tests_6_convolution.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H_VtX8nP-lwE"
      },
      "source": [
        "# Init\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "id": "TLV1vk1tc0y2"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "\n",
        "from icecream import ic\n",
        "\n",
        "from Lineaire.Linear import *\n",
        "from Loss.CELoss import CELoss\n",
        "from Activation.ReLu import ReLU\n",
        "from Activation.SoftMax import SoftMax\n",
        "from Encapsulation.Sequentiel import Sequentiel\n",
        "from Encapsulation.Optim import SGD\n",
        "from Encapsulation import fonctions as fn\n",
        "from Convolution.Convolution1D import *\n",
        "from Convolution.Convolution2D import *\n",
        "from Convolution.Flatten import Flatten\n",
        "\n",
        "\n",
        "from utils import tools as tl\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "yTv86KrLel3O"
      },
      "outputs": [],
      "source": [
        "def evaluer(net, couts):\n",
        "    plt.plot(np.arange(len(couts)), couts, color='red', label=\"Evolution du cout\")\n",
        "    plt.xlabel(\"iter\")\n",
        "    plt.title(\"variation de la fonction de cout\")\n",
        "\n",
        "    _, alltrainy = load_usps(uspsdatatrain)\n",
        "    _, alltesty = load_usps(uspsdatatest)\n",
        "\n",
        "    alltrainy = alltrainy[:nb_train]\n",
        "    alltesty = alltesty[:nb_test]\n",
        "\n",
        "    predict = net.forward(alltrainx)\n",
        "    # print((np.where(predict == alltrainy, 1, 0)).mean() )\n",
        "    score_train = fn.score(alltrainy, predict)\n",
        "    print(\"Le score d'accuracy en train = \", score_train)\n",
        "\n",
        "    predict = net.forward(alltestx)\n",
        "    score_test = fn.score(alltesty,predict)\n",
        "    print(\"Le score d'accuracy en test = \", score_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yylVSwRCJcOU"
      },
      "source": [
        "# Conv1D"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "EsFQjD8ec0y8"
      },
      "outputs": [],
      "source": [
        "uspsdatatrain = \"dataset/USPS_train.txt\"\n",
        "uspsdatatest = \"dataset/USPS_test.txt\" \n",
        "\n",
        "alltrainx, alltrainy = tl.load_usps(uspsdatatrain)\n",
        "alltestx, alltesty = tl.load_usps(uspsdatatest)\n",
        "\n",
        "\n",
        "nb_train = 1100\n",
        "nb_test = 500\n",
        "\n",
        "alltrainx, alltrainy = alltrainx[:nb_train],alltrainy[:nb_train]\n",
        "alltestx, alltesty = alltestx[:nb_test],alltesty[:nb_test]\n",
        "\n",
        "alltrainy = fn.transform_one_hot(alltrainy)\n",
        "alltesty= fn.transform_one_hot(alltesty)\n",
        "alltrainx = alltrainx.reshape(alltrainx.shape[0], alltrainx.shape[1], 1)\n",
        "alltestx = alltestx.reshape(alltestx.shape[0], alltestx.shape[1], 1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "DPzXF-BCc0y9"
      },
      "outputs": [],
      "source": [
        "iteration = 100\n",
        "gradient_step = 1e-3\n",
        "nb_batches = 1\n",
        "\n",
        "net = Sequentiel(*[Conv1D(3, 1, 32, stride=1),\n",
        "                      MaxPool1D(2, 2),\n",
        "                      Flatten(),\n",
        "                      Linear(4064, 100, init_type=1),\n",
        "                      ReLU(),\n",
        "                      Linear(100, 10,init_type=1),\n",
        "                      SoftMax()\n",
        "                        ])\n",
        "\n",
        "loss_ce = CELoss()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3eJTaTa_fmZ5",
        "outputId": "6aaa4138-2261-4db7-c10d-2261f5cecdb6"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ic| X.shape: (1100, 256, 1)\n",
            "ic| Y.shape: (1100, 10)\n",
            "100%|██████████| 100/100 [01:19<00:00,  1.26it/s]\n"
          ]
        }
      ],
      "source": [
        "net, couts, opt = SGD(net, alltrainx, alltrainy, nb_batch=nb_batches, loss=loss_ce, nb_epochs=iteration,shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "wQhPDd1SnEG5",
        "outputId": "40a7161f-bf09-4846-a723-6fc0851f572b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Le score d'accuracy en train =  0.519090909090909\n",
            "Le score d'accuracy en train =  0.51\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "evaluer(net, couts, iteration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DG_D2UV3nLP2"
      },
      "source": [
        "## Tests 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "-D-N23MAsSim"
      },
      "outputs": [],
      "source": [
        "iteration = 100\n",
        "gradient_step = 1e-3\n",
        "nb_batches = 100\n",
        "\n",
        "net = Sequentiel(*[Conv1D(3, 1, 32, stride=1),\n",
        "                      MaxPool1D(2, 2),\n",
        "                      Flatten(),\n",
        "                      Linear(4064, 100, init_type=1),\n",
        "                      ReLU(),\n",
        "                      Linear(100, 10,init_type=1),\n",
        "                      SoftMax()\n",
        "                        ])\n",
        "\n",
        "loss_ce = CELoss()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDqRjkposcdW",
        "outputId": "ae21b703-8b4d-4b2b-962a-3f429517f39d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ic| X.shape: (1100, 256, 1)\n",
            "ic| Y.shape: (1100, 10)\n",
            "100%|██████████| 100/100 [04:06<00:00,  2.46s/it]\n"
          ]
        }
      ],
      "source": [
        "net, couts, opt = SGD(net, alltrainx, alltrainy, nb_batch=nb_batches, loss=loss_ce, nb_epochs=iteration,shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "RtWFNUC2seKY",
        "outputId": "adba9941-ec9e-4283-84e0-99847ee5f99a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Le score d'accuracy en train =  0.5745454545454546\n",
            "Le score d'accuracy en test =  0.548\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "evaluer(net, couts, iteration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BtoZyM1sJYEJ"
      },
      "source": [
        "# Conv2D"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "uspsdatatrain = \"dataset/USPS_train.txt\"\n",
        "uspsdatatest = \"dataset/USPS_test.txt\" \n",
        "\n",
        "alltrainx, alltrainy = tl.load_usps(uspsdatatrain)\n",
        "alltestx, alltesty = tl.load_usps(uspsdatatest)\n",
        "\n",
        "nb_train = 1000\n",
        "nb_test = 200\n",
        "\n",
        "alltrainx, alltrainy = alltrainx[:nb_train],alltrainy[:nb_train]\n",
        "alltestx, alltesty = alltestx[:nb_test],alltesty[:nb_test]\n",
        "\n",
        "alltrainy = fn.transform_one_hot(alltrainy)\n",
        "alltesty= fn.transform_one_hot(alltesty)\n",
        "\n",
        "alltrainx=alltrainx.reshape(alltrainx.shape[0],16,16,1)\n",
        "alltestx=alltestx.reshape(alltestx.shape[0],16,16,1)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1apAMjDmxSY"
      },
      "source": [
        "## Test 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "TUGH1kY6Ko03"
      },
      "outputs": [],
      "source": [
        "iteration = 150\n",
        "gradient_step = 1e-2\n",
        "nb_batches = 1\n",
        "\n",
        "net = Sequentiel(*[Conv2D(3, 1, 32, stride=1),\n",
        "                      MaxPool2D(2, 2),\n",
        "                      Flatten(),\n",
        "                      Linear(1568, 100, init_type=1),\n",
        "                      ReLU(),\n",
        "                      Linear(100, 10,init_type=1),\n",
        "                      SoftMax()\n",
        "                        ])\n",
        "\n",
        "loss_ce = CELoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFz1GrOVLbqK",
        "outputId": "899fdaa4-11f5-4228-c57f-18af702412dc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ic| X.shape: (1000, 16, 16, 1)\n",
            "ic| Y.shape: (1000, 10)\n",
            "100%|██████████| 150/150 [01:03<00:00,  2.36it/s]\n"
          ]
        }
      ],
      "source": [
        "net, couts, opt = SGD(net, alltrainx, alltrainy, nb_batch=nb_batches, loss=loss_ce, nb_epochs=iteration,shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "CGDGxalme8Vo",
        "outputId": "af30c6c9-a0d7-4e62-b025-65f4ce60215e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Le score d'accuracy en train =  0.54\n",
            "Le score d'accuracy en test =  0.475\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "evaluer(net, couts, iteration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NSx31AwB845g"
      },
      "source": [
        "## Test 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Im0eEkxV8ZmE"
      },
      "outputs": [],
      "source": [
        "iteration = 160\n",
        "gradient_step = 1e-1\n",
        "batch_size = 1\n",
        "\n",
        "net = Sequentiel(*[Conv2D(3, 1, 32, stride=1),\n",
        "                      MaxPool2D(2, 2),\n",
        "                      Flatten(),\n",
        "                      Linear(1568, 100, init_type=1),\n",
        "                      ReLU(),\n",
        "                      Linear(100, 10,init_type=1),\n",
        "                      SoftMax()\n",
        "                        ])\n",
        "\n",
        "loss_ce = CELoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bThtuHBX8dlF",
        "outputId": "068b945c-9b0d-48d0-9aac-c570042d2d57"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ic| X.shape: (1000, 16, 16, 1)\n",
            "ic| Y.shape: (1000, 10)\n",
            "100%|██████████| 160/160 [01:03<00:00,  2.54it/s]\n"
          ]
        }
      ],
      "source": [
        "net, couts, opt = SGD(net, alltrainx, alltrainy, nb_batch=nb_batches, loss=loss_ce, nb_epochs=iteration,shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "APa80XJwfS9M",
        "outputId": "fcc05f87-e67f-4e68-887b-436989732c0a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Le score d'accuracy en train =  0.571\n",
            "Le score d'accuracy en test =  0.5\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "evaluer(net, couts, iteration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "seY6gYt2U0-2"
      },
      "source": [
        "## Test 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "2QcQcmBiU3DV"
      },
      "outputs": [],
      "source": [
        "iteration = 160\n",
        "gradient_step = 1e-2\n",
        "batch_size = 50\n",
        "\n",
        "net = Sequentiel(*[Conv2D(3, 1, 32, stride=1),\n",
        "                      MaxPool2D(2, 2),\n",
        "                      Flatten(),\n",
        "                      Linear(1568, 100, init_type=1),\n",
        "                      ReLU(),\n",
        "                      Linear(100, 10,init_type=1),\n",
        "                      SoftMax()\n",
        "                        ])\n",
        "\n",
        "loss_ce = CELoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7m6pfAlU5qB",
        "outputId": "5ff95b3a-ebe4-4244-d6ba-72537f6fdb42"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ic| X.shape: (1000, 16, 16, 1)\n",
            "ic| Y.shape: (1000, 10)\n",
            "100%|██████████| 160/160 [03:37<00:00,  1.36s/it]\n"
          ]
        }
      ],
      "source": [
        "net, couts, opt = SGD(net, alltrainx, alltrainy, nb_batch=batch_size, loss=loss_ce, nb_epochs=iteration, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "okpw19EIf9GS",
        "outputId": "86a2a2d4-6160-45eb-af5b-5ddd9799ad0b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Le score d'accuracy en train =  0.499\n",
            "Le score d'accuracy en test =  0.445\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "evaluer(net, couts, iteration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DX_70MYcW6EK"
      },
      "source": [
        "## Test 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "-HitEhQyW6ug"
      },
      "outputs": [],
      "source": [
        "iteration = 160\n",
        "gradient_step = 1e-1\n",
        "batch_size = 50\n",
        "\n",
        "net = Sequentiel(*[Conv2D(3, 1, 32, stride=1),\n",
        "                      MaxPool2D(2, 2),\n",
        "                      Flatten(),\n",
        "                      Linear(1568, 100, init_type=1),\n",
        "                      ReLU(),\n",
        "                      Linear(100, 10,init_type=1),\n",
        "                      SoftMax()\n",
        "                        ])\n",
        "\n",
        "loss_ce = CELoss()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "umsrsDHUW8ZK",
        "outputId": "dadd9913-da8b-4f5c-fecb-882169501dd6"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ic| X.shape: (1000, 16, 16, 1)\n",
            "ic| Y.shape: (1000, 10)\n",
            "100%|██████████| 160/160 [03:10<00:00,  1.19s/it]\n"
          ]
        }
      ],
      "source": [
        "net, couts, opt = SGD(net, alltrainx, alltrainy, nb_batch=batch_size, loss=loss_ce, nb_epochs=iteration,shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "DKSwB1oSiUpD",
        "outputId": "f7413870-4a70-4a09-b3c7-fa08b645575c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Le score d'accuracy en train =  0.571\n",
            "Le score d'accuracy en test =  0.5\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "evaluer(net, couts, iteration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ur3ZfDxklUo"
      },
      "source": [
        "## Test 5 (meilleur)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "EHaeJbFBkkmu"
      },
      "outputs": [],
      "source": [
        "iteration = 165\n",
        "gradient_step = 1e-1\n",
        "batch_size = 100\n",
        "\n",
        "net = Sequentiel(*[Conv2D(3, 1, 32, stride=1),\n",
        "                      MaxPool2D(2, 2),\n",
        "                      Flatten(),\n",
        "                      Linear(1568, 100, init_type=1),\n",
        "                      ReLU(),\n",
        "                      Linear(100, 10,init_type=1),\n",
        "                      SoftMax()\n",
        "                        ])\n",
        "\n",
        "loss_ce = CELoss()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GOIlgBuRkoaE",
        "outputId": "19f88ddf-d52b-43a5-d935-d3909d77e431"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ic| X.shape: (1000, 16, 16, 1)\n",
            "ic| Y.shape: (1000, 10)\n",
            "100%|██████████| 165/165 [01:11<00:00,  2.32it/s]\n"
          ]
        }
      ],
      "source": [
        "net, couts, opt = SGD(net, alltrainx, alltrainy, nb_batch=1, loss=loss_ce, nb_epochs=iteration,shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "9WnJwDXBkp3t",
        "outputId": "e19e9102-9369-4603-ba7b-68f012b090f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Le score d'accuracy en train =  0.598\n",
            "Le score d'accuracy en train =  0.51\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "evaluer(net, couts, iteration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6x_b267vtsrc"
      },
      "source": [
        "## Test 6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "h5Fljynhttic"
      },
      "outputs": [],
      "source": [
        "iteration = 165\n",
        "gradient_step = 1e-1\n",
        "nb_batches = 100\n",
        "\n",
        "net = Sequentiel(*[Conv2D(3, 1, 32, stride=1),\n",
        "                      MaxPool2D(2, 2),\n",
        "                      Flatten(),\n",
        "                      Linear(1568, 100, init_type=1),\n",
        "                      ReLU(),\n",
        "                      Linear(100, 10,init_type=1),\n",
        "                      SoftMax()\n",
        "                        ])\n",
        "\n",
        "loss_ce = CELoss()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dpwCobAHtvFh",
        "outputId": "a07b4178-b0ed-445b-85c6-3b517410f914"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ic| X.shape: (1000, 16, 16, 1)\n",
            "ic| Y.shape: (1000, 10)\n",
            "100%|██████████| 165/165 [05:51<00:00,  2.13s/it]\n"
          ]
        }
      ],
      "source": [
        "net, couts, opt = SGD(net, alltrainx, alltrainy, nb_batch=nb_batches, loss=loss_ce, nb_epochs=iteration,shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "KWe_-39Mtz1q",
        "outputId": "b6ea3e50-5552-4f52-bbeb-73984f146495"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Le score d'accuracy en train =  0.58\n",
            "Le score d'accuracy en test =  0.505\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "evaluer(net, couts, iteration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Note\n",
        "Certains graphes sont différents parce que parfois on sauvegardait la loss après chaque mise à jour du gradient et autrefois après chaque époch (donc parfois on avait plus de valeurs) "
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "NSx31AwB845g",
        "seY6gYt2U0-2",
        "DX_70MYcW6EK",
        "3liENvJtjOnF"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}