto al Drive)\n",
        "!unzip /content/drive/MyDrive/ESM/KinDeNet/FIDs.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "70kAQ6kMbye2"
      },
      "source": [
        "# FUNZIONI PER IL SALVATAGGIO E CARICAMENTO SU FILE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhaOaXi1KOHF"
      },
      "source": [
        "Utilizziamo prettamente file .npy per salvare le varie variabili per non occupare memoria.\n",
        "\n",
        "Questo tipo di file gode di un'estrema velocità di lettura e scrittura.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_34SA01UMxH4"
      },
      "outputs": [],
      "source": [
        "def array_on_file(path, array):\n",
        "  '''\n",
        "      Salva un array su un file binario\n",
        "        # PRECONDIZIONE: array caricato\n",
        "        # POSTCONDIZIONE: restituisce un file con gli elementi dell'array\n",
        "\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        path: percorso del file\n",
        "            string\n",
        "        array:\n",
        "            numpy.ndarray\n",
        "  '''\n",
        "  with open(path, 'wb') as f:                                                   # wb = write binary\n",
        "    np.save(f, array)\n",
        "  f.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o9kQuYkSM0EL"
      },
      "outputs": [],
      "source": [
        "def array_from_file(path):\n",
        "  '''\n",
        "      Legge un array da un file binario\n",
        "        # PRECONDIZIONE: array salvato in un file binario\n",
        "        # POSTCONDIZIONE: restituisce un array\n",
        "\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        path: percorso del file\n",
        "            string\n",
        "\n",
        "      ----------\n",
        "      RETURN:\n",
        "        numpy.ndarray\n",
        "  '''\n",
        "  with open(path, 'rb') as f:                                                   # rb = read binary\n",
        "    array = np.load(f, allow_pickle = True)                                     # Consente il caricamento di oggetti complessi salvati in precedenza (liste, np.array)\n",
        "  return array"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_XyMVfo4yui"
      },
      "source": [
        "Utilizziamo prettamente file csv per memorizzare liste per la comodità di utilizzo tramite libreria pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dxi0aJcbbwWC"
      },
      "outputs": [],
      "source": [
        "def list_on_csv(path, listname, filename):\n",
        "  '''\n",
        "      Salva una lista su un file csv\n",
        "        # PRECONDIZIONE: lista in input\n",
        "        # POSTCONDIZIONE: restituisce un csv con gli elementi della lista\n",
        "\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        path: percorso della cartella contenente il file\n",
        "            str\n",
        "        listname: lista da salvare\n",
        "            list\n",
        "        filename: nome del file\n",
        "            string (\"filename.ext\")\n",
        "  '''\n",
        "  df = pd.DataFrame(listname)                                                   # Creazione di una variabile Dataframe\n",
        "  df.to_csv(path + '/' + filename + '.csv', header=None, index=None)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def list_on_csv_wH(path, listname, filename):\n",
        "  '''\n",
        "      Salva una lista su un file csv con l'header incluso\n",
        "        # PRECONDIZIONE: lista in input\n",
        "        # POSTCONDIZIONE: restituisce un csv con gli elementi della lista\n",
        "\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        path: percorso della cartella contenente il file\n",
        "            str\n",
        "        listname: lista da salvare\n",
        "            list\n",
        "        filename: nome del file\n",
        "            string (\"filename.ext\")\n",
        "  '''\n",
        "  df = pd.DataFrame(listname)                                                   # Creazione di una variabile Dataframe\n",
        "  df.to_csv(path + '/' + filename + '.csv', header=True, index=None)"
      ],
      "metadata": {
        "id": "Q2omTNA6m9Vk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BpACRIY8DNEv"
      },
      "outputs": [],
      "source": [
        "def list_from_csv(path):\n",
        "  '''\n",
        "      Legge una lista da un file csv\n",
        "        # PRECONDIZIONE: lista salvata su un file csv\n",
        "        # POSTCONDIZIONE: restituisce una lista\n",
        "\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        path: percorso del file\n",
        "            string\n",
        "\n",
        "      ----------\n",
        "      RETURN:\n",
        "        list\n",
        "  '''\n",
        "  list_csv = list()\n",
        "  df = pd.read_csv(path, delimiter=',')                                         # Creazione di un Dataframe dalla lettura del CSV\n",
        "  list_csv = df.values.tolist()                                                 # Conversione della variabile DataFrame in Lista\n",
        "\n",
        "  return list_csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmGQ-L3zqL6h"
      },
      "source": [
        "# FUNZIONI PER IL PREPROCESSING SUGLI INPUT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wiifJhftV69K"
      },
      "outputs": [],
      "source": [
        "def img_preprocessing(img_path, dimension):\n",
        "  '''\n",
        "    Stretcha e normalizza le immagini in ingresso\n",
        "      # PRECONDIZIONE: path dell'immagine da processare\n",
        "      # POSTCONDIZIONE: immagine pronta per la rete\n",
        "\n",
        "    ----------\n",
        "    PARAMETRI:\n",
        "      img_path: indirizzo dell'immagine da processare\n",
        "          string\n",
        "      dimension: dimensione desiderata per l'immagine\n",
        "          int\n",
        "\n",
        "    ----------\n",
        "    RETURN:\n",
        "      img_stretched: immagine stretchata e normalizzata\n",
        "          numpy.array\n",
        "  '''\n",
        "  img = np.float32(io.imread(img_path))/255                                     # Legge e normalizza le immagini\n",
        "  img_stretched = resize(img, (dimension, dimension), order=1)                  # Ridimensiona le immagini e applica interpolazione bilineare\n",
        "\n",
        "  return img_stretched"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0561mCsPz8-4"
      },
      "source": [
        "**Lazy Loading:** per ridurre l'elevato carico di memoria durante l'addestramento del modello, abbiamo implementato dei generatori personalizzati che caricano e preelaborano le triple di immagini per la rete siamese e le coppie di immagini per la rete con fine tuning in modo dinamico, solo quando vengono richieste durante l'iterazione dell'addestramento. Questo approccio ci consente di ridurre il consumo complessivo di memoria, in quanto le immagini vengono caricate in memoria solo quando effettivamente necessario."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9Gh_GrgwXw47"
      },
      "outputs": [],
      "source": [
        "def preprocess_triplets(anchor_path, positive_path, negative_path, dimension, batch_size):\n",
        "  '''\n",
        "    Generatore della tripla per la TRIPLET LOSS\n",
        "      # PRECONDIZIONE: immagini di anchor, positive embedded e negative embedded\n",
        "      # POSTCONDIZIONE: tripla di immagini in batch pronte per il generatore della TRIPLET LOSS\n",
        "\n",
        "    ----------\n",
        "    PARAMETRI:\n",
        "      anchor_path: path dell'input di riferimento\n",
        "          string\n",
        "      positive_path: path dell'input positivo corrispondente\n",
        "          string\n",
        "      negative_path: path dell'input negativo corrispondente\n",
        "          string\n",
        "      dimension: dimensione delle immagini\n",
        "          int\n",
        "      batch_size: dimensione dei batch\n",
        "          int\n",
        "  '''\n",
        "  # LETTURA DEI CSV PER ANCHOR-POSITIVE-NEGATIVE\n",
        "  anchor_list = list_from_csv(anchor_path)\n",
        "  positive_list = list_from_csv(positive_path)\n",
        "  negative_list = list_from_csv(negative_path)\n",
        "\n",
        "  # CALCOLO DEL NUMERO TOTALE DI CAMPIONI NELL'ANCHOR LIST\n",
        "  num_samples = len(anchor_list)\n",
        "  all_indices = np.arange(num_samples)\n",
        "\n",
        "  while True:                                                                   # Ciclo infinito per creare i batch in modo continuo\n",
        "\n",
        "    np.random.shuffle(all_indices)                                              # Mischia gli indici\n",
        "    unused_indices = all_indices.copy()                                         # Copia gli indici in una lista che conterrà gli indici inutilizzati (inizialmente tutti)\n",
        "\n",
        "    while len(unused_indices) >= batch_size:                                    # Ciclo iterativo precondizionato se ci sono abbastanza indici inutilizzati\n",
        "      indices = unused_indices[:batch_size]                                     # Prendi gli indici per il batch\n",
        "      unused_indices = unused_indices[batch_size:]                              # Rimuovi gli indici utilizzati\n",
        "\n",
        "      # CREAZIONE LISTE VUOTE PER ANCHOR-POSITIVE-NEGATIVE\n",
        "      batch_anchor = list()\n",
        "      batch_positive = list()\n",
        "      batch_negative = list()\n",
        "\n",
        "      for index in indices:                                                     # Scorre i vari indici\n",
        "\n",
        "        # PRE-PROCESSING SULLE IMMAGINI PER IL BATCH DI ANCHOR-POSITIVE-NEGATIVE\n",
        "        batch_anchor.append(img_preprocessing(anchor_list[index][0], dimension))\n",
        "        batch_positive.append(img_preprocessing(positive_list[index][0], dimension))\n",
        "        batch_negative.append(img_preprocessing(negative_list[index][0], dimension))\n",
        "\n",
        "      # CONVERSIONE DELLE LISTE DI ANCHOR-POSITIVE-NEGATIVE IN NUMPY-ARRAY\n",
        "      batch_anchor = np.array(batch_anchor)\n",
        "      batch_positive = np.array(batch_positive)\n",
        "      batch_negative = np.array(batch_negative)\n",
        "\n",
        "      yield [batch_anchor, batch_positive, batch_negative], batch_anchor        # Restituisce i batch come output di una funzione generatrice\n",
        "      # N.B. Viene restituito anche batch_anchor come target anche se non viene utilizzato durante l'addestraemento\n",
        "\n",
        "      del batch_anchor, batch_positive, batch_negative                          # Libera la GPU-RAM dealloccando gli array del batch\n",
        "\n",
        "  # RIAVVIO DEGLI INDICI AL MOMENTO CHE TUTTI GLI INDICI SONO STATI UTILIZZATI\n",
        "  if len(unused_indices) == 0:\n",
        "    unused_indices = all_indices.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W4eOvijRsqk4"
      },
      "outputs": [],
      "source": [
        "def preprocess_couplets_binary(image1_path, image2_path, labels_path, dimension, batch_size):\n",
        "  '''\n",
        "    Generatore delle coppie per il classificatore Binario\n",
        "      # PRECONDIZIONE: immagine 1 e 2 embedded\n",
        "      # POSTCONDIZIONE: coppia di immagini pronte per il Classificatore Binario\n",
        "\n",
        "    ----------\n",
        "    PARAMETRI:\n",
        "      image1_path: path dei csv della prima immagine\n",
        "          string\n",
        "      image2_path: path dei csv della seconda immagine\n",
        "          string\n",
        "      labels_path: path dei csv delle labels\n",
        "          string\n",
        "      dimension: dimensione delle immagini\n",
        "          int\n",
        "      batch_size: dimensione dei batch\n",
        "          int\n",
        "  '''\n",
        "\n",
        "  # DEFINIZIONE DI NA\n",
        "  na = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]\n",
        "\n",
        "  # LETTURA DEI CSV PER IMAGE1-IMAGE2\n",
        "  image1_list = list_from_csv(image1_path)\n",
        "  image2_list = list_from_csv(image2_path)\n",
        "  labels = list_from_csv(labels_path)\n",
        "\n",
        "  # CALCOLO DEL NUMERO TOTALE DI CAMPIONI DELLA PRIMA IMMAGINE DELLE COPPIE\n",
        "  num_samples = len(image1_list)\n",
        "  all_indices = np.arange(num_samples)\n",
        "\n",
        "  while True:                                                                   # Ciclo infinito per creare i batch in modo continuo\n",
        "\n",
        "    np.random.shuffle(all_indices)                                              # Mischia gli indici\n",
        "    unused_indices = all_indices.copy()                                         # Copia gli indici in una lista che conterrà gli indici inutilizzati (inizialmente tutti)\n",
        "\n",
        "    while len(unused_indices) >= batch_size:                                    # Ciclo iterativo precondizionato se ci sono abbastanza indici inutilizzati\n",
        "      indices = unused_indices[:batch_size]                                     # Prendi gli indici per il batch\n",
        "      unused_indices = unused_indices[batch_size:]                              # Rimuovi gli indici utilizzati\n",
        "\n",
        "      # CREAZIONE LISTE VUOTE PER IMG1-IMG2-LABELS\n",
        "      batch_image1 = list()\n",
        "      batch_image2 = list()\n",
        "      batch_labels = list()\n",
        "\n",
        "      for index in indices:                                                     # Scorre i vari indici\n",
        "        index1 = int(index)\n",
        "\n",
        "        # PRE-PROCESSING SULLE IMMAGINI PER IL BATCH DI IMG1-IMG2\n",
        "        batch_image1.append(img_preprocessing(image1_list[index1][0], dimension))\n",
        "        batch_image2.append(img_preprocessing(image2_list[index1][0], dimension))\n",
        "\n",
        "        # CREAZIONE DELLE LABELS 0/1\n",
        "        if (labels[index1] == na):\n",
        "          batch_labels.append(1)\n",
        "        else:\n",
        "          batch_labels.append(0)\n",
        "\n",
        "      # CONVERSIONE DELLE LISTE DI IMG1-IMG2-LABELS IN NUMPY-ARRAY\n",
        "      batch_image1 = np.array(batch_image1)\n",
        "      batch_image2 = np.array(batch_image2)\n",
        "      batch_labels = np.array(batch_labels)\n",
        "\n",
        "      yield ([batch_image1, batch_image2], batch_labels)        # Restituisce i batch come output di una funzione generatrice\n",
        "\n",
        "      del batch_image1, batch_image2, batch_labels                          # Libera la GPU-RAM dealloccando gli array del batch\n",
        "\n",
        "    # RIAVVIO DEGLI INDICI AL MOMENTO CHE TUTTI GLI INDICI SONO STATI UTILIZZATI\n",
        "    if len(unused_indices) < batch_size:\n",
        "      unused_indices = all_indices.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kXTfpMwR9JwJ"
      },
      "outputs": [],
      "source": [
        "def preprocess_couplets_3class(image1_path, image2_path, labels_path, dimension, batch_size):\n",
        "  '''\n",
        "    Generatore delle coppie per il classificatore a 3 classi\n",
        "      # PRECONDIZIONE: immagine 1 e 2 embedded\n",
        "      # POSTCONDIZIONE: coppia di immagini pronte per il Classificatore a 3 classi\n",
        "\n",
        "    ----------\n",
        "    PARAMETRI:\n",
        "      image1_path: path dei csv della prima immagine\n",
        "          string\n",
        "      image2_path: path dei csv della seconda immagine\n",
        "          string\n",
        "      labels_path: path dei csv delle labels\n",
        "          string\n",
        "      dimension: dimensione delle immagini\n",
        "          int\n",
        "      batch_size: dimensione dei batch\n",
        "          int\n",
        "  '''\n",
        "\n",
        "  # DEFINIZIONE DELLE LABELS\n",
        "  bb = np.array(0)\n",
        "  fd = np.array(1)\n",
        "  fs = np.array(2)\n",
        "  gfgd = np.array(3)\n",
        "  gfgs = np.array(4)\n",
        "  gmgd = np.array(5)\n",
        "  gmgs = np.array(6)\n",
        "  md = np.array(7)\n",
        "  ms = np.array(8)\n",
        "  na = np.array(9)\n",
        "  sibs = np.array(10)\n",
        "  ss = np.array(11)\n",
        "\n",
        "  # LETTURA DEI CSV PER IMAGE1-IMAGE2\n",
        "  image1_list = list_from_csv(image1_path)\n",
        "  image2_list = list_from_csv(image2_path)\n",
        "  labels = list_from_csv(labels_path)\n",
        "\n",
        "  # CALCOLO DEL NUMERO TOTALE DI CAMPIONI DELLA PRIMA IMMAGINE DELLE COPPIE\n",
        "  num_samples = len(image1_list)\n",
        "  all_indices = np.arange(num_samples)\n",
        "\n",
        "  while True:                                                                   # Ciclo infinito per creare i batch in modo continuo\n",
        "\n",
        "    counter = 0\n",
        "    np.random.shuffle(all_indices)                                              # Mischia gli indici\n",
        "    unused_indices = all_indices.copy()                                         # Copia gli indici in una lista che conterrà gli indici inutilizzati (inizialmente tutti)\n",
        "\n",
        "    while len(unused_indices) >= batch_size:                                    # Ciclo iterativo precondizionato se ci sono abbastanza indici inutilizzati\n",
        "      # CREAZIONE LISTE VUOTE PER IMG1-IMG2-LABELS\n",
        "      batch_image1 = list()\n",
        "      batch_image2 = list()\n",
        "      batch_labels = list()\n",
        "\n",
        "      indices = unused_indices[:batch_size]                                     # Prendi gli indici per il batch\n",
        "      unused_indices = unused_indices[batch_size:]                              # Rimuovi gli indici utilizzati\n",
        "\n",
        "      for index in indices:                                                     # Scorre i vari indici\n",
        "        label = np.argmax(np.array(labels[index]))\n",
        "        if (not (np.array_equal(label, na))):\n",
        "          # PRE-PROCESSING SULLE IMMAGINI PER IL BATCH DI IMG1-IMG2\n",
        "          batch_image1.append(img_preprocessing(image1_list[index][0], dimension))\n",
        "          batch_image2.append(img_preprocessing(image2_list[index][0], dimension))\n",
        "\n",
        "          # CREAZIONE DELLE LABELS 0/1/2\n",
        "          if (np.array_equal(label, fs) or\n",
        "            np.array_equal(label, fd) or\n",
        "            np.array_equal(label, ms) or\n",
        "            np.array_equal(label, md)):\n",
        "            batch_labels.append(0)\n",
        "          elif (np.array_equal(label, bb) or\n",
        "            np.array_equal(label, ss) or\n",
        "            np.array_equal(label, sibs)):\n",
        "            batch_labels.append(1)\n",
        "          elif (np.array_equal(label, gfgs) or\n",
        "            np.array_equal(label, gfgd) or\n",
        "            np.array_equal(label, gmgs) or\n",
        "            np.array_equal(label, gmgd)):\n",
        "            batch_labels.append(2)\n",
        "\n",
        "      # CONVERSIONE DELLE LISTE DI IMG1-IMG2-LABELS IN NUMPY-ARRAY\n",
        "      batch_image1 = np.array(batch_image1)\n",
        "      batch_image2 = np.array(batch_image2)\n",
        "      batch_labels = to_categorical(batch_labels, 3)\n",
        "      batch_labels = np.array(batch_labels)\n",
        "\n",
        "      yield ([batch_image1, batch_image2], batch_labels)        # Restituisce i batch come output di una funzione generatrice\n",
        "\n",
        "      del batch_image1, batch_image2, batch_labels                          # Libera la GPU-RAM dealloccando gli array del batch\n",
        "\n",
        "    # RIAVVIO DEGLI INDICI AL MOMENTO CHE TUTTI GLI INDICI SONO STATI UTILIZZATI\n",
        "    if len(unused_indices) < batch_size:\n",
        "      unused_indices = all_indices.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# FUNZIONI PER GRAFICI E RAPPRESENTAZIONI"
      ],
      "metadata": {
        "id": "NfdCZ8I0m_xo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uSc19HrzBzoO"
      },
      "outputs": [],
      "source": [
        "def plot_history(history, metrics):\n",
        "  '''\n",
        "    Genera una rappresentazione mediante grafico delle metriche successive al training\n",
        "      # PRECONDIZIONE: training effettuato\n",
        "      # POSTCONDIZIONE: grafico con la rappresentazione della variazione delle metriche durante il training\n",
        "\n",
        "    ----------\n",
        "    PARAMETRI:\n",
        "      history: tiene traccia delle metriche durante il training\n",
        "        History (Keras Object)\n",
        "      metrics: lista di metriche da far visualizzare\n",
        "        list\n",
        "  '''\n",
        "  for metric in metrics:                                                        # Fa il plot delle metriche definite nei parametri\n",
        "    plt.plot(history.history[metric], label=metric)\n",
        "\n",
        "  plt.xlabel('Epochs')                                                          # Etichetta per l'asse delle ascisse\n",
        "  plt.ylabel('Metrics')                                                         # Etichetta per l'asse delle ordinate\n",
        "  plt.legend()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def confusionMatrix(base_model, splitted_features, splitted_labels, flagModel):\n",
        "  '''\n",
        "    Genera una rappresentazione mediante matrice di confusione\n",
        "      # PRECONDIZIONE: base model compilato\n",
        "      # POSTCONDIZIONE: grafico con la rappresentazione della matrice di confusione\n",
        "\n",
        "    ----------\n",
        "    PARAMETRI:\n",
        "      base_model: classificatore utilizzato nel fit\n",
        "        Sequential\n",
        "      splitted_features: array di features\n",
        "        numpy.array\n",
        "      splitted_labels: array di labels\n",
        "        numpy.array\n",
        "      flagModel: indica se è un classificatore binario o n-classi (0--> binario, 1--> n-classi)\n",
        "        numpy.array\n",
        "  '''\n",
        "  # VALUTAZIONE DELLE PERFORMANCE DI LOSS E ACCURACY\n",
        "  splitted_loss, splitted_accuracy = base_model.evaluate(splitted_features, splitted_labels)\n",
        "  print('Test Loss:', splitted_loss)\n",
        "  print('Test Accuracy:', splitted_accuracy)\n",
        "\n",
        "  y_pred = base_model.predict(splitted_features)\n",
        "  if (flagModel == 0):\n",
        "    y_pred_labels = (y_pred > 0.5).astype(int)                                  # Converte le probabilità in etichette binarie\n",
        "    cm = confusion_matrix(splitted_labels, y_pred_labels)                       # Calcola la matrice di confusione\n",
        "  elif (flagModel == 1):\n",
        "    y_pred_labels = np.argmax(y_pred, axis=1)                                   # Converte le predizioni in etichette di classe\n",
        "    cm = confusion_matrix(np.argmax(splitted_labels,axis=1), y_pred_labels)     # Calcola la matrice di confusione\n",
        "  else:\n",
        "    print(\"FLAG ERRATO\")\n",
        "    return 0\n",
        "\n",
        "  # STAMPA DELLA MATRICE DI CONFUSIONE\n",
        "  plt.figure(figsize=(8, 6))\n",
        "  sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "  plt.xlabel(\"Etichetta Predetta\")\n",
        "  plt.ylabel(\"Etichetta Reale\")\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "_87WWWDwhVjO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def histogram_plot(splitted_labels, splitted_features, num_classes):\n",
        "  '''\n",
        "    Genera una rappresentazione mediante istogramma della frequenza di predizione e delle etichette vere\n",
        "      # PRECONDIZIONE: fit effettuato\n",
        "      # POSTCONDIZIONE: istogramma delle predizioni\n",
        "\n",
        "    ----------\n",
        "    PARAMETRI:\n",
        "      splitted_features: array di features\n",
        "        numpy.array\n",
        "      splitted_labels: array di labels\n",
        "        numpy.array\n",
        "      num_classes: numero di classi da rappresentare\n",
        "        int\n",
        "  '''\n",
        "  label_counts = np.sum(splitted_labels, axis=0)                                # Calcolo delle frequenze delle etichette del dataset di test\n",
        "\n",
        "  # PREDIZIONE E CALCOLO DELLA FREQUENZA DELLE PREDIZIONI\n",
        "  predictions = np.argmax(classifier_model.predict(splitted_features), axis=1)\n",
        "  class_counts = np.bincount(predictions, minlength=num_classes)\n",
        "\n",
        "  class_labels = np.arange(num_classes)                                         # Etichette delle classi\n",
        "\n",
        "  # CREAZIONE DELL'ISTOGRAMMA\n",
        "  width = 0.35\n",
        "  fig, ax = plt.subplots()\n",
        "  rects1 = ax.bar(class_labels - width/2, class_counts, width, label='Predizioni')\n",
        "  rects2 = ax.bar(class_labels + width/2, label_counts, width, label='Etichette vere')\n",
        "  ax.set_xlabel('Classe')\n",
        "  ax.set_ylabel('Frequenza')\n",
        "  ax.set_title('Predizioni e Etichette vere sul test set')\n",
        "\n",
        "  # CREAZIONE DELLA LEGGENDA\n",
        "  legend_labels = ['Predizioni', 'Etichette vere']\n",
        "  legend_colors = ['blue', 'darkorange']\n",
        "  legend_patches = [mpatches.Patch(color=c, label=l) for c, l in zip(legend_colors, legend_labels)]\n",
        "  plt.legend(handles=legend_patches)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "YldXc4ucjJcd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EoU06CdPBE74"
      },
      "source": [
        "# CREAZIONE DEI MID.CSV E DEL PAIR.CSV"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FmuCZr1gDIRw"
      },
      "source": [
        "**Esempio etichettatura persona 1 in una famiglia di 5 persone:**\n",
        "> [0 1 4 5 4 0]\n",
        "*   indice 0: 0--> persona 1 non imparentata con persona 1\n",
        "*   indice 1: 1--> persona 1 figlio di persona 2\n",
        "*   indice 2: 4--> persona 1 padre di persona 3\n",
        "*   indice 3: 5--> persona 1 marito di persona 4\n",
        "*   indice 4: 4--> persona 1 padre di persona 5\n",
        "*   indice 5: 0--> maschio\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6SDSR9EnBxPy"
      },
      "outputs": [],
      "source": [
        "@lru_cache(maxsize=None)          # Dimensione della cache (default:128, con None non diamo un limite ma consumiamo molte risorse --> Colab)\n",
        "def mid2newmid(batch_path):\n",
        "  '''\n",
        "    Crea un nuovo file csv con la rimozione delle colonne non utilizzate e la conversione in int dell'informazione sul sesso (utilizza lru_cache)\n",
        "      # PRECONDIZIONE: csv divisi per batch delle famiglie\n",
        "      # POSTCONDIZIONE: restituisce un nuovo csv da un array di interi contenente le informazioni sui batch delle famiglie\n",
        "\n",
        "    ----------\n",
        "    PARAMETRI:\n",
        "      batch_path: indirizzo dei batch delle famiglie del dataset\n",
        "          string\n",
        "  '''\n",
        "  with tqdm(total = len(glob(batch_path + '/F*')), ncols = 80) as pbar:         # Setta la barra di avanzamento\n",
        "    for fname in sorted(glob(batch_path + '/F*')):                              # Scorre i batch delle famiglie presenti nel dataset\n",
        "      file_csv = open(fname + \"/mid.csv\", \"r\")                                  # Apertura dei mid.csv del Dataset in modalità lettura\n",
        "      mid_label = list(csv.reader(file_csv, delimiter=\",\"))                     # Converte il file csv delle famiglie in una lista\n",
        "      file_csv.close()\n",
        "\n",
        "      mid_label= mid_label[1:]                                                  # Elimina la prima riga della lista (header)\n",
        "\n",
        "      for i in range(len(mid_label)):                                           # Scorre la lista con lo scopo di convertire il sesso m/f in intero 0/1\n",
        "        if (mid_label[i][-1] == 'm'):\n",
        "          mid_label[i][-1] = 0\n",
        "        elif (mid_label[i][-1] == 'f'):\n",
        "          mid_label[i][-1] = 1\n",
        "        else:\n",
        "          mid_label[i][-1] = 2\n",
        "\n",
        "      for i in range(len(mid_label)):                                           # Scorre la lista con lo scopo di eliminare delle colonne non utilizzate\n",
        "        mid_label[i].pop(-2)\n",
        "\n",
        "      np_label= np.array(mid_label, dtype=int)                                  # Conversione della lista in un NumPy-Array di interi\n",
        "      np_label = np_label[:,1:]\n",
        "\n",
        "      df = pd.DataFrame(np_label)\n",
        "      df.to_csv(fname + '/' + 'new_mid.csv', header=None, index=None)           # Salvataggio del NumPy-Array su un nuovo file csv\n",
        "\n",
        "      pbar.update(1)                                                            # Aggiorna la barra di avanzamento\n",
        "\n",
        "    pbar.set_postfix_str(\"MID.CSV Completato\")                                  # Notifica di completamento"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Do7d9JHFKqIs"
      },
      "outputs": [],
      "source": [
        "# CHIAMATA DELLA FUNZIONE PER LA CREAZIONE DEL NEW_MID.CSV\n",
        "mid2newmid(batch_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DjciNd-NniIv"
      },
      "outputs": [],
      "source": [
        "@lru_cache(maxsize=None)\n",
        "def mid2pair(fid1t, fid2t, mid1t, mid2t, kinshipt, batch_path, filename, dataset_path):\n",
        "  '''\n",
        "    Crea un nuovo file csv dove sono presenti tutte le informazioni sulle parentele di due immagini di uno specifico batch (utilizza lru_cache)\n",
        "      # PRECONDIZIONE: csv divisi per batch delle famiglie\n",
        "      # POSTCONDIZIONE: restituisce un nuovo csv contenente le parentele globali di tutto il dataset organizzato per famiglia e per coppie di persone\n",
        "\n",
        "    ----------\n",
        "    PARAMETRI:\n",
        "      fid1t: tupla per l'id della famiglia della prima persona\n",
        "          tuple\n",
        "      fid2t: tupla per l'id della famiglia della seconda persona\n",
        "          tuple\n",
        "      mid1t: tupla per l'id della prima persona\n",
        "          tuple\n",
        "      mid2t: tupla per l'id della seconda persona\n",
        "          tuple\n",
        "      kinshipt: tupla per il grado di parentela\n",
        "          tuple\n",
        "      batch_path: indirizzo dei batch delle famiglie del dataset\n",
        "          string\n",
        "      filename: nome del file csv di partenza\n",
        "          string ('filename.ext')\n",
        "      dataset_path:  indirizzo del dataset\n",
        "          string\n",
        "  '''\n",
        "  # CONVERSIONE TUPLE IN LISTE\n",
        "  fid1= list(fid1t)\n",
        "  fid2= list(fid2t)\n",
        "  mid1= list(mid1t)\n",
        "  mid2= list(mid2t)\n",
        "  kinship= list(kinshipt)\n",
        "\n",
        "  with tqdm(total = len(glob(batch_path + '/F*')), ncols = 80) as pbar1:        # Setta la barra di avanzamento\n",
        "    for fname in sorted(glob(batch_path + '/F*')):                              # Scorre i batch delle famiglie presenti nel dataset\n",
        "\n",
        "      file_csv = open(fname + \"/new_mid.csv\", \"r\")\n",
        "      list_csv = list((csv.reader(file_csv, delimiter=\",\")))                    # Converte il file csv delle famiglie in una lista\n",
        "      file_csv.close()\n",
        "\n",
        "      np_label= np.array(list_csv, dtype=int)                                   # Converto la lista in un NumPy-Array\n",
        "\n",
        "      for i in range (len(np_label)):                                           # Scorro le varie persone della famiglia presenti nel NumPy-Array\n",
        "        for j in range (i+1,len(np_label[i])-1):                                # Scorro le varie relazioni tra i familiari presenti nel NumPy-Array\n",
        "\n",
        "          fid1.append(fname[-4:])                                               # Identificativo della famiglia persona 1\n",
        "          fid2.append(fname[-4:])                                               # Identificatore della famiglia persona 2\n",
        "\n",
        "          match np_label[i][j]:\n",
        "            case 0:                                                             # NA\n",
        "              mid1.append(i+1)                                                    # Persona 1\n",
        "              mid2.append(j+1)                                                    # Persona 2\n",
        "\n",
        "              kinship.append('na')                                                # Nessuna parentela\n",
        "\n",
        "            case 1:                                                             # CHILD\n",
        "              mid1.append(j+1)                                                    # Genitore\n",
        "              mid2.append(i+1)                                                    # Figlio\n",
        "\n",
        "              if (np_label[i][-1] == 0):                                          # Figlio maschio\n",
        "                if (np_label[j][-1] == 0):                                          # Genitore maschio\n",
        "                  kinship.append('fs')                                                # Padre-Figlio\n",
        "                elif (np_label[j][-1] == 1):                                        # Genitore femmina\n",
        "                  kinship.append('ms')                                                # Madre-Figlio\n",
        "                else:                                                               # Genitore di genere non specificato\n",
        "                  kinship.append('na')                                                # Parentela non specificata\n",
        "              elif (np_label[i][-1] == 1):                                        # Figlia femmina\n",
        "                if (np_label[j][-1] == 0):                                          # Genitore maschio\n",
        "                  kinship.append('fd')                                                # Padre-Figlia\n",
        "                elif (np_label[j][-1] == 1):                                        # Genitore femmina\n",
        "                  kinship.append('md')                                                # Madre-Figlia\n",
        "                else:                                                               # Genitore di genere non specificato\n",
        "                  kinship.append('na')                                                # Parentela non specificata\n",
        "              else:                                                               # Figlio di genere non specificato\n",
        "                kinship.append('na')                                                # Parentela non specificata\n",
        "\n",
        "            case 2:                                                             # SIBLING\n",
        "              mid1.append(i+1)                                                    # Sibling1\n",
        "              mid2.append(j+1)                                                    # Sibling2\n",
        "\n",
        "              if (np_label[i][-1] == 0):                                          # Fratello\n",
        "                if (np_label[j][-1] == 0):                                          # Fratello\n",
        "                  kinship.append('bb')                                                # Fratello-Fratello\n",
        "                elif (np_label[j][-1] == 1):                                        # Sorella\n",
        "                  kinship.append('sibs')                                              # Fratello-Sorella\n",
        "                else:                                                               # Sibling2 di genere non specificato\n",
        "                  kinship.append('na')                                                # Parentela non specificata\n",
        "              elif (np_label[i][-1] == 1):                                        # Sorella\n",
        "                if (np_label[j][-1] == 0):                                          # Fratello\n",
        "                  kinship.append('sibs')                                              # Sorella-Fratello\n",
        "                elif (np_label[j][-1] == 1):                                        # Sorella\n",
        "                  kinship.append('ss')                                                # Sorella-Sorella\n",
        "                else:                                                               # Sibling2 di genere non specificato\n",
        "                  kinship.append('na')                                                # Parentela non specificata\n",
        "              else:                                                               # Sibling1 di genere non specificato\n",
        "                kinship.append('na')                                                # Parentela non specificata\n",
        "\n",
        "            case 3:                                                             # GRANDCHILD\n",
        "              mid1.append(j+1)                                                    # Grandparent\n",
        "              mid2.append(i+1)                                                    # Nipote\n",
        "\n",
        "              if (np_label[i][-1] == 0):                                          # Nipote maschio\n",
        "                if (np_label[j][-1] == 0):                                          # Nonno\n",
        "                  kinship.append('gfgs')                                              # Nonno-Nipote maschio\n",
        "                elif (np_label[j][-1] == 1):                                        # Nonna\n",
        "                  kinship.append('gmgs')                                              # Nonna-Nipote maschio\n",
        "                else:                                                               # Grandparent di genere non specificato\n",
        "                  kinship.append('na')                                                # Parentela non specificata\n",
        "              elif (np_label[i][-1] == 1):                                        # Nipote femmina\n",
        "                if (np_label[j][-1] == 0):                                          # Nonno\n",
        "                  kinship.append('gfgd')                                              # Nonno-Nipote femmina\n",
        "                elif (np_label[j][-1] == 1):                                        # Nonna\n",
        "                  kinship.append('gmgd')                                              # Nonna-Nipote femmina\n",
        "                else:                                                               # Grandparent di genere non specificato\n",
        "                  kinship.append('na')                                                # Parentela non specificata\n",
        "              else:                                                               # Nipote di genere non specificato\n",
        "                kinship.append('na')                                                # Parentela non specificata\n",
        "\n",
        "            case 4:                                                             # PARENT\n",
        "              mid1.append(i+1)                                                    # Genitore\n",
        "              mid2.append(j+1)                                                    # Figlio\n",
        "\n",
        "              if (np_label[i][-1] == 0):                                          # Genitore maschio\n",
        "                if (np_label[j][-1] == 0):                                          # Figlio maschio\n",
        "                  kinship.append('fs')                                                # Padre-Figlio\n",
        "                elif (np_label[j][-1] == 1):                                        # Figlia femmina\n",
        "                  kinship.append('fd')                                                # Padre-Figlia\n",
        "                else:                                                               # Figlio di genere non specificato\n",
        "                  kinship.append('na')                                                # Parentela non specificata\n",
        "              elif (np_label[i][-1] == 1):                                        # Genitore femmina\n",
        "                if (np_label[j][-1] == 0):                                          # Figlio maschio\n",
        "                  kinship.append('ms')                                                # Madre-Figlio\n",
        "                elif (np_label[j][-1] == 1):                                        # Figlia femmina\n",
        "                  kinship.append('md')                                                # Madre-Figlia\n",
        "                else:                                                               # Figlio di genere non specificato\n",
        "                  kinship.append('na')                                                # Parentela non specificata\n",
        "              else:                                                               # Genitore di genere non specificato\n",
        "                kinship.append('na')                                                # Parentela non specificata\n",
        "\n",
        "            case 5:                                                             # SPOUSE\n",
        "              mid1.append(i+1)                                                    # Spouse1\n",
        "              mid2.append(j+1)                                                    # Spouse2\n",
        "\n",
        "              kinship.append('na')                                                # Nessuna parentela\n",
        "\n",
        "            case 6:                                                             # GRANDPARENT\n",
        "              mid1.append(i+1)                                                    # Grandparent\n",
        "              mid2.append(j+1)                                                    # Nipote\n",
        "\n",
        "              if (np_label[i][-1] == 0):                                          # Nonno\n",
        "                if (np_label[j][-1] == 0):                                          # Nipote maschio\n",
        "                  kinship.append('gfgs')                                              # Nonno-Nipote maschio\n",
        "                elif (np_label[j][-1] == 1):                                        # Nipote femmina\n",
        "                  kinship.append('gfgd')                                              # Nonno-Nipote femmina\n",
        "                else:                                                               # Nipote di genere non specificato\n",
        "                  kinship.append('na')                                                # Parentela non specificata\n",
        "              elif (np_label[i][-1] == 1):                                        # Nonna\n",
        "                if (np_label[j][-1] == 0):                                          # Nipote maschio\n",
        "                  kinship.append('gmgs')                                              # Nonna-Nipote maschio\n",
        "                elif (np_label[j][-1] == 1):                                        # Nipote femmina\n",
        "                  kinship.append('gmgd')                                              # Nonna-Nipote femmina\n",
        "                else:                                                               # Nipote di genere non specificato\n",
        "                  kinship.append('na')                                                # Parentela non specificata\n",
        "              else:                                                               # Grandparent di genere non specificato\n",
        "                kinship.append('na')                                                # Parentela non specificata\n",
        "\n",
        "            case 7:                                                             # GREAT GRANDCHILD\n",
        "              mid1.append(j+1)                                                    # Great Grandparent\n",
        "              mid2.append(i+1)                                                    # Pronipote\n",
        "\n",
        "              kinship.append('na')                                                # Nessuna parentela (Futura implementazione per risorse limitate)\n",
        "\n",
        "            case 8:                                                             # GREAT GRANDPARENT\n",
        "              mid1.append(i+1)                                                    # Great Grandparent\n",
        "              mid2.append(j+1)                                                    # Pronipote\n",
        "\n",
        "              kinship.append('na')                                                # Nessuna parentela (Futura implementazione per risorse limitate)\n",
        "\n",
        "            case 9:                                                             # TO BE DONE\n",
        "              mid1.append(i+1)                                                    # Persona 1\n",
        "              mid2.append(j+1)                                                    # Persona 2\n",
        "\n",
        "              kinship.append('na')                                                # Nessuna parentela\n",
        "\n",
        "            case other:                                                         # DEFAULT\n",
        "              mid1.append(i+1)                                                    # Persona 1\n",
        "              mid2.append(j+1)                                                    # Persona 2\n",
        "\n",
        "              kinship.append('na')                                                # Nessuna parentela\n",
        "\n",
        "      dict = {'FID1': fid1, 'MID1': mid1,\n",
        "              'FID2': fid2, 'MID2': mid2,\n",
        "              'Parentela': kinship }                                            # Dizionario per il csv\n",
        "      df = pd.DataFrame(dict)\n",
        "      df.to_csv(dataset_path + 'pair.csv', header=None, index=None)             # Salvataggio su un file csv creato nella cartella del dataset\n",
        "\n",
        "      pbar1.update(1)                                                           # Aggiorna la barra di avanzamento\n",
        "\n",
        "    pbar1.set_postfix_str(\"PAIR.CSV Completato\")                                # Notifica di completamento della barra"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dFSqik-7goxw"
      },
      "outputs": [],
      "source": [
        "# INIZIALIZZAZIONE TUPLE PER DIZIONARIO\n",
        "fid1= tuple()\n",
        "fid2= tuple()\n",
        "mid1= tuple()\n",
        "mid2= tuple()\n",
        "kinship= tuple()\n",
        "\n",
        "# INIZIALIZZAZIONE NOME DEI FILE CSV DA CUI CREARE IL PAIR\n",
        "filename= 'new_mid.csv'\n",
        "\n",
        "# CHIAMATA DELLA FUNZIONE PER LA CREAZIONE DI PAIR.CSV\n",
        "mid2pair(fid1, fid2, mid1, mid2, kinship, batch_path, filename, dataset_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjnRt5utpp13"
      },
      "source": [
        "# SPLITTAGGIO DEL DATASET"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xawWcGCOBFFM",
        "outputId": "c7360a0f-95f3-4f7e-b5ad-631b4410a607"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "978 FAMIGLIE presenti nel DATASET:\n",
            "\t• Le FAMIGLIE per il TRAINING saranno: 686 (~70%)\n",
            "\t• Le FAMIGLIE per il VALIDATION saranno: 149 (~16%)\n",
            "\t• Le FAMIGLIE per il TEST saranno: 143 (~14%)\n"
          ]
        }
      ],
      "source": [
        "# BLOCCO DI CODICE AI FINI STATISTICI SUL NUMERO DI FAMIGLIE TOTALI E LA SCELTA DELLA DIVISIONE IN TRAINING-VALIDATION-TEST\n",
        "family_counter = 0\n",
        "for fname in sorted(glob(batch_path + '/F*')):\n",
        "  family_counter = family_counter + 1\n",
        "\n",
        "print(str(family_counter) + \" FAMIGLIE presenti nel DATASET:\")\n",
        "print(\"\\t• Le FAMIGLIE per il TRAINING saranno: \" + str(round(family_counter*0.7010)) + \" (~70%)\")\n",
        "print(\"\\t• Le FAMIGLIE per il VALIDATION saranno: \" + str(round(family_counter*0.1525)) + \" (~16%)\")\n",
        "print(\"\\t• Le FAMIGLIE per il TEST saranno: \" + str(round(family_counter*0.1465)) + \" (~14%)\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ocWcLJSmMRQ9"
      },
      "outputs": [],
      "source": [
        "@lru_cache(maxsize=None)          # Dimensione della cache (default:128, con None non diamo un limite ma consumiamo molte risorse --> Colab)\n",
        "def f_pair2npapath(dataset_path, local_batch_path, batch_path, training_path, validation_path, test_path, ntraining, nvalidation, ntest, flag):\n",
        "  '''\n",
        "    Dal csv pair crea degli NumPy-Array contenente i path delle immagini e le relazioni tra le persone della stessa famiglia (utilizza lru_cache)\n",
        "      # PRECONDIZIONE: csv contenente le parentele globali di tutto il dataset organizzato per famiglia e per coppie di persone\n",
        "      # POSTCONDIZIONE: restituisce NumPy-Array nei path di training, validation e test\n",
        "\n",
        "    ----------\n",
        "    PARAMETRI:\n",
        "      dataset_path: indirizzo del dataset\n",
        "          string\n",
        "      local_batch_path: indirizzo locale dei batch delle famiglie del dataset\n",
        "          string\n",
        "      batch_path: indirizzo dei batch delle famiglie del dataset\n",
        "          string\n",
        "      training_path: indirizzo della cartella per il training\n",
        "          string\n",
        "      validation_path: indirizzo della cartella per il validation\n",
        "          string\n",
        "      test_path: indirizzo della cartella per il test\n",
        "          string\n",
        "      ntraining: numero di famiglie in training\n",
        "          int\n",
        "      nvalidation: numero di famiglie in validation\n",
        "          int\n",
        "      ntest: numero di famiglie in test\n",
        "          int\n",
        "      flag: 0--> splittaggio senza relazioni 'na' (triplet loss), 1---> splittaggio completo\n",
        "          int\n",
        "  '''\n",
        "  logger.info(\"INIZIO della funzione di Splittaggio\")\n",
        "\n",
        "  counter = 0                                                                   # Inizializzazione del counter\n",
        "  old_fid = 0                                                                   # Inizializzazione del fid per controllar l'avanzamento del counter\n",
        "\n",
        "  pair_csv = open(dataset_path + \"pair.csv\", \"r\")                               # Apre il pair.csv\n",
        "  pair_label = list(csv.reader(pair_csv, delimiter=\",\"))                        # Converte il file csv delle famiglie in una lista\n",
        "  pair_csv.close()\n",
        "\n",
        "  np_label= np.array(pair_label)                                                # Conversione della lista in un NumPy-Array\n",
        "\n",
        "  with tqdm(total = len(np_label), ncols = 80) as pbar:                         # Setta la barra di avanzamento\n",
        "    for row in range (len(np_label)):\n",
        "\n",
        "      pbar.update(1)                                                            # Aggiorna la barra di avanzamento\n",
        "      if (flag != 0 and flag != 1):\n",
        "        logger.error(\"Flag errato, usa 0 per creare il pair.csv senza na e 1 per creare il pair.csv con na\")\n",
        "        pass\n",
        "\n",
        "      elif (np_label[row][4] == 'na' and flag == 0):                            # Escludiamo le relazioni na per poter ottenere facilmente anchor e positive successivamente\n",
        "        continue\n",
        "\n",
        "      else:\n",
        "        fid= 'F' + np_label[row][0]\n",
        "        mid1= 'MID' + np_label[row][1]\n",
        "        mid2= 'MID' + np_label[row][3]\n",
        "\n",
        "        mid1_path = batch_path + '/' + fid + '/' + mid1                         # Crea il path per il mid della prima persona presa in considerazione\n",
        "        mid2_path = batch_path + '/' + fid + '/' + mid2                         # Crea il path per il mid della seconda persona presa in considerazione\n",
        "\n",
        "        mid1_lpath = local_batch_path + '/' + fid + '/' + mid1                  # Crea il path locale per il mid della prima persona presa in considerazione\n",
        "        mid2_lpath = local_batch_path + '/' + fid + '/' + mid2                  # Crea il path locale per il mid della seconda persona presa in considerazione\n",
        "\n",
        "        try:\n",
        "          list_pair = list()                                                    # Inizializzazione della lista contenente le triple dei path delle due immagini e le relazioni\n",
        "\n",
        "          for fname in os.listdir(mid1_path):\n",
        "\n",
        "            path_img1 = mid1_lpath + '/' + fname                                # Path della prima immagine\n",
        "\n",
        "            try:\n",
        "              for fname in os.listdir(mid2_path):\n",
        "                path_img2 = mid2_lpath + '/' + fname                            # Path della seconda immagine\n",
        "\n",
        "                tripla = list()                                                 # Inizializzazione della tripla\n",
        "\n",
        "                tripla.append(path_img1)                                        # Aggiunta alla tripla della prima immagine\n",
        "                tripla.append(path_img2)                                        # Aggiunta alla tripla della seconda immagine\n",
        "                tripla.append(np_label[row][4])                                 # Aggiunta alla tripla della relazione tra le due persone nelle immagini\n",
        "\n",
        "                list_pair.append(tripla)                                        # Aggiunge la tripla alla lista\n",
        "\n",
        "              npa_pair = np.array(list_pair, dtype=object)                      # Conversione della lista in NumPy-Array\n",
        "\n",
        "              if (old_fid != fid):\n",
        "                counter = counter + 1                                           # Avanzament del Dataset in caso sia cambiado il Fid\n",
        "\n",
        "              if (counter <= ntraining):\n",
        "                array_on_file(training_path + '/' + fid + '_' + mid1 + '_' + mid2, npa_pair)    # Salvataggio dell'array nel training set\n",
        "\n",
        "              elif (counter <= (ntraining + nvalidation)):\n",
        "                array_on_file(validation_path + '/' + fid + '_' + mid1 + '_' + mid2, npa_pair)  # Salvataggio dell'array nel validation set\n",
        "\n",
        "              elif (counter <= (ntraining + nvalidation + ntest)):\n",
        "                array_on_file(test_path + '/' + fid + '_' + mid1 + '_' + mid2, npa_pair)        # Salvataggio dell'array nel test set\n",
        "\n",
        "              else:\n",
        "                logger.error(\"Dataset più grande di quanto stabilito, modificare le ntraining, nvalidation e ntest\")   # ERRORE: DATASET ERRATO\n",
        "                pass\n",
        "\n",
        "              old_fid = fid\n",
        "\n",
        "            except OSError as e:\n",
        "              logger.error( fid + \"_\" + mid2 + \" non trovata in iterazione interna\")        # EXCEPTION: Salta il MID2 non presente (errore di costruzione del Dataset)\n",
        "              continue\n",
        "\n",
        "        except OSError as e:\n",
        "          logger.error( fid + \"_\" + mid1 + \" non trovata in iterazione esterna\")            # EXCEPTION: Salta il MID1 non presente (errore di costruzione del Dataset)\n",
        "          continue\n",
        "\n",
        "    pbar.set_postfix_str(\"Split Completato\")                                    # Notifica di completamento\n",
        "    logger.info(\"FINE funzione di Splittaggio\")                                 # Notifica di completamento sul logger"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BNrxusm6Sj65",
        "outputId": "3ccd9c0f-8409-4be5-95b9-fb723b675e86"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|█████████████████| 14708/14708 [1:34:03<00:00,  2.61it/s, Split Completato]\n",
            "100%|███████████████████| 14708/14708 [35:53<00:00,  6.83it/s, Split Completato]\n"
          ]
        }
      ],
      "source": [
        "ntraining = 686                                                                 # Numero di famiglie in training\n",
        "nvalidation = 149                                                               # Numero di famiglie in validation\n",
        "ntest = 143                                                                     # Numero di famiglie in test\n",
        "flag_wona = 0                                                                   # Flag per indicare di non prendere le relazioni 'na'(Without NA)\n",
        "flag_wna = 1                                                                    # Flag per indicare di prendere le relazioni 'na' (WITH NA)\n",
        "\n",
        "f_pair2npapath(dataset_path, local_batch_path, batch_path, training_path, validation_path, test_path, ntraining, nvalidation, ntest, flag_wna)\n",
        "f_pair2npapath(dataset_path, local_batch_path, batch_path, trainingTL_path, validationTL_path, testTL_path, ntraining, nvalidation, ntest, flag_wona)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgTmngYBy5Ng",
        "outputId": "8914da2e-702e-4575-b61a-18d3d0884132"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "13336 RELAZIONI:\n",
            "\t• Le RELAZIONI presenti nel TRAINING sono: 9065 (~68%)\n",
            "\t• Le RELAZIONI presenti nel VALIDATION sono: 1886 (~14%)\n",
            "\t• Le RELAZIONI presenti nel TEST sono: 2385 (~18%)\n",
            "\n",
            "8290 RELAZIONI per la TRIPLET LOSS:\n",
            "\t• Le RELAZIONI presenti nel TRAINING per la TRIPLET-LOSS sono: 5782 (~70%)\n",
            "\t• Le RELAZIONI presenti nel VALIDATION per la TRIPLET-LOSS sono: 1300 (~16%)\n",
            "\t• Le RELAZIONI presenti nel TEST per la TRIPLET-LOSS sono: 1208 (~15%)\n"
          ]
        }
      ],
      "source": [
        "# BLOCCO DI CODICE AI FINI STATISTII SUL CONTEGGIO DELLE RELAZIONI\n",
        "training_counter = 0\n",
        "validation_counter = 0\n",
        "test_counter = 0\n",
        "\n",
        "for fname in os.listdir(training_path):\n",
        "  training_counter = training_counter + 1\n",
        "for fname in os.listdir(validation_path):\n",
        "  validation_counter = validation_counter + 1\n",
        "for fname in os.listdir(test_path):\n",
        "  test_counter = test_counter + 1\n",
        "\n",
        "total_counter = training_counter + validation_counter + test_counter\n",
        "print(str(total_counter) + \" RELAZIONI:\")\n",
        "print(\"\\t• Le RELAZIONI presenti nel TRAINING sono: \" + str(training_counter) + \" (~\" + str(round((training_counter/total_counter)*100)) + \"%)\")\n",
        "print(\"\\t• Le RELAZIONI presenti nel VALIDATION sono: \" + str(validation_counter) + \" (~\" + str(round((validation_counter/total_counter)*100)) + \"%)\")\n",
        "print(\"\\t• Le RELAZIONI presenti nel TEST sono: \" + str(test_counter)+ \" (~\" + str(round((test_counter/total_counter)*100)) + \"%)\\n\")\n",
        "\n",
        "\n",
        "trainingTL_counter = 0\n",
        "validationTL_counter = 0\n",
        "testTL_counter = 0\n",
        "\n",
        "# N.B. I valori di trainingTL_counter e validationTL_counter devono essere diminuiti di 3 se è già stata creato il txt per l'anchor, la positive e il negative\n",
        "for fname in os.listdir(trainingTL_path):\n",
        "  trainingTL_counter = trainingTL_counter + 1\n",
        "for fname in os.listdir(validationTL_path):\n",
        "  validationTL_counter = validationTL_counter + 1\n",
        "for fname in os.listdir(testTL_path):\n",
        "  testTL_counter = testTL_counter + 1\n",
        "\n",
        "totalTL_counter = (trainingTL_counter-3) + (validationTL_counter-3) + testTL_counter\n",
        "print(str(totalTL_counter) + \" RELAZIONI per la TRIPLET LOSS:\")\n",
        "print(\"\\t• Le RELAZIONI presenti nel TRAINING per la TRIPLET-LOSS sono: \" + str(trainingTL_counter-3) + \" (~\" + str(round(((trainingTL_counter-3)/totalTL_counter)*100)) + \"%)\")\n",
        "print(\"\\t• Le RELAZIONI presenti nel VALIDATION per la TRIPLET-LOSS sono: \" + str(validationTL_counter-3) + \" (~\" + str(round(((validationTL_counter-3)/totalTL_counter)*100)) + \"%)\")\n",
        "print(\"\\t• Le RELAZIONI presenti nel TEST per la TRIPLET-LOSS sono: \" + str(testTL_counter) + \" (~\" + str(round((testTL_counter/totalTL_counter)*100)) + \"%)\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yqFQeSogrTgl"
      },
      "source": [
        "**ERRORI DEL DATASET:**\n",
        "* 978 famiglie presenti invece di 1018 come da numerazione tramite FID;\n",
        "* Alcune famiglie con MID non classificati all'interno del mid.csv e quindi non utilizzabili (Es. F0601);\n",
        "* F0601 e F1017 con mid.csv errato con numero di relazioni per persona e numero di persone non coincidente;\n",
        "* 124 famiglie con MID assenti rispetto a quanto scritto nel mid.csv;\n",
        "* F1018 con MID5 privo di immagini;\n",
        "* 60 famiglie con cartella \"unrelated_and_nonfaces\" vuota;\n",
        "* 34 famiglie con solo relazioni \"na\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u0ESGk7AZFr1"
      },
      "source": [
        "# PREPARAZIONE DEGLI INPUT PER LA TRIPLET LOSS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2ZQMGVsY5AF"
      },
      "source": [
        "**TRIPLET LOSS:** funzione di loss in cui un input di riferimento (**ANCHOR**) viene confrontato con un input corrispondente (**POSITIVE EMBEDDING**) e un input non corrispondente (**NEGATIVE EMBEDDING**)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6pLY2KqX3BPp"
      },
      "outputs": [],
      "source": [
        "def triplet_constructor(splittedTL_path, local_batch_path, batch_path, flag):\n",
        "  '''\n",
        "      Salva una lista di path rispettivamente per le anchor, positive e negative che sfrutteremo per la Triplet Loss\n",
        "        # PRECONDIZIONE: file npy contenenti i path delle immagini e relazioni tra i vari familiari\n",
        "        # POSTCONDIZIONE: restituisce tre liste contenenti path di immagini\n",
        "\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        splittedTL_path: path per la cartella di Training/Validation/Test che sfrutteremo per la Triplet Loss\n",
        "            str\n",
        "        local_batch_path: path dei batch locali contenenti le famiglie\n",
        "            str\n",
        "        batch_path: path dei batch contenenti le famiglie\n",
        "            str\n",
        "        flag: flag per individuare se si tratta di training (0), validation (1) o test (2)\n",
        "            int\n",
        "\n",
        "      ----------\n",
        "      RETURN:\n",
        "        anchor_listpath: lista di path per le anchor\n",
        "            list\n",
        "        positive_listpath: lista di path per le positive\n",
        "            list\n",
        "        negative_listpath: lista di path per i negative\n",
        "            list\n",
        "  '''\n",
        "  anchor_listpath = list()                                                      # Inizializzazione lista per i path delle immagini di anchor\n",
        "  positive_listpath = list()                                                    # Inizializzazione lista per i path delle immagini di positive embedded\n",
        "  negative_listpath = list()                                                    # Inizializzazione lista per i path delle immagini di negative embedded\n",
        "  offset = 0\n",
        "\n",
        "  if(flag == 0):\n",
        "    offset = 74                                                                 # Offset del path in caso di training\n",
        "  elif(flag == 1):\n",
        "    offset = 76                                                                 # Offset del path in caso di validation\n",
        "  elif(flag == 2):\n",
        "    offset = 70                                                                 # Offset del path in caso di test\n",
        "  else:\n",
        "    print(\"FLAG ERRATO\")\n",
        "    return anchor_listpath, positive_listpath, negative_listpath\n",
        "\n",
        "  with tqdm(total = len(glob(splittedTL_path + '/F*')), ncols = 80) as pbar:    # Setta la barra di avanzamento\n",
        "    for fname in sorted(glob(splittedTL_path + '/F*')):                         # Scorriamo gli npy contenenti i path delle immagini e relazioni tra i vari familiari\n",
        "      pbar.update(1)                                                            # Aggiorna la barra di avanzamento\n",
        "\n",
        "      np_training = array_from_file(fname)                                      # Caricamento di un array con i contenuti del .npy tra MID di una famiglia\n",
        "\n",
        "      try:\n",
        "        for i in range(len(np_training)):\n",
        "          # Prendiamo un'immagine casuale tra gli unrelated della famiglia di anchor e positive\n",
        "          negatives = os.listdir(batch_path + '/' + fname[offset:offset+5] + '/' + 'unrelated_and_nonfaces')\n",
        "          if(len(negatives) > 0):\n",
        "            negative_listpath.append(str(local_batch_path + '/' + fname[offset:offset+5] + '/' + 'unrelated_and_nonfaces' + '/' + negatives[rnd.randint(0, len(negatives)-1)]))\n",
        "\n",
        "            anchor_listpath.append(str(np_training[i][0]))                      # Aggiungiamo la prima immagine presente negli npy come anchor\n",
        "            positive_listpath.append(str(np_training[i][1]))                    # Aggiungiamo la seconda immagine presente negli npy come positive\n",
        "          else:\n",
        "            print(\"Unrelated vuota in \" + fname[offset:offset+5])\n",
        "\n",
        "      except OSError as e:                                                      # In caso di errore nel Dataset per la presenza di una cartella unrelated vuota\n",
        "        print(\"Unrelated non presenti in \" + fname[offset:offset+5])\n",
        "        continue\n",
        "\n",
        "    pbar.set_postfix_str(\"Costruzione Triplet Completata\")                      # Notifica di completamento\n",
        "\n",
        "  return anchor_listpath, positive_listpath, negative_listpath"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "28Lk_GfY82qT"
      },
      "outputs": [],
      "source": [
        "anchor_listpathTR = list()\n",
        "positive_listpathTR = list()\n",
        "negative_listpathTR = list()\n",
        "flagTR = 0                                                                      # Flag per indicare il training\n",
        "\n",
        "anchor_listpathTR, positive_listpathTR, negative_listpathTR = triplet_constructor(trainingTL_path, local_batch_path, batch_path, flagTR)\n",
        "\n",
        "list_on_csv(trainingTL_path, anchor_listpathTR, 'anchor')                       # Creazione del csv per le anchor in training\n",
        "list_on_csv(trainingTL_path, positive_listpathTR, 'positive')                   # Creazione del csv per le positive in training\n",
        "list_on_csv(trainingTL_path, negative_listpathTR, 'negative')                   # Creazione del csv per le negative in training\n",
        "\n",
        "\n",
        "anchor_listpathV = list()\n",
        "positive_listpathV = list()\n",
        "negative_listpathV = list()\n",
        "flagV = 1                                                                       # Flag per indicare la valdiation\n",
        "\n",
        "anchor_listpathV, positive_listpathV, negative_listpathV = triplet_constructor(validationTL_path, local_batch_path, batch_path, flagV)\n",
        "\n",
        "\n",
        "list_on_csv(validationTL_path, anchor_listpathV, 'anchor')                      # Creazione del csv per le anchor in validation\n",
        "list_on_csv(validationTL_path, positive_listpathV, 'positive')                  # Creazione del csv per le positive in validation\n",
        "list_on_csv(validationTL_path, negative_listpathV, 'negative')                  # Creazione del csv per le negative in validation\n",
        "\n",
        "\n",
        "# N.B. Per il test non è necessario avere triple di immagini, eseguiamo il calcolo ai fini statistici\n",
        "anchor_listpathT = list()\n",
        "positive_listpathT = list()\n",
        "negative_listpathT = list()\n",
        "flagT = 2                                                                       # Flag per indicare il test\n",
        "\n",
        "anchor_listpathT, positive_listpathT, negative_listpathT = triplet_constructor(testTL_path, local_batch_path, batch_path, flagT)\n",
        "\n",
        "list_on_csv(testTL_path, anchor_listpathT, 'anchor')                            # Creazione del csv per le anchor in test\n",
        "list_on_csv(testTL_path, positive_listpathT, 'positive')                        # Creazione del csv per le positive in test\n",
        "list_on_csv(testTL_path, negative_listpathT, 'negative')                        # Creazione del csv per le negative in test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Thw2fdzw1bfe"
      },
      "outputs": [],
      "source": [
        "# BLOCCO DI CODICE AI FINI STATISTICI SUL NUMERO DI TRIPLE DI IMMAGINI (ANCHOR-POSITIVE-NEGATIVE), DIVISE IN TRAINING-VALIDATION-TEST\n",
        "totalTriplet_listpath = anchor_listpathTR + anchor_listpathV + anchor_listpathT\n",
        "\n",
        "print(str(len(totalTriplet_listpath)) + \" TRIPLE di IMMAGINI (ANCHOR-POSITIVE-NEGATIVE): \")\n",
        "print(\"\\t• Il numero di TRIPLE di immagini per il TRAINING sono: \" + str(len(anchor_listpathTR)-1) + \" (~\" + str(round((len(anchor_listpathTR)/len(totalTriplet_listpath))*100)) + \"%)\")\n",
        "print(\"\\t• Il numero di TRIPLE di immagini per la VALIDATION sono: \" + str(len(anchor_listpathV)-1) + \" (~\" + str(round((len(anchor_listpathV)/len(totalTriplet_listpath))*100)) + \"%)\")\n",
        "print(\"\\t• Il numero di TRIPLE di immagini per il TEST sono: \" + str(len(anchor_listpathT)) + \" (~\" + str(round((len(anchor_listpathT)/len(totalTriplet_listpath))*100)) + \"%)\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-pv1ACzf1z5"
      },
      "source": [
        "# DEFINIZIONE RETE CON TRIPLET LOSS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mHDKoYUTzb0D",
        "outputId": "630dd60e-c751-4382-df7d-589e7d58f207"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_notop_resnet50.h5\n",
            "94694792/94694792 [==============================] - 1s 0us/step\n",
            "Model: \"vggface_resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv1/7x7_s2 (Conv2D)          (None, 112, 112, 64  9408        ['input_1[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv1/7x7_s2/bn (BatchNormaliz  (None, 112, 112, 64  256        ['conv1/7x7_s2[0][0]']           \n",
            " ation)                         )                                                                 \n",
            "                                                                                                  \n",
            " activation (Activation)        (None, 112, 112, 64  0           ['conv1/7x7_s2/bn[0][0]']        \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " max_pooling2d (MaxPooling2D)   (None, 55, 55, 64)   0           ['activation[0][0]']             \n",
            "                                                                                                  \n",
            " conv2_1_1x1_reduce (Conv2D)    (None, 55, 55, 64)   4096        ['max_pooling2d[0][0]']          \n",
            "                                                                                                  \n",
            " conv2_1_1x1_reduce/bn (BatchNo  (None, 55, 55, 64)  256         ['conv2_1_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_1 (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_1[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_1_3x3/bn (BatchNormaliza  (None, 55, 55, 64)  256         ['conv2_1_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_2 (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_2[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_1_1x1_proj (Conv2D)      (None, 55, 55, 256)  16384       ['max_pooling2d[0][0]']          \n",
            "                                                                                                  \n",
            " conv2_1_1x1_increase/bn (Batch  (None, 55, 55, 256)  1024       ['conv2_1_1x1_increase[0][0]']   \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " conv2_1_1x1_proj/bn (BatchNorm  (None, 55, 55, 256)  1024       ['conv2_1_1x1_proj[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " add (Add)                      (None, 55, 55, 256)  0           ['conv2_1_1x1_increase/bn[0][0]',\n",
            "                                                                  'conv2_1_1x1_proj/bn[0][0]']    \n",
            "                                                                                                  \n",
            " activation_3 (Activation)      (None, 55, 55, 256)  0           ['add[0][0]']                    \n",
            "                                                                                                  \n",
            " conv2_2_1x1_reduce (Conv2D)    (None, 55, 55, 64)   16384       ['activation_3[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_2_1x1_reduce/bn (BatchNo  (None, 55, 55, 64)  256         ['conv2_2_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_4 (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_4[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_2_3x3/bn (BatchNormaliza  (None, 55, 55, 64)  256         ['conv2_2_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_5 (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_5[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_2_1x1_increase/bn (Batch  (None, 55, 55, 256)  1024       ['conv2_2_1x1_increase[0][0]']   \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_1 (Add)                    (None, 55, 55, 256)  0           ['conv2_2_1x1_increase/bn[0][0]',\n",
            "                                                                  'activation_3[0][0]']           \n",
            "                                                                                                  \n",
            " activation_6 (Activation)      (None, 55, 55, 256)  0           ['add_1[0][0]']                  \n",
            "                                                                                                  \n",
            " conv2_3_1x1_reduce (Conv2D)    (None, 55, 55, 64)   16384       ['activation_6[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_3_1x1_reduce/bn (BatchNo  (None, 55, 55, 64)  256         ['conv2_3_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_7 (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_7[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_3_3x3/bn (BatchNormaliza  (None, 55, 55, 64)  256         ['conv2_3_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_8 (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_8[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_3_1x1_increase/bn (Batch  (None, 55, 55, 256)  1024       ['conv2_3_1x1_increase[0][0]']   \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_2 (Add)                    (None, 55, 55, 256)  0           ['conv2_3_1x1_increase/bn[0][0]',\n",
            "                                                                  'activation_6[0][0]']           \n",
            "                                                                                                  \n",
            " activation_9 (Activation)      (None, 55, 55, 256)  0           ['add_2[0][0]']                  \n",
            "                                                                                                  \n",
            " conv3_1_1x1_reduce (Conv2D)    (None, 28, 28, 128)  32768       ['activation_9[0][0]']           \n",
            "                                                                                                  \n",
            " conv3_1_1x1_reduce/bn (BatchNo  (None, 28, 28, 128)  512        ['conv3_1_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_10 (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_10[0][0]']          \n",
            "                                                                                                  \n",
            " conv3_1_3x3/bn (BatchNormaliza  (None, 28, 28, 128)  512        ['conv3_1_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_11 (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_11[0][0]']          \n",
            "                                                                                                  \n",
            " conv3_1_1x1_proj (Conv2D)      (None, 28, 28, 512)  131072      ['activation_9[0][0]']           \n",
            "                                                                                                  \n",
            " conv3_1_1x1_increase/bn (Batch  (None, 28, 28, 512)  2048       ['conv3_1_1x1_increase[0][0]']   \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " conv3_1_1x1_proj/bn (BatchNorm  (None, 28, 28, 512)  2048       ['conv3_1_1x1_proj[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " add_3 (Add)                    (None, 28, 28, 512)  0           ['conv3_1_1x1_increase/bn[0][0]',\n",
            "                                                                  'conv3_1_1x1_proj/bn[0][0]']    \n",
            "                                                                                                  \n",
            " activation_12 (Activation)     (None, 28, 28, 512)  0           ['add_3[0][0]']                  \n",
            "                                                                                                  \n",
            " conv3_2_1x1_reduce (Conv2D)    (None, 28, 28, 128)  65536       ['activation_12[0][0]']          \n",
            "                                                                                                  \n",
            " conv3_2_1x1_reduce/bn (BatchNo  (None, 28, 28, 128)  512        ['conv3_2_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_13 (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_13[0][0]']          \n",
            "                                                                                                  \n",
            " conv3_2_3x3/bn (BatchNormaliza  (None, 28, 28, 128)  512        ['conv3_2_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_14 (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_14[0][0]']          \n",
            "                                                                                                  \n",
            " conv3_2_1x1_increase/bn (Batch  (None, 28, 28, 512)  2048       ['conv3_2_1x1_increase[0][0]']   \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_4 (Add)                    (None, 28, 28, 512)  0           ['conv3_2_1x1_increase/bn[0][0]',\n",
            "                                                                  'activation_12[0][0]']          \n",
            "                                                                                                  \n",
            " activation_15 (Activation)     (None, 28, 28, 512)  0           ['add_4[0][0]']                  \n",
            "                                                                                                  \n",
            " conv3_3_1x1_reduce (Conv2D)    (None, 28, 28, 128)  65536       ['activation_15[0][0]']          \n",
            "                                                                                                  \n",
            " conv3_3_1x1_reduce/bn (BatchNo  (None, 28, 28, 128)  512        ['conv3_3_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_16 (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_16[0][0]']          \n",
            "                                                                                                  \n",
            " conv3_3_3x3/bn (BatchNormaliza  (None, 28, 28, 128)  512        ['conv3_3_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_17 (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_17[0][0]']          \n",
            "                                                                                                  \n",
            " conv3_3_1x1_increase/bn (Batch  (None, 28, 28, 512)  2048       ['conv3_3_1x1_increase[0][0]']   \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_5 (Add)                    (None, 28, 28, 512)  0           ['conv3_3_1x1_increase/bn[0][0]',\n",
            "                                                                  'activation_15[0][0]']          \n",
            "                                                                                                  \n",
            " activation_18 (Activation)     (None, 28, 28, 512)  0           ['add_5[0][0]']                  \n",
            "                                                                                                  \n",
            " conv3_4_1x1_reduce (Conv2D)    (None, 28, 28, 128)  65536       ['activation_18[0][0]']          \n",
            "                                                                                                  \n",
            " conv3_4_1x1_reduce/bn (BatchNo  (None, 28, 28, 128)  512        ['conv3_4_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_19 (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_19[0][0]']          \n",
            "                                                                                                  \n",
            " conv3_4_3x3/bn (BatchNormaliza  (None, 28, 28, 128)  512        ['conv3_4_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_20 (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_20[0][0]']          \n",
            "                                                                                                  \n",
            " conv3_4_1x1_increase/bn (Batch  (None, 28, 28, 512)  2048       ['conv3_4_1x1_increase[0][0]']   \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_6 (Add)                    (None, 28, 28, 512)  0           ['conv3_4_1x1_increase/bn[0][0]',\n",
            "                                                                  'activation_18[0][0]']          \n",
            "                                                                                                  \n",
            " activation_21 (Activation)     (None, 28, 28, 512)  0           ['add_6[0][0]']                  \n",
            "                                                                                                  \n",
            " conv4_1_1x1_reduce (Conv2D)    (None, 14, 14, 256)  131072      ['activation_21[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_1_1x1_reduce/bn (BatchNo  (None, 14, 14, 256)  1024       ['conv4_1_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_22 (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_22[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_1_3x3/bn (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_1_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_23 (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_23[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_1_1x1_proj (Conv2D)      (None, 14, 14, 1024  524288      ['activation_21[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_1_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_1_1x1_increase[0][0]']   \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " conv4_1_1x1_proj/bn (BatchNorm  (None, 14, 14, 1024  4096       ['conv4_1_1x1_proj[0][0]']       \n",
            " alization)                     )                                                                 \n",
            "                                                                                                  \n",
            " add_7 (Add)                    (None, 14, 14, 1024  0           ['conv4_1_1x1_increase/bn[0][0]',\n",
            "                                )                                 'conv4_1_1x1_proj/bn[0][0]']    \n",
            "                                                                                                  \n",
            " activation_24 (Activation)     (None, 14, 14, 1024  0           ['add_7[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_2_1x1_reduce (Conv2D)    (None, 14, 14, 256)  262144      ['activation_24[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_2_1x1_reduce/bn (BatchNo  (None, 14, 14, 256)  1024       ['conv4_2_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_25 (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_25[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_2_3x3/bn (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_2_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_26 (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_26[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_2_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_2_1x1_increase[0][0]']   \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " add_8 (Add)                    (None, 14, 14, 1024  0           ['conv4_2_1x1_increase/bn[0][0]',\n",
            "                                )                                 'activation_24[0][0]']          \n",
            "                                                                                                  \n",
            " activation_27 (Activation)     (None, 14, 14, 1024  0           ['add_8[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_3_1x1_reduce (Conv2D)    (None, 14, 14, 256)  262144      ['activation_27[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_3_1x1_reduce/bn (BatchNo  (None, 14, 14, 256)  1024       ['conv4_3_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_28 (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_28[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_3_3x3/bn (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_3_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_29 (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_29[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_3_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_3_1x1_increase[0][0]']   \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " add_9 (Add)                    (None, 14, 14, 1024  0           ['conv4_3_1x1_increase/bn[0][0]',\n",
            "                                )                                 'activation_27[0][0]']          \n",
            "                                                                                                  \n",
            " activation_30 (Activation)     (None, 14, 14, 1024  0           ['add_9[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_4_1x1_reduce (Conv2D)    (None, 14, 14, 256)  262144      ['activation_30[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_4_1x1_reduce/bn (BatchNo  (None, 14, 14, 256)  1024       ['conv4_4_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_31 (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_31[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_4_3x3/bn (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_4_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_32 (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_32[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_4_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_4_1x1_increase[0][0]']   \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " add_10 (Add)                   (None, 14, 14, 1024  0           ['conv4_4_1x1_increase/bn[0][0]',\n",
            "                                )                                 'activation_30[0][0]']          \n",
            "                                                                                                  \n",
            " activation_33 (Activation)     (None, 14, 14, 1024  0           ['add_10[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_5_1x1_reduce (Conv2D)    (None, 14, 14, 256)  262144      ['activation_33[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_5_1x1_reduce/bn (BatchNo  (None, 14, 14, 256)  1024       ['conv4_5_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_34 (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_34[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_5_3x3/bn (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_5_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_35 (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_35[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_5_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_5_1x1_increase[0][0]']   \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " add_11 (Add)                   (None, 14, 14, 1024  0           ['conv4_5_1x1_increase/bn[0][0]',\n",
            "                                )                                 'activation_33[0][0]']          \n",
            "                                                                                                  \n",
            " activation_36 (Activation)     (None, 14, 14, 1024  0           ['add_11[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_6_1x1_reduce (Conv2D)    (None, 14, 14, 256)  262144      ['activation_36[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_6_1x1_reduce/bn (BatchNo  (None, 14, 14, 256)  1024       ['conv4_6_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_37 (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_37[0][0]']          \n",
            "                                                                                                  \n",
            " conv4_6_3x3/bn (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_6_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_38 (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_38[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_6_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_6_1x1_increase[0][0]']   \n",
            " Normalization)                 )                                                                 \n",
            "                                                                                                  \n",
            " add_12 (Add)                   (None, 14, 14, 1024  0           ['conv4_6_1x1_increase/bn[0][0]',\n",
            "                                )                                 'activation_36[0][0]']          \n",
            "                                                                                                  \n",
            " activation_39 (Activation)     (None, 14, 14, 1024  0           ['add_12[0][0]']                 \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv5_1_1x1_reduce (Conv2D)    (None, 7, 7, 512)    524288      ['activation_39[0][0]']          \n",
            "                                                                                                  \n",
            " conv5_1_1x1_reduce/bn (BatchNo  (None, 7, 7, 512)   2048        ['conv5_1_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_40 (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_40[0][0]']          \n",
            "                                                                                                  \n",
            " conv5_1_3x3/bn (BatchNormaliza  (None, 7, 7, 512)   2048        ['conv5_1_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_41 (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_41[0][0]']          \n",
            "                                                                                                  \n",
            " conv5_1_1x1_proj (Conv2D)      (None, 7, 7, 2048)   2097152     ['activation_39[0][0]']          \n",
            "                                                                                                  \n",
            " conv5_1_1x1_increase/bn (Batch  (None, 7, 7, 2048)  8192        ['conv5_1_1x1_increase[0][0]']   \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " conv5_1_1x1_proj/bn (BatchNorm  (None, 7, 7, 2048)  8192        ['conv5_1_1x1_proj[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " add_13 (Add)                   (None, 7, 7, 2048)   0           ['conv5_1_1x1_increase/bn[0][0]',\n",
            "                                                                  'conv5_1_1x1_proj/bn[0][0]']    \n",
            "                                                                                                  \n",
            " activation_42 (Activation)     (None, 7, 7, 2048)   0           ['add_13[0][0]']                 \n",
            "                                                                                                  \n",
            " conv5_2_1x1_reduce (Conv2D)    (None, 7, 7, 512)    1048576     ['activation_42[0][0]']          \n",
            "                                                                                                  \n",
            " conv5_2_1x1_reduce/bn (BatchNo  (None, 7, 7, 512)   2048        ['conv5_2_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_43 (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_43[0][0]']          \n",
            "                                                                                                  \n",
            " conv5_2_3x3/bn (BatchNormaliza  (None, 7, 7, 512)   2048        ['conv5_2_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_44 (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_44[0][0]']          \n",
            "                                                                                                  \n",
            " conv5_2_1x1_increase/bn (Batch  (None, 7, 7, 2048)  8192        ['conv5_2_1x1_increase[0][0]']   \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_14 (Add)                   (None, 7, 7, 2048)   0           ['conv5_2_1x1_increase/bn[0][0]',\n",
            "                                                                  'activation_42[0][0]']          \n",
            "                                                                                                  \n",
            " activation_45 (Activation)     (None, 7, 7, 2048)   0           ['add_14[0][0]']                 \n",
            "                                                                                                  \n",
            " conv5_3_1x1_reduce (Conv2D)    (None, 7, 7, 512)    1048576     ['activation_45[0][0]']          \n",
            "                                                                                                  \n",
            " conv5_3_1x1_reduce/bn (BatchNo  (None, 7, 7, 512)   2048        ['conv5_3_1x1_reduce[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_46 (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_46[0][0]']          \n",
            "                                                                                                  \n",
            " conv5_3_3x3/bn (BatchNormaliza  (None, 7, 7, 512)   2048        ['conv5_3_3x3[0][0]']            \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " activation_47 (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_47[0][0]']          \n",
            "                                                                                                  \n",
            " conv5_3_1x1_increase/bn (Batch  (None, 7, 7, 2048)  8192        ['conv5_3_1x1_increase[0][0]']   \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " add_15 (Add)                   (None, 7, 7, 2048)   0           ['conv5_3_1x1_increase/bn[0][0]',\n",
            "                                                                  'activation_45[0][0]']          \n",
            "                                                                                                  \n",
            " activation_48 (Activation)     (None, 7, 7, 2048)   0           ['add_15[0][0]']                 \n",
            "                                                                                                  \n",
            " avg_pool (AveragePooling2D)    (None, 1, 1, 2048)   0           ['activation_48[0][0]']          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 23,561,152\n",
            "Trainable params: 23,508,032\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ARCHITETTURA PRE-ADDESTRATA RESNET50 (VGGFace)\n",
        "resnet_model = VGGFace(model = 'resnet50', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "resnet_model.summary()                                                          # Riepilogo Architettura ResNet50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xiiw97XK4AZg"
      },
      "outputs": [],
      "source": [
        "def net_def(base_model, train_after_layer, input_dimension, input_channels, anchor_path, positive_path, negative_path, learning_rate, margin = 0.2):\n",
        "  '''\n",
        "      Crea un modello di rete siamese per il calcolo della triplet loss\n",
        "        # PRECONDIZIONE: definizione dell'architettura di rete e path validi\n",
        "        # POSTCONDIZIONE: modello di rete siamese per triplet loss definito\n",
        "\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        base_model: modello base di rete\n",
        "            keras_vggface\n",
        "        train_after_layer: numero di layer dove i pesi not trainable\n",
        "            int\n",
        "        input_dimension: dimensione di input dell'immagine\n",
        "            int\n",
        "        input_channels: numero di canali di input dell'immagine\n",
        "            int\n",
        "        anchor_path: percorso per le immagini di anchor\n",
        "            string\n",
        "        positive_path: percorso per le immagini positive embedded\n",
        "            string\n",
        "        negative_path: percorso per le immagini negative embedded\n",
        "            string\n",
        "        learning_rate: tasso di apprendimento\n",
        "            float\n",
        "        margin: margine utilizzato dalla triplet loss\n",
        "            float\n",
        "\n",
        "      ----------\n",
        "      RETURN:\n",
        "        Model\n",
        "  '''\n",
        "  for layer in base_model.layers[:train_after_layer]:                           # Congela tutti i pesi del modello base fino al layer indicato\n",
        "      layer.trainable = False\n",
        "\n",
        "  # DEFINIZIONE DEGLI INPUT PER ANCHOR-POSITIVE-NEGATIVE\n",
        "  anchor_input = Input(shape=(input_dimension, input_dimension, input_channels))\n",
        "  positive_input = Input(shape=(input_dimension, input_dimension, input_channels))\n",
        "  negative_input = Input(shape=(input_dimension, input_dimension, input_channels))\n",
        "\n",
        "  # ESTRAZIONE DELLE FEATURE TRAMITE BASE_MODEL PER ANCHOR-POSITIVE-NEGATIVE\n",
        "  anchor_features = base_model(anchor_input)\n",
        "  positive_features = base_model(positive_input)\n",
        "  negative_features = base_model(negative_input)\n",
        "\n",
        "  # CALCOLO DELLE DISTANZE EUCLIDEE TRA ANCHOR-POSITIVE E ANCHOR-NEGATIVE\n",
        "  positive_distance = Lambda(lambda tensors: tf.square(tensors[0] - tensors[1]))([anchor_features, positive_features])\n",
        "  negative_distance = Lambda(lambda tensors: tf.square(tensors[0] - tensors[1]))([anchor_features, negative_features])\n",
        "  '''\n",
        "  Lambda viene adoperata per calcolare la distanza euclidea tra due tensori, viene applicata sulla lista di feature estratte dagli input del base_model,\n",
        "  permette di personalizzare il comportamento dei layer in modo flessibile e di eseguire operazioni specifiche all'interno di un modello senza dover definire un layer.\n",
        "  '''\n",
        "\n",
        "  # CALCOLO DELLA TRIPLET LOSS COMBINANDO LE DISTANZE EUCLIDEE\n",
        "  triplet_loss = Lambda(lambda tensors: tf.maximum(tensors[0] - tensors[1] + 0.2, 0.0))([positive_distance, negative_distance])\n",
        "\n",
        "  # CREAZIONE DEL MODELLO SIAMESE\n",
        "  siamese_model = Model(inputs=[anchor_input, positive_input, negative_input], outputs=triplet_loss)\n",
        "\n",
        "  # COMPILAZIONE DEL MODELLO CON OPTIMIZER E TRIPLET LOSS\n",
        "  siamese_model.compile(optimizer=Adam(learning_rate = learning_rate), loss=lambda y_true, y_pred: y_pred)\n",
        "\n",
        "  siamese_model.summary()                                                       # Riepilogo dell'architettura del modello siamese\n",
        "\n",
        "  return siamese_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JiMZmR3g6_RY",
        "outputId": "a158dfae-3161-4111-e445-cea8ad125625"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " input_4 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " vggface_resnet50 (Functional)  (None, 1, 1, 2048)   23561152    ['input_2[0][0]',                \n",
            "                                                                  'input_3[0][0]',                \n",
            "                                                                  'input_4[0][0]']                \n",
            "                                                                                                  \n",
            " lambda (Lambda)                (None, 1, 1, 2048)   0           ['vggface_resnet50[0][0]',       \n",
            "                                                                  'vggface_resnet50[1][0]']       \n",
            "                                                                                                  \n",
            " lambda_1 (Lambda)              (None, 1, 1, 2048)   0           ['vggface_resnet50[0][0]',       \n",
            "                                                                  'vggface_resnet50[2][0]']       \n",
            "                                                                                                  \n",
            " lambda_2 (Lambda)              (None, 1, 1, 2048)   0           ['lambda[0][0]',                 \n",
            "                                                                  'lambda_1[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 23,561,152\n",
            "Trainable params: 14,964,736\n",
            "Non-trainable params: 8,596,416\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "base_model = resnet_model                                                       # Modello di rete scelto\n",
        "train_after_layer = 141                                                         # Numero di layer nonn-trainable\n",
        "learning_rate = 1e-6                                                            # Learning-Rate del modello\n",
        "\n",
        "siameseRES_model = net_def(base_model, train_after_layer, input_dimension, input_channels, anchorTR_path, positiveTR_path, negativeTR_path, learning_rate)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2gtTeBkPvi_h"
      },
      "source": [
        "# TRAINING E HISTORY RETE CON TRIPLET LOSS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ovidG0N0KB4X"
      },
      "outputs": [],
      "source": [
        "# SALVATAGGIO DEI CSV PER ANCHOR-POSITIVE-NEGATIVE DEL TRAINING IN LOCALE (maggiore velocità per il training a scapito di un dispendio di memoria minimo)\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Training_TL/anchor.csv\" \"anchorTR.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Training_TL/positive.csv\" \"positiveTR.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Training_TL/negative.csv\" \"negativeTR.csv\"\n",
        "\n",
        "# SALVATAGGIO DEI CSV PER ANCHOR-POSITIVE-NEGATIVE DEL VALIDATION IN LOCALE (maggiore velocità per il training a scapito di un dispendio di memoria minimo)\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Validation_TL/anchor.csv\" \"anchorV.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Validation_TL/positive.csv\" \"positiveV.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Validation_TL/negative.csv\" \"negativeV.csv\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lM3gqmTie3oX"
      },
      "outputs": [],
      "source": [
        "# INIZIALIZZAZIONE VARIABILI\n",
        "epochs = 10                                                                     # Numero di epoche per il fit\n",
        "batch_size = 300                                                                # Grandezza di ogni batch per il fit\n",
        "total_tripletsTR = 298474                                                       # Numero totale di triple del training set (troppo elevato per le risorse a disposizione)\n",
        "total_tripletsV = 36353                                                         # Numero totale di triple del validation set (troppo elevato per le risorse a disposizione)\n",
        "total_reduced_tripletsTR = 50000                                                # Numero di triple di training considerate per il fit\n",
        "total_reduced_tripletsV = 11000                                                 # Numero di triple di validation considerare per il fit\n",
        "patience_LR = 1                                                                 # Valore di patience per la callback di riduzione del learning rate\n",
        "patience_ES = 3                                                                 # Valore di patience per la callback di stop preoce dell'addestramento\n",
        "saved_epoch = 0                                                                 # Variabile per il salvataggio del valore della best-epoch\n",
        "\n",
        "# CREAZIONE GENERATORI DI TRAINING E VALIDATION\n",
        "genT = preprocess_triplets(local_anchorTR_path, local_positiveTR_path, local_negativeTR_path, input_dimension, batch_size)\n",
        "genV = preprocess_triplets(local_anchorV_path, local_positiveV_path, local_negativeV_path, input_dimension, batch_size)\n",
        "\n",
        "# FUNZIONE PER IL SALVATAGGIO DEL NUMERO DELLA BEST EPOCH\n",
        "def save_epoch_number(epoch, logs):\n",
        "    global saved_epoch\n",
        "    saved_epoch = epoch + 1                                                     # N.B. Aggiungiamo 1 perchè le epoche partono da 1\n",
        "\n",
        "# CALLBACK: Salva i pesi del modello durante l'addestramento\n",
        "model_checkpoint_callback = keras.callbacks.ModelCheckpoint(\n",
        "    filepath = local_ResWeights_path,                                           # Percorso in locale del file in cui salvare i pesi\n",
        "    filename = 'weights_{epoch:02d}.h5',                                        # Nome del file dove salvare i pesi\n",
        "    save_weights_only = True,                                                   # Salva solo i pesi anziché l'intero modello\n",
        "    monitor = 'val_loss',                                                       # Metrica da monitorare per il salvataggio dei pesi migliori\n",
        "    mode = 'min',                                                               # Indica che la metrica monitorata deve essere minimizzata\n",
        "    save_best_only = True,                                                      # Salva solo i pesi corrispondenti al miglior valore della metrica monitorata\n",
        "    save_freq = 'epoch',                                                        # Frequenza di salvataggio dei pesi\n",
        "    callbacks = [save_epoch_number]                                             # Callback per il salvataggio dell'epoca migliore\n",
        ")\n",
        "\n",
        "# CALLBACK: Riduce il learning rate quando una particolare metrica smette di migliorare\n",
        "reduce_lr = keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor = 'val_loss',                                                       # Monitora la loss del validation set\n",
        "    factor = 0.1,                                                               # Fattore di riduzione del learning rate (10%)\n",
        "    patience = patience_LR,                                                     # Numero di epoche senza miglioramenti dopo il quale il learning rate viene ridotto\n",
        "    verbose = 1,                                                                # Stampa un messaggio quando il learning rate viene ridotto\n",
        "    mode = 'min',                                                               # Modalità di monitoraggio ('min' per minimizzare la loss)\n",
        "    min_delta = 0.0001,                                                         # Soglia per considerare un miglioramento significativo\n",
        "    min_lr = 1e-8                                                               # Learning rate minimo consentito\n",
        ")\n",
        "\n",
        "# CALLBACK: Se una particolare metrica smette di migliorare l'addestramento viene interrotto\n",
        "early_stopping = keras.callbacks.EarlyStopping(\n",
        "    monitor = \"val_loss\",                                                       # Monitora la loss del validation set\n",
        "    min_delta = 0,                                                              # Differenza minima ritenuta come miglioramento\n",
        "    patience = patience_ES,                                                     # Numero di epoche consecutive senza miglioramento prima di interrompere l'addestramento\n",
        "    verbose = 1,                                                                # Stampa un messaggio quando l'addestramento viene interrotto\n",
        "    mode = \"min\",                                                               # Indica che la metrica monitorata deve essere minimizzata\n",
        "    baseline = None,                                                            # Valore di riferimento per la metrica monitorata\n",
        "    restore_best_weights = True,                                                # Ripristina i pesi del modello al miglior punto durante l'addestramento\n",
        "    start_from_epoch = 0,                                                       # Indica da quale epoca iniziare il conteggio delle epoche senza miglioramento\n",
        ")\n",
        "\n",
        "# CALLBACK: Salvataggio delle metriche di training e validazione in formato csv\n",
        "csv_logger = keras.callbacks.CSVLogger(local_CSVLogger_path)\n",
        "\n",
        "# ADDESTRAMENTO DEL MODELLO SULLE TRIPLE (ANCHOR-POSITIVE-NEGATIVE)\n",
        "history = siameseRES_model.fit(\n",
        "    x=genT,                                                                       # Generatore di training\n",
        "    validation_data=genV,                                                         # Generatore di validazione\n",
        "    epochs=epochs,                                                                # Numero di epoche di addestramento\n",
        "    steps_per_epoch=total_tripletsTR//batch_size,                                 # Numero di passi per epoca di addestramento\n",
        "    validation_steps=total_reduced_tripletsV//batch_size,                         # Numero di passi per epoca di validazione\n",
        "    verbose=True,                                                                 # Messaggi di avviso sulle metriche\n",
        "    callbacks=[model_checkpoint_callback, reduce_lr, early_stopping, csv_logger]  # Callback da utilizzare durante l'addestramento\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XBXjV2dbNW2t"
      },
      "outputs": [],
      "source": [
        "# SALVATAGGIO DEI PESI SUL DRIVE\n",
        "weights_path = \"/content/drive/MyDrive/ESM/KinDeNet/Weights/Weights_lr\" + str(learning_rate) + \"_batch\" + str(batch_size) + \"_epochs\" + str(epochs) + \"_bestEpoch\" + str(saved_epoch) + \".h5\"\n",
        "siameseRES_model.save_weights(weights_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "erlOHmlaCgi-",
        "outputId": "63dc4f67-6ef5-4f14-fe7d-9ab9025bb7c9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# STAMPA DEL GRAFICO DELLE METRICHE DI ADDESTRAMENTO\n",
        "plot_history(history, metrics=['loss', 'val_loss'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ESTRAZIONE MAPPE DELLE CLASSI DI ATTIVAZIONE"
      ],
      "metadata": {
        "id": "pqSQsf_lYcIB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# CARICAMENTO DEI PESI DELLA RETE ADDESTRATA CON TRIPLET LOSS\n",
        "siameseRES_model.load_weights(ResWeights_path + \"Weights_lr1e-05_batch150_epochs20_bestEpoch1.hdf5\")"
      ],
      "metadata": {
        "id": "uPV1pLkH7kRL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# INIZIALIZZAZIONE DELLE VARIABILI\n",
        "index = 1000                                                                    # Indice della tripletta di esempio di cui si vogliono estrarre le mappe di attivazione\n",
        "\n",
        "# LETTURA DEI CSV PER ANCHOR-POSITIVE-NEGATIVE\n",
        "anchor_list = list_from_csv(local_anchorTR_path)\n",
        "positive_list = list_from_csv(local_positiveTR_path)\n",
        "negative_list = list_from_csv(local_negativeTR_path)\n",
        "\n",
        "# ESTRAZIONE DELLE TRE IMMAGINI\n",
        "anchor_image = img_preprocessing(anchor_list[index][0], input_dimension)\n",
        "positive_image = img_preprocessing(positive_list[index][0], input_dimension)\n",
        "negative_image = img_preprocessing(negative_list[index][0], input_dimension)\n",
        "\n",
        "# ESTRAZIONE DI UNO DEI TRE RAMI DELLA RETE\n",
        "model = siameseRES_model.get_layer('vggface_resnet50')\n",
        "\n",
        "# DEFINIZIONE DEL LAYER CONVOLUZIONALE DA CUI ESTRARRE LE MAPPE DI ATTIVAZIONE\n",
        "layer_names = [\"conv5_3_1x1_increase\"]\n",
        "\n",
        "# CREAZIONE DEI MODELLI DI ATTIVAZIONE\n",
        "activation_models = []                                                          # Lista per archiviare i modelli di attivazione per ogni livello\n",
        "for layer_name in layer_names:\n",
        "    activation_model = tf.keras.Model(inputs=model.input, outputs=model.get_layer(layer_name).output)\n",
        "    activation_models.append(activation_model)"
      ],
      "metadata": {
        "id": "m6U4l5kmMBkU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_activation_map(image, input_dimension, activation_models):\n",
        "  '''\n",
        "      Stampa della mappa di attivazione per l'immagine specificata\n",
        "        # PRECONDIZIONE: creati i modelli di attivazione\n",
        "        # POSTCONDIZIONE: mappa di attivazione per l'immagine scelta\n",
        "\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        image: immagine per cui creare la mappa di attivazione (anchor/positive/negative)\n",
        "            numpy.array\n",
        "        input_dimension: dimensione di input dell'immagine\n",
        "            int\n",
        "        activation_models: lista contenenti i modelli di attivazione per ogni livello\n",
        "            list\n",
        "  '''\n",
        "\n",
        "  # ESTRAZIONE DELLE CLASSI DI ATTIVAZIONE PER OGNI LIVELLO\n",
        "  activations = []                                                              # Lista per archiviare le attivazioni estratte da ogni livello\n",
        "  for activation_model in activation_models:\n",
        "      activations.append(activation_model.predict(np.expand_dims(image, axis=0)))\n",
        "\n",
        "  # VISUALIZZAZIONE DELL'IMMAGINE ORIGINALE\n",
        "  plt.subplot(1, len(activations)+1, 1)\n",
        "  image = resize(image, (2*input_dimension, 2*input_dimension), order=1)\n",
        "  plt.imshow(image)\n",
        "\n",
        "  # VISUALIZZAZIONE DELL'EVOLVERSI DELLE MAPPE DI ATTIVAZIONE PER OGNI LIVELLO\n",
        "  for i, activation_map in enumerate(activations):\n",
        "      plt.subplot(1, len(activations)+1, i+2)\n",
        "      map = np.max(np.array(activation_map).squeeze(), axis = 2)                # Prendiamo il valore massimo tra i diversi canali\n",
        "      map = resize(map, (2*input_dimension, 2*input_dimension), order=1)\n",
        "      plt.imshow(map, cmap='jet')\n",
        "      plt.title(layer_names[i])\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "s4cakhlR0YjB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# STAMPA DELLA MAPPA DI ATTIVAZIONE DELL'ANCHOR IMAGE\n",
        "plot_activation_map(anchor_image, input_dimension, activation_models)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "e0fd3e18-3b1f-4766-80e0-ecbcf28d2d27",
        "id": "yhdPCO37YcID"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 720ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# STAMPA DELLA MAPPA DI ATTIVAZIONE DELLA POSITIVE IMAGE\n",
        "plot_activation_map(positive_image, input_dimension, activation_models)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "KE6Buo8ygRM5",
        "outputId": "670b4158-743e-420c-aed2-784e384e54b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 28ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# STAMPA DELLA MAPPA DI ATTIVAZIONE DELLA NEGATIVE IMAGE\n",
        "plot_activation_map(negative_image, input_dimension, activation_models)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "WiN7smoogR_3",
        "outputId": "383b5471-948f-4f02-8674-b0475eadd4ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 24ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ulkazTpqCg5r"
      },
      "source": [
        "# CREAZIONE DEI CSV DELLE IMMAGINI E RELAZIONI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xhKyYUdeCkia"
      },
      "outputs": [],
      "source": [
        "def label_constructor(splitted_path):\n",
        "  '''\n",
        "      Salva due liste di path per le coppie di immagini e una lista di labels che useremo\n",
        "      per la feature extraction e la classificazione\n",
        "        # PRECONDIZIONE: file npy contenenti i path delle immagini e relazioni tra i vari familiari\n",
        "        # POSTCONDIZIONE: restituisce due liste contenenti path di immagini e una lista contenente labels\n",
        "\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        splitted_path: path per la cartella di Training/Validation/Test\n",
        "            str\n",
        "\n",
        "      ----------\n",
        "      RETURN:\n",
        "        img1_listpath: lista di path della prima immagine di ogni coppia\n",
        "            list\n",
        "        img2_listpath: lista di path della seconda immagine di ogni coppia\n",
        "            list\n",
        "        label_list: lista delle label contenenti le relazioni\n",
        "            list\n",
        "  '''\n",
        "  img1_listpath = list()                                                        # Inizializzazione lista per i path della prima immagine\n",
        "  img2_listpath = list()                                                        # Inizializzazione lista per i path della seconda immagine\n",
        "  label_list = list()                                                           # Inizializzazione lista per le label di relazione di parentela\n",
        "\n",
        "  with tqdm(total = len(glob(splitted_path + '/F*')), ncols = 80) as pbar:      # Setta la barra di avanzamento\n",
        "    for fname in sorted(glob(splitted_path + '/F*')):                           # Scorriamo gli npy contenenti i path delle immagini e relazioni tra i vari familiari\n",
        "\n",
        "      pbar.update(1)                                                            # Aggiornamento della barra di avanzamento\n",
        "      np_training = array_from_file(fname)                                      # Caricamento di un array con i contenuti del .npy tra MID di una famiglia\n",
        "\n",
        "      for i in range(len(np_training)):\n",
        "        img1_listpath.append(str(np_training[i][0]))                            # Aggiungiamo la prima immagine presente negli npy\n",
        "        img2_listpath.append(str(np_training[i][1]))                            # Aggiungiamo la seconda immagine presente negli npy\n",
        "        label_list.append(str(np_training[i][2]))                               # Aggiundiamo la label tra le persone presenti nelle due immaigni\n",
        "\n",
        "    pbar.set_postfix_str(\"Creazione Label.csv Completata\")                      # Notifica di completamento\n",
        "\n",
        "  return img1_listpath, img2_listpath, label_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HK3rjf5fHQJJ",
        "outputId": "81d5e6a3-050b-4436-9718-abf77891f959"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████| 9065/9065 [02:47<00:00, 53.96it/s, Creazione Label.csv Completata]\n",
            "100%|███████| 1886/1886 [00:24<00:00, 75.61it/s, Creazione Label.csv Completata]\n",
            "100%|███████| 2385/2385 [00:33<00:00, 71.12it/s, Creazione Label.csv Completata]\n"
          ]
        }
      ],
      "source": [
        "img1_listpathTR = list()\n",
        "img2_listpathTR = list()\n",
        "label_listTR = list()\n",
        "\n",
        "img1_listpathTR, img2_listpathTR, label_listTR = label_constructor(training_path)\n",
        "\n",
        "list_on_csv(training_path, img1_listpathTR, 'img1')                             # Creazione del csv per la prima immagine di ogni coppia\n",
        "list_on_csv(training_path, img2_listpathTR, 'img2')                             # Creazione del csv per la seconda immagine di ogni coppia\n",
        "list_on_csv(training_path, label_listTR, 'label')                               # Creazione del csv con le relazioni\n",
        "\n",
        "\n",
        "img1_listpathV = list()\n",
        "img2_listpathV = list()\n",
        "label_listV = list()\n",
        "\n",
        "img1_listpathV, img2_listpathV, label_listV = label_constructor(validation_path)\n",
        "\n",
        "list_on_csv(validation_path, img1_listpathV, 'img1')                            # Creazione del csv per la prima immagine di ogni coppia\n",
        "list_on_csv(validation_path, img2_listpathV, 'img2')                            # Creazione del csv per la seconda immagine di ogni coppia\n",
        "list_on_csv(validation_path, label_listV, 'label')                              # Creazione del csv con le relazioni\n",
        "\n",
        "\n",
        "img1_listpathT = list()\n",
        "img2_listpathT = list()\n",
        "label_listT = list()\n",
        "\n",
        "img1_listpathT, img2_listpathT, label_listT = label_constructor(test_path)\n",
        "\n",
        "list_on_csv(test_path, img1_listpathT, 'img1')                                  # Creazione del csv per la prima immagine di ogni coppia\n",
        "list_on_csv(test_path, img2_listpathT, 'img2')                                  # Creazione del csv per la seconda immagine di ogni coppia\n",
        "list_on_csv(test_path, label_listT, 'label')                                    # Creazione del csv con le relazioni"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fo4nOnPfjBFi"
      },
      "source": [
        "# DEFINIZIONE RETE PER ESTRAZIONE DI FEATURE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PJ8cuBhkfvyp"
      },
      "outputs": [],
      "source": [
        "def create_siamese_network(base_network, input_dimension, input_channels, weights_path, filename):\n",
        "  '''\n",
        "      Crea un modello di rete siamese per l'estrazione di features\n",
        "        # PRECONDIZIONE: definizione dell'architettura di rete e path dei pesi pre-addestrati valido\n",
        "        # POSTCONDIZIONE: modello di rete siamese per feature extraction\n",
        "\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        base_model: modello base di rete\n",
        "            keras_vggface\n",
        "        input_dimension: dimensione di input dell'immagine\n",
        "            int\n",
        "        input_channels: numero di canali di input dell'immagine\n",
        "            int\n",
        "        anchor_path: percorso dei pesi della rete pre-addestrata con triplet loss\n",
        "            str\n",
        "        filename: nome dei pesi specifici da prendere\n",
        "            str\n",
        "\n",
        "      ----------\n",
        "      RETURN:\n",
        "        Model\n",
        "  '''\n",
        "  base_network.load_weights(weights_path + filename)                            # Caricamento dei pesi sul base_network\n",
        "\n",
        "  base_network = base_network.get_layer(\"vggface_resnet50\")                     # Estrazione dei layer corrispondenti a ResNet50\n",
        "\n",
        "  # DEFINIZIONE DEGLI INPUT\n",
        "  input_1 = Input(shape = (input_dimension, input_dimension, input_channels))\n",
        "  input_2 = Input(shape = (input_dimension, input_dimension, input_channels))\n",
        "\n",
        "  # SELEZIONE DEI LAYER DI INTERESSE\n",
        "  desired_layers = [\"activation_9\", \"activation_21\", \"activation_39\", \"activation_48\"]\n",
        "  selected_features = []\n",
        "\n",
        "  # ESTRAZIONE DELLE FEATURE DAI LAYER DI INTERESSE\n",
        "  for layer_name in desired_layers:\n",
        "    layer_output = base_network.get_layer(layer_name).output                    # Estrazione delle feature dallo strato corrente\n",
        "\n",
        "    pooled_features = tf.keras.layers.GlobalAveragePooling2D()(layer_output)    # Averaging Pooling 1x1\n",
        "\n",
        "    normalized_features = tf.keras.layers.BatchNormalization()(pooled_features) # Aggiunta di un layer di BatchNormalization\n",
        "\n",
        "    selected_features.append(normalized_features)                               # Salvataggio delle feature estratte per lo strato corrente\n",
        "\n",
        "  concatenated = tf.keras.layers.Concatenate()(selected_features)               # Concatenazione delle feature per tutti gli strati\n",
        "\n",
        "  # CREAZIONE DI UN NUOVO MODELLO CON INPUT E FEATURE ESTRATTE\n",
        "  new_model = Model(inputs = base_network.input, outputs = concatenated)\n",
        "\n",
        "  # APPLICAZIONE DELLE DUE IMMAGINI IN INGRESSO AL NUOVO MODELLO\n",
        "  total_features_1 = new_model(input_1)\n",
        "  total_features_2 = new_model(input_2)\n",
        "\n",
        "  # CALCOLO DELLA DIFFERENZA IN VALORE ASSOLUTO TRA LE FEATURE DELLE DUE IMMAGINI (L1)\n",
        "  diff_features = tf.math.abs(tf.subtract(total_features_1, total_features_2))\n",
        "\n",
        "  # CREAZIONE DEL MODELLO DI RETE SIAMESE CON INPUT LE DUE IMMAGINI E OUTPUT LA L1 DELLE FEATURES\n",
        "  siamese_model = Model(inputs=[input_1,input_2], outputs=diff_features)\n",
        "\n",
        "  return siamese_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ZkBzdoNYewv",
        "outputId": "5fb65d75-facb-40a2-9f03-920787a4252e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_5 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " input_6 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " model_1 (Functional)           (None, 3840)         23576512    ['input_5[0][0]',                \n",
            "                                                                  'input_6[0][0]']                \n",
            "                                                                                                  \n",
            " tf.math.subtract (TFOpLambda)  (None, 3840)         0           ['model_1[0][0]',                \n",
            "                                                                  'model_1[1][0]']                \n",
            "                                                                                                  \n",
            " tf.math.abs (TFOpLambda)       (None, 3840)         0           ['tf.math.subtract[0][0]']       \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 23,576,512\n",
            "Trainable params: 14,972,416\n",
            "Non-trainable params: 8,604,096\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "filename = \"Weights_lr1e-05_batch150_epochs20_bestEpoch1.hdf5\"                  # Nome dei pesi scelti\n",
        "\n",
        "# DEFINIZIONE RETE SIAMESE PER FEATURE EXTRACTION\n",
        "siamese_model = create_siamese_network(siameseRES_model, input_dimension, input_channels, ResWeights_path, filename)\n",
        "siamese_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqBkIgRVhU4w"
      },
      "source": [
        "# ESTRAZIONE DEI PATH E DELLE LABEL DAI CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VjBWVknZV0-8"
      },
      "outputs": [],
      "source": [
        "# SALVATAGGIO DEI CSV PER IMG1-IMG2-LABEL DEL TRAINING IN LOCALE\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Training/img1.csv\" \"img1TR.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Training/img2.csv\" \"img2TR.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Training/label.csv\" \"labelTR.csv\"\n",
        "\n",
        "# SALVATAGGIO DEI CSV PER IMG1-IMG2-LABEL DEL VALIDATION IN LOCALE\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Validation/img1.csv\" \"img1V.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Validation/img2.csv\" \"img2V.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Validation/label.csv\" \"labelV.csv\"\n",
        "\n",
        "# SALVATAGGIO DEI CSV PER IMG1-IMG2-LABEL DEL TEST IN LOCALE\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Test/img1.csv\" \"img1T.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Test/img2.csv\" \"img2T.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Test/label.csv\" \"labelT.csv\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r_igNHU73eG7"
      },
      "outputs": [],
      "source": [
        "# CARICA PATH DELLE IMMAGINI E LABEL DI TRAINING DAI CSV\n",
        "img1TR_paths= list_from_csv(local_img1TR_path)\n",
        "img2TR_paths = list_from_csv(local_img2TR_path)\n",
        "train_labels = list_from_csv(local_labelTR_path)\n",
        "\n",
        "# CARICA PATH DELLE IMMAGINI E LABEL DI VALIDATION DAI CSV\n",
        "img1V_paths= list_from_csv(local_img1V_path)\n",
        "img2V_paths = list_from_csv(local_img2V_path)\n",
        "val_labels = list_from_csv(local_labelV_path)\n",
        "\n",
        "# CARICA PATH DELLE IMMAGINI E LABEL DI TEST DAI CSV\n",
        "img1T_paths= list_from_csv(local_img1T_path)\n",
        "img2T_paths = list_from_csv(local_img2T_path)\n",
        "test_labels = list_from_csv(local_labelT_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bsv8O9hOve3Q"
      },
      "outputs": [],
      "source": [
        "all_labels = train_labels + val_labels + test_labels                            # Uniamo le liste di labels in un'unica lista\n",
        "\n",
        "# CREAZIONE DELL'ENCODER DI LABELS\n",
        "label_encoder = LabelEncoder()\n",
        "label_encoder.fit(all_labels)\n",
        "\n",
        "# CODIDICA DELLE LABEL IN FORMATO NUMERICO DAL FORMATO STRINGA\n",
        "train_encode_labels = label_encoder.transform(np.ravel(train_labels))\n",
        "val_encode_labels = label_encoder.transform(np.ravel(val_labels))\n",
        "test_encode_labels = label_encoder.transform(np.ravel(test_labels))\n",
        "# N.B. np.ravel() appiattisce la lista di liste di label\n",
        "\n",
        "# CREAZIONE NOTAZIONE ONE-HOT\n",
        "train_labels = to_categorical(train_encode_labels, num_classes)\n",
        "val_labels = to_categorical(val_encode_labels, num_classes)\n",
        "test_labels = to_categorical(test_encode_labels, num_classes)\n",
        "\n",
        "# LIBERAZIONE MEMORIA RAM\n",
        "del train_encode_labels, val_encode_labels, test_encode_labels\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# FEATURE EXTRACTION"
      ],
      "metadata": {
        "id": "dQfRrcPqU5aV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def features_extraction(image_paths_1, image_paths_2, labels, siamese_model, input_dimension, input_channel, coppie_tot):\n",
        "  '''\n",
        "      Estrae le features a partire dalle immagini specificate dai path\n",
        "        # PRECONDIZIONE: paths delle coppie di immagini da cui estrarre le features\n",
        "        # POSTCONDIZIONE: lista di features estratte e di labels\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        image_paths_1: lista di paths della prima immagine\n",
        "            list\n",
        "        image_paths_2: lista di paths della seconda immagine\n",
        "            list\n",
        "        labels: lista di labels\n",
        "            list\n",
        "        siamese_model: modello siamese di rete per la feature extraction\n",
        "            keras_vggface\n",
        "        input_dimension: dimensione desiderata dell'immagine\n",
        "            int\n",
        "        input_channel: numero di canali dell'immagine\n",
        "            int\n",
        "        coppie_tot: numero di coppie per relazione da considerare\n",
        "            int\n",
        "      ----------\n",
        "      RETURN:\n",
        "        all_features: lista delle features estratte\n",
        "            list\n",
        "        all_labels: lista delle labels relative alle coppie considerate\n",
        "            list\n",
        "  '''\n",
        "  logger.info(\"Inizio Features Extraction\")\n",
        "\n",
        "  all_features = []                                                             # Inizializzazione della lista contenenti tutti i vettori di feature estratti\n",
        "  all_labels = []                                                               # Inizializzazione della lista conteneti tutte le labels\n",
        "  with tqdm(total=coppie_tot, ncols=80) as pbar:                                # Setta la barra di avanzamento\n",
        "      for i in range(coppie_tot):\n",
        "          pbar.update(1)                                                        # Avanzamento della barra\n",
        "          image_1 = img_preprocessing(image_paths_1[i][0], input_dimension)     # Pre-processing della prima immagine della coppia\n",
        "          image_1 = np.reshape(image_1, (-1, input_dimension, input_dimension, input_channel))\n",
        "          image_2 = img_preprocessing(image_paths_2[i][0], input_dimension)     # Pre-processing della seconda immagine della coppia\n",
        "          image_2 = np.reshape(image_2, (-1, input_dimension, input_dimension, input_channel))\n",
        "          predicted_features = siamese_model.predict([image_1, image_2], verbose=0) # Utilizzo il modello di rete siamese per estrarre il vettore di feature\n",
        "          all_features.append(predicted_features[0])                            # Aggiungo il vettore di feature estratto (batch_features deve essere una matrice)\n",
        "          all_labels.append(labels[i])\n",
        "  pbar.set_postfix_str(\"Creazione Label.csv Completata\")\n",
        "  return all_features, all_labels"
      ],
      "metadata": {
        "id": "GRIuBnvKTJ-G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# COPPIE PER IL TRAINING-VALIDATION-TEST (70%-15%-15%)\n",
        "coppie_totTR = 50000\n",
        "coppie_totV = 10715\n",
        "coppie_totT = 10715"
      ],
      "metadata": {
        "id": "eEC5CBdiTprx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CHIAMATA PER FEATURE EXTRACTION DEL TRAINING\n",
        "training_features, reduced_train_labels = features_extraction(img1TR_paths, img2TR_paths, train_labels, siamese_model, input_dimension, input_channels, coppie_totTR)\n",
        "\n",
        "# SALVATAGGIO DELLE FEATURES E DELLE LABELS ESTRATTE SU NPY BINARI PER IL TRAINING\n",
        "array_on_file(features_labels_pathTR + str(coppie_totTR) + 'R_training_feature', np.array(training_features))\n",
        "array_on_file(features_labels_pathTR + str(coppie_totTR) + 'R_reduced_train_labels', np.array(reduced_train_labels))\n",
        "\n",
        "# LIBERAZIONE MEMORIA RAM\n",
        "del training_features, reduced_train_labels, img1TR_paths, img2TR_paths, train_labels\n",
        "gc.collect()"
      ],
      "metadata": {
        "id": "PhZDxkOoTKfb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CHIAMATA PER FEATURE EXTRACTION DEL VALIDATION\n",
        "validation_features, reduced_val_labels = features_extraction(img1V_paths, img2V_paths, val_labels, siamese_model, input_dimension, input_channels, coppie_totV)\n",
        "\n",
        "# SALVATAGGIO DELLE FEATURES E DELLE LABELS ESTRATTE SU NPY BINARI PER LA VALIDATION\n",
        "array_on_file(features_labels_pathV + str(coppie_totV) + 'R_validation_feature', np.array(validation_features))\n",
        "array_on_file(features_labels_pathV + str(coppie_totV) + 'R_reduced_val_labels', np.array(reduced_val_labels))\n",
        "\n",
        "# LIBERAZIONE MEMORIA RAM\n",
        "del validation_features, reduced_val_labels, img1V_paths, img2V_paths, val_labels\n",
        "gc.collect()"
      ],
      "metadata": {
        "id": "Fcyyw8iGTa8z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d4d5e8c-8809-4ba9-fe27-1e54a206a2ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|█████████████████████████████████████| 10715/10715 [50:25<00:00,  3.54it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "177951"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CHIAMATA PER FEATURE EXTRACTION DEL TEST\n",
        "test_features, reduced_test_labels = features_extraction(img1T_paths, img2T_paths, test_labels, siamese_model, input_dimension, input_channels, coppie_totT)\n",
        "\n",
        "# SALVATAGGIO DELLE FEATURES E DELLE LABELS ESTRATTE SU NPY BINARI PER IL TEST\n",
        "array_on_file(features_labels_pathT + str(coppie_totT) + 'R_test_feature', np.array(test_features))\n",
        "array_on_file(features_labels_pathT + str(coppie_totT) + 'R_reduced_test_labels', np.array(reduced_test_labels))\n",
        "\n",
        "# LIBERAZIONE MEMORIA RAM\n",
        "del test_features, reduced_test_labels, img1T_paths, img2T_paths, test_labels\n",
        "gc.collect()"
      ],
      "metadata": {
        "id": "UhkkBJSjTezK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1f2a677-3511-4ad9-ee12-de1b15514cf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|█████████████████████████████████████| 10715/10715 [49:01<00:00,  3.64it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "190223"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gg-RgqkaG0jc"
      },
      "source": [
        "# DEFINIZIONE RETE CLASSIFICATORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a9HVBIvBF65J"
      },
      "outputs": [],
      "source": [
        "def create_classifier(num_classes, siamese_model, feature_dimension, flagClassifier, learning_rate):\n",
        "  '''\n",
        "      Crea un modello di classificatore\n",
        "        # PRECONDIZIONE: definizione dell'architettura del modello siamese di estrazione delle feature e numero di classi per le label\n",
        "        # POSTCONDIZIONE: modello di classificatore\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        num_classes: numero di classi per le label di relazione parentale\n",
        "            int\n",
        "        siamese_model: modello siamese di rete per la feature extraction\n",
        "            keras_vggface\n",
        "        feature_dimension: dimensione del vettore delle features\n",
        "            int\n",
        "        flagClassifier: flag per indicare se classificatore binario (0) o classificatore a classi multiple (1)\n",
        "            int\n",
        "        learning_rate: valore del learning rate\n",
        "            float\n",
        "      ----------\n",
        "      RETURN:\n",
        "        Sequential\n",
        "  '''\n",
        "  # CREAZIONE DEL MODELLO DEL CLASSIFICATORE\n",
        "  classifier_model = Sequential([\n",
        "  # LAYER FC CON 512 NEURONI E ACTIVATION FUNCTION 'relu'\n",
        "  Dense(512, activation='relu', input_shape=(feature_dimension,)),\n",
        "  Dense(256, activation='relu'),\n",
        "  Dense(128, activation='relu'),\n",
        "  Dense(64, activation='relu'),\n",
        "  ])\n",
        "\n",
        "  # LAYER DI OUTPUT CON NUMERO DI CLASSI DERIDERATO E ACTIVATION FUNCTION\n",
        "  if flagClassifier == 1:\n",
        "    classifier_model.add(Dense(num_classes, activation='softmax'))\n",
        "  elif flagClassifier == 0:\n",
        "    classifier_model.add(Dense(num_classes, activation='sigmoid'))\n",
        "  else:\n",
        "    print(\"ERRORE FLAG ERRATO\")\n",
        "    return 0\n",
        "\n",
        "  classifier_model.summary()                                                    # Stampa del modello\n",
        "\n",
        "  # COMPILAZIONE DEL MODELLO\n",
        "  if (flagClassifier == 1):\n",
        "    classifier_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = learning_rate),\n",
        "                            loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                            metrics=[keras.metrics.CategoricalAccuracy()])\n",
        "  elif (flagClassifier == 0):\n",
        "    classifier_model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate = learning_rate),\n",
        "                            loss=keras.losses.binary_crossentropy,\n",
        "                            metrics=['accuracy'])\n",
        "  else:\n",
        "    print(\"ERRORE FLAG ERRATO\")\n",
        "    return 0\n",
        "\n",
        "  return classifier_model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "flagClassifierNA = 0\n",
        "num_classesNA = 1\n",
        "learning_rate = 1e-4\n",
        "\n",
        "# CLASSIFICATORE PER RELAZIONE BINARIA\n",
        "relation_classifier_model = create_classifier(num_classesNA, siamese_model, feature_dimension, flagClassifierNA, learning_rate)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "589tZJJkCC8k",
        "outputId": "dcf49f25-a512-4278-d0d0-1bca690adc72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 512)               1966592   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,139,137\n",
            "Trainable params: 2,139,137\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ix6s9hPcgkHf",
        "outputId": "c830b422-38c1-4634-ce63-4554b0b2c7fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_5 (Dense)             (None, 512)               1966592   \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 3)                 195       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,139,267\n",
            "Trainable params: 2,139,267\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "flagClassifierR = 1\n",
        "num_classesR = 3\n",
        "learning_rate = 1e-4\n",
        "\n",
        "# CLASSIFICATORE PER GRADO DI RELAZIONE\n",
        "classifier_model = create_classifier(num_classesR, siamese_model, feature_dimension, flagClassifierR, learning_rate)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PREPARAZIONE DELLE LABELS E FEATURES PER IL FIT"
      ],
      "metadata": {
        "id": "uE1Cu2DSkyZM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ESTRAZIONE DELLE FEATURES E DELLE LABELS ESTRATTE DA NPY BINARI\n",
        "training_features = array_from_file(features_labels_pathTR + str(coppie_totTR) + 'R_training_feature')\n",
        "validation_features = array_from_file(features_labels_pathV + str(coppie_totV) + 'R_validation_feature')\n",
        "test_features = array_from_file(features_labels_pathT + str(coppie_totT) + 'R_test_feature')\n",
        "reduced_train_labels = array_from_file(features_labels_pathTR + str(coppie_totTR) + 'R_reduced_train_labels')\n",
        "reduced_val_labels = array_from_file(features_labels_pathV + str(coppie_totV) + 'R_reduced_val_labels')\n",
        "reduced_test_labels = array_from_file(features_labels_pathT + str(coppie_totT) + 'R_reduced_test_labels')"
      ],
      "metadata": {
        "id": "8zThy2gWBdwJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zabEhSff9r8F",
        "outputId": "482c2180-7763-4800-9a2d-44c0aef4f501"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LA DIMENSIONE DEI VETTORI DI FEATURE È: \n",
            "(50000, 3840)\n",
            "(10715, 3840)\n",
            "(10715, 3840)\n",
            "LA DIMENSIONE DEI DELLE LABELS È: \n",
            "(50000, 12)\n",
            "(10715, 12)\n",
            "(10715, 12)\n"
          ]
        }
      ],
      "source": [
        "# STAMPA DELLA DIMENSIONE DEI VETTORI DI FEATURE\n",
        "print(\"LA DIMENSIONE DEI VETTORI DI FEATURE È: \")\n",
        "print(training_features.shape)\n",
        "print(validation_features.shape)\n",
        "print(test_features.shape)\n",
        "\n",
        "# STAMPA DELLA DIMENSIONE DEI VETTORI DI LABELS\n",
        "print(\"LA DIMENSIONE DEI DELLE LABELS È: \")\n",
        "print(reduced_train_labels.shape)\n",
        "print(reduced_val_labels.shape)\n",
        "print(reduced_test_labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TRASFORMAZIONE DELLE LABELS DA NOTAZIONE ONE-HOT A NOTAZIONE DECIMALE\n",
        "reduced_train_labels = np.argmax(reduced_train_labels, axis = 1)\n",
        "reduced_val_labels = np.argmax(reduced_val_labels, axis = 1)\n",
        "reduced_test_labels = np.argmax(reduced_test_labels, axis = 1)"
      ],
      "metadata": {
        "id": "DNT7KhsJed5M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def relation_binary(splitted_features, splitted_labels):\n",
        "  '''\n",
        "      Trasforma le labels in \"parenti\"(0) e \"non parenti\"(1)\n",
        "        # PRECONDIZIONE: array di labels contenente 12 classi tra cui la classe 'na'\n",
        "        # POSTCONDIZIONE: nuovo array di features e di labels con classi 0/1\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        splitted_features: array contenente le features relative alle coppie di immagini\n",
        "            np_array\n",
        "        splitted_labels: array contenente le labels relative alle coppie di immagini\n",
        "            np_array\n",
        "      ----------\n",
        "      RETURN:\n",
        "        splitted_features: array contenente le nuove features\n",
        "            np_array\n",
        "        splitted_labels: array contenente le nuove labels\n",
        "            np_array\n",
        "  '''\n",
        "  na = np.array(9)                                                              # La classe 'na' è indicata con il valore 9\n",
        "\n",
        "  # INIZIALIZZAZIONE VARIABILI DI CONTEGGIO\n",
        "  counterNA = 0\n",
        "  counterR = 0\n",
        "\n",
        "  # INIZIALIZZAZIONE LISTE PER CONTENERE LE NUOVE FEATURES E LE NUOVE LABELS\n",
        "  new_splitted_labels = []\n",
        "  new_splitted_features = []\n",
        "\n",
        "  # CREAZIONE DELLE NUOVE LABELS\n",
        "  for i in range(len(splitted_labels)):\n",
        "    if (np.array_equal(na, splitted_labels[i])):\n",
        "      new_splitted_labels.append(1)\n",
        "      new_splitted_features.append(splitted_features[i])\n",
        "      counterNA = counterNA + 1\n",
        "    else:\n",
        "      new_splitted_labels.append(0)\n",
        "      new_splitted_features.append(splitted_features[i])\n",
        "      counterR = counterR + 1\n",
        "\n",
        "  # TRASFORMAZIONE DELLE LISTE IN ARRAY\n",
        "  splitted_labels = np.array(new_splitted_labels)\n",
        "  splitted_features = np.array(new_splitted_features)\n",
        "\n",
        "  # STAMPA DEL NUMERO DI FEATURES DIVISO PER CLASSE\n",
        "  print(\"SONO PRESENTI:\")\n",
        "  print(\"\\t\" + str(counterNA) + \" NON RELAZIONI\")\n",
        "  print(\"\\t\" + str(counterR) + \" RELAZIONI\")\n",
        "\n",
        "  return splitted_features, splitted_labels"
      ],
      "metadata": {
        "id": "EjTgo_3VHOBV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TRASFORMAZIONE DELLE LABELS DI TRAINING IN PARENTI/NON PARENTI\n",
        "print(\"TRAINING\")\n",
        "btraining_features, btrain_labels = relation_binary(training_features, reduced_train_labels)\n",
        "\n",
        "# TRASFORMAZIONE DELLE LABELS DI VALIDATION IN PARENTI/NON PARENTI\n",
        "print(\"\\nVALIDATION\")\n",
        "bvalidation_features, bval_labels = relation_binary(validation_features, reduced_val_labels)\n",
        "\n",
        "# TRASFORMAZIONE DELLE LABELS DI TEST IN PARENTI/NON PARENTI\n",
        "print(\"\\nTEST\")\n",
        "btest_features, btest_labels = relation_binary(test_features, reduced_test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEvvvX7-HOvY",
        "outputId": "322c3bec-7f72-4554-ccba-ba648c23dc8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TRAINING\n",
            "SONO PRESENTI:\n",
            "\t12272 NON RELAZIONI\n",
            "\t37728 RELAZIONI\n",
            "\n",
            "VALIDATION\n",
            "SONO PRESENTI:\n",
            "\t2258 NON RELAZIONI\n",
            "\t8457 RELAZIONI\n",
            "\n",
            "TEST\n",
            "SONO PRESENTI:\n",
            "\t3197 NON RELAZIONI\n",
            "\t7518 RELAZIONI\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def relation_unity(splitted_features, splitted_labels):\n",
        "  '''\n",
        "      Trasforma le labels in \"siblings\"(0), \"parent-child\"(1) e \"grandparent-grandchild\"(2)\n",
        "        # PRECONDIZIONE: array di labels contenente 12 classi\n",
        "        # POSTCONDIZIONE: nuovo array di features e di labels con classi 0/1/2\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        splitted_features: array contenente le features relative alle coppie di immagini\n",
        "            np_array\n",
        "        splitted_labels: array contenente le labels relative alle coppie di immagini\n",
        "            np_array\n",
        "      ----------\n",
        "      RETURN:\n",
        "        splitted_features: array contenente le nuove features\n",
        "            np_array\n",
        "        splitted_labels: array contenente le nuove labels\n",
        "            np_array\n",
        "  '''\n",
        "\n",
        "  # INIZIALIZZAZIONE LISTE PER CONTENERE LE NUOVE FEATURES E LE NUOVE LABELS\n",
        "  new_splitted_features = []\n",
        "  new_splitted_labels = []\n",
        "\n",
        "  # DEFINIZIONE DEI VALORI DECIMALI RELATIVI ALLE VARIE LABELS\n",
        "  bb = np.array(0)\n",
        "  fd = np.array(1)\n",
        "  fs = np.array(2)\n",
        "  gfgd = np.array(3)\n",
        "  gfgs = np.array(4)\n",
        "  gmgd = np.array(5)\n",
        "  gmgs = np.array(6)\n",
        "  md = np.array(7)\n",
        "  ms = np.array(8)\n",
        "  na = np.array(9)\n",
        "  sibs = np.array(10)\n",
        "  ss = np.array(11)\n",
        "\n",
        "  # CREAZIONE DELLE NUOVE LABELS\n",
        "  for i in range(len(splitted_features)):\n",
        "    if (np.array_equal(bb, splitted_labels[i]) or\n",
        "        np.array_equal(ss, splitted_labels[i]) or\n",
        "        np.array_equal(sibs, splitted_labels[i])):\n",
        "        new_splitted_labels.append(0)\n",
        "        new_splitted_features.append(splitted_features[i])\n",
        "    elif (np.array_equal(fs, splitted_labels[i]) or\n",
        "        np.array_equal(fd, splitted_labels[i]) or\n",
        "        np.array_equal(ms, splitted_labels[i]) or\n",
        "        np.array_equal(md, splitted_labels[i])):\n",
        "        new_splitted_labels.append(1)\n",
        "        new_splitted_features.append(splitted_features[i])\n",
        "    elif (np.array_equal(gfgs, splitted_labels[i]) or\n",
        "        np.array_equal(gfgd, splitted_labels[i]) or\n",
        "        np.array_equal(gmgs, splitted_labels[i]) or\n",
        "        np.array_equal(gmgd, splitted_labels[i])):\n",
        "        new_splitted_labels.append(2)\n",
        "        new_splitted_features.append(splitted_features[i])\n",
        "\n",
        "  # TRASFORMAZIONE DELLE LISTE IN ARRAY\n",
        "  splitted_features = np.array(new_splitted_features)\n",
        "  splitted_labels = np.array(new_splitted_labels)\n",
        "\n",
        "  return splitted_features, splitted_labels"
      ],
      "metadata": {
        "id": "09m4kTprfvfx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TRASFORMAZIONE DELLE LABELS DI TRAINING IN SIBLING/PARENT-CHILD/GRANDPARENT-GRANDCHILD\n",
        "training_features, reduced_train_labels = relation_unity(training_features, reduced_train_labels)\n",
        "\n",
        "# TRASFORMAZIONE DELLE LABELS DI VALIDARION IN SIBLING/PARENT-CHILD/GRANDPARENT-GRANDCHILD\n",
        "validation_features, reduced_val_labels = relation_unity(validation_features, reduced_val_labels)\n",
        "\n",
        "# TRASFORMAZIONE DELLE LABELS DI TEST IN SIBLING/PARENT-CHILD/GRANDPARENT-GRANDCHILD\n",
        "test_features, reduced_test_labels = relation_unity(test_features, reduced_test_labels)"
      ],
      "metadata": {
        "id": "ejxGtzB5gZKG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def relation_balance3C(splitted_features, splitted_labels, threshold):\n",
        "  '''\n",
        "      Bilancia le classi all'interno del dataset\n",
        "        # PRECONDIZIONE: lista di labels contenente 3 classi\n",
        "        # POSTCONDIZIONE: nuova lista di features e di labels bilanciate\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        splitted_features: array contenente le features relative alle coppie di immagini\n",
        "            np_array\n",
        "        splitted_labels: array contenente le labels relative alle coppie di immagini\n",
        "            np_array\n",
        "        threshold: numero minimo di campioni per classe da considerare\n",
        "            int\n",
        "      ----------\n",
        "      RETURN:\n",
        "        splitted_features: array contenente le nuove features bilanciate\n",
        "            np_array\n",
        "        spltted_labels: array contenente le nuove labels bilanciate\n",
        "            np_array\n",
        "  '''\n",
        "\n",
        "  # DEFINIZIONE DEI VALORI RELATIVI ALLE VARIE LABELS\n",
        "  sibs = np.array(0)\n",
        "  pc = np.array(1)\n",
        "  gpgc = np.array(2)\n",
        "\n",
        "  # INIZIALIZZAZIONE DELLE VARIABILI DI CONTEGGIO\n",
        "  counterSIBS = 0\n",
        "  counterPC = 0\n",
        "  counterGPGC = 0\n",
        "\n",
        "  # INIZIALIZZAZIONE LISTE PER CONTENERE LE NUOVE FEATURES E LE NUOVE LABELS\n",
        "  new_splitted_labels = []\n",
        "  new_splitted_features = []\n",
        "\n",
        "  # CONTEGGIO DEL NUMERO DI COPPIE PER OGNI CLASSE\n",
        "  for i in range(len(splitted_labels)):\n",
        "    if (np.array_equal(sibs, splitted_labels[i])):\n",
        "      counterSIBS = counterSIBS + 1\n",
        "    elif (np.array_equal(pc, splitted_labels[i])):\n",
        "      counterPC = counterPC + 1\n",
        "    elif (np.array_equal(gpgc, splitted_labels[i])):\n",
        "      counterGPGC = counterGPGC + 1\n",
        "    else:\n",
        "      print(\"ERRORE LABELS ERRATE\")\n",
        "      return 0\n",
        "\n",
        "  # STAMPA DEL NUMERO DI COPPIE PER OGNI CLASSE\n",
        "  print(\"SONO PRESENTI:\")\n",
        "  print(\"\\t\" + str(counterSIBS) + \" RELAZIONI DI SIBLINGS\")\n",
        "  print(\"\\t\" + str(counterPC) + \" RELAZIONI DI PARENT-CHILD\")\n",
        "  print(\"\\t\" + str(counterGPGC) + \" RELAZIONI DI GRANDPARENT-CHILD\")\n",
        "\n",
        "  # CALCOLO DEL VALORE MINIMO DI COPPIE PER OGNI CLASSE DA CONSIDERARE\n",
        "  min_value = float('inf')\n",
        "  if counterSIBS > threshold:\n",
        "    min_value = min(min_value, counterSIBS)\n",
        "  if counterPC > threshold:\n",
        "    min_value = min(min_value, counterPC)\n",
        "  if counterGPGC > threshold:\n",
        "    min_value = min(min_value, counterGPGC)\n",
        "\n",
        "  # RE-INIZIALIZZAZIONE DELLE VARIABILI DI CONTEGGIO\n",
        "  counterSIBS = 0\n",
        "  counterPC = 0\n",
        "  counterGPGC = 0\n",
        "\n",
        "  # CREAZIONE DELLE NUOVE LABELS E DELLE NUOVE FEATURES\n",
        "  for i in range(len(splitted_labels)):\n",
        "    if (np.array_equal(sibs, splitted_labels[i])):\n",
        "      if (counterSIBS < min_value):\n",
        "        new_splitted_labels.append(splitted_labels[i])\n",
        "        new_splitted_features.append(splitted_features[i])\n",
        "        counterSIBS = counterSIBS + 1\n",
        "    elif (np.array_equal(pc, splitted_labels[i])):\n",
        "      if (counterPC < min_value):\n",
        "        new_splitted_labels.append(splitted_labels[i])\n",
        "        new_splitted_features.append(splitted_features[i])\n",
        "        counterPC = counterPC + 1\n",
        "    elif (np.array_equal(gpgc, splitted_labels[i])):\n",
        "      if (counterGPGC < min_value):\n",
        "        new_splitted_labels.append(splitted_labels[i])\n",
        "        new_splitted_features.append(splitted_features[i])\n",
        "        counterGPGC = counterGPGC + 1\n",
        "    else:\n",
        "      print(\"ERRORE LABELS ERRATE\")\n",
        "      return 0\n",
        "\n",
        "  # TRASFORMAZIONE DELLE LISTE IN ARRAY\n",
        "  splitted_labels = np.array(new_splitted_labels)\n",
        "  splitted_features = np.array(new_splitted_features)\n",
        "\n",
        "  # STAMPA DEL NUMERO DI FEATURES DIVISO PER CLASSE\n",
        "  print(\"DOPO AVER EQUILIBRATO SONO PRESENTI:\")\n",
        "  print(\"\\t\" + str(counterSIBS) + \" RELAZIONI DI SIBLINGS\")\n",
        "  print(\"\\t\" + str(counterPC) + \" RELAZIONI DI PARENT-CHILD\")\n",
        "  print(\"\\t\" + str(counterGPGC) + \" RELAZIONI DI GRANDPARENT-CHILD\")\n",
        "\n",
        "  return splitted_features, splitted_labels"
      ],
      "metadata": {
        "id": "g0bM_xFfDzBv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# BILANCIAMENTO DELLE CLASSI PER IL TRAINING SET\n",
        "thresholdTR = len(reduced_train_labels) * 0.15                                  # Numero minimo di coppie per classe da mantenere per il training set\n",
        "print(\"TRAINING\")\n",
        "training_features, reduced_train_labels = relation_balance3C(training_features, reduced_train_labels, thresholdTR)\n",
        "\n",
        "# BILANCIAMENTO DELLE CLASSI PER IL VALIDATION SET\n",
        "thresholdV = len(reduced_val_labels) * 0.15                                     # Numero minimo di coppie per classe da mantenere per il validation set\n",
        "print(\"\\nVALIDATION\")\n",
        "validation_features, reduced_val_labels = relation_balance3C(validation_features, reduced_val_labels, thresholdV)\n",
        "\n",
        "# BILANCIAMENTO DELLE CLASSI PER IL TEST SET\n",
        "thresholdT = len(reduced_test_labels) * 0.15                                    # Numero minimo di coppie per classe da mantenere per il test set\n",
        "print(\"\\nTEST\")\n",
        "test_features, reduced_test_labels = relation_balance3C(test_features, reduced_test_labels, thresholdT)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GzbejnxCDzfm",
        "outputId": "bf060118-c92f-4b84-85c4-d12c822509b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TRAINING\n",
            "SONO PRESENTI:\n",
            "\t13233 RELAZIONI DI SIBLINGS\n",
            "\t13233 RELAZIONI DI PARENT-CHILD\n",
            "\t1165 RELAZIONI DI GRANDPARENT-CHILD\n",
            "DOPO AVER EQUILIBRATO SONO PRESENTI:\n",
            "\t13233 RELAZIONI DI SIBLINGS\n",
            "\t13233 RELAZIONI DI PARENT-CHILD\n",
            "\t1165 RELAZIONI DI GRANDPARENT-CHILD\n",
            "\n",
            "VALIDATION\n",
            "SONO PRESENTI:\n",
            "\t3172 RELAZIONI DI SIBLINGS\n",
            "\t3172 RELAZIONI DI PARENT-CHILD\n",
            "\t323 RELAZIONI DI GRANDPARENT-CHILD\n",
            "DOPO AVER EQUILIBRATO SONO PRESENTI:\n",
            "\t3172 RELAZIONI DI SIBLINGS\n",
            "\t3172 RELAZIONI DI PARENT-CHILD\n",
            "\t323 RELAZIONI DI GRANDPARENT-CHILD\n",
            "\n",
            "TEST\n",
            "SONO PRESENTI:\n",
            "\t1400 RELAZIONI DI SIBLINGS\n",
            "\t1400 RELAZIONI DI PARENT-CHILD\n",
            "\t359 RELAZIONI DI GRANDPARENT-CHILD\n",
            "DOPO AVER EQUILIBRATO SONO PRESENTI:\n",
            "\t1400 RELAZIONI DI SIBLINGS\n",
            "\t1400 RELAZIONI DI PARENT-CHILD\n",
            "\t359 RELAZIONI DI GRANDPARENT-CHILD\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = 3                                                                 # Numero di classi\n",
        "\n",
        "# CREAZIONE NOTAZIONE ONE-HOT\n",
        "train_labels = to_categorical(reduced_train_labels, num_classes)\n",
        "val_labels = to_categorical(reduced_val_labels, num_classes)\n",
        "test_labels = to_categorical(reduced_test_labels, num_classes)\n",
        "\n",
        "# STAMPA DELLA DIMENSIONE DEI VETTORI DI FEATURES\n",
        "print(\"LA DIMENSIONE DEI VETTORI DI FEATURE È: \")\n",
        "print(training_features.shape)\n",
        "print(validation_features.shape)\n",
        "print(test_features.shape)\n",
        "\n",
        "# STAMPA DELLA DIMENSIONE DEI VETTORI DI LABELS\n",
        "print(\"LA DIMENSIONE DEI DELLE LABELS È: \")\n",
        "print(train_labels.shape)\n",
        "print(val_labels.shape)\n",
        "print(test_labels.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "huYe88w-P4RQ",
        "outputId": "a802be19-6abb-4dbb-bae4-36200be5118f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LA DIMENSIONE DEI VETTORI DI FEATURE È: \n",
            "(49427, 3840)\n",
            "(10166, 3840)\n",
            "(9005, 3840)\n",
            "LA DIMENSIONE DEI DELLE LABELS È: \n",
            "(49427, 3)\n",
            "(10166, 3)\n",
            "(9005, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FX-_5Tf9G5tI"
      },
      "source": [
        "# TRAINING E HISTORY CLASSIFICATORE RELAZIONE BINARIA"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ISTANZA DEL CLASSIFICATORE SMOTE\n",
        "smote = SMOTE()\n",
        "\n",
        "# OVERSAMPLING DEI SET DI TRAINING-VALIDATION-TEST (Dataset sbilanciato)\n",
        "btraining_featuresOS, btrain_labelsOS = smote.fit_resample(btraining_features, btrain_labels)\n",
        "bvalidation_featuresOS, bval_labelsOS = smote.fit_resample(bvalidation_features, bval_labels)\n",
        "btest_featuresOS, btest_labelsOS = smote.fit_resample(btest_features, btest_labels)"
      ],
      "metadata": {
        "id": "ujlp1CrzM-Cw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# INIZIALIZZAZIONE VARIABILI\n",
        "num_epochs = 150                                                                # Numero di epoche per il fit\n",
        "batch_size = 100                                                                # Grandezza di ogni batch per il fit\n",
        "patience_LR = 3                                                                 # Valore di patience per la callback di riduzione del learning rate\n",
        "patience_ES = 30                                                                # Valore di patience per la callback di stop preoce dell'addestramento\n",
        "flagClassifierNA = 0                                                            # Flag per indicare il classificatore Relazione/Non-Relazione\n",
        "num_classesNA = 1                                                               # Numero di classi per il classificatore (0= Relazione, 1= Non relazione)\n",
        "learning_rate = 0.00015                                                         # Learning rate del classificatore\n",
        "\n",
        "# CALLBACK: Riduce il learning rate quando una particolare metrica smette di migliorare\n",
        "reduce_lr = keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor = 'val_accuracy',                                                   # Monitora l'accuracy del validation set\n",
        "    factor = 0.1,                                                               # Fattore di riduzione del learning rate (10%)\n",
        "    patience = patience_LR,                                                     # Numero di epoche senza miglioramenti dopo il quale il learning rate viene ridotto\n",
        "    verbose = 1,                                                                # Stampa un messaggio quando il learning rate viene ridotto\n",
        "    mode = 'max',                                                               # Modalità di monitoraggio ('max' per massimizzare l'accuracy)\n",
        "    min_delta = 0.0001,                                                         # Soglia per considerare un miglioramento significativo\n",
        "    min_lr = 1e-9                                                               # Learning rate minimo consentito\n",
        ")\n",
        "\n",
        "# CALLBACK: Se una particolare metrica smette di migliorare l'addestramento viene interrotto\n",
        "early_stopping = keras.callbacks.EarlyStopping(\n",
        "    monitor = \"val_accuracy\",                                                   # Monitora l'accuracy del validation set\n",
        "    min_delta = 0,                                                              # Differenza minima ritenuta come miglioramento\n",
        "    patience = patience_ES,                                                     # Numero di epoche consecutive senza miglioramento prima di interrompere l'addestramento\n",
        "    verbose = 1,                                                                # Stampa un messaggio quando l'addestramento viene interrotto\n",
        "    mode = \"max\",                                                               # Indica che la metrica monitorata deve essere massimizzata\n",
        "    baseline = None,                                                            # Valore di riferimento per la metrica monitorata\n",
        "    restore_best_weights = True,                                                # Ripristina i pesi del modello al miglior punto durante l'addestramento\n",
        "    start_from_epoch = 0,                                                       # Indica da quale epoca iniziare il conteggio delle epoche senza miglioramento\n",
        ")\n",
        "\n",
        "# CALLBACK: Salvataggio delle metriche di training e validazione in formato csv\n",
        "csv_logger = keras.callbacks.CSVLogger(local_CSVLoggerClassifier_path)\n",
        "\n",
        "# CLASSIFICATORE PER RELAZIONE BINARIA\n",
        "relation_classifier_model = create_classifier(num_classesNA, siamese_model, feature_dimension, flagClassifierNA, learning_rate)\n",
        "\n",
        "# ADDESTRAMENTO CLASSIFICATORE BINARIO\n",
        "history_classifier = relation_classifier_model.fit(x = btraining_featuresOS,\n",
        "                                          y = btrain_labelsOS,\n",
        "                                          batch_size = batch_size,\n",
        "                                          epochs = num_epochs,\n",
        "                                          validation_data = (bvalidation_featuresOS, bval_labelsOS),\n",
        "                                          verbose = True,\n",
        "                                          callbacks=[csv_logger, early_stopping])"
      ],
      "metadata": {
        "id": "R6TzE8IM5Qrf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CREAZIONE INSOGRAMMA DELLA HISTORY\n",
        "plot_history(history_classifier, ['val_accuracy'])"
      ],
      "metadata": {
        "id": "u3vco5wSvcRF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "flagModelBC = 0\n",
        "\n",
        "# CREAZIONE MATRICE DI CONFUSIONE PER IL TEST SET\n",
        "confusionMatrix(relation_classifier_model, btest_featuresOS, btest_labelsOS, flagModelBC)"
      ],
      "metadata": {
        "id": "QVa1l2nkhc21"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TRAINING E HISTORY CLASSIFICATORE GRADI DI PARENTELA"
      ],
      "metadata": {
        "id": "Ij9kA39z8tVZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TQbKxN_VaJKn",
        "outputId": "7991bf16-2a26-49c0-fd25-52a2ab8a0639"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_70 (Dense)            (None, 512)               1966592   \n",
            "                                                                 \n",
            " dense_71 (Dense)            (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_72 (Dense)            (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_73 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_74 (Dense)            (None, 3)                 195       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,139,267\n",
            "Trainable params: 2,139,267\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/200\n",
            "142/142 [==============================] - 3s 17ms/step - loss: 0.9211 - categorical_accuracy: 0.4876 - val_loss: 1.0671 - val_categorical_accuracy: 0.4453\n",
            "Epoch 2/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.8866 - categorical_accuracy: 0.5096 - val_loss: 1.0295 - val_categorical_accuracy: 0.4580\n",
            "Epoch 3/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.8718 - categorical_accuracy: 0.5152 - val_loss: 1.0175 - val_categorical_accuracy: 0.4637\n",
            "Epoch 4/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.8630 - categorical_accuracy: 0.5293 - val_loss: 1.0243 - val_categorical_accuracy: 0.4638\n",
            "Epoch 5/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.8584 - categorical_accuracy: 0.5394 - val_loss: 1.0262 - val_categorical_accuracy: 0.4669\n",
            "Epoch 6/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.8538 - categorical_accuracy: 0.5452 - val_loss: 1.0457 - val_categorical_accuracy: 0.4612\n",
            "Epoch 7/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.8472 - categorical_accuracy: 0.5556 - val_loss: 1.0234 - val_categorical_accuracy: 0.4608\n",
            "Epoch 8/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.8429 - categorical_accuracy: 0.5593 - val_loss: 1.0400 - val_categorical_accuracy: 0.4811\n",
            "Epoch 9/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.8381 - categorical_accuracy: 0.5641 - val_loss: 1.0246 - val_categorical_accuracy: 0.4827\n",
            "Epoch 10/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.8315 - categorical_accuracy: 0.5707 - val_loss: 1.0290 - val_categorical_accuracy: 0.4820\n",
            "Epoch 11/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.8249 - categorical_accuracy: 0.5807 - val_loss: 1.0274 - val_categorical_accuracy: 0.4648\n",
            "Epoch 12/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.8235 - categorical_accuracy: 0.5770 - val_loss: 1.0397 - val_categorical_accuracy: 0.4708\n",
            "Epoch 13/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.8154 - categorical_accuracy: 0.5861 - val_loss: 1.0132 - val_categorical_accuracy: 0.4786\n",
            "Epoch 14/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.8084 - categorical_accuracy: 0.5946 - val_loss: 1.0296 - val_categorical_accuracy: 0.4768\n",
            "Epoch 15/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.8065 - categorical_accuracy: 0.5955 - val_loss: 1.0193 - val_categorical_accuracy: 0.4733\n",
            "Epoch 16/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7952 - categorical_accuracy: 0.6088 - val_loss: 1.0171 - val_categorical_accuracy: 0.4914\n",
            "Epoch 17/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7905 - categorical_accuracy: 0.6110 - val_loss: 1.0314 - val_categorical_accuracy: 0.4640\n",
            "Epoch 18/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7843 - categorical_accuracy: 0.6150 - val_loss: 1.0235 - val_categorical_accuracy: 0.4868\n",
            "Epoch 19/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7810 - categorical_accuracy: 0.6181 - val_loss: 1.0792 - val_categorical_accuracy: 0.4611\n",
            "Epoch 20/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7782 - categorical_accuracy: 0.6207 - val_loss: 1.0117 - val_categorical_accuracy: 0.4931\n",
            "Epoch 21/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7676 - categorical_accuracy: 0.6300 - val_loss: 1.0284 - val_categorical_accuracy: 0.4839\n",
            "Epoch 22/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7572 - categorical_accuracy: 0.6385 - val_loss: 1.0314 - val_categorical_accuracy: 0.4830\n",
            "Epoch 23/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.7560 - categorical_accuracy: 0.6395 - val_loss: 1.0570 - val_categorical_accuracy: 0.4893\n",
            "Epoch 24/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.7475 - categorical_accuracy: 0.6473 - val_loss: 1.0495 - val_categorical_accuracy: 0.4918\n",
            "Epoch 25/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.7405 - categorical_accuracy: 0.6531 - val_loss: 1.0437 - val_categorical_accuracy: 0.4823\n",
            "Epoch 26/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7350 - categorical_accuracy: 0.6541 - val_loss: 1.0341 - val_categorical_accuracy: 0.4984\n",
            "Epoch 27/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.7303 - categorical_accuracy: 0.6578 - val_loss: 1.0508 - val_categorical_accuracy: 0.4844\n",
            "Epoch 28/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7246 - categorical_accuracy: 0.6607 - val_loss: 1.0566 - val_categorical_accuracy: 0.4810\n",
            "Epoch 29/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7165 - categorical_accuracy: 0.6668 - val_loss: 1.0266 - val_categorical_accuracy: 0.4895\n",
            "Epoch 30/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7103 - categorical_accuracy: 0.6722 - val_loss: 1.0555 - val_categorical_accuracy: 0.4769\n",
            "Epoch 31/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.7079 - categorical_accuracy: 0.6751 - val_loss: 1.0562 - val_categorical_accuracy: 0.4886\n",
            "Epoch 32/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.7021 - categorical_accuracy: 0.6771 - val_loss: 1.0484 - val_categorical_accuracy: 0.4920\n",
            "Epoch 33/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.6928 - categorical_accuracy: 0.6843 - val_loss: 1.0883 - val_categorical_accuracy: 0.4866\n",
            "Epoch 34/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.6970 - categorical_accuracy: 0.6803 - val_loss: 1.0893 - val_categorical_accuracy: 0.4908\n",
            "Epoch 35/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6862 - categorical_accuracy: 0.6886 - val_loss: 1.0631 - val_categorical_accuracy: 0.4834\n",
            "Epoch 36/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6778 - categorical_accuracy: 0.6950 - val_loss: 1.1029 - val_categorical_accuracy: 0.4875\n",
            "Epoch 37/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6728 - categorical_accuracy: 0.6951 - val_loss: 1.0744 - val_categorical_accuracy: 0.4920\n",
            "Epoch 38/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6666 - categorical_accuracy: 0.7022 - val_loss: 1.0578 - val_categorical_accuracy: 0.4860\n",
            "Epoch 39/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.6616 - categorical_accuracy: 0.7063 - val_loss: 1.1068 - val_categorical_accuracy: 0.4898\n",
            "Epoch 40/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.6611 - categorical_accuracy: 0.7031 - val_loss: 1.1287 - val_categorical_accuracy: 0.4719\n",
            "Epoch 41/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.6525 - categorical_accuracy: 0.7110 - val_loss: 1.1051 - val_categorical_accuracy: 0.4796\n",
            "Epoch 42/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.6442 - categorical_accuracy: 0.7166 - val_loss: 1.1078 - val_categorical_accuracy: 0.4727\n",
            "Epoch 43/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6389 - categorical_accuracy: 0.7187 - val_loss: 1.1654 - val_categorical_accuracy: 0.4879\n",
            "Epoch 44/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6366 - categorical_accuracy: 0.7174 - val_loss: 1.0953 - val_categorical_accuracy: 0.4855\n",
            "Epoch 45/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6295 - categorical_accuracy: 0.7228 - val_loss: 1.1542 - val_categorical_accuracy: 0.4826\n",
            "Epoch 46/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.6295 - categorical_accuracy: 0.7237 - val_loss: 1.1026 - val_categorical_accuracy: 0.4812\n",
            "Epoch 47/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6213 - categorical_accuracy: 0.7283 - val_loss: 1.0981 - val_categorical_accuracy: 0.4853\n",
            "Epoch 48/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6134 - categorical_accuracy: 0.7339 - val_loss: 1.1163 - val_categorical_accuracy: 0.4812\n",
            "Epoch 49/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6095 - categorical_accuracy: 0.7358 - val_loss: 1.1848 - val_categorical_accuracy: 0.4636\n",
            "Epoch 50/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6067 - categorical_accuracy: 0.7363 - val_loss: 1.1360 - val_categorical_accuracy: 0.4816\n",
            "Epoch 51/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.6045 - categorical_accuracy: 0.7371 - val_loss: 1.1765 - val_categorical_accuracy: 0.4801\n",
            "Epoch 52/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.5941 - categorical_accuracy: 0.7443 - val_loss: 1.1322 - val_categorical_accuracy: 0.4738\n",
            "Epoch 53/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.5864 - categorical_accuracy: 0.7476 - val_loss: 1.1828 - val_categorical_accuracy: 0.4827\n",
            "Epoch 54/200\n",
            "142/142 [==============================] - 2s 13ms/step - loss: 0.5880 - categorical_accuracy: 0.7484 - val_loss: 1.2184 - val_categorical_accuracy: 0.4824\n",
            "Epoch 55/200\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.5826 - categorical_accuracy: 0.7499 - val_loss: 1.1714 - val_categorical_accuracy: 0.4740\n",
            "Epoch 56/200\n",
            "139/142 [============================>.] - ETA: 0s - loss: 0.5800 - categorical_accuracy: 0.7518Restoring model weights from the end of the best epoch: 26.\n",
            "142/142 [==============================] - 2s 14ms/step - loss: 0.5797 - categorical_accuracy: 0.7523 - val_loss: 1.1825 - val_categorical_accuracy: 0.4801\n",
            "Epoch 56: early stopping\n"
          ]
        }
      ],
      "source": [
        "# INIZIALIZZAZIONE VARIABILI\n",
        "num_epochs = 200                                                                # Numero di epoche per il fit\n",
        "batch_size = 350                                                                # Grandezza di ogni batch per il fit\n",
        "patience_LR = 10                                                                 # Valore di patience per la callback di riduzione del learning rate\n",
        "patience_ES = 30                                                                # Valore di patience per la callback di stop preoce dell'addestramento\n",
        "flagClassifierR = 1                                                             # Flag per indicare il classificatore\n",
        "num_classesR = 3                                                                # Numero di classi (0= Siblings, 1= Parent-Child, 2= GrandParent-GrandChildren)\n",
        "learning_rate = 3e-5                                                            # Learning rate del classificatore\n",
        "\n",
        "\n",
        "# CALLBACK: Riduce il learning rate quando una particolare metrica smette di migliorare\n",
        "reduce_lr = keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor = 'val_categorical_accuracy',                                       # Monitora l'accuracy del validation set\n",
        "    factor = 0.1,                                                               # Fattore di riduzione del learning rate (10%)\n",
        "    patience = patience_LR,                                                     # Numero di epoche senza miglioramenti dopo il quale il learning rate viene ridotto\n",
        "    verbose = 1,                                                                # Stampa un messaggio quando il learning rate viene ridotto\n",
        "    mode = 'max',                                                               # Modalità di monitoraggio ('max' per massimizzare l'accuracy)\n",
        "    min_delta = 0.0001,                                                         # Soglia per considerare un miglioramento significativo\n",
        "    min_lr = 1e-9                                                               # Learning rate minimo consentito\n",
        ")\n",
        "\n",
        "# CALLBACK: Se una particolare metrica smette di migliorare l'addestramento viene interrotto\n",
        "early_stopping = keras.callbacks.EarlyStopping(\n",
        "    monitor = \"val_categorical_accuracy\",                                       # Monitora l'accuracy del validation set\n",
        "    min_delta = 0,                                                              # Differenza minima ritenuta come miglioramento\n",
        "    patience = patience_ES,                                                     # Numero di epoche consecutive senza miglioramento prima di interrompere l'addestramento\n",
        "    verbose = 1,                                                                # Stampa un messaggio quando l'addestramento viene interrotto\n",
        "    mode = \"max\",                                                               # Indica che la metrica monitorata deve essere massimizzata\n",
        "    baseline = None,                                                            # Valore di riferimento per la metrica monitorata\n",
        "    restore_best_weights = True,                                                # Ripristina i pesi del modello al miglior punto durante l'addestramento\n",
        "    start_from_epoch = 0,                                                       # Indica da quale epoca iniziare il conteggio delle epoche senza miglioramento\n",
        ")\n",
        "\n",
        "# CALLBACK: Salvataggio delle metriche di training e validazione in formato csv\n",
        "csv_logger = keras.callbacks.CSVLogger(local_CSVLoggerClassifier_path)\n",
        "\n",
        "# CAMBIO IL PESO DELLE CLASSI\n",
        "sample_weights = np.ones(len(training_features))\n",
        "sample_weights[np.all(train_labels == np.array([1,0,0]), axis=1)] = 0.6\n",
        "sample_weights[np.all(train_labels == np.array([0,1,0]), axis=1)] = 0.4\n",
        "sample_weights[np.all(train_labels == np.array([0,0,1]), axis=1)] = 2.0\n",
        "\n",
        "# CLASSIFICATORE PER CLASSI MULTIPLE\n",
        "classifier_model = create_classifier(num_classesR, siamese_model, feature_dimension, flagClassifierR, learning_rate)\n",
        "\n",
        "# ADDESTRAMENTO CLASSIFICATORE\n",
        "history_classifier = classifier_model.fit(x = training_features,\n",
        "                                          y = train_labels,\n",
        "                                          batch_size = batch_size,\n",
        "                                          epochs = num_epochs,\n",
        "                                          validation_data = (validation_features, val_labels),\n",
        "                                          verbose = True,\n",
        "                                          #sample_weight = sample_weights,\n",
        "                                          callbacks=[early_stopping, csv_logger])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CREAZIONE INSOTGRAMMA DELLE PREDIZIONI\n",
        "histogram_plot(test_labels, test_features, num_classesR)"
      ],
      "metadata": {
        "id": "IdkOip5Kiyjq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "ae8181ec-5b2c-4b9e-c119-1413daea3adf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "282/282 [==============================] - 1s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "flagModelC = 1\n",
        "\n",
        "# CREAZIONE MATRICE DI CONFUSIONE\n",
        "confusionMatrix(classifier_model, test_features, test_labels, flagModelC)"
      ],
      "metadata": {
        "id": "3XQl-CsLhRq1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "49852e64-68eb-4611-892f-12f965ffa5b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1545/1545 [==============================] - 4s 2ms/step - loss: 0.7355 - categorical_accuracy: 0.6475\n",
            "Test Loss: 0.7354826927185059\n",
            "Test Accuracy: 0.647459864616394\n",
            "1545/1545 [==============================] - 3s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_VztqY2CcpM1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "13ba6bf0-7b5c-45de-bbfe-f20694e29539"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# CREAZIONE INSOGRAMMA DELLA HISTORY\n",
        "plot_history(history_classifier, ['val_categorical_accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J4f4dCaMGK6X"
      },
      "outputs": [],
      "source": [
        "# SALVATAGGIO DEI PESI SU DRIVE\n",
        "weights_path = \"/content/drive/MyDrive/ESM/KinDeNet/Weights/ClassifierWeights_lr\" + str(learning_rate) + \"_batch\" + str(batch_size) + \"_epochs\" + str(num_epochs) + \".h5\"\n",
        "classifier_model.save_weights(weights_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dLnzY1b_WFa9"
      },
      "source": [
        "# FUNZIONE DI PREDIZIONE SU IMMAGINI SOLITI IGNOTI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fm_qfU8HWFa-"
      },
      "outputs": [],
      "source": [
        "def predict_similarity(image_1_path, image_2_path, siamese_model, classifier_model, input_dimension, input_channel):\n",
        "  '''\n",
        "      Predice la presenza o meno di parentela tra due immagini\n",
        "        # PRECONDIZIONE: definizione dell'architettura del modello siamese di estrazione delle feature e numero di classi per le label\n",
        "        # POSTCONDIZIONE: predizione sulla coppia di immagini\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        image_1_path: path della prima immagine\n",
        "            str\n",
        "        image_2_path: path della seconda immagine\n",
        "            str\n",
        "        siamese_model: modello siamese di rete per la feature extraction\n",
        "            keras_vggface\n",
        "        classifier_model: rete neurale fully-connected per la classificazione\n",
        "            keras_model\n",
        "        input_dimension: dimensione desiderata dell'immagine\n",
        "            int\n",
        "        input_channel: numero di canali dell'immagine\n",
        "            int\n",
        "      ----------\n",
        "      RETURN:\n",
        "        prediction: predizione sulla coppia di immagini\n",
        "            np_array\n",
        "  '''\n",
        "\n",
        "  # PREPARAZIONE DELLA PRIMA IMMAGINE\n",
        "  image_1 = img_preprocessing(image_1_path, input_dimension)\n",
        "  image_1 = image_1[:,:,:input_channel]                                         #leviamo il 4 canale\n",
        "  image_1 = np.reshape(image_1, (-1, input_dimension, input_dimension, input_channel))\n",
        "\n",
        "  # PREPARAZIONE DELLA SECONDA IMMAGINE\n",
        "  image_2 = img_preprocessing(image_2_path, input_dimension)\n",
        "  image_2 = image_2[:,:,:input_channel]                                         #leviamo il 4 canale\n",
        "  image_2 = np.reshape(image_2, (-1, input_dimension, input_dimension, input_channel))\n",
        "\n",
        "  # ESTRAZIONE DELLE FEATURES DALLA COPPIA DI IMMAGINI\n",
        "  features_1 = siamese_model.predict([image_1, image_2])\n",
        "\n",
        "  # PREDIZIONE DELLA CLASSE DALLE FEATURES\n",
        "  prediction = classifier_model.predict(features_1)\n",
        "\n",
        "  print(prediction)\n",
        "  return prediction"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# UNZIP DELLA CARTELLA SOLITI_IGNOTI IN LOCALE\n",
        "!unzip /content/drive/MyDrive/ESM/KinDeNet/Soliti_Ignoti.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_7Wu3o5WFa-",
        "outputId": "bc8c43ba-888c-4497-b806-5f8f08478918"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/drive/MyDrive/ESM/KinDeNet/Soliti_Ignoti.zip\n",
            "   creating: Soliti_Ignoti/Ignoti/\n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_1.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_2.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_3.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_4.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_5.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_6.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_7.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_8.jpg  \n",
            "  inflating: Soliti_Ignoti/indiziato.jpg  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kASzpeTTWFa_"
      },
      "outputs": [],
      "source": [
        "image_1_path = '/content/Soliti_Ignoti/indiziato.jpg'                           # Definiamo il percorso dell'immagine dell'indiziato\n",
        "\n",
        "# PREDIZIONE PER OGNUNO DEGLI 8 IGNOTI\n",
        "predictions = []                                                                # Lista vuota per contenere le predizioni\n",
        "for elem in sorted(glob('/content/Soliti_Ignoti/Ignoti/*.jpg')):\n",
        "  predictions.append(predict_similarity(image_1_path, elem, siamese_model, classifier_model, input_dimension, input_channels))\n",
        "\n",
        "predictions = np.array(predictions)                                             # Trasformiamo la lista di predizioni in un array numpy\n",
        "index = np.argmax(predictions)                                                  # Prendiamo l'indice della predizione più alta\n",
        "\n",
        "# STAMPA DEL RISULTATO\n",
        "print('Il parente misterioso è il numero ' + str(index+1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2NjGgU0Nt0YL"
      },
      "source": [
        "# DEFINIZIONE RETE CLASSIFICATORE [RETE FINE TUNING]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z7Mn81I-t0Yp"
      },
      "outputs": [],
      "source": [
        "def create_siamese_classifier_network(num_classes, base_network, train_after_layer, flagClassifier, input_dimension, input_channels, weights_path, filename):\n",
        "  '''\n",
        "      Crea un modello di rete siamese per l'estrazione di features e la classificazione\n",
        "        # PRECONDIZIONE: definizione dell'architettura di rete e path dei pesi pre-addestrati valido\n",
        "        # POSTCONDIZIONE: modello di rete per feature extraction e classificazione\n",
        "\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        num_classes: numero di classi per le label di relazione parentale\n",
        "            int\n",
        "        base_network: modello base di rete\n",
        "            keras_model\n",
        "        train_after_layer: numero dell'ultimo livello da non trainare\n",
        "            int\n",
        "        flagClassifier: flag per indicare se classificatore binario (0) o classificatore a classi multiple (1)\n",
        "            int\n",
        "        input_dimension: dimensione di input dell'immagine\n",
        "            int\n",
        "        input_channels: numero di canali di input dell'immagine\n",
        "            int\n",
        "        weights_path: percorso dei pesi della rete pre-addestrata con triplet loss\n",
        "            str\n",
        "        filename: nome dei pesi specifici da prendere\n",
        "            str\n",
        "\n",
        "      ----------\n",
        "      RETURN:\n",
        "        Model\n",
        "  '''\n",
        "  base_network.load_weights(weights_path + filename)                            # Caricamento dei pesi sul base_network\n",
        "\n",
        "  base_network = base_network.get_layer(\"vggface_resnet50\")                     # Estrazione dei layer corrispondenti a ResNet50\n",
        "\n",
        "  for layer in base_network.layers[:train_after_layer]:                         # Congela tutti i pesi del modello base fino al layer indicato\n",
        "      layer.trainable = False\n",
        "\n",
        "  # DEFINIZIONE DEGLI INPUT\n",
        "  input_1 = Input(shape = (input_dimension, input_dimension, input_channels))\n",
        "  input_2 = Input(shape = (input_dimension, input_dimension, input_channels))\n",
        "\n",
        "  # SELEZIONE DEI LAYER DI INTERESSE\n",
        "  desired_layers = [\"activation_9\", \"activation_21\", \"activation_39\", \"activation_48\"]\n",
        "  selected_features = []\n",
        "\n",
        "  # ESTRAZIONE DELLE FEATURE DAI LAYER DI INTERESSE\n",
        "  for layer_name in desired_layers:\n",
        "    layer_output = base_network.get_layer(layer_name).output                    # Estrazione delle feature dallo strato corrente\n",
        "    pooled_features = tf.keras.layers.GlobalAveragePooling2D()(layer_output)    # Averaging Pooling 1x1\n",
        "    normalized_features = tf.keras.layers.BatchNormalization()(pooled_features) # Aggiunta di un layer di BatchNormalization\n",
        "    selected_features.append(normalized_features)                               # Salvataggio delle feature estratte per lo strato corrente\n",
        "\n",
        "  # CONCATENAZIONE DELLE FEATURES ESTRATTE\n",
        "  concatenated = tf.keras.layers.Concatenate()(selected_features)\n",
        "\n",
        "  # CREAZIONE DI UN NUOVO MODELLO CON INPUT E FEATURE ESTRATTE\n",
        "  new_model = Model(inputs = base_network.input, outputs = concatenated)\n",
        "\n",
        "  # APPLICAZIONE DELLE DUE IMMAGINI IN INGRESSO AL NUOVO MODELLO\n",
        "  total_features_1 = new_model(input_1)\n",
        "  total_features_2 = new_model(input_2)\n",
        "\n",
        "  # CALCOLO DELLA DIFFERENZA IN VALORE ASSOLUTO TRA LE FEATURE DELLE DUE IMMAGINI (L1)\n",
        "  diff_features = tf.math.abs(tf.subtract(total_features_1, total_features_2))\n",
        "\n",
        "  # AGGIUNTA DI LIVELLI FULLY-CONNECTED\n",
        "  fc_layer1 = Dense(512, activation='relu')(diff_features)\n",
        "  fc_layer2 = Dense(128, activation='relu')(fc_layer1)\n",
        "\n",
        "  # AGGIUNTA DEL LAYER DI OUTPUT CON NUMERO DI CLASSI DERIDERATO E ACTIVATION FUNCTION\n",
        "  if flagClassifier == 1:\n",
        "    output = Dense(num_classes, activation='softmax')(fc_layer2)\n",
        "  elif flagClassifier == 0:\n",
        "    output = Dense(num_classes, activation='sigmoid')(fc_layer2)\n",
        "  else:\n",
        "    print(\"ERRORE FLAG ERRATO\")\n",
        "    return 0\n",
        "\n",
        "  # CREAZIONE DEL MODELLO DI RETE SIAMESE CON INPUT LE DUE IMMAGINI E OUTPUT LA L1 DELLE FEATURES\n",
        "  siamese_classifier_model = Model(inputs=[input_1,input_2], outputs=output)\n",
        "\n",
        "  # COMPILAZIONE DEL CLASSIFICATORE\n",
        "  if (flagClassifier == 1):\n",
        "    accuracy = tf.keras.metrics.CategoricalAccuracy(name = \"accuracy\")\n",
        "    top_2_accuracy = tf.keras.metrics.TopKCategoricalAccuracy(name = \"top_2_accuracy\", k=2)\n",
        "    siamese_classifier_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = 0.001),\n",
        "                            loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                            metrics=[accuracy, top_2_accuracy])\n",
        "  elif (flagClassifier == 0):\n",
        "    siamese_classifier_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = 0.001),\n",
        "                            loss=keras.losses.binary_crossentropy,\n",
        "                            metrics=['accuracy'])\n",
        "  else:\n",
        "    print(\"ERRORE FLAG ERRATO\")\n",
        "    return 0\n",
        "\n",
        "  return siamese_classifier_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gbHvA4YTt0Yq",
        "outputId": "748c1ad8-1b73-4903-f65b-6e3bae1ecb72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_8\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_11 (InputLayer)          [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " input_12 (InputLayer)          [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " model_7 (Functional)           (None, 3840)         23576512    ['input_11[0][0]',               \n",
            "                                                                  'input_12[0][0]']               \n",
            "                                                                                                  \n",
            " tf.math.subtract_3 (TFOpLambda  (None, 3840)        0           ['model_7[0][0]',                \n",
            " )                                                                'model_7[1][0]']                \n",
            "                                                                                                  \n",
            " tf.math.abs_3 (TFOpLambda)     (None, 3840)         0           ['tf.math.subtract_3[0][0]']     \n",
            "                                                                                                  \n",
            " dense_9 (Dense)                (None, 512)          1966592     ['tf.math.abs_3[0][0]']          \n",
            "                                                                                                  \n",
            " dense_10 (Dense)               (None, 128)          65664       ['dense_9[0][0]']                \n",
            "                                                                                                  \n",
            " dense_11 (Dense)               (None, 1)            129         ['dense_10[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 25,608,897\n",
            "Trainable params: 17,004,801\n",
            "Non-trainable params: 8,604,096\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "filename = \"Weights_lr1e-05_batch150_epochs20_bestEpoch1.hdf5\"                  # Nome dei pesi scelti\n",
        "train_after_layer = 141                                                         # Numero dell'ultimo layer non trainabile\n",
        "flagClassifierNA = 0                                                            # Flag che indica la creazione del classificatore binario\n",
        "num_classesNA = 1                                                               # Numero di classi del classificatore binario\n",
        "\n",
        "# DEFINIZIONE RETE PER FEATURE EXTRACTION E CLASSIFICAZIONE\n",
        "relation_siamese_classifier_model = create_siamese_classifier_network(num_classesNA, siameseRES_model, train_after_layer, flagClassifierNA, input_dimension, input_channels, ResWeights_path, filename)\n",
        "relation_siamese_classifier_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a56141e-c327-4598-bd96-9e11c9d7ace3",
        "id": "pXU4KEdB5pMi"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_7 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " input_8 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " model_3 (Functional)           (None, 3840)         23576512    ['input_7[0][0]',                \n",
            "                                                                  'input_8[0][0]']                \n",
            "                                                                                                  \n",
            " tf.math.subtract_1 (TFOpLambda  (None, 3840)        0           ['model_3[0][0]',                \n",
            " )                                                                'model_3[1][0]']                \n",
            "                                                                                                  \n",
            " tf.math.abs_1 (TFOpLambda)     (None, 3840)         0           ['tf.math.subtract_1[0][0]']     \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 512)          1966592     ['tf.math.abs_1[0][0]']          \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 128)          65664       ['dense_3[0][0]']                \n",
            "                                                                                                  \n",
            " dense_5 (Dense)                (None, 3)            387         ['dense_4[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 25,609,155\n",
            "Trainable params: 17,005,059\n",
            "Non-trainable params: 8,604,096\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "filename = \"Weights_lr1e-05_batch150_epochs20_bestEpoch1.hdf5\"                  # Nome dei pesi scelti\n",
        "num_classes = 3                                                                 # Numero di classi da classificare\n",
        "train_after_layer = 30                                                          # Numero dell'ultimo livello non trainabile\n",
        "flagClassifierR = 1                                                             # Flag che indica la creazione del classificatore dei gradi di parentela\n",
        "\n",
        "# DEFINIZIONE RETE SIAMESE PER FEATURE EXTRACTION\n",
        "siamese_classifier_model = create_siamese_classifier_network(num_classes, siameseRES_model, train_after_layer, flagClassifierR, input_dimension, input_channels, ResWeights_path, filename)\n",
        "siamese_classifier_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ESTRAZIONE DEI PATH E DELLE LABEL DAI CSV [RETE FINE TUNING]"
      ],
      "metadata": {
        "id": "c4LSaJJQ3gYe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uVGNEb5uOaL9"
      },
      "outputs": [],
      "source": [
        "# SALVATAGGIO DEI CSV PER IMG1-IMG2-LABEL DEL TRAINING IN LOCALE\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Training/img1.csv\" \"img1TR.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Training/img2.csv\" \"img2TR.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Training/label.csv\" \"labelTR.csv\"\n",
        "\n",
        "# SALVATAGGIO DEI CSV PER IMG1-IMG2-LABEL DEL VALIDATION IN LOCALE\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Validation/img1.csv\" \"img1V.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Validation/img2.csv\" \"img2V.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Validation/label.csv\" \"labelV.csv\"\n",
        "\n",
        "# SALVATAGGIO DEI CSV PER IMG1-IMG2-LABEL DEL TEST IN LOCALE\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Test/img1.csv\" \"img1T.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Test/img2.csv\" \"img2T.csv\"\n",
        "!cp \"/content/drive/MyDrive/ESM/KinDeNet/Dataset_Splitted/Test/label.csv\" \"labelT.csv\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1XRAJGntOaL9"
      },
      "outputs": [],
      "source": [
        "num_classes = 12\n",
        "\n",
        "# APERTURA DEI CSV RELATIVI ALLE LABELS\n",
        "train_labels = list_from_csv(local_labelTR_path)\n",
        "val_labels = list_from_csv(local_labelV_path)\n",
        "test_labels = list_from_csv(local_labelT_path)\n",
        "\n",
        "# CREAZIONE DELL'ENCODER DI LABELS\n",
        "label_encoder = LabelEncoder()\n",
        "all_labels = train_labels + val_labels + test_labels\n",
        "label_encoder.fit(all_labels)\n",
        "\n",
        "# CODIDICA DELLE LABEL IN FORMATO NUMERICO DAL FORMATO STRINGA\n",
        "train_encode_labels = label_encoder.transform(np.ravel(train_labels))\n",
        "val_encode_labels = label_encoder.transform(np.ravel(val_labels))\n",
        "test_encode_labels = label_encoder.transform(np.ravel(test_labels))\n",
        "# N.B. np.ravel() appiattisce la lista di liste di label\n",
        "\n",
        "# CREAZIONE NOTAZIONE ONE-HOT\n",
        "train_labels = to_categorical(train_encode_labels, num_classes)\n",
        "val_labels = to_categorical(val_encode_labels, num_classes)\n",
        "test_labels = to_categorical(test_encode_labels, num_classes)\n",
        "\n",
        "# SALVATAGGIO IN LOCALE DELLE LABELS\n",
        "list_on_csv_wH('/content', train_labels.tolist(), 'labelTR')\n",
        "list_on_csv_wH('/content', val_labels.tolist(), 'labelV')\n",
        "list_on_csv_wH('/content', test_labels.tolist(), 'labelT')\n",
        "\n",
        "# LIBERAZIONE MEMORIA RAM\n",
        "del train_encode_labels, val_encode_labels, test_encode_labels\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GvsIqvYdOaL1"
      },
      "source": [
        "# TRAINING E HISTORY CLASSIFICATORE BINARIO [RETE FINE TUNING]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# APERTURA DEI CSV RELATIVI ALLE LABELS\n",
        "train_labels = list_from_csv(local_labelTR_path)\n",
        "val_labels = list_from_csv(local_labelV_path)\n",
        "test_labels = list_from_csv(local_labelT_path)"
      ],
      "metadata": {
        "id": "TDBP4_7D34Vn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "002fad12-3d04-4b8f-d5f0-a994eb092162",
        "id": "cRL3rEP5OaL-"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "500/500 [==============================] - 3405s 7s/step - loss: 0.6528 - accuracy: 0.6227 - val_loss: 0.5967 - val_accuracy: 0.7179 - lr: 0.0010\n",
            "Epoch 2/2\n",
            "500/500 [==============================] - ETA: 0s - loss: 0.5817 - accuracy: 0.7002\n",
            "Epoch 2: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "Restoring model weights from the end of the best epoch: 1.\n",
            "500/500 [==============================] - 3386s 7s/step - loss: 0.5817 - accuracy: 0.7002 - val_loss: 0.6259 - val_accuracy: 0.6837 - lr: 0.0010\n",
            "Epoch 2: early stopping\n"
          ]
        }
      ],
      "source": [
        "# INIZIALIZZAZIONE VARIABILI\n",
        "epochs = 2                                                                      # Numero di epoche per il fit\n",
        "batch_size = 200                                                                # Grandezza di ogni batch per il fit\n",
        "total_coupletsTR = len(train_labels )                                           # Numero totale di coppie del training set (troppo elevato per le risorse a disposizione)\n",
        "total_coupletsV = len(val_labels)                                               # Numero totale di coppie del validation set (troppo elevato per le risorse a disposizione)\n",
        "total_coupletsT = len(test_labels)                                              # Numero totale di coppie del test set (troppo elevato per le risorse a disposizione)\n",
        "reduced_coupletsTR = 100000                                                     # Numero di coppie di training considerate per il fit\n",
        "reduced_coupletsV = 21429                                                       # Numero di coppie di validation considerate per il fit\n",
        "reduced_coupletsT = 21429                                                       # Numero di coppie di test considerate per il predict\n",
        "patience_LR = 1                                                                 # Valore di patience per la callback di riduzione del learning rate\n",
        "patience_ES = 1                                                                 # Valore di patience per la callback di stop preoce dell'addestramento\n",
        "saved_epoch = 0                                                                 # Variabile per il salvataggio del valore della best-epoch\n",
        "\n",
        "# CREAZIONE GENERATORI DI TRAINING E VALIDATION\n",
        "genT = preprocess_couplets_binary(local_img1TR_path, local_img2TR_path, local_labelTR_path, input_dimension, batch_size)\n",
        "genV = preprocess_couplets_binary(local_img1V_path, local_img2V_path, local_labelV_path, input_dimension, batch_size)\n",
        "\n",
        "# FUNZIONE PER IL SALVATAGGIO DEL NUMERO DELLA BEST EPOCH\n",
        "def save_epoch_number(epoch, logs):\n",
        "    global saved_epoch\n",
        "    saved_epoch = epoch + 1                                                     # N.B. Aggiungiamo 1 perchè le epoche partono da 1\n",
        "\n",
        "# CALLBACK: Salva i pesi del modello durante l'addestramento\n",
        "model_checkpoint_callback = keras.callbacks.ModelCheckpoint(\n",
        "    filepath = local_ResWeights_path,                                           # Percorso in locale del file in cui salvare i pesi\n",
        "    filename = 'weights_{epoch:02d}.h5',                                        # Nome del file dove salvare i pesi\n",
        "    save_weights_only = True,                                                   # Salva solo i pesi anziché l'intero modello\n",
        "    monitor = 'val_accuracy',                                                   # Metrica da monitorare per il salvataggio dei pesi migliori\n",
        "    mode = 'max',                                                               # Indica che la metrica monitorata deve essere massimizzata\n",
        "    save_best_only = True,                                                      # Salva solo i pesi corrispondenti al miglior valore della metrica monitorata\n",
        "    save_freq = 'epoch',                                                        # Frequenza di salvataggio dei pesi\n",
        "    callbacks = [save_epoch_number]                                             # Callback per il salvataggio dell'epoca migliore\n",
        ")\n",
        "\n",
        "# CALLBACK: Riduce il learning rate quando una particolare metrica smette di migliorare\n",
        "reduce_lr = keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor = 'val_accuracy',                                                   # Monitora la loss del validation set\n",
        "    factor = 0.1,                                                               # Fattore di riduzione del learning rate (10%)\n",
        "    patience = patience_LR,                                                     # Numero di epoche senza miglioramenti dopo il quale il learning rate viene ridotto\n",
        "    verbose = 1,                                                                # Stampa un messaggio quando il learning rate viene ridotto\n",
        "    mode = 'max',                                                               # Modalità di monitoraggio ('max' per massimizzare la loss)\n",
        "    min_delta = 0.0001,                                                         # Soglia per considerare un miglioramento significativo\n",
        "    min_lr = 1e-10                                                              # Learning rate minimo consentito\n",
        ")\n",
        "\n",
        "# CALLBACK: Se una particolare metrica smette di migliorare l'addestramento viene interrotto\n",
        "early_stopping = keras.callbacks.EarlyStopping(\n",
        "    monitor = \"val_accuracy\",                                                   # Monitora la loss del validation set\n",
        "    min_delta = 0,                                                              # Differenza minima ritenuta come miglioramento\n",
        "    patience = patience_ES,                                                     # Numero di epoche consecutive senza miglioramento prima di interrompere l'addestramento\n",
        "    verbose = 1,                                                                # Stampa un messaggio quando l'addestramento viene interrotto\n",
        "    mode = \"max\",                                                               # Indica che la metrica monitorata deve essere massimizzata\n",
        "    baseline = None,                                                            # Valore di riferimento per la metrica monitorata\n",
        "    restore_best_weights = True,                                                # Ripristina i pesi del modello al miglior punto durante l'addestramento\n",
        "    start_from_epoch = 0,                                                       # Indica da quale epoca iniziare il conteggio delle epoche senza miglioramento\n",
        ")\n",
        "\n",
        "# CALLBACK: Salvataggio delle metriche di training e validazione in formato csv\n",
        "csv_logger = keras.callbacks.CSVLogger(local_CSVLogger_path)\n",
        "\n",
        "# ADDESTRAMENTO DEL MODELLO SULLE COPPIE (IMAGE_1-IMAGE_2)\n",
        "history = relation_siamese_classifier_model.fit(\n",
        "    genT,                                                                       # Generatore di training\n",
        "    validation_data=genV,                                                       # Generatore di validazione\n",
        "    epochs=epochs,                                                              # Numero di epoche di addestramento\n",
        "    steps_per_epoch=reduced_coupletsTR//batch_size,                             # Numero di passi per epoca di addestramento\n",
        "    validation_steps=reduced_coupletsV//batch_size,                             # Numero di passi per epoca di validazione\n",
        "    verbose=True,                                                               # Messaggi di avviso sulle metriche\n",
        "    callbacks=[model_checkpoint_callback, reduce_lr, early_stopping, csv_logger] # Callback da utilizzare durante l'addestramento\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R7CzuOhYOaMA"
      },
      "outputs": [],
      "source": [
        "# SALVATAGGIO DEI PESI SUL DRIVE\n",
        "weights_path = \"/content/drive/MyDrive/ESM/KinDeNet/Weights/Weights_lr\" + str(learning_rate) + \"_batch\" + str(batch_size) + \"_epochs\" + str(epochs) + \"_bestEpoch\" + str(saved_epoch) + \".h5\"\n",
        "siamese_classifier_model.save_weights(weights_path)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SALVATAGGIO DEL MODELLO ADDESTRATO\n",
        "siamese_classifier_model.save(\"/content/rete_completa_binaria.h5\")"
      ],
      "metadata": {
        "id": "yXM3zXHFqIq-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# COPIA SUL DRIVE DEL MODELLO ADDESTRATO\n",
        "!cp \"/content/rete_completa_binaria.h5\" \"/content/drive/MyDrive/ESM/KinDeNet/Models\""
      ],
      "metadata": {
        "id": "DiLWLVkAhMl4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CARICAMENTO DEL MODELLO ADDESTRATO\n",
        "siamese_classifier_model = tf.keras.models.load_model(\"/content/rete_completa_binaria.h5\")"
      ],
      "metadata": {
        "id": "Vmq2aEX9q5TH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SdxfvEpuOaMB"
      },
      "outputs": [],
      "source": [
        "# STAMPA DELL'HISTORY DELLE METRICHE\n",
        "plot_history(history, metrics=['accuracy', 'val_accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wR8-JLccJEpJ"
      },
      "outputs": [],
      "source": [
        "# ESTRAZIONE DELLE FEATURES E LABELS DI TEST MEDIANTE GENERATORE\n",
        "genT = preprocess_couplets_binary(local_img1T_path, local_img2T_path, local_labelT_path, input_dimension, reduced_coupletsT)\n",
        "test_features, test_labels = next(genT)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "flagModelBC = 0\n",
        "\n",
        "# CREAZIONE MATRICE DI CONFUSIONE PER IL TEST SET\n",
        "confusionMatrix(siamese_classifier_model, test_features, test_labels, flagModelBC)"
      ],
      "metadata": {
        "id": "Xo-eLj9W0hMo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yt7Egnc_64Ow"
      },
      "source": [
        "# TRAINING E HISTORY CLASSIFICATORE GRADI DI PARENTELA [RETE FINE TUNING]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zZWCN7UX64PA"
      },
      "outputs": [],
      "source": [
        "# APERTURA DEI CSV RELATIVI ALLE LABELS\n",
        "train_labels = list_from_csv(local_labelTR_path)\n",
        "val_labels = list_from_csv(local_labelV_path)\n",
        "test_labels = list_from_csv(local_labelT_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2dee144d-def3-48be-8a57-1f645fc7b53b",
        "id": "tahp9Rju64PE"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_7 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " input_8 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " model_3 (Functional)           (None, 3840)         23576512    ['input_7[0][0]',                \n",
            "                                                                  'input_8[0][0]']                \n",
            "                                                                                                  \n",
            " tf.math.subtract_1 (TFOpLambda  (None, 3840)        0           ['model_3[0][0]',                \n",
            " )                                                                'model_3[1][0]']                \n",
            "                                                                                                  \n",
            " tf.math.abs_1 (TFOpLambda)     (None, 3840)         0           ['tf.math.subtract_1[0][0]']     \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 2048)         7866368     ['tf.math.abs_1[0][0]']          \n",
            "                                                                                                  \n",
            " dense_5 (Dense)                (None, 512)          1049088     ['dense_4[0][0]']                \n",
            "                                                                                                  \n",
            " dense_6 (Dense)                (None, 128)          65664       ['dense_5[0][0]']                \n",
            "                                                                                                  \n",
            " dense_7 (Dense)                (None, 3)            387         ['dense_6[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 32,558,019\n",
            "Trainable params: 23,953,923\n",
            "Non-trainable params: 8,604,096\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/3\n",
            "500/500 [==============================] - 2268s 4s/step - loss: 0.9452 - accuracy: 0.5788 - top_2_accuracy: 0.8642 - val_loss: 0.8519 - val_accuracy: 0.5950 - val_top_2_accuracy: 0.9298 - lr: 0.0010\n",
            "Epoch 2/3\n",
            "500/500 [==============================] - 1928s 4s/step - loss: 0.8539 - accuracy: 0.6032 - top_2_accuracy: 0.9108 - val_loss: 0.8166 - val_accuracy: 0.6403 - val_top_2_accuracy: 0.9398 - lr: 0.0010\n",
            "Epoch 3/3\n",
            "500/500 [==============================] - 1874s 4s/step - loss: 0.8116 - accuracy: 0.6263 - top_2_accuracy: 0.9247 - val_loss: 0.8189 - val_accuracy: 0.6272 - val_top_2_accuracy: 0.9354 - lr: 0.0010\n"
          ]
        }
      ],
      "source": [
        "# INIZIALIZZAZIONE VARIABILI\n",
        "epochs = 3                                                                      # Numero di epoche per il fit\n",
        "batch_size = 200                                                                # Grandezza di ogni batch per il fit\n",
        "total_coupletsTR = len(train_labels)                                            # Numero totale di triple del training set (troppo elevato per le risorse a disposizione)\n",
        "total_coupletsV = len(val_labels)                                               # Numero totale di triple del validation set (troppo elevato per le risorse a disposizione)\n",
        "total_coupletsT = len(test_labels)                                              # Numero totale di coppie del test set (troppo elevato per le risorse a disposizione)\n",
        "reduced_coupletsTR = 100000                                                     # Numero di triple di training considerate per il fit\n",
        "reduced_coupletsV = 21429                                                       # Numero di triple di validation considerare per il fit\n",
        "reduced_coupletsT = 21429                                                       # Numero di coppie di test considerate per il predict\n",
        "patience_LR = 3                                                                 # Valore di patience per la callback di riduzione del learning rate\n",
        "patience_ES = 7                                                                 # Valore di patience per la callback di stop preoce dell'addestramento\n",
        "saved_epoch = 0                                                                 # Variabile per il salvataggio del valore della best-epoch\n",
        "filename = \"Weights_lr1e-05_batch150_epochs20_bestEpoch1.hdf5\"                  # Nome dei pesi scelti\n",
        "num_classes = 3                                                                 # Numero di classi del classificatore\n",
        "\n",
        "# CREAZIONE GENERATORI DI TRAINING E VALIDATION\n",
        "genT = preprocess_couplets_3class(local_img1TR_path, local_img2TR_path, local_labelTR_path, input_dimension, batch_size)\n",
        "genV = preprocess_couplets_3class(local_img1V_path, local_img2V_path, local_labelV_path, input_dimension, batch_size)\n",
        "\n",
        "# FUNZIONE PER IL SALVATAGGIO DEL NUMERO DELLA BEST EPOCH\n",
        "def save_epoch_number(epoch, logs):\n",
        "    global saved_epoch\n",
        "    saved_epoch = epoch + 1                                                     # N.B. Aggiungiamo 1 perchè le epoche partono da 1\n",
        "\n",
        "# CALLBACK: Salva i pesi del modello durante l'addestramento\n",
        "model_checkpoint_callback = keras.callbacks.ModelCheckpoint(\n",
        "    filepath = local_ResWeights_path,                                           # Percorso in locale del file in cui salvare i pesi\n",
        "    filename = 'weights_{epoch:02d}.h5',                                        # Nome del file dove salvare i pesi\n",
        "    save_weights_only = True,                                                   # Salva solo i pesi anziché l'intero modello\n",
        "    monitor = 'val_accuracy',                                                       # Metrica da monitorare per il salvataggio dei pesi migliori\n",
        "    mode = 'max',                                                               # Indica che la metrica monitorata deve essere minimizzata\n",
        "    save_best_only = True,                                                      # Salva solo i pesi corrispondenti al miglior valore della metrica monitorata\n",
        "    save_freq = 'epoch',                                                        # Frequenza di salvataggio dei pesi\n",
        "    callbacks = [save_epoch_number]                                             # Callback per il salvataggio dell'epoca migliore\n",
        ")\n",
        "\n",
        "# CALLBACK: Riduce il learning rate quando una particolare metrica smette di migliorare\n",
        "reduce_lr = keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor = 'val_accuracy',                                                       # Monitora la loss del validation set\n",
        "    factor = 0.1,                                                               # Fattore di riduzione del learning rate (10%)\n",
        "    patience = patience_LR,                                                     # Numero di epoche senza miglioramenti dopo il quale il learning rate viene ridotto\n",
        "    verbose = 1,                                                                # Stampa un messaggio quando il learning rate viene ridotto\n",
        "    mode = 'max',                                                               # Modalità di monitoraggio ('min' per minimizzare la loss)\n",
        "    min_delta = 0.0001,                                                         # Soglia per considerare un miglioramento significativo\n",
        "    min_lr = 1e-10                                                               # Learning rate minimo consentito\n",
        ")\n",
        "\n",
        "# CALLBACK: Se una particolare metrica smette di migliorare l'addestramento viene interrotto\n",
        "early_stopping = keras.callbacks.EarlyStopping(\n",
        "    monitor = \"val_accuracy\",                                                   # Monitora la loss del validation set\n",
        "    min_delta = 0,                                                              # Differenza minima ritenuta come miglioramento\n",
        "    patience = patience_ES,                                                     # Numero di epoche consecutive senza miglioramento prima di interrompere l'addestramento\n",
        "    verbose = 1,                                                                # Stampa un messaggio quando l'addestramento viene interrotto\n",
        "    mode = \"max\",                                                               # Indica che la metrica monitorata deve essere minimizzata\n",
        "    baseline = None,                                                            # Valore di riferimento per la metrica monitorata\n",
        "    restore_best_weights = True,                                                # Ripristina i pesi del modello al miglior punto durante l'addestramento\n",
        "    start_from_epoch = 0,                                                       # Indica da quale epoca iniziare il conteggio delle epoche senza miglioramento\n",
        ")\n",
        "\n",
        "# CALLBACK: Salvataggio delle metriche di training e validazione in formato csv\n",
        "csv_logger = keras.callbacks.CSVLogger(local_CSVLogger_path)\n",
        "\n",
        "# ADDESTRAMENTO DEL MODELLO SULLE TRIPLE (ANCHOR-POSITIVE-NEGATIVE)\n",
        "history = siamese_classifier_model.fit(\n",
        "    genT,                                                                        # Generatore di training\n",
        "    validation_data=genV,                                                        # Generatore di validazione\n",
        "    epochs=epochs,                                                               # Numero di epoche di addestramento\n",
        "    steps_per_epoch=reduced_coupletsTR//batch_size,                              # Numero di passi per epoca di addestramento\n",
        "    validation_steps=reduced_coupletsV//batch_size,                              # Numero di passi per epoca di validazione\n",
        "    verbose=True,                                                                # Messaggi di avviso sulle metriche\n",
        "    callbacks=[model_checkpoint_callback, reduce_lr, early_stopping, csv_logger] # Callback da utilizzare durante l'addestramento\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SALVATAGGIO DEL MODELLO ADDESTRATO\n",
        "siamese_classifier_model.save(\"/content/rete_completa_3_classi.h5\")"
      ],
      "metadata": {
        "id": "sqbaKGFrOugl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# COPIA SUL DRIVE DEL MODELLO ADDESTRATO\n",
        "!cp \"/content/rete_completa_3_classi.h5\" \"/content/drive/MyDrive/ESM/KinDeNet/Models\""
      ],
      "metadata": {
        "id": "coWC68g60O6r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CARICAMENTO DEL MODELLO ADDESTRATO\n",
        "siamese_classifier_model = tf.keras.models.load_model(\"/content/drive/MyDrive/ESM/KinDeNet/Models/rete_completa_3_classi.h5\")"
      ],
      "metadata": {
        "id": "bgCRrrJfPHtW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b1I0eaPo64PF"
      },
      "outputs": [],
      "source": [
        "# SALVATAGGIO DEI PESI SUL DRIVE\n",
        "weights_path = \"/content/drive/MyDrive/ESM/KinDeNet/Weights/Weights_lr\" + str(learning_rate) + \"_batch\" + str(batch_size) + \"_epochs\" + str(epochs) + \"_bestEpoch\" + str(saved_epoch) + \".h5\"\n",
        "siamese_classifier_model.save_weights(weights_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y7XHvcCv64PE"
      },
      "outputs": [],
      "source": [
        "# ESTRAZIONE DELLE FEATURES E LABELS DI TEST MEDIANTE GENERATORE\n",
        "genT = preprocess_couplets_3class(local_img1T_path, local_img2T_path, local_labelT_path, input_dimension, reduced_coupletsT)\n",
        "test_features, test_labels = next(genT)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "flagModelC = 1\n",
        "\n",
        "# CREAZIONE MATRICE DI CONFUSIONE PER IL TEST SET\n",
        "confusionMatrix(siamese_classifier_model, test_features, test_labels, flagModelC)"
      ],
      "metadata": {
        "id": "t54N1ZTX2CsA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CREAZIONE INSOTGRAMMA DELLE PREDIZIONI\n",
        "histogram_plot(test_labels, test_features, num_classes)"
      ],
      "metadata": {
        "id": "U_vMaT3PzfJN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7iQqv9f364PF"
      },
      "outputs": [],
      "source": [
        "# STAMPA DELL'HISTORY DELLE METRICHE\n",
        "plot_history(history, metrics=['accuracy', 'val_accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kvGLAojFS7ve"
      },
      "source": [
        "# FUNZIONE DI PREDIZIONE SU IMMAGINI SOLITI IGNOTI [RETE FINE TUNING]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TFzZgoTzS7vg"
      },
      "outputs": [],
      "source": [
        "def predict_similarity_full(image_1_path, image_2_path, siamese_classifier_model, input_dimension, input_channel):\n",
        "  '''\n",
        "      Predice la presenza o meno di parentela tra due immagini\n",
        "        # PRECONDIZIONE: definizione dell'architettura della rete di estrazione delle features e classificazione\n",
        "        e numero di classi per le label\n",
        "        # POSTCONDIZIONE: predizione sulla coppia di immagini\n",
        "      ----------\n",
        "      PARAMETRI:\n",
        "        image_1_path: path della prima immagine\n",
        "            str\n",
        "        image_2_path: path della seconda immagine\n",
        "            str\n",
        "        siamese_classifier_model: modello siamese di rete per la feature extraction e la classificazione\n",
        "            keras_model\n",
        "        input_dimension: dimensione desiderata dell'immagine\n",
        "            int\n",
        "        input_channel: numero di canali dell'immagine\n",
        "            int\n",
        "      ----------\n",
        "      RETURN:\n",
        "        prediction: predizione sulla coppia di immagini\n",
        "            np_array\n",
        "  '''\n",
        "\n",
        "  # PREPARAZIONE DELLA PRIMA IMMAGINE\n",
        "  image_1 = img_preprocessing(image_1_path, input_dimension)\n",
        "  image_1 = image_1[:,:,:input_channel]                                         #leviamo il 4 canale\n",
        "  image_1 = np.reshape(image_1, (-1, input_dimension, input_dimension, input_channel))\n",
        "\n",
        "  # PREPARAZIONE DELLA SECONDA IMMAGINE\n",
        "  image_2 = img_preprocessing(image_2_path, input_dimension)\n",
        "  image_2 = image_2[:,:,:input_channel]                                         #leviamo il 4 canale\n",
        "  image_2 = np.reshape(image_2, (-1, input_dimension, input_dimension, input_channel))\n",
        "\n",
        "  # PREDIZIONE DELLA CLASSE A PARTIRE DALLA COPPIA DI IMMAGINI\n",
        "  prediction = siamese_classifier_model.predict([image_1, image_2])\n",
        "\n",
        "  print(prediction)\n",
        "  return prediction"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# UNZIP DELLA CARTELLA SOLITI_IGNOTI IN LOCALE\n",
        "!unzip /content/drive/MyDrive/ESM/KinDeNet/Soliti_Ignoti.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc8c43ba-888c-4497-b806-5f8f08478918",
        "id": "SChXxyqITFsF"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/drive/MyDrive/ESM/KinDeNet/Soliti_Ignoti.zip\n",
            "   creating: Soliti_Ignoti/Ignoti/\n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_1.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_2.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_3.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_4.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_5.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_6.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_7.jpg  \n",
            "  inflating: Soliti_Ignoti/Ignoti/Ignoto_8.jpg  \n",
            "  inflating: Soliti_Ignoti/indiziato.jpg  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-4sVjcHPS7vh"
      },
      "outputs": [],
      "source": [
        "image_1_path = '/content/Soliti_Ignoti/indiziato.jpg'                           # Definiamo il percorso dell'immagine dell'indiziato\n",
        "\n",
        "# PREDIZIONE PER OGNUNO DEGLI 8 IGNOTI\n",
        "predictions = []                                                                # Lista vuota per contenere le predizioni\n",
        "for elem in sorted(glob('/content/Soliti_Ignoti/Ignoti/*.jpg')):\n",
        "  predictions.append(predict_similarity_full(image_1_path, elem, siamese_model, classifier_model, input_dimension, input_channels))\n",
        "\n",
        "predictions = np.array(predictions)                                             # Trasformiamo la lista di predizioni in un array numpy\n",
        "index = np.argmax(predictions)                                                  # Prendiamo l'indice della predizione più alta\n",
        "\n",
        "# STAMPA DEL RISULTATO\n",
        "print('Il parente misterioso è il numero ' + str(index+1))"
      ]
    }
  ],
  "metadata": {
    "colab": {
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      "machine_shape": "hm",
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
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}