Toolbox_projetML.ipynb

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    "colab": {
      "name": "Toolbox_projetML.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GeoLabUniLaSalle/Python/blob/main/Toolbox_projetML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1svx_l_KYciP"
      },
      "source": [
        "# Toolbox projet PA numérique AE\n",
        "Cette boite à outils vous donnes accès à certaines bibliothèques python qui vous seront utiles dans la réalisation de votre projet. \n",
        "Elles sont à mettre en perspectives avec les différents cours que vous suivrez au cours de la semaine \n",
        "En cas de problème ou de question : jean-marie.vient@univ-brest.\n",
        "\n",
        "Voila un rappel des éléments attendus (avec un petit planning à titre indicatif):\n",
        "* J1 : Récupération et traitement de vos données (fusion de matrices, ajout des latitudes longitudes, traietement input Torch) et préparation de votre Présentation des données \n",
        "* J2 : Dimensionnement et architecture de votre autoEncoder et application des PCA \n",
        "* J3 : PArametrage de votre modèle AE et comparaison avec PCA\n",
        "* J4 : Finalisation des traitements et préparation de votre présentation\n",
        "\n",
        "Amusez-vous bien "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhbInGhxYciU"
      },
      "source": [
        "## Domaines d'études \n",
        "3 ZOIsont proposées,voila leur coordonnées dans la matrice de données spim: \n",
        "* [:,1650:1778,970:1098]\n",
        "* [:,1000:1128,372:500]\n",
        "* [:,1201:1329,275:403]\n",
        "\n",
        "Ils sont situés en 3 zones différentes de votre zone d'études entre Mer du nord, Mediterranée et Atlantique\n",
        "C'est à vous de trouver leurs coordonnées excates avec les autres variables de votre fichier NetCDF\n",
        "\n",
        "Les fichiers sont stockés dans le NAS : 'Geolab/PA_Numerique/Projet ML'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cDUQJ08sYciV"
      },
      "source": [
        "## NetCDF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nlela9frYciW",
        "outputId": "b9797b2f-1610-4d4c-e13e-678b67e5f55f"
      },
      "source": [
        "import netCDF4 \n",
        "import bz2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "with bz2.open(\"mon_nom_de_fichier.nc.bz2\", \"rb\") as b:#pour décompresser un fichier bz2\n",
        "    f = netCDF4.Dataset(\"mon_nom_de_fichier\")# Pour ouvir un fichier netcdf \n",
        "f.variables #vous affiche l'ensemble des variables contenu dans le netcdf a vous d'detifier celle qui sont importantes pour votre travail"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'time': <class 'netCDF4._netCDF4.Variable'>\n",
              " int32 time(time)\n",
              "     long_name: reference time of spim field\n",
              "     standard_name: time\n",
              "     axis: T\n",
              "     units: seconds since 1998-01-01 00:00:00\n",
              " unlimited dimensions: \n",
              " current shape = (1,)\n",
              " filling on, default _FillValue of -2147483647 used,\n",
              " 'lat': <class 'netCDF4._netCDF4.Variable'>\n",
              " float32 lat(lat)\n",
              "     long_name: latitude\n",
              "     standard_name: latitude\n",
              "     axis: Y\n",
              "     units: degrees_north\n",
              " unlimited dimensions: \n",
              " current shape = (2401,)\n",
              " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
              " 'lon': <class 'netCDF4._netCDF4.Variable'>\n",
              " float32 lon(lon)\n",
              "     long_name: longitude\n",
              "     standard_name: longitude\n",
              "     axis: X\n",
              "     units: degrees_east\n",
              " unlimited dimensions: \n",
              " current shape = (1667,)\n",
              " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
              " 'analysed_spim': <class 'netCDF4._netCDF4.Variable'>\n",
              " int16 analysed_spim(time, lat, lon)\n",
              "     long_name: analysed inorganic suspended matters\n",
              "     standard_name: spim\n",
              "     type: foundation\n",
              "     units: g m-3\n",
              "     _FillValue: -32768\n",
              "     add_offset: 0\n",
              "     scale_factor: 0.01\n",
              "     valid_min: -300\n",
              "     valid_max: 4500\n",
              "     coordinates: lon lat\n",
              " unlimited dimensions: \n",
              " current shape = (1, 2401, 1667)\n",
              " filling on,\n",
              " 'analysed_spim_anomaly': <class 'netCDF4._netCDF4.Variable'>\n",
              " int16 analysed_spim_anomaly(time, lat, lon)\n",
              "     long_name: spim anomaly\n",
              "     units: g m-3\n",
              "     scale_factor: 0.01\n",
              "     add_offset: 0\n",
              "     valid_min: -4000\n",
              "     valid_max: 4000\n",
              "     _FillValue: -32768\n",
              "     comment: spim anomaly calculated from a monthly climatology (1998-2007) (Saulquin, 2009)\n",
              "     coordinates: lon lat\n",
              " unlimited dimensions: \n",
              " current shape = (1, 2401, 1667)\n",
              " filling on,\n",
              " 'analysis_error': <class 'netCDF4._netCDF4.Variable'>\n",
              " int16 analysis_error(time, lat, lon)\n",
              "     long_name: stdev estimated error\n",
              "     units: g m-3\n",
              "     _FillValue: -32768\n",
              "     scale_factor: 0.01\n",
              "     add_offset: 0\n",
              "     valid_min: 0\n",
              "     valid_max: 10000\n",
              "     coordinates: lon lat\n",
              " unlimited dimensions: \n",
              " current shape = (1, 2401, 1667)\n",
              " filling on,\n",
              " 'mask': <class 'netCDF4._netCDF4.Variable'>\n",
              " int8 mask(time, lat, lon)\n",
              "     long_name: sea/land/cloud-nodata field composite mask\n",
              "     flag_mask: 0b 1b 2b\n",
              "     flag_meanings: sea land cloud-nodata\n",
              " unlimited dimensions: \n",
              " current shape = (1, 2401, 1667)\n",
              " filling on, default _FillValue of -127 ignored}"
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLDOIy-yYciY",
        "outputId": "421aa3db-a1ce-4218-aa42-a46d065334c1"
      },
      "source": [
        "spim=f.variables['analysed_spim'][:]# Vous avez ici la matrice correspondant à la variable analysed_spim"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1, 2401, 1667)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jc7k_3DrYciY",
        "outputId": "db803b33-2193-4db8-c366-daa76f691b48"
      },
      "source": [
        "print(spim.mask)#on remarque que la donnée est masquée"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[[False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  [False False False ... False False False]\n",
            "  ...\n",
            "  [ True  True  True ...  True  True  True]\n",
            "  [ True  True  True ...  True  True  True]\n",
            "  [ True  True  True ...  True  True  True]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AchYAt-yYciZ",
        "outputId": "89050a99-9b73-419a-8fff-dda658963f5e"
      },
      "source": [
        "spimsansmask=spim.data #ajouter .data pour ne récupérer que la variables sans la partie masquée \n",
        "print(spimsansmask)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[[ 4.2999998e-01  3.8999999e-01  3.8999999e-01 ...  3.5999998e-01\n",
            "    3.4999999e-01  3.5999998e-01]\n",
            "  [ 3.7000000e-01  3.4000000e-01  3.5999998e-01 ...  3.4999999e-01\n",
            "    3.4999999e-01  3.5999998e-01]\n",
            "  [ 3.8000000e-01  3.2999998e-01  3.8999999e-01 ...  3.4999999e-01\n",
            "    3.4999999e-01  3.5999998e-01]\n",
            "  ...\n",
            "  [-3.2768000e+04 -3.2768000e+04 -3.2768000e+04 ... -3.2768000e+04\n",
            "   -3.2768000e+04 -3.2768000e+04]\n",
            "  [-3.2768000e+04 -3.2768000e+04 -3.2768000e+04 ... -3.2768000e+04\n",
            "   -3.2768000e+04 -3.2768000e+04]\n",
            "  [-3.2768000e+04 -3.2768000e+04 -3.2768000e+04 ... -3.2768000e+04\n",
            "   -3.2768000e+04 -3.2768000e+04]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUXUCqhmYciZ",
        "outputId": "2c291990-b02e-4b46-ad2e-3c9266fd98bb"
      },
      "source": [
        "plt.imshow(spim,origin='lower')# l'image nous donnes un erreur, celle-ci vient de nos dimensions, voir plus haut "
      ],
      "execution_count": null,
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "Invalid shape (1, 2401, 1667) for image data",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-36-cecec2186c6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m# l'image nous donnes un erreur, celle-ci vient de nos dimensions, voir plus haut\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m   2722\u001b[0m         \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2723\u001b[0m         data=None, **kwargs):\n\u001b[0;32m-> 2724\u001b[0;31m     __ret = gca().imshow(\n\u001b[0m\u001b[1;32m   2725\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maspect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2726\u001b[0m         \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1445\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1446\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1447\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1449\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   5521\u001b[0m                               resample=resample, **kwargs)\n\u001b[1;32m   5522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5523\u001b[0;31m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5524\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5525\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    709\u001b[0m         if not (self._A.ndim == 2\n\u001b[1;32m    710\u001b[0m                 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n\u001b[0;32m--> 711\u001b[0;31m             raise TypeError(\"Invalid shape {} for image data\"\n\u001b[0m\u001b[1;32m    712\u001b[0m                             .format(self._A.shape))\n\u001b[1;32m    713\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Invalid shape (1, 2401, 1667) for image data"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdZ2xZfaYcia",
        "outputId": "af3c122a-07fb-4759-a61b-18d75c59e082"
      },
      "source": [
        "plt.imshow(spim.reshape(spim.shape[1],spim.shape[2]),origin='lower')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe5e5fb8760>"
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnymhO6oYcib",
        "outputId": "76305313-dc7a-437b-c8f9-e17fbc727f8f"
      },
      "source": [
        "extrait= f.variables['analysed_spim'][:,732:1517,456:1234]#Si vous souhaitez extraire un bloc spécifique \n",
        "extrait.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1, 785, 778)"
            ]
          },
          "execution_count": 127,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-8Pdzp5Ycib",
        "outputId": "73dd05c8-7e5b-4ffa-b9fb-7eace20bedfd"
      },
      "source": [
        "plt.imshow(extrait.reshape(extrait.shape[1],extrait.shape[2]),origin='lower')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe5e5f8cf10>"
            ]
          },
          "execution_count": 128,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7IpJ8rwYcic"
      },
      "source": [
        "## ACP "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8wwJOdLYcic"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_eof)#(nb modes ou valeur entre 0 et 1) dans le projet, je vous demande 95% de Variance expliquée\n",
        "Valeurs propres = pca.singular_values_\n",
        "Vecteurs propres = pca.components_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HIcS6TqtYcid"
      },
      "source": [
        "# AE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nko_3wpUYcid"
      },
      "source": [
        "#voila un exemple d'auto encodeur basique fait sous pytorch, il ne s'agit ici que de la \n",
        "class AE(nn.Module):\n",
        "    def __init__(self, **kwargs):\n",
        "        super().__init__()\n",
        "        self.encoder_hidden_layer = nn.Linear(\n",
        "            in_features=kwargs[\"input_shape\"], out_features=128\n",
        "        )\n",
        "        self.encoder_output_layer = nn.Linear(\n",
        "            in_features=128, out_features=128\n",
        "        )\n",
        "        self.decoder_hidden_layer = nn.Linear(\n",
        "            in_features=128, out_features=128\n",
        "        )\n",
        "        self.decoder_output_layer = nn.Linear(\n",
        "            in_features=128, out_features=kwargs[\"input_shape\"]\n",
        "        )\n",
        "\n",
        "    def forward(self, features):\n",
        "        activation = self.encoder_hidden_layer(features)\n",
        "        activation = torch.relu(activation)\n",
        "        code = self.encoder_output_layer(activation)\n",
        "        code = torch.relu(code)\n",
        "        activation = self.decoder_hidden_layer(code)\n",
        "        activation = torch.relu(activation)\n",
        "        activation = self.decoder_output_layer(activation)\n",
        "        reconstructed = torch.relu(activation)\n",
        "        return reconstructed"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}