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Tf dataset.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "Tf dataset.ipynb", | |
| "version": "0.3.2", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "accelerator": "GPU" | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/ia35/cfe64164b85cf617a4f4fc373d9f3e28/tf-dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "lW8d0ezZU799", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 114 | |
| }, | |
| "outputId": "a8f97437-9409-4b10-c852-0868b765f6bc" | |
| }, | |
| "source": [ | |
| "!pip install -q tensorflow==2.0.0-alpha0\n", | |
| "import tensorflow as tf" | |
| ], | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\u001b[K |████████████████████████████████| 79.9MB 1.3MB/s \n", | |
| "\u001b[K |████████████████████████████████| 3.0MB 42.7MB/s \n", | |
| "\u001b[K |████████████████████████████████| 419kB 44.3MB/s \n", | |
| "\u001b[K |████████████████████████████████| 61kB 21.1MB/s \n", | |
| "\u001b[?25h" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "RcmhUWFsc005", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "Exemple fortement inspiré de la doc ce [TensorFlow](https://www.tensorflow.org/datasets/overview) " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "8TfDW6rXbrsX", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "Dorénavant, les exemples utiliseront TensorFlow 2.0" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "S7CY4z_Sb0B8", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "On importe TensorFlow DataSet Library. \n", | |
| "\n", | |
| "Il n'est pas utile de l'installer si on utilise tf > 2.0" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ZiofaIeXVRmB", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "import tensorflow_datasets as tfds" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "okrFlDGUVfnQ", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 287 | |
| }, | |
| "outputId": "6906becd-b201-4516-ba66-87eead0e03b8" | |
| }, | |
| "source": [ | |
| "dataset = tfds.load(name=\"mnist\")" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\u001b[1mDownloading and preparing dataset mnist (11.06 MiB) to /root/tensorflow_datasets/mnist/1.0.0...\u001b[0m\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
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| "\n", | |
| "\n", | |
| "\n", | |
| "\n" | |
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| "text": [ | |
| "WARNING: Logging before flag parsing goes to stderr.\n", | |
| "W0521 07:41:38.557350 140118336792448 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_datasets/core/file_format_adapter.py:247: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n", | |
| "Instructions for updating:\n", | |
| "Use eager execution and: \n", | |
| "`tf.data.TFRecordDataset(path)`\n" | |
| ], | |
| "name": "stderr" | |
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| "output_type": "stream", | |
| "text": [ | |
| "\r\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "W7yNdIaBVlWx", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "dataset??" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "RvsrHljlWCOO", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "1e9572d5-6f6d-4e54-d074-af6e5123ffd6" | |
| }, | |
| "source": [ | |
| "dataset[\"test\"]" | |
| ], | |
| "execution_count": 34, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<_OptionsDataset shapes: {image: (28, 28, 1), label: ()}, types: {image: tf.uint8, label: tf.int64}>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 34 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "f09BYfkFcEYj", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "La liste des jeux de données disponibles :" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "8d6W0wTSXxng", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1190 | |
| }, | |
| "outputId": "2b836c4f-0d29-4eb2-8872-b60bae78a57c" | |
| }, | |
| "source": [ | |
| "tfds.list_builders()" | |
| ], | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['abstract_reasoning',\n", | |
| " 'bair_robot_pushing_small',\n", | |
| " 'caltech101',\n", | |
| " 'cats_vs_dogs',\n", | |
| " 'celeb_a',\n", | |
| " 'celeb_a_hq',\n", | |
| " 'chexpert',\n", | |
| " 'cifar10',\n", | |
| " 'cifar100',\n", | |
| " 'cifar10_corrupted',\n", | |
| " 'clevr',\n", | |
| " 'cnn_dailymail',\n", | |
| " 'coco2014',\n", | |
| " 'colorectal_histology',\n", | |
| " 'colorectal_histology_large',\n", | |
| " 'cycle_gan',\n", | |
| " 'diabetic_retinopathy_detection',\n", | |
| " 'dsprites',\n", | |
| " 'dtd',\n", | |
| " 'dummy_dataset_shared_generator',\n", | |
| " 'dummy_mnist',\n", | |
| " 'emnist',\n", | |
| " 'fashion_mnist',\n", | |
| " 'flores',\n", | |
| " 'glue',\n", | |
| " 'groove',\n", | |
| " 'higgs',\n", | |
| " 'horses_or_humans',\n", | |
| " 'image_label_folder',\n", | |
| " 'imagenet2012',\n", | |
| " 'imagenet2012_corrupted',\n", | |
| " 'imdb_reviews',\n", | |
| " 'iris',\n", | |
| " 'kmnist',\n", | |
| " 'lm1b',\n", | |
| " 'lsun',\n", | |
| " 'mnist',\n", | |
| " 'moving_mnist',\n", | |
| " 'multi_nli',\n", | |
| " 'nsynth',\n", | |
| " 'omniglot',\n", | |
| " 'open_images_v4',\n", | |
| " 'oxford_flowers102',\n", | |
| " 'oxford_iiit_pet',\n", | |
| " 'para_crawl',\n", | |
| " 'quickdraw_bitmap',\n", | |
| " 'rock_paper_scissors',\n", | |
| " 'shapes3d',\n", | |
| " 'smallnorb',\n", | |
| " 'squad',\n", | |
| " 'starcraft_video',\n", | |
| " 'sun397',\n", | |
| " 'svhn_cropped',\n", | |
| " 'ted_hrlr_translate',\n", | |
| " 'ted_multi_translate',\n", | |
| " 'tf_flowers',\n", | |
| " 'titanic',\n", | |
| " 'ucf101',\n", | |
| " 'voc2007',\n", | |
| " 'wikipedia',\n", | |
| " 'wmt14_translate',\n", | |
| " 'wmt15_translate',\n", | |
| " 'wmt16_translate',\n", | |
| " 'wmt17_translate',\n", | |
| " 'wmt18_translate',\n", | |
| " 'wmt19_translate',\n", | |
| " 'wmt_t2t_translate',\n", | |
| " 'wmt_translate',\n", | |
| " 'xnli']" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 15 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "JauGJQiZcP51", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "On a vu précédemment que le dataset est un dictionary : 'test', 'train'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "jkYEwG1AYRFq", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "mnist_train, mnist_test = dataset[\"train\"], dataset[\"test\"]" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "1UHGjix3hU4X", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "5657165c-27e2-4a1e-b237-58246b161c56" | |
| }, | |
| "source": [ | |
| "type(mnist_train)" | |
| ], | |
| "execution_count": 63, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "tensorflow.python.data.ops.dataset_ops._OptionsDataset" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 63 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "5uwwlSL1jYU8", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "take(https://www.tensorflow.org/api_docs/python/tf/data/Dataset)count)\n", | |
| "\n", | |
| "Creates a Dataset with at most count elements from this dataset." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "jJsuQWe9YxH5", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "mnist_example, = mnist_train.take(1)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "WWZ5rnnWYyTx", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "25773f4a-6a23-4f93-e639-6a2fb4f90166" | |
| }, | |
| "source": [ | |
| "type(mnist_example)" | |
| ], | |
| "execution_count": 69, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "dict" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 69 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "TO9kVFRpftvU", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "37ecb590-e3f7-4498-f579-cd7b8a89881a" | |
| }, | |
| "source": [ | |
| "len(mnist_example[\"image\"])" | |
| ], | |
| "execution_count": 70, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "28" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 70 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "lSjsvNora8Xk", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "image, label = mnist_example[\"image\"], mnist_example[\"label\"]" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "2QdTvuYIgFBK", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "aff9f60d-efc3-4123-943b-4bf2e2b8d7b6" | |
| }, | |
| "source": [ | |
| "image.shape" | |
| ], | |
| "execution_count": 72, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "TensorShape([28, 28, 1])" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 72 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "L2RsNA6NbcbD", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "uBFIfv7QlkMN", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "Pour afficher l'image, on la convertit avec numpy" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "yY5Txetbbh4M", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 286 | |
| }, | |
| "outputId": "a8f4e7ea-55d8-4463-dfa2-c07e7225ca4a" | |
| }, | |
| "source": [ | |
| "plt.imshow(image.numpy()[:, :, 0].astype(np.float32), cmap=plt.get_cmap(\"gray\"))\n", | |
| "print(\"Label: %d\" % label.numpy())" | |
| ], | |
| "execution_count": 33, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Label: 1\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRfiBpAg/kBThB5L6H0cU9DTfPY2mAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "boDSmVsMbk1L", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "mnist_train = mnist_train.repeat(5).batch(32)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "TXONi9HkqmnT", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "dataset = tfds.load(name=\"mnist\", batch_size=32, as_supervised=True)\n", | |
| "mnist_train = dataset[\"train\"].repeat().prefetch(1)" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "bTH3ILb9rYOc", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "dyl044KBrar2", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": 0, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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