{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hUYO09lse0Ew"
      },
      "outputs": [],
      "source": [
        "from tensorflow.python.ops.gen_array_ops import tensor_scatter_min_eager_fallback\n",
        "from resnet18 import ResNet18\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def snake(x):\n",
        "    return x + tf.sin(x)**2\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Chargement et normalisation des données\n",
        "resnet18 = tf.keras.datasets.cifar10\n",
        "(train_images, train_labels), (test_images, test_labels) = resnet18.load_data()\n",
        "train_images = train_images.astype('float32')\n",
        "test_images = test_images.astype('float32')\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_images, val_images, train_labels, val_labels = train_test_split(train_images,train_labels, test_size = 0.2,shuffle = True)\n",
        "\n",
        "train_images = train_images / 255.0\n",
        "val_images = val_images /255.0\n",
        "test_images = test_images / 255.0\n",
        "\n",
        "# POUR LES CNN : un tenseur d'ordre 3 pour les images en couleurs\n",
        "train_images = train_images.reshape(max(np.shape(train_images)),32,32,3)\n",
        "val_images = val_images.reshape(max(np.shape(val_images)),32,32,3)\n",
        "test_images = test_images.reshape(max(np.shape(test_images)),32,32,3)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "-iczeI91fEND"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# One hot encoding\n",
        "train_labels = tf.keras.utils.to_categorical(train_labels)\n",
        "val_labels = tf.keras.utils.to_categorical(val_labels)\n",
        "test_labels = tf.keras.utils.to_categorical(test_labels)\n",
        "\n",
        "filter_size_conv1 = (3,3)\n",
        "\n",
        "\n",
        "#création du réseau ResNet18\n",
        "\n",
        "model = ResNet18(10)\n",
        "model.build(input_shape = (None,32,32,3))\n",
        "print(model.summary())\n"
      ],
      "metadata": {
        "id": "9UGNJFoRfGTz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Adam comme optimizer et categorical-crossentropy comme norme\n",
        "sgd = tf.keras.optimizers.Adam()\n",
        "model.compile(sgd, loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "\n",
        "\n",
        "history = model.fit(train_images,\n",
        "         train_labels,\n",
        "         batch_size=64,\n",
        "         epochs=100,\n",
        "         validation_data=(val_images, val_labels),\n",
        "         )\n"
      ],
      "metadata": {
        "id": "PtY0q1p5fM-p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Evaluation du modèle \n",
        "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
        "print('Test accuracy:', test_acc)\n",
        "\n"
      ],
      "metadata": {
        "id": "FcehZmotfPCx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## on affiche et on sauvegarde les images\n",
        "\n",
        "fig, axs = plt.subplots(2, 1, figsize=(15,15))\n",
        "\n",
        "axs[0].plot(history.history['loss'])\n",
        "axs[0].plot(history.history['val_loss'])\n",
        "axs[0].title.set_text('Training Loss vs Validation Loss')\n",
        "axs[0].legend(['Train', 'Val'])\n",
        "\n",
        "axs[1].plot(history.history['accuracy'])\n",
        "axs[1].plot(history.history['val_accuracy'])\n",
        "axs[1].title.set_text('Training Accuracy vs Validation Accuracy')\n",
        "axs[1].legend(['Train', 'Val'])\n",
        "plt.savefig('./resnet18snake.png')"
      ],
      "metadata": {
        "id": "8USwL2YTfRGN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}