From 15675d5d808f58602bb2c8c13c79bd848dfce6b9 Mon Sep 17 00:00:00 2001 From: Masatoshi Itagaki Date: Mon, 25 Mar 2019 21:34:26 +0900 Subject: [PATCH 1/6] add images.ipynb, tf-records.ipynb --- site/ja/tutorials/load_data/images.ipynb | 3254 ++++++++++++++++++ site/ja/tutorials/load_data/tf-records.ipynb | 1687 +++++++++ 2 files changed, 4941 insertions(+) create mode 100644 site/ja/tutorials/load_data/images.ipynb create mode 100644 site/ja/tutorials/load_data/tf-records.ipynb diff --git a/site/ja/tutorials/load_data/images.ipynb b/site/ja/tutorials/load_data/images.ipynb new file mode 100644 index 00000000000..dd176f8a9f1 --- /dev/null +++ b/site/ja/tutorials/load_data/images.ipynb @@ -0,0 +1,3254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mt9dL5dIir8X" + }, + "source": [ + "##### Copyright 2018 The TensorFlow Authors." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "cellView": "form", + "colab": {}, + "colab_type": "code", + "id": "ufPx7EiCiqgR" + }, + "outputs": [], + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ucMoYase6URl" + }, + "source": [ + "# tf.dataを使って画像をロードする" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_Wwu5SXZmEkB" + }, + "source": [ + "\n", + " \n", + " \n", + " \n", + "
\n", + " View on TensorFlow.org\n", + " \n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Oxw4WahM7DU9" + }, + "source": [ + "このチュートリアルでは、'tf.data'を使って画像データセットをロードする簡単な例を示します。\n", + "\n", + "このチュートリアルで使用するデータセットは、クラスごとに別々のディレクトリに別れた形で配布されています。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hoQQiZDB6URn" + }, + "source": [ + "## 設定" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "QGXxBuPyKJw1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: tf-nightly in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (1.14.1.dev20190301)\n", + "Requirement already satisfied: keras-preprocessing>=1.0.5 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.0.5)\n", + "Requirement already satisfied: wheel>=0.26 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (0.32.2)\n", + "Requirement already satisfied: tf-estimator-nightly in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.14.0.dev2019022801)\n", + "Requirement already satisfied: astor>=0.6.0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (0.7.1)\n", + "Requirement already satisfied: absl-py>=0.7.0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (0.7.0)\n", + "Requirement already satisfied: numpy<2.0,>=1.14.5 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.16.2)\n", + "Requirement already satisfied: gast>=0.2.0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (0.2.0)\n", + "Requirement already satisfied: termcolor>=1.1.0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.1.0)\n", + "Requirement already satisfied: six>=1.10.0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.11.0)\n", + "Requirement already satisfied: protobuf>=3.6.1 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (3.6.1)\n", + "Requirement already satisfied: keras-applications>=1.0.6 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.0.6)\n", + "Requirement already satisfied: grpcio>=1.8.6 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.16.0)\n", + "Requirement already satisfied: tb-nightly<1.15.0a0,>=1.14.0a0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.14.0a20190301)\n", + "Requirement already satisfied: google-pasta>=0.1.2 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (0.1.4)\n", + "Requirement already satisfied: setuptools in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from protobuf>=3.6.1->tf-nightly) (39.0.1)\n", + "Requirement already satisfied: h5py in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from keras-applications>=1.0.6->tf-nightly) (2.8.0)\n", + "Requirement already satisfied: markdown>=2.6.8 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tb-nightly<1.15.0a0,>=1.14.0a0->tf-nightly) (3.0.1)\n", + "Requirement already satisfied: werkzeug>=0.11.15 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tb-nightly<1.15.0a0,>=1.14.0a0->tf-nightly) (0.14.1)\n" + ] + } + ], + "source": [ + "!pip install tf-nightly" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "DHz3JONNEHlj" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.13.1'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tensorflow as tf\n", + "tf.enable_eager_execution()\n", + "tf.VERSION" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KT6CcaqgQewg" + }, + "outputs": [], + "source": [ + "AUTOTUNE = tf.data.experimental.AUTOTUNE" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rxndJHNC8YPM" + }, + "source": [ + "## データセットのダウンロードと検査" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wO0InzL66URu" + }, + "source": [ + "### 画像の取得\n", + "\n", + "訓練を始める前に、ネットワークに認識すべき新しいクラスを教えるために画像のセットが必要です。最初に使うためのクリエイティブ・コモンズでライセンスされた花の画像のアーカイブを作成してあります。" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "rN-Pc6Zd6awg" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/masatoshi/.keras/datasets/flower_photos\n" + ] + } + ], + "source": [ + "import pathlib\n", + "data_root = tf.keras.utils.get_file('flower_photos','https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz', untar=True)\n", + "data_root = pathlib.Path(data_root)\n", + "print(data_root)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rFkFK74oO--g" + }, + "source": [ + "218MBをダウンロードすると、花の画像のコピーが使えるようになっているはずです。" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "7onR_lWE7Njj" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/masatoshi/.keras/datasets/flower_photos/roses\n", + "/Users/masatoshi/.keras/datasets/flower_photos/sunflowers\n", + "/Users/masatoshi/.keras/datasets/flower_photos/daisy\n", + "/Users/masatoshi/.keras/datasets/flower_photos/dandelion\n", + "/Users/masatoshi/.keras/datasets/flower_photos/tulips\n", + "/Users/masatoshi/.keras/datasets/flower_photos/LICENSE.txt\n" + ] + } + ], + "source": [ + "for item in data_root.iterdir():\n", + " print(item)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4yYX3ZRqGOuq" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3670" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "all_image_paths = list(data_root.glob('*/*'))\n", + "all_image_paths = [str(path) for path in all_image_paths]\n", + "random.shuffle(all_image_paths)\n", + "\n", + "image_count = len(all_image_paths)\n", + "image_count" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "t_BbYnLjbltQ" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['/Users/masatoshi/.keras/datasets/flower_photos/dandelion/7179487220_56e4725195_m.jpg',\n", + " '/Users/masatoshi/.keras/datasets/flower_photos/dandelion/18282528206_7fb3166041.jpg',\n", + " '/Users/masatoshi/.keras/datasets/flower_photos/daisy/2713919471_301fcc941f.jpg',\n", + " 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'/Users/masatoshi/.keras/datasets/flower_photos/roses/4325834819_ab56661dcc_m.jpg',\n", + " '/Users/masatoshi/.keras/datasets/flower_photos/sunflowers/13959937305_2f5c532886_n.jpg',\n", + " '/Users/masatoshi/.keras/datasets/flower_photos/tulips/8520488975_a50d377f91.jpg',\n", + " '/Users/masatoshi/.keras/datasets/flower_photos/sunflowers/1044296388_912143e1d4.jpg',\n", + " '/Users/masatoshi/.keras/datasets/flower_photos/sunflowers/18843967474_9cb552716b.jpg',\n", + " '/Users/masatoshi/.keras/datasets/flower_photos/dandelion/2389720627_8923180b19.jpg',\n", + " '/Users/masatoshi/.keras/datasets/flower_photos/tulips/8668974855_8389ecbdca_m.jpg',\n", + " ...]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_image_paths" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vkM-IpB-6URx" + }, + "source": [ + "### 画像の検査\n", + "\n", + "扱っている画像について知るために、画像のいくつかを見てみましょう。" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "wNGateQJ6UR1" + }, + "outputs": [], + "source": [ + "attributions = (data_root/\"LICENSE.txt\").read_text(encoding=\"utf8\").splitlines()[4:]\n", + "attributions = [line.split(' CC-BY') for line in attributions]\n", + "attributions = dict(attributions)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "jgowG2xu88Io" + }, + "outputs": [], + "source": [ + "import IPython.display as display\n", + "\n", + "def caption_image(image_path):\n", + " image_rel = pathlib.Path(image_path).relative_to(data_root)\n", + " return \"Image (CC BY 2.0) \" + ' - '.join(attributions[str(image_rel)].split(' - ')[:-1])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "YIjLi-nX0txI" + }, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image (CC BY 2.0) by Marilena Marchese\n", + "\n" + ] + } + ], + "source": [ + "for n in range(3):\n", + " image_path = random.choice(all_image_paths)\n", + " display.display(display.Image(image_path))\n", + " print(caption_image(image_path))\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OaNOr-co3WKk" + }, + "source": [ + "### 各画像のラベルの決定" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-weOQpDw2Jnu" + }, + "source": [ + "ラベルを一覧してみます。" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ssUZ7Qh96UR3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "label_names = sorted(item.name for item in data_root.glob('*/') if item.is_dir())\n", + "label_names" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9l_JEBql2OzS" + }, + "source": [ + "ラベルにインデックスを割り当てます。" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Y8pCV46CzPlp" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "label_to_index = dict((name, index) for index,name in enumerate(label_names))\n", + "label_to_index" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VkXsHg162T9F" + }, + "source": [ + "ファイルとラベルのインデックスの一覧を作成します。" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "q62i1RBP4Q02" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First 10 labels indices: [1, 1, 0, 1, 4, 4, 2, 0, 4, 1]\n" + ] + } + ], + "source": [ + "all_image_labels = [label_to_index[pathlib.Path(path).parent.name]\n", + " for path in all_image_paths]\n", + "\n", + "print(\"First 10 labels indices: \", all_image_labels[:10])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "i5L09icm9iph" + }, + "source": [ + "### イメージのロードと整形" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "SbqqRUS79ooq" + }, + "source": [ + "TensorFlowには画像をロードして処理するために必要なツールが備わっています。" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "jQZdySHvksOu" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/masatoshi/.keras/datasets/flower_photos/dandelion/7179487220_56e4725195_m.jpg'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_path = all_image_paths[0]\n", + "img_path" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2t2h2XCcmK1Y" + }, + "source": [ + "これが生のデータです。" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "LJfkyC_Qkt7A" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "img_tensor = tf.image.decode_image(img_raw)\n", + "\n", + "print(img_tensor.shape)\n", + "print(img_tensor.dtype)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3k-Of2Tfmbeq" + }, + "source": [ + "モデルに合わせてリサイズします。" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "XFpz-3_vlJgp" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(192, 192, 3)\n", + "0.0\n", + "1.0\n" + ] + } + ], + "source": [ + "img_final = tf.image.resize_images(img_tensor, [192, 192])\n", + "img_final = img_final/255.0\n", + "print(img_final.shape)\n", + "print(img_final.numpy().min())\n", + "print(img_final.numpy().max())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aCsAa4Psl4AQ" + }, + "source": [ + "このあと使用するために、簡単な関数にまとめます。" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "HmUiZJNU73vA" + }, + "outputs": [], + "source": [ + "def preprocess_image(image):\n", + " image = tf.image.decode_jpeg(image, channels=3)\n", + " image = tf.image.resize_images(image, [192, 192])\n", + " image /= 255.0 # normalize to [0,1] range\n", + "\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "einETrJnO-em" + }, + "outputs": [], + "source": [ + "def load_and_preprocess_image(path):\n", + " image = tf.read_file(path)\n", + " return preprocess_image(image)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "3brWQcdtz78y" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "image_path = all_image_paths[0]\n", + "label = all_image_labels[0]\n", + "\n", + "plt.imshow(load_and_preprocess_image(img_path))\n", + "plt.grid(False)\n", + "plt.xlabel(caption_image(img_path))\n", + "plt.title(label_names[label].title())\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "n2TCr1TQ8pA3" + }, + "source": [ + "## `tf.data.Dataset`の構築" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6H9Z5Mq63nSH" + }, + "source": [ + "### 画像のデータセット" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GN-s04s-6Luq" + }, + "source": [ + "`tf.data.Dataset`を構築する最も簡単な方法は、`from_tensor_slices`メソッドを使うことです。\n", + "\n", + "文字列の配列をスライスすると、文字列のデータセットが出来上がります。" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "6oRPG3Jz3ie_" + }, + "outputs": [], + "source": [ + "path_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "uML4JeMmIAvO" + }, + "source": [ + "`output_shapes`と`output_types`という2つのフィールドが、データセット中の要素の中身を示しています。この場合には、バイナリ文字列というスカラーのセットです。" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "mIsNflFbIK34" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: TensorShape([])\n", + "type: \n", + "\n", + "\n" + ] + } + ], + "source": [ + "print('shape: ', repr(path_ds.output_shapes))\n", + "print('type: ', path_ds.output_types)\n", + "print()\n", + "print(path_ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ZjyGcM8OwBJ2" + }, + "source": [ + "`preprocess_image`をファイルパスのデータセットにマップすることで、画像を実行時にロードし整形する新しいデータセットを作成します。" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "D1iba6f4khu-" + }, + "outputs": [], + "source": [ + "image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "JLUPs2a-lEEJ" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py:532: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(8,8))\n", + "for n,image in enumerate(image_ds.take(4)):\n", + " plt.subplot(2,2,n+1)\n", + " plt.imshow(image)\n", + " plt.grid(False)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.xlabel(caption_image(all_image_paths[n]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "P6FNqPbxkbdx" + }, + "source": [ + "### `(image, label)`のペアのデータセット" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "YgvrWLKG67-x" + }, + "source": [ + "同じ`from_tensor_slices`メソッドを使ってラベルのデータセットを作ることができます。" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "AgBsAiV06udj" + }, + "outputs": [], + "source": [ + "label_ds = tf.data.Dataset.from_tensor_slices(tf.cast(all_image_labels, tf.int64))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "HEsk5nN0vyeX" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dandelion\n", + "dandelion\n", + "daisy\n", + "dandelion\n", + "tulips\n", + "tulips\n", + "roses\n", + "daisy\n", + "tulips\n", + "dandelion\n" + ] + } + ], + "source": [ + "for label in label_ds.take(10):\n", + " print(label_names[label.numpy()])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jHjgrEeTxyYz" + }, + "source": [ + "これらのデータセットは同じ順番なので、zipすることで`(image, label)`というペアのデータセットができます。" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "AOEWNMdQwsbN" + }, + "outputs": [], + "source": [ + "image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "yA2F09SJLMuM" + }, + "source": [ + "新しいデータセットの`shapes`と`types`は、それぞれのフィールドを示すシェイプと型のタプルです。" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "DuVYNinrLL-N" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image shape: (192, 192, 3)\n", + "label shape: ()\n", + "types: (tf.float32, tf.int64)\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print('image shape: ', image_label_ds.output_shapes[0])\n", + "print('label shape: ', image_label_ds.output_shapes[1])\n", + "print('types: ', image_label_ds.output_types)\n", + "print()\n", + "print(image_label_ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2WYMikoPWOQX" + }, + "source": [ + "注：`all_image_labels`や`all_image_paths`の配列がある場合、`tf.data.dataset.Dataset.zip`メソッドの代わりとなるのは、配列のペアをスライスすることです。" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "HOFwZI-2WhzV" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = tf.data.Dataset.from_tensor_slices((all_image_paths, all_image_labels))\n", + "\n", + "# The tuples are unpacked into the positional arguments of the mapped function\n", + "# タプルは解体され、マップ関数の位置引数に割り当てられます\n", + "def load_and_preprocess_from_path_label(path, label):\n", + " return load_and_preprocess_image(path), label\n", + "\n", + "image_label_ds = ds.map(load_and_preprocess_from_path_label)\n", + "image_label_ds" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vYGCgJuR_9Qp" + }, + "source": [ + "### 基本的な訓練手法" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wwZavzgsIytz" + }, + "source": [ + "このデータセットを使ってモデルの訓練を行うには、データが\n", + "\n", + "* よくシャッフルされ\n", + "* バッチ化され\n", + "* 限りなく繰り返され\n", + "* バッチが出来るだけ早く利用できる\n", + "\n", + "ことが必要です。\n", + "\n", + "これらの特性は`tf.data`APIを使えば簡単に付け加えることができます。" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "uZmZJx8ePw_5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "BATCH_SIZE = 32\n", + "\n", + "# シャッフルバッファのサイズをデータセットと同じに設定することで、データが完全にシャッフルされる\n", + "# ようにできます。\n", + "ds = image_label_ds.shuffle(buffer_size=image_count)\n", + "ds = ds.repeat()\n", + "ds = ds.batch(BATCH_SIZE)\n", + "# `prefetch`を使うことで、モデルの訓練中にバックグラウンドでデータセットがバッチを取得できます。\n", + "ds = ds.prefetch(buffer_size=AUTOTUNE)\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6JsM-xHiFCuW" + }, + "source": [ + "注意すべきことがいくつかあります。\n", + "\n", + "1. 順番が重要です。\n", + "\n", + " * `.repeat`の前に`.shuffle`すると、エポックの境界を越えて要素がシャッフルされます。（他の要素がすべて出現する前に2回出現する要素があるかもしれません）\n", + " * `.batch`の後に`.shuffle`すると、バッチの順番がシャッフルされますが、要素がバッチを越えてシャッフルされることはありません。\n", + "\n", + "1. 完全なシャッフルのため、`buffer_size`をデータセットと同じサイズに設定します。データセットのサイズ未満の場合、値が大きいほど良くランダム化されますが、より多くのメモリーを使用します。\n", + "\n", + "1. シャッフルバッファがいっぱいになってから要素が取り出されます。そのため、大きな`buffer_size`が`Dataset`を使い始める際の遅延の原因になります。\n", + "\n", + "1. シャッフルされたデータセットは、シャッフルバッファが完全に空になるまでデータセットが終わりであることを伝えません。`.repeat`によって`Dataset`が再起動されると、シャッフルバッファが一杯になるまでもう一つの待ち時間が発生します。\n", + "\n", + "最後の問題は、`tf.data.Dataset.apply`メソッドを、融合された`tf.data.experimental.shuffle_and_repeat`関数を使うことで対処できます。" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Ocr6PybXNDoO" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = image_label_ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds = ds.batch(BATCH_SIZE)\n", + "ds = ds.prefetch(buffer_size=AUTOTUNE)\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GBBZMSuAmQVL" + }, + "source": [ + "### データセットをモデルにつなぐ\n", + "\n", + "`tf.keras.applications`からMobileNet v2のコピーを取得します。\n", + "\n", + "これを簡単な転移学習のサンプルに使用します。\n", + "\n", + "MobileNetの重みを訓練不可に設定します。" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KbJrXn9omO_g" + }, + "outputs": [], + "source": [ + "mobile_net = tf.keras.applications.MobileNetV2(input_shape=(192, 192, 3), include_top=False)\n", + "mobile_net.trainable=False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Y7NVWiLF3Vbf" + }, + "source": [ + "このモデルは、入力が`[-1,1]`の範囲に正規化されていることを想定しています。\n", + "\n", + "```\n", + "help(keras_applications.mobilenet_v2.preprocess_input)\n", + "```\n", + "\n", + "
\n",
+    "...\n",
+    "This function applies the \"Inception\" preprocessing which converts\n",
+    "the RGB values from [0, 255] to [-1, 1] \n",
+    "...\n",
+    "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CboYya6LmdQI" + }, + "source": [ + "このため、データをMobileNetモデルに渡す前に、入力を`[0,1]`の範囲から`[-1,1]`の範囲に変換する必要があります。" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "SNOkHUGv3FYq" + }, + "outputs": [], + "source": [ + "def change_range(image,label):\n", + " return 2*image-1, label\n", + "\n", + "keras_ds = ds.map(change_range)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QDzZ3Nye5Rpv" + }, + "source": [ + "MobileNetは画像ごとに`6x6`の特徴量の空間を返します。\n", + "\n", + "バッチを１つ渡してみましょう。" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "OzAhGkEK6WuE" + }, + "outputs": [], + "source": [ + "# シャッフルバッファがいっぱいになるまで、データセットは何秒かかかります。\n", + "image_batch, label_batch = next(iter(keras_ds))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "LcFdiWpO5WbV" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(32, 6, 6, 1280)\n" + ] + } + ], + "source": [ + "feature_map_batch = mobile_net(image_batch)\n", + "print(feature_map_batch.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vrbjEvaC5XmU" + }, + "source": [ + "MobileNetをラップしたモデルを作り、出力層である`tf.keras.layers.Dense`の前に、`tf.keras.layers.GlobalAveragePooling2D`で空間の軸に沿って平均値を求めます。" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "X0ooIU9fNjPJ" + }, + "outputs": [], + "source": [ + "model = tf.keras.Sequential([\n", + " mobile_net,\n", + " tf.keras.layers.GlobalAveragePooling2D(),\n", + " tf.keras.layers.Dense(len(label_names))])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "foQYUJs97V4V" + }, + "source": [ + "期待したとおりのシェイプの出力が得られます。" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "1nwYxvpj7ZEf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "min logit: -3.2900493\n", + "max logit: 2.763081\n", + "\n", + "Shape: (32, 5)\n" + ] + } + ], + "source": [ + "logit_batch = model(image_batch).numpy()\n", + "\n", + "print(\"min logit:\", logit_batch.min())\n", + "print(\"max logit:\", logit_batch.max())\n", + "print()\n", + "\n", + "print(\"Shape:\", logit_batch.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pFc4I_J2nNOJ" + }, + "source": [ + "訓練手法を記述するためにモデルをコンパイルします。" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ZWGqLEWYRNvv" + }, + "outputs": [], + "source": [ + "model.compile(optimizer=tf.train.AdamOptimizer(), \n", + " loss=tf.keras.losses.sparse_categorical_crossentropy,\n", + " metrics=[\"accuracy\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tF1mO6haBOSd" + }, + "source": [ + "訓練可能な変数は2つ、全結合層の`weights`と`bias`です。" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "pPQ5yqyKBJMm" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(model.trainable_variables) " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "kug5Wg66UJjl" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "mobilenetv2_1.00_192 (Model) (None, 6, 6, 1280) 2257984 \n", + "_________________________________________________________________\n", + "global_average_pooling2d (Gl (None, 1280) 0 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 5) 6405 \n", + "=================================================================\n", + "Total params: 2,264,389\n", + "Trainable params: 6,405\n", + "Non-trainable params: 2,257,984\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "f_glpYZ-nYC_" + }, + "source": [ + "モデルを訓練します。\n", + "\n", + "普通は、エポックごとの本当のステップ数を指定しますが、ここではデモの目的なので3ステップだけとします。" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "AnXPRNWoTypI" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "115.0" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "steps_per_epoch=tf.ceil(len(all_image_paths)/BATCH_SIZE).numpy()\n", + "steps_per_epoch" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "q_8sabaaSGAp" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3/3 [==============================] - 30s 10s/step - loss: 8.3349 - acc: 0.2812\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(ds, epochs=1, steps_per_epoch=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UMVnoBcG_NlQ" + }, + "source": [ + "## 性能\n", + "\n", + "注：このセクションでは性能の向上に役立ちそうな簡単なトリックをいくつか紹介します。詳しくは、[Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets)を参照してください。\n", + "\n", + "上記の単純なパイプラインは、エポックごとにそれぞれのファイルを一つずつ読み込みます。これは、CPUを使ったローカルでの訓練では問題になりませんが、GPUを使った訓練では十分ではなく、いかなる分散訓練でも使うべきではありません。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "oNmQqgGhLWie" + }, + "source": [ + "調査のため、まず、データセットの性能をチェックする簡単な関数を定義します。" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "_gFVe1rp_MYr" + }, + "outputs": [], + "source": [ + "import time\n", + "\n", + "def timeit(ds, batches=2*steps_per_epoch+1):\n", + " overall_start = time.time()\n", + " # タイマーをスタートする前に、パイプラインの初期化の（シャッフルバッファを埋める）ため、\n", + " # バッチを１つ取得します\n", + " it = iter(ds.take(batches+1))\n", + " next(it)\n", + "\n", + " start = time.time()\n", + " for i,(images,labels) in enumerate(it):\n", + " if i%10 == 0:\n", + " print('.',end='')\n", + " print()\n", + " end = time.time()\n", + "\n", + " duration = end-start\n", + " print(\"{} batches: {} s\".format(batches, duration))\n", + " print(\"{:0.5f} Images/s\".format(BATCH_SIZE*batches/duration))\n", + " print(\"Total time: {}s\".format(end-overall_start))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TYiOr4vdLcNX" + }, + "source": [ + "現在のデータセットの性能は次のとおりです。" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ZDxLwMJOReVe" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = image_label_ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds = ds.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "IjouTJadRxyp" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................\n", + "231.0 batches: 25.843446016311646 s\n", + "286.02997 Images/s\n", + "Total time: 43.207932233810425s\n" + ] + } + ], + "source": [ + "timeit(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HsLlXMO7EWBR" + }, + "source": [ + "### キャッシュ" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "lV1NOn2zE2lR" + }, + "source": [ + "`tf.data.Dataset.cache`を使うと、エポックを越えて計算結果を簡単にキャッシュできます。特に、データがメモリに収まるときには効果的です。\n", + "\n", + "ここでは、画像が前処理（デコードとリサイズ）された後でキャッシュされます。" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "qj_U09xpDvOg" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = image_label_ds.cache()\n", + "ds = ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds = ds.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "rdxpvQ7VEo3y" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................\n", + "231.0 batches: 1.0587589740753174 s\n", + "6981.75900 Images/s\n", + "Total time: 14.936384201049805s\n" + ] + } + ], + "source": [ + "timeit(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "usIv7MqqZQps" + }, + "source": [ + "メモリキャッシュを使う際の欠点のひとつは、実行の都度キャッシュを再構築しなければならないことです。このため、データセットがスタートするたびに同じだけ起動のための遅延が発生します。" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "eKX6ergKb_xd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................\n", + "231.0 batches: 1.0269150733947754 s\n", + "7198.25835 Images/s\n", + "Total time: 15.162395000457764s\n" + ] + } + ], + "source": [ + "timeit(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jUzpG4lYNkN-" + }, + "source": [ + "データがメモリに収まらない場合には、キャッシュファイルを使用します。" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vIvF8K4GMq0g" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = image_label_ds.cache(filename='./cache.tf-data')\n", + "ds = ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds = ds.batch(BATCH_SIZE).prefetch(1)\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "eTIj6IOmM4yA" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................\n", + "231.0 batches: 12.766232967376709 s\n", + "579.02750 Images/s\n", + "Total time: 33.048365116119385s\n" + ] + } + ], + "source": [ + "timeit(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qqo3dyB0Z4t2" + }, + "source": [ + "キャッシュファイルには、キャッシュを再構築することなくデータセットを再起動できるという利点もあります。2回めがどれほど早いか見てみましょう。" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "hZhVdR8MbaUj" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................\n", + "231.0 batches: 9.893965005874634 s\n", + "747.12211 Images/s\n", + "Total time: 14.534404039382935s\n" + ] + } + ], + "source": [ + "timeit(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "WqOVlf8tFrDU" + }, + "source": [ + "### TFRecord ファイル" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "y1llOTwWFzmR" + }, + "source": [ + "#### 生の画像データ\n", + "\n", + "TFRecordファイルは、バイナリの大きなオブジェクトのシーケンスを保存するための単純なフォーマットです。複数のサンプルを同じファイルに詰め込むことで、TensorFlowは複数のサンプルを一度に読み込むことができます。これは、特にGCSのようなリモートストレージサービスを使用する際の性能にとって重要です。\n", + "\n", + "最初に、生の画像データからTFRecordファイルを構築します。" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EqtARqKuHQLu" + }, + "outputs": [], + "source": [ + "image_ds = tf.data.Dataset.from_tensor_slices(all_image_paths).map(tf.read_file)\n", + "tfrec = tf.data.experimental.TFRecordWriter('images.tfrec')\n", + "tfrec.write(image_ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "flR2GXWFKcO1" + }, + "source": [ + "次に、TFRecordファイルを読み込み、以前定義した`preprocess_image`関数を使って画像のデコード/リフォーマットを行うデータセットを構築します。" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "j9PVUL2SFufn" + }, + "outputs": [], + "source": [ + "image_ds = tf.data.TFRecordDataset('images.tfrec').map(preprocess_image)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "cRp1eZDRKzyN" + }, + "source": [ + "これを、前に定義済みのラベルデータセットとzipし、予定される`(image,label)`のペアを得ます。" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "7XI_nDU2KuhS" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = tf.data.Dataset.zip((image_ds, label_ds))\n", + "ds = ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds=ds.batch(BATCH_SIZE).prefetch(AUTOTUNE)\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "3ReSapoPK22E" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................\n", + "231.0 batches: 25.64651608467102 s\n", + "288.22628 Images/s\n", + "Total time: 38.50603103637695s\n" + ] + } + ], + "source": [ + "timeit(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wb7VyoKNOMms" + }, + "source": [ + "これは、`cache`バージョンよりも低速です。前処理をキャッシュしていないからです。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NF9W-CTKkM-f" + }, + "source": [ + "#### シリアライズしたテンソル" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "J9HzljSPkxt0" + }, + "source": [ + "前処理をTFRecordファイルに保存するには、前やったように前処理した画像のデータセットを作ります。" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "OzS0Azukkjyw" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "paths_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)\n", + "image_ds = paths_ds.map(load_and_preprocess_image)\n", + "image_ds" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "onWOwLpYlzJQ" + }, + "source": [ + "`.jpeg`文字列のデータセットではなく、これはテンソルのデータセットです。\n", + "\n", + "これをTFRecordファイルにシリアライズするには、まず、テンソルのデータセットを文字列のデータセットに変換します。" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "xxZSwnRllyf0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = image_ds.map(tf.serialize_tensor)\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "w9N6hJWAkKPC" + }, + "outputs": [], + "source": [ + "tfrec = tf.data.experimental.TFRecordWriter('images.tfrec')\n", + "tfrec.write(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OlFc9dJSmcx0" + }, + "source": [ + "前処理をキャッシュしたことにより、データはTFRecordファイルから非常に効率的にロードできます。テンソルを使用する前にデシリアライズすることを忘れないでください。" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "BsqFyTBFmSCZ" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "RESTORE_TYPE = image_ds.output_types\n", + "RESTORE_SHAPE = image_ds.output_shapes\n", + "\n", + "ds = tf.data.TFRecordDataset('images.tfrec')\n", + "\n", + "def parse(x):\n", + " result = tf.parse_tensor(x, out_type=RESTORE_TYPE)\n", + " result = tf.reshape(result, RESTORE_SHAPE)\n", + " return result\n", + "\n", + "ds = ds.map(parse, num_parallel_calls=AUTOTUNE)\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OPs_sLV9pQg5" + }, + "source": [ + "次にラベルを追加し、以前と同じような標準的な処理を適用します。" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "XYxBwaLYnGop" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = tf.data.Dataset.zip((ds, label_ds))\n", + "ds = ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds=ds.batch(BATCH_SIZE).prefetch(AUTOTUNE)\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "W8X6RmGan1-P" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................\n", + "231.0 batches: 9.515093088150024 s\n", + "776.87101 Images/s\n", + "Total time: 13.438390016555786s\n" + ] + } + ], + "source": [ + "timeit(ds)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "images.ipynb", + "private_outputs": true, + "provenance": [], + "toc_visible": true, + "version": "0.3.2" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "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": "3.6.5" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/site/ja/tutorials/load_data/tf-records.ipynb b/site/ja/tutorials/load_data/tf-records.ipynb new file mode 100644 index 00000000000..238da53524c --- /dev/null +++ b/site/ja/tutorials/load_data/tf-records.ipynb @@ -0,0 +1,1687 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pL--_KGdYoBz" + }, + "source": [ + "##### Copyright 2018 The TensorFlow Authors." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "cellView": "both", + "colab": {}, + "colab_type": "code", + "id": "uBDvXpYzYnGj" + }, + "outputs": [], + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HQzaEQuJiW_d" + }, + "source": [ + "# TFRecords と `tf.Example` の使用法\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "
\n", + " View on TensorFlow.org\n", + " \n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3pkUd_9IZCFO" + }, + "source": [ + "データの読み込みを効率的にするには、データをシリアライズし、連続的に読み込めるファイルのセット（各ファイルは100-200MB）に保存することが有効です。データをネットワーク経由で流そうとする場合には、特にそうです。また、データの前処理をキャッシングする際にも役立ちます。\n", + "\n", + "TFRecord形式は、バイナリレコードの系列を保存するための単純な形式です。\n", + "\n", + "[プロトコルバッファ](https://developers.google.com/protocol-buffers/) は、構造化データを効率的にシリアライズする、プラットフォームや言語に依存しないライブラリです。\n", + "\n", + "プロトコルメッセージは`.proto`という拡張子のファイルで表されます。メッセージの型を識別する最も簡単な方法です。\n", + "\n", + "`tf.Example`メッセージ（あるいはプロトコルバッファ）は、`{\"string\": value}`形式のマッピングを表現する柔軟なメッセージタイプです。これは、TensorFlow用に設計され、[TFX](https://www.tensorflow.org/tfx/)のような上位レベルのAPIで共通に使用されています。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Ac83J0QxjhFt" + }, + "source": [ + "このノートブックでは、`tf.Example`の作成、解析と使用法をデモし、その後、`tf.Example`メッセージを`.tfrecord`に書き出し、読み取る方法を示します。\n", + "\n", + "注：こうした構造は有用ですが必ずそうしなければならなというものではありません。[`tf.data`](https://www.tensorflow.org/guide/datasets) を使っていて、データの読み込みが訓練のボトルネックである場合でなければ、既存のコードをTFRecordsを使用するために変更する必要はありません。データセットの性能改善のヒントは、 [Data Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets)を参照ください。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "WkRreBf1eDVc" + }, + "source": [ + "## 設定" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Ja7sezsmnXph" + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import\n", + "from __future__ import division\n", + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "tf.enable_eager_execution()\n", + "\n", + "import numpy as np\n", + "import IPython.display as display" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "e5Kq88ccUWQV" + }, + "source": [ + "## `tf.Example`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VrdQHgvNijTi" + }, + "source": [ + "### `tf.Example`用のデータ型" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "lZw57Qrn4CTE" + }, + "source": [ + "基本的には`tf.Example`は`{\"string\": tf.train.Feature}`というマッピングです。\n", + "\n", + "`tf.train.Feature`メッセージ型は次の3つの型のうち1つをとることができます。（[.proto file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto)を参照）この他の一般的なデータ型のほとんどは、強制的にこれらのうちの1つにすること可能です。\n", + "\n", + "1. `tf.train.BytesList` (次の型のデータを扱うことが可能)\n", + " - `string`\n", + " - `byte` \n", + "1. `tf.train.FloatList` (次の型のデータを扱うことが可能)\n", + " - `float` (`float32`)\n", + " - `double` (`float64`) \n", + "1. `tf.train.Int64List` (次の型のデータを扱うことが可能)\n", + " - `bool`\n", + " - `enum`\n", + " - `int32`\n", + " - `uint32`\n", + " - `int64`\n", + " - `uint64`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_e3g9ExathXP" + }, + "source": [ + "通常のTensorFlowの型を`tf.Example`互換の `tf.train.Feature`に変換するには、次のショートカット関数を使うことができます。\n", + "\n", + "どの関数も、1個のスカラー値を入力とし、上記の3つの`list`型のうちの一つを含む`tf.train.Feature`を返します。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "mbsPOUpVtYxA" + }, + "outputs": [], + "source": [ + "# 下記の関数を使うと値を tf.Exampleと互換性の有る型に変換できる\n", + "\n", + "def _bytes_feature(value):\n", + " \"\"\"string / byte 型から byte_listを返す\"\"\"\n", + " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n", + "\n", + "def _float_feature(value):\n", + " \"\"\"float / double 型から float_listを返す\"\"\"\n", + " return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n", + "\n", + "def _int64_feature(value):\n", + " \"\"\"bool / enum / int / uint 型から Int64_listを返す\"\"\"\n", + " return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Wst0v9O8hgzy" + }, + "source": [ + "注：単純化のため、このサンプルではスカラー値の入力のみを扱っています。スカラー値ではない特徴を扱う最も簡単な方法は、`tf.serialize_tensor`を使ってテンソルをバイナリ文字列に変換する方法です。TensorFlowでは文字列はスカラー値として扱います。バイナリ文字列をテンソルに戻すには、`tf.parse_tensor`を使用します。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vsMbkkC8xxtB" + }, + "source": [ + "上記の関数の使用例を下記に示します。入力が様々な型であるのに対して、出力が標準化されていることに注目してください。入力が、強制変換できない型であった場合、例外が発生します。（例：`_int64_feature(1.0)`はエラーとなります。`1.0`が浮動小数点数であるためで、代わりに`_float_feature`関数を使用すべきです）" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "hZzyLGr0u73y" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bytes_list {\n", + " value: \"test_string\"\n", + "}\n", + "\n", + "bytes_list {\n", + " value: \"test_bytes\"\n", + "}\n", + "\n", + "float_list {\n", + " value: 2.7182817459106445\n", + "}\n", + "\n", + "int64_list {\n", + " value: 1\n", + "}\n", + "\n", + "int64_list {\n", + " value: 1\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "print(_bytes_feature(b'test_string'))\n", + "print(_bytes_feature(u'test_bytes'.encode('utf-8')))\n", + "\n", + "print(_float_feature(np.exp(1)))\n", + "\n", + "print(_int64_feature(True))\n", + "print(_int64_feature(1))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nj1qpfQU5qmi" + }, + "source": [ + "主要なメッセージはすべて`.SerializeToString` を使ってバイナリ文字列にシリアライズすることができます。" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "5afZkORT5pjm" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "b'\\x12\\x06\\n\\x04T\\xf8-@'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature = _float_feature(np.exp(1))\n", + "\n", + "feature.SerializeToString()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "laKnw9F3hL-W" + }, + "source": [ + "### `tf.Example` メッセージの作成" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b_MEnhxchQPC" + }, + "source": [ + "既存のデータから`tf.Example`を作成したいとします。実際には、データセットの出処はどこでも良いのですが、1件の観測記録から`tf.Example`メッセージを作る手順は同じです。\n", + "\n", + "1. 観測記録それぞれにおいて、各値は上記の関数を使って3種類の互換性のある型をからなる`tf.train.Feature`に変換する必要があります。\n", + "\n", + "1. 次に、特徴の名前を表す文字列と、#1で作ったエンコード済みの特徴量を対応させたマップ（ディクショナリ）を作成します。\n", + "\n", + "1. #2で作成したマップを[特徴量メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)に変換します。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4EgFQ2uHtchc" + }, + "source": [ + "このノートブックでは、NumPyを使ってデータセットを作成します。\n", + "\n", + "このデータセットには4つの特徴量があります。\n", + "- `False` または `True`を表す論理値。出現確率は等しいものとします。\n", + "- ランダムなバイト値。全体において一様であるとします。\n", + "- `[-10000, 10000)`の範囲から一様にサンプリングした整数値。\n", + "- 標準正規分布からサンプリングした浮動小数点数。\n", + "\n", + "サンプルは上記の分布から独立して同じ様に分布した10,000件の観測記録からなるものとします。" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "CnrguFAy3YQv" + }, + "outputs": [], + "source": [ + "# データセットに含まれる観測結果の件数\n", + "n_observations = int(1e4)\n", + "\n", + "# ブール特徴量 FalseまたはTrueとしてエンコードされている\n", + "feature0 = np.random.choice([False, True], n_observations)\n", + "\n", + "# 整数特徴量 -10000 から 10000 の間の乱数\n", + "feature1 = np.random.randint(0, 5, n_observations)\n", + "\n", + "# バイト特徴量\n", + "strings = np.array([b'cat', b'dog', b'chicken', b'horse', b'goat'])\n", + "feature2 = strings[feature1]\n", + "\n", + "# 浮動小数点数特徴量 標準正規分布から発生\n", + "feature3 = np.random.randn(n_observations)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aGrscehJr7Jd" + }, + "source": [ + "これらの特徴量は、`_bytes_feature`, `_float_feature`, `_int64_feature`のいずれかを使って`tf.Example`互換の型に強制変換されます。その後、エンコード済みの特徴量から`tf.Example`メッセージを作成できます。" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "RTCS49Ij_kUw" + }, + "outputs": [], + "source": [ + "def serialize_example(feature0, feature1, feature2, feature3):\n", + " \"\"\"\n", + " Creates a tf.Example message ready to be written to a file.\n", + " ファイル出力可能なtf.Exampleメッセージを作成する\n", + " \"\"\"\n", + "\n", + " # 特徴量名とtf.Example互換データ型との対応ディクショナリを作成\n", + "\n", + " feature = {\n", + " 'feature0': _int64_feature(feature0),\n", + " 'feature1': _int64_feature(feature1),\n", + " 'feature2': _bytes_feature(feature2),\n", + " 'feature3': _float_feature(feature3),\n", + " }\n", + "\n", + " # tf.train.Exampleを用いて特徴メッセージを作成\n", + "\n", + " example_proto = tf.train.Example(features=tf.train.Features(feature=feature))\n", + " return example_proto.SerializeToString()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XftzX9CN_uGT" + }, + "source": [ + "例えば、データセットに`[False, 4, bytes('goat'), 0.9876]`という1つの観測記録があるとします。`create_message()`を使うとこの観測記録から`tf.Example`メッセージを作成し印字できます。上記のように、観測記録一つ一つが`Features`メッセージとして書かれています。`tf.Example` [メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88)は、この`Features` メッセージを包むラッパーに過ぎないことに注意してください。" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "N8BtSx2RjYcb" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "b'\\nR\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04[\\xd3|?\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x04\\n\\x14\\n\\x08feature2\\x12\\x08\\n\\x06\\n\\x04goat'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# データセットからの観測記録の例\n", + "\n", + "example_observation = []\n", + "\n", + "serialized_example = serialize_example(False, 4, b'goat', 0.9876)\n", + "serialized_example" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_pbGATlG6u-4" + }, + "source": [ + "メッセージをデコードするには、`tf.train.Example.FromString`メソッドを使用します。" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "dGim-mEm6vit" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "features {\n", + " feature {\n", + " key: \"feature0\"\n", + " value {\n", + " int64_list {\n", + " value: 0\n", + " }\n", + " }\n", + " }\n", + " feature {\n", + " key: \"feature1\"\n", + " value {\n", + " int64_list {\n", + " value: 4\n", + " }\n", + " }\n", + " }\n", + " feature {\n", + " key: \"feature2\"\n", + " value {\n", + " bytes_list {\n", + " value: \"goat\"\n", + " }\n", + " }\n", + " }\n", + " feature {\n", + " key: \"feature3\"\n", + " value {\n", + " float_list {\n", + " value: 0.9876000285148621\n", + " }\n", + " }\n", + " }\n", + "}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example_proto = tf.train.Example.FromString(serialized_example)\n", + "example_proto" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "y-Hjmee-fbLH" + }, + "source": [ + "## `tf.data`を使用したTFRecordファイル" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GmehkCCT81Ez" + }, + "source": [ + "`tf.data`モジュールには、TensorFlowでデータを読み書きするツールが含まれます。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1FISEuz8ubu3" + }, + "source": [ + "### TFRecordファイルの書き出し\n", + "\n", + "データをデータセットにする最も簡単な方法は`from_tensor_slices`メソッドです。\n", + "\n", + "配列に適用すると、このメソッドはスカラー値のデータセットを返します。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "mXeaukvwu5_-" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.data.Dataset.from_tensor_slices(feature1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "f-q0VKyZvcad" + }, + "source": [ + "配列のタプルに適用すると、タプルのデータセットが返されます。" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "H5sWyu1kxnvg" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features_dataset = tf.data.Dataset.from_tensor_slices((feature0, feature1, feature2, feature3))\n", + "features_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "m1C-t71Nywze" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py:532: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n", + "tf.Tensor(False, shape=(), dtype=bool)\n", + "tf.Tensor(4, shape=(), dtype=int64)\n", + "tf.Tensor(b'goat', shape=(), dtype=string)\n", + "tf.Tensor(-0.2768728503385437, shape=(), dtype=float64)\n" + ] + } + ], + "source": [ + "# データセットから1つのサンプルだけを取り出すには`take(1)` を使います。\n", + "for f0,f1,f2,f3 in features_dataset.take(1):\n", + " print(f0)\n", + " print(f1)\n", + " print(f2)\n", + " print(f3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mhIe63awyZYd" + }, + "source": [ + "`Dataset`のそれぞれの要素に関数を適用するには、`tf.data.Dataset.map`メソッドを使用します。\n", + "\n", + "マップされる関数はTensorFlowのグラフモードで動作する必要があります。関数は`tf.Tensors`を処理し、返す必要があります。`create_example`のような非テンソル関数は、互換性のため`tf.py_func`でラップすることができます。\n", + "\n", + "`tf.py_func`を使用する際には、シェイプと型は取得できないため、指定する必要があります。" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "apB5KYrJzjPI" + }, + "outputs": [], + "source": [ + "def tf_serialize_example(f0,f1,f2,f3):\n", + " tf_string = tf.py_func(\n", + " serialize_example, \n", + " (f0,f1,f2,f3), # pass these args to the above function.\n", + " tf.string) # the return type is `tf.string`.\n", + " return tf.reshape(tf_string, ()) # The result is a scalar" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CrFZ9avE3HUF" + }, + "source": [ + "この関数をデータセットのそれぞれの要素に適用します。" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VDeqYVbW3ww9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :5: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "tf.py_func is deprecated in TF V2. Instead, use\n", + " tf.py_function, which takes a python function which manipulates tf eager\n", + " tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n", + " an ndarray (just call tensor.numpy()) but having access to eager tensors\n", + " means `tf.py_function`s can use accelerators such as GPUs as well as\n", + " being differentiable using a gradient tape.\n", + " \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "serialized_features_dataset = features_dataset.map(tf_serialize_example)\n", + "serialized_features_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "p6lw5VYpjZZC" + }, + "source": [ + "TFRecordファイルに書き出します。" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vP1VgTO44UIE" + }, + "outputs": [], + "source": [ + "filename = 'test.tfrecord'\n", + "writer = tf.data.experimental.TFRecordWriter(filename)\n", + "writer.write(serialized_features_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6aV0GQhV8tmp" + }, + "source": [ + "### TFRecordファイルの読み込み" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "o3J5D4gcSy8N" + }, + "source": [ + "`tf.data.TFRecordDataset`クラスを使ってTFRecordファイルを読み込むこともできます。\n", + "\n", + "`tf.data`を使ってTFRecordファイルを取り扱う際の詳細については、[こちら](https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data)を参照ください。 \n", + "\n", + "`TFRecordDataset`を使うことは、入力データを標準化し、パフォーマンスを最適化するのに有用です。" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "6OjX6UZl-bHC" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filenames = [filename]\n", + "raw_dataset = tf.data.TFRecordDataset(filenames)\n", + "raw_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6_EQ9i2E_-Fz" + }, + "source": [ + "この時点で、データセットにはシリアライズされた`tf.train.Example`メッセージが含まれています。データセットをイテレートすると、スカラーの文字列テンソルが返ってきます。\n", + "\n", + "`.take`メソッドを使って最初の10レコードだけを表示します。\n", + "\n", + "注：`tf.data.Dataset`をイテレートできるのは、Eager Executionが有効になっている場合のみです。" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "hxVXpLz_AJlm" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x03'>\n", + "\n", + "\n", + "\n", + "'>\n", + "'>\n", + "\\xbf'>\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for raw_record in raw_dataset.take(10):\n", + " print(repr(raw_record))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W-6oNzM4luFQ" + }, + "source": [ + "これらのテンソルは下記の関数でパースできます。\n", + "\n", + "注：ここでは、`feature_description`が必要です。データセットはグラフ実行を使用するため、この記述を使ってシェイプと型を構築するのです。" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "zQjbIR1nleiy" + }, + "outputs": [], + "source": [ + "# 特徴の記述\n", + "feature_description = {\n", + " 'feature0': tf.FixedLenFeature([], tf.int64, default_value=0),\n", + " 'feature1': tf.FixedLenFeature([], tf.int64, default_value=0),\n", + " 'feature2': tf.FixedLenFeature([], tf.string, default_value=''),\n", + " 'feature3': tf.FixedLenFeature([], tf.float32, default_value=0.0),\n", + "}\n", + "\n", + "def _parse_function(example_proto):\n", + " # 上記の記述を使って入力のtf.Exampleを処理\n", + " return tf.parse_single_example(example_proto, feature_description)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "gWETjUqhEQZf" + }, + "source": [ + "あるいは、`tf.parse example`を使ってバッチ全体を一度にパースします。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AH73hav6Bnmg" + }, + "source": [ + "`tf.data.Dataset.map`メソッドを使って、データセットの各アイテムにこの関数を適用します。" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "6Ob7D-zmBm1w" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parsed_dataset = raw_dataset.map(_parse_function)\n", + "parsed_dataset " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "sNV-XclGnOvn" + }, + "source": [ + "Eager Execution を使ってデータセット中の観測記録を表示します。このデータセットには10,000件の観測記録がありますが、最初の10個だけ表示します。 \n", + "データは特徴量のディクショナリの形で表示されます。それぞれの項目は`tf.Tensor`であり、このテンソルの`numpy` 要素は特徴量を表します。" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x2LT2JCqhoD_" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for parsed_record in parsed_dataset.take(10):\n", + " print(repr(raw_record))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Cig9EodTlDmg" + }, + "source": [ + "ここでは、`tf.parse_example` が`tf.Example`のフィールドを通常のテンソルに展開しています。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jyg1g3gU7DNn" + }, + "source": [ + "## tf.python_ioを使ったTFRecordファイル" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3FXG3miA7Kf1" + }, + "source": [ + "`tf.python_io`モジュールには、TFRecordファイルの読み書きのための純粋なPython関数も含まれています。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CKn5uql2lAaN" + }, + "source": [ + "### TFRecordファイルの書き出し" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LNW_FA-GQWXs" + }, + "source": [ + "次にこの10,000件の観測記録を`test.tfrecords`ファイルに出力します。観測記録はそれぞれ`tf.Example`メッセージに変換され、ファイルに出力されます。その後、`test.tfrecords`ファイルが作成されたことを確認することができます。" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "MKPHzoGv7q44" + }, + "outputs": [], + "source": [ + "# `tf.Example`観測記録をファイルに出力\n", + "with tf.python_io.TFRecordWriter(filename) as writer:\n", + " for i in range(n_observations):\n", + " example = serialize_example(feature0[i], feature1[i], feature2[i], feature3[i])\n", + " writer.write(example)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EjdFHHJMpUUo" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "images.ipynb images.tfrecords test.tfrecord tf-records.ipynb\r\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wtQ7k0YWQ1cz" + }, + "source": [ + "### TFRecordファイルの読み込み" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "utkozytkQ-2K" + }, + "source": [ + "モデルに入力にするため、このデータを読み込みたいとしましょう。\n", + "\n", + "次の例では、データをそのまま、`tf.Example`メッセージとしてインポートします。これは、ファイルが期待されるデータを含んでいるかを確認するのに役に立ちます。これは、また、入力データがTFRecordとして保存されているが、[この](https://www.tensorflow.org/guide/datasets#consuming_numpy_arrays)例のようにNumPyデータ（またはそれ以外のデータ型）として入力したい場合に有用です。このコーディング例では値そのものを読み取れるからです。\n", + "\n", + "入力ファイルの中のTFRecordをイテレートして、`tf.Example`メッセージを取り出し、その中の値を読み取って保存できます。" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "36ltP9B8OezA" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :1: 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", + "features {\n", + " feature {\n", + " key: \"feature0\"\n", + " value {\n", + " int64_list {\n", + " value: 0\n", + " }\n", + " }\n", + " }\n", + " feature {\n", + " key: \"feature1\"\n", + " value {\n", + " int64_list {\n", + " value: 4\n", + " }\n", + " }\n", + " }\n", + " feature {\n", + " key: \"feature2\"\n", + " value {\n", + " bytes_list {\n", + " value: \"goat\"\n", + " }\n", + " }\n", + " }\n", + " feature {\n", + " key: \"feature3\"\n", + " value {\n", + " float_list {\n", + " value: -0.276872843503952\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "record_iterator = tf.python_io.tf_record_iterator(path=filename)\n", + "\n", + "for string_record in record_iterator:\n", + " example = tf.train.Example()\n", + " example.ParseFromString(string_record)\n", + " \n", + " print(example)\n", + " \n", + " # Exit after 1 iteration as this is purely demonstrative.\n", + " # 純粋にデモであるため、イテレーションの1回目で終了\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "i3uquiiGTZTK" + }, + "source": [ + "（上記で作成した`tf.Example`型の）`example`オブジェクトの特徴量は（他のプロトコルバッファメッセージと同様に）ゲッターを使ってアクセス可能です。`example.features`は`repeated feature`メッセージを返し、`feature`メッセージをを取得すると（Pythonのディクショナリとして保存された）特徴量の名前と特徴量の値のマッピングが得られます。" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "-UNzS7vsUBs0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'feature0': int64_list {\n", + " value: 0\n", + "}\n", + ", 'feature1': int64_list {\n", + " value: 4\n", + "}\n", + ", 'feature2': bytes_list {\n", + " value: \"goat\"\n", + "}\n", + ", 'feature3': float_list {\n", + " value: -0.276872843503952\n", + "}\n", + "}\n" + ] + } + ], + "source": [ + "print(dict(example.features.feature))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "u1M-WrbqUUVW" + }, + "source": [ + "このディクショナリから、指定した値をディクショナリとして得ることができます。" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "2yCBu70IUb2H" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "float_list {\n", + " value: -0.276872843503952\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "print(example.features.feature['feature3'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4dw6_OI9UiNZ" + }, + "source": [ + "次に、ゲッターを使って値にアクセスできます。" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "BdDYjDnDUlFe" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.276872843503952]\n" + ] + } + ], + "source": [ + "print(example.features.feature['feature3'].float_list.value)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "S0tFDrwdoj3q" + }, + "source": [ + "## ウォークスルー： 画像データの読み書き" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rjN2LFxFpcR9" + }, + "source": [ + "以下は、TFRecordを使って画像データを読み書きする方法の例です。この例の目的は、データ（この場合は画像）を入力し、そのデータをTFRecordファイルに書き込んで、再びそのファイルを読み込み、画像を表示するという手順を最初から最後まで示すことです。\n", + "\n", + "これは、例えば、同じ入力データセットを使って複数のモデルを構築するといった場合に役立ちます。画像データをそのまま保存する代わりに、TFRecord形式に前処理しておき、その後の処理やモデル構築に使用することができます。\n", + "\n", + "まずは、雪の中の猫の[画像](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg)と、ニューヨーク市にあるウイリアムズバーグ橋の [写真](https://upload.wikimedia.org/wikipedia/commons/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg)をダウンロードしましょう。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5Lk2qrKvN0yu" + }, + "source": [ + "### 画像の取得" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "3a0fmwg8lHdF" + }, + "outputs": [], + "source": [ + "cat_in_snow = tf.keras.utils.get_file('320px-Felis_catus-cat_on_snow.jpg', 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/Felis_catus-cat_on_snow.jpg/320px-Felis_catus-cat_on_snow.jpg')\n", + "williamsburg_bridge = tf.keras.utils.get_file('194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg','https://upload.wikimedia.org/wikipedia/commons/thumb/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "7aJJh7vENeE4" + }, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "source" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display.display(display.Image(filename=williamsburg_bridge))\n", + "display.display(display.HTML('source'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VSOgJSwoN5TQ" + }, + "source": [ + "### TFRecordファイルの書き出し" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Azx83ryQEU6T" + }, + "source": [ + "上記で行ったように、この特徴量を`tf.Example`と互換のデータ型にエンコードできます。この場合には、生の画像文字列を特徴として保存するだけではなく、縦、横のサイズにチャネル数、更に画像を保存する際に猫の画像と橋の画像を区別するための`label`特徴量を付け加えます。猫の画像には`0`を、橋の画像には`1`を使うことにしましょう。" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "kC4TS1ZEONHr" + }, + "outputs": [], + "source": [ + "image_labels = {\n", + " cat_in_snow : 0,\n", + " williamsburg_bridge : 1,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "c5njMSYNEhNZ" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "features {\n", + " feature {\n", + " key: \"depth\"\n", + " value {\n", + " int64_list {\n", + " value: 3\n", + " }\n", + " }\n", + " }\n", + " feature {\n", + " key: \"height\"\n", + " value {\n", + " int64_list {\n", + " value: 213\n", + " }\n", + "...\n" + ] + } + ], + "source": [ + "# 猫の画像を使った例\n", + "image_string = open(cat_in_snow, 'rb').read()\n", + "\n", + "label = image_labels[cat_in_snow]\n", + "\n", + "# 関連する特徴量のディクショナリを作成\n", + "def image_example(image_string, label):\n", + " image_shape = tf.image.decode_jpeg(image_string).shape\n", + "\n", + " feature = {\n", + " 'height': _int64_feature(image_shape[0]),\n", + " 'width': _int64_feature(image_shape[1]),\n", + " 'depth': _int64_feature(image_shape[2]),\n", + " 'label': _int64_feature(label),\n", + " 'image_raw': _bytes_feature(image_string),\n", + " }\n", + "\n", + " return tf.train.Example(features=tf.train.Features(feature=feature))\n", + "\n", + "for line in str(image_example(image_string, label)).split('\\n')[:15]:\n", + " print(line)\n", + "print('...')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2G_o3O9MN0Qx" + }, + "source": [ + "ご覧のように、すべての特徴量が`tf.Example`メッセージに保存されました。上記のコードを関数化し、このサンプルメッセージを`images.tfrecords`ファイルに書き込みます。" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "qcw06lQCOCZU" + }, + "outputs": [], + "source": [ + "# 生の画像をimages.tfrecordsファイルに書き出す\n", + "# まず、2つの画像をtf.Exampleメッセージに変換し、\n", + "# 次に.tfrecordsファイルに書き出す\n", + "with tf.python_io.TFRecordWriter('images.tfrecords') as writer:\n", + " for filename, label in image_labels.items():\n", + " image_string = open(filename, 'rb').read()\n", + " tf_example = image_example(image_string, label)\n", + " writer.write(tf_example.SerializeToString())" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "yJrTe6tHPCfs" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "images.ipynb images.tfrecords test.tfrecord tf-records.ipynb\r\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jJSsCkZLPH6K" + }, + "source": [ + "### TFRecordファイルの読み込み\n", + "\n", + "これで、`images.tfrecords`ファイルができました。このファイルの中のレコードをイテレートし、書き込んだものを読み出します。このユースケースでは、画像を復元するだけなので、必要なのは生画像の文字列だけです。上記のゲッター、すなわち、`example.features.feature['image_raw'].bytes_list.value[0]`を使って抽出することができます。猫と橋のどちらであるかを決めるため、ラベルも使用します。" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "M6Cnfd3cTKHN" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_image_dataset = tf.data.TFRecordDataset('images.tfrecords')\n", + "\n", + "# 特徴量を記述するディクショナリを作成\n", + "image_feature_description = {\n", + " 'height': tf.FixedLenFeature([], tf.int64),\n", + " 'width': tf.FixedLenFeature([], tf.int64),\n", + " 'depth': tf.FixedLenFeature([], tf.int64),\n", + " 'label': tf.FixedLenFeature([], tf.int64),\n", + " 'image_raw': tf.FixedLenFeature([], tf.string),\n", + "}\n", + "\n", + "def _parse_image_function(example_proto):\n", + " # 入力のtf.Exampleのプロトコルバッファを上記のディクショナリを使って解釈\n", + " return tf.parse_single_example(example_proto, image_feature_description)\n", + "\n", + "parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n", + "parsed_image_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "0PEEFPk4NEg1" + }, + "source": [ + "TFRecordファイルから画像を復元しましょう。" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "yZf8jOyEIjSF" + }, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for image_features in parsed_image_dataset:\n", + " image_raw = image_features['image_raw'].numpy()\n", + " display.display(display.Image(data=image_raw))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "pL--_KGdYoBz" + ], + "name": "tf-records.ipynb", + "private_outputs": true, + "provenance": [], + "toc_visible": true, + "version": "0.3.2" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "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": "3.6.5" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 819f9c7ee5c5ec447464371fa7ff669d846b0051 Mon Sep 17 00:00:00 2001 From: masa-ita Date: Wed, 3 Apr 2019 22:55:38 +0900 Subject: [PATCH 2/6] Changed after the review by ohtaman. --- site/ja/tutorials/load_data/images.ipynb | 4833 ++++++++-------------- 1 file changed, 1617 insertions(+), 3216 deletions(-) diff --git a/site/ja/tutorials/load_data/images.ipynb b/site/ja/tutorials/load_data/images.ipynb index dd176f8a9f1..65d23e50720 100644 --- a/site/ja/tutorials/load_data/images.ipynb +++ b/site/ja/tutorials/load_data/images.ipynb @@ -1,3254 +1,1655 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "mt9dL5dIir8X" - }, - "source": [ - "##### Copyright 2018 The TensorFlow Authors." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "cellView": "form", - "colab": {}, - "colab_type": "code", - "id": "ufPx7EiCiqgR" - }, - "outputs": [], - "source": [ - "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "ucMoYase6URl" - }, - "source": [ - "# tf.dataを使って画像をロードする" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "_Wwu5SXZmEkB" - }, - "source": [ - "\n", - " \n", - " \n", - " \n", - "
\n", - " View on TensorFlow.org\n", - " \n", - " Run in Google Colab\n", - " \n", - " View source on GitHub\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Oxw4WahM7DU9" - }, - "source": [ - "このチュートリアルでは、'tf.data'を使って画像データセットをロードする簡単な例を示します。\n", - "\n", - "このチュートリアルで使用するデータセットは、クラスごとに別々のディレクトリに別れた形で配布されています。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "hoQQiZDB6URn" - }, - "source": [ - "## 設定" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "QGXxBuPyKJw1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: tf-nightly in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (1.14.1.dev20190301)\n", - "Requirement already satisfied: keras-preprocessing>=1.0.5 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.0.5)\n", - "Requirement already satisfied: wheel>=0.26 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (0.32.2)\n", - "Requirement already satisfied: tf-estimator-nightly in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.14.0.dev2019022801)\n", - "Requirement already satisfied: astor>=0.6.0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (0.7.1)\n", - "Requirement already satisfied: absl-py>=0.7.0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (0.7.0)\n", - "Requirement already satisfied: numpy<2.0,>=1.14.5 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.16.2)\n", - "Requirement already satisfied: gast>=0.2.0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (0.2.0)\n", - "Requirement already satisfied: termcolor>=1.1.0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.1.0)\n", - "Requirement already satisfied: six>=1.10.0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.11.0)\n", - "Requirement already satisfied: protobuf>=3.6.1 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (3.6.1)\n", - "Requirement already satisfied: keras-applications>=1.0.6 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.0.6)\n", - "Requirement already satisfied: grpcio>=1.8.6 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.16.0)\n", - "Requirement already satisfied: tb-nightly<1.15.0a0,>=1.14.0a0 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (1.14.0a20190301)\n", - "Requirement already satisfied: google-pasta>=0.1.2 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tf-nightly) (0.1.4)\n", - "Requirement already satisfied: setuptools in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from protobuf>=3.6.1->tf-nightly) (39.0.1)\n", - "Requirement already satisfied: h5py in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from keras-applications>=1.0.6->tf-nightly) (2.8.0)\n", - "Requirement already satisfied: markdown>=2.6.8 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tb-nightly<1.15.0a0,>=1.14.0a0->tf-nightly) (3.0.1)\n", - "Requirement already satisfied: werkzeug>=0.11.15 in /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages (from tb-nightly<1.15.0a0,>=1.14.0a0->tf-nightly) (0.14.1)\n" - ] + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "images.ipynb", + "version": "0.3.2", + "provenance": [], + "private_outputs": true, + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } - ], - "source": [ - "!pip install tf-nightly" - ] }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "DHz3JONNEHlj" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.13.1'" + "cells": [ + { + "metadata": { + "colab_type": "text", + "id": "mt9dL5dIir8X" + }, + "cell_type": "markdown", + "source": [ + "##### Copyright 2018 The TensorFlow Authors." ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import tensorflow as tf\n", - "tf.enable_eager_execution()\n", - "tf.VERSION" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "KT6CcaqgQewg" - }, - "outputs": [], - "source": [ - "AUTOTUNE = tf.data.experimental.AUTOTUNE" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "rxndJHNC8YPM" - }, - "source": [ - "## データセットのダウンロードと検査" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "wO0InzL66URu" - }, - "source": [ - "### 画像の取得\n", - "\n", - "訓練を始める前に、ネットワークに認識すべき新しいクラスを教えるために画像のセットが必要です。最初に使うためのクリエイティブ・コモンズでライセンスされた花の画像のアーカイブを作成してあります。" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "rN-Pc6Zd6awg" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/masatoshi/.keras/datasets/flower_photos\n" - ] - } - ], - "source": [ - "import pathlib\n", - "data_root = tf.keras.utils.get_file('flower_photos','https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz', untar=True)\n", - "data_root = pathlib.Path(data_root)\n", - "print(data_root)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "rFkFK74oO--g" - }, - "source": [ - "218MBをダウンロードすると、花の画像のコピーが使えるようになっているはずです。" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "7onR_lWE7Njj" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/masatoshi/.keras/datasets/flower_photos/roses\n", - "/Users/masatoshi/.keras/datasets/flower_photos/sunflowers\n", - "/Users/masatoshi/.keras/datasets/flower_photos/daisy\n", - "/Users/masatoshi/.keras/datasets/flower_photos/dandelion\n", - "/Users/masatoshi/.keras/datasets/flower_photos/tulips\n", - "/Users/masatoshi/.keras/datasets/flower_photos/LICENSE.txt\n" - ] - } - ], - "source": [ - "for item in data_root.iterdir():\n", - " print(item)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "4yYX3ZRqGOuq" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "3670" + }, + { + "metadata": { + "cellView": "form", + "colab_type": "code", + "id": "ufPx7EiCiqgR", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "ucMoYase6URl" + }, + "cell_type": "markdown", + "source": [ + "# tf.dataを使って画像をロードする" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import random\n", - "all_image_paths = list(data_root.glob('*/*'))\n", - "all_image_paths = [str(path) for path in all_image_paths]\n", - "random.shuffle(all_image_paths)\n", - "\n", - "image_count = len(all_image_paths)\n", - "image_count" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "t_BbYnLjbltQ" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['/Users/masatoshi/.keras/datasets/flower_photos/dandelion/7179487220_56e4725195_m.jpg',\n", - " '/Users/masatoshi/.keras/datasets/flower_photos/dandelion/18282528206_7fb3166041.jpg',\n", - " '/Users/masatoshi/.keras/datasets/flower_photos/daisy/2713919471_301fcc941f.jpg',\n", - " '/Users/masatoshi/.keras/datasets/flower_photos/dandelion/9011235009_58c7b244c1_n.jpg',\n", - " '/Users/masatoshi/.keras/datasets/flower_photos/tulips/2232289392_9a79a0c5cb_n.jpg',\n", - 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" '/Users/masatoshi/.keras/datasets/flower_photos/tulips/8520488975_a50d377f91.jpg',\n", - " '/Users/masatoshi/.keras/datasets/flower_photos/sunflowers/1044296388_912143e1d4.jpg',\n", - " '/Users/masatoshi/.keras/datasets/flower_photos/sunflowers/18843967474_9cb552716b.jpg',\n", - " '/Users/masatoshi/.keras/datasets/flower_photos/dandelion/2389720627_8923180b19.jpg',\n", - " '/Users/masatoshi/.keras/datasets/flower_photos/tulips/8668974855_8389ecbdca_m.jpg',\n", - " ...]" + }, + { + "metadata": { + "colab_type": "text", + "id": "_Wwu5SXZmEkB" + }, + "cell_type": "markdown", + "source": [ + "\n", + " \n", + " \n", + " \n", + "
\n", + " View on TensorFlow.org\n", + " \n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
" ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_image_paths" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "vkM-IpB-6URx" - }, - "source": [ - "### 画像の検査\n", - "\n", - "扱っている画像について知るために、画像のいくつかを見てみましょう。" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "wNGateQJ6UR1" - }, - "outputs": [], - "source": [ - "attributions = (data_root/\"LICENSE.txt\").read_text(encoding=\"utf8\").splitlines()[4:]\n", - "attributions = [line.split(' CC-BY') for line in attributions]\n", - "attributions = dict(attributions)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "jgowG2xu88Io" - }, - "outputs": [], - "source": [ - "import IPython.display as display\n", - "\n", - "def caption_image(image_path):\n", - " image_rel = pathlib.Path(image_path).relative_to(data_root)\n", - " return \"Image (CC BY 2.0) \" + ' - '.join(attributions[str(image_rel)].split(' - ')[:-1])\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "YIjLi-nX0txI" - }, - "outputs": [ - { - "data": { - "image/jpeg": 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\n", - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "text", + "id": "Oxw4WahM7DU9" + }, + "cell_type": "markdown", + "source": [ + "このチュートリアルでは、'tf.data'を使って画像データセットをロードする簡単な例を示します。\n", + "\n", + "このチュートリアルで使用するデータセットは、クラスごとに別々のディレクトリに別れた形で配布されています。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "hoQQiZDB6URn" + }, + "cell_type": "markdown", + "source": [ + "## 設定" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Image (CC BY 2.0) by Norbert Reimer\n", - "\n" - ] + "metadata": { + "colab_type": "code", + "id": "DHz3JONNEHlj", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "tf.enable_eager_execution()\n", + "tf.__version__" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "KT6CcaqgQewg", + "colab": {} + }, + "cell_type": "code", + "source": [ + "AUTOTUNE = tf.data.experimental.AUTOTUNE" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "rxndJHNC8YPM" + }, + "cell_type": "markdown", + "source": [ + "## データセットのダウンロードと検査" + ] }, { - "data": { - "image/jpeg": 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\n", - "text/plain": [ - "" + "metadata": { + "colab_type": "text", + "id": "wO0InzL66URu" + }, + "cell_type": "markdown", + "source": [ + "### 画像の取得\n", + "\n", + "訓練を始める前に、ネットワークに認識すべき新しいクラスを教えるために画像のセットが必要です。最初に使うためのクリエイティブ・コモンズでライセンスされた花の画像のアーカイブを作成してあります。" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Image (CC BY 2.0) by elbfoto\n", - "\n" - ] + "metadata": { + "colab_type": "code", + "id": "rN-Pc6Zd6awg", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pathlib\n", + "data_root = tf.keras.utils.get_file('flower_photos','https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz', untar=True)\n", + "data_root = pathlib.Path(data_root)\n", + "print(data_root)" + ], + "execution_count": 0, + "outputs": [] }, { - "data": { - "image/jpeg": 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\n", - "text/plain": [ - "" + "metadata": { + "colab_type": "text", + "id": "rFkFK74oO--g" + }, + "cell_type": "markdown", + "source": [ + "218MBをダウンロードすると、花の画像のコピーが使えるようになっているはずです。" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Image (CC BY 2.0) by Marilena Marchese\n", - "\n" - ] - } - ], - "source": [ - "for n in range(3):\n", - " image_path = random.choice(all_image_paths)\n", - " display.display(display.Image(image_path))\n", - " print(caption_image(image_path))\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "OaNOr-co3WKk" - }, - "source": [ - "### 各画像のラベルの決定" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "-weOQpDw2Jnu" - }, - "source": [ - "ラベルを一覧してみます。" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "ssUZ7Qh96UR3" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']" + "metadata": { + "colab_type": "code", + "id": "7onR_lWE7Njj", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for item in data_root.iterdir():\n", + " print(item)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "4yYX3ZRqGOuq", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import random\n", + "all_image_paths = list(data_root.glob('*/*'))\n", + "all_image_paths = [str(path) for path in all_image_paths]\n", + "random.shuffle(all_image_paths)\n", + "\n", + "image_count = len(all_image_paths)\n", + "image_count" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "t_BbYnLjbltQ", + "colab": {} + }, + "cell_type": "code", + "source": [ + "all_image_paths" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "vkM-IpB-6URx" + }, + "cell_type": "markdown", + "source": [ + "### 画像の検査\n", + "\n", + "扱っている画像について知るために、画像のいくつかを見てみましょう。" ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "label_names = sorted(item.name for item in data_root.glob('*/') if item.is_dir())\n", - "label_names" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "9l_JEBql2OzS" - }, - "source": [ - "ラベルにインデックスを割り当てます。" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Y8pCV46CzPlp" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}" + }, + { + "metadata": { + "colab_type": "code", + "id": "wNGateQJ6UR1", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import os\n", + "attributions = (data_root/\"LICENSE.txt\").open(encoding='utf-8').readlines()[4:]\n", + "attributions = [line.split(' CC-BY') for line in attributions]\n", + "attributions = dict(attributions)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "jgowG2xu88Io", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import IPython.display as display\n", + "\n", + "def caption_image(image_path):\n", + " image_rel = pathlib.Path(image_path).relative_to(data_root)\n", + " return \"Image (CC BY 2.0) \" + ' - '.join(attributions[str(image_rel)].split(' - ')[:-1])\n", + " " + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "YIjLi-nX0txI", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for n in range(3):\n", + " image_path = random.choice(all_image_paths)\n", + " display.display(display.Image(image_path))\n", + " print(caption_image(image_path))\n", + " print()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "OaNOr-co3WKk" + }, + "cell_type": "markdown", + "source": [ + "### 各画像のラベルの決定" ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "label_to_index = dict((name, index) for index,name in enumerate(label_names))\n", - "label_to_index" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "VkXsHg162T9F" - }, - "source": [ - "ファイルとラベルのインデックスの一覧を作成します。" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "q62i1RBP4Q02" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "First 10 labels indices: [1, 1, 0, 1, 4, 4, 2, 0, 4, 1]\n" - ] - } - ], - "source": [ - "all_image_labels = [label_to_index[pathlib.Path(path).parent.name]\n", - " for path in all_image_paths]\n", - "\n", - "print(\"First 10 labels indices: \", all_image_labels[:10])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "i5L09icm9iph" - }, - "source": [ - "### イメージのロードと整形" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "SbqqRUS79ooq" - }, - "source": [ - "TensorFlowには画像をロードして処理するために必要なツールが備わっています。" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "jQZdySHvksOu" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/masatoshi/.keras/datasets/flower_photos/dandelion/7179487220_56e4725195_m.jpg'" + }, + { + "metadata": { + "colab_type": "text", + "id": "-weOQpDw2Jnu" + }, + "cell_type": "markdown", + "source": [ + "ラベルを一覧してみます。" ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "img_path = all_image_paths[0]\n", - "img_path" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "2t2h2XCcmK1Y" - }, - "source": [ - "これが生のデータです。" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "LJfkyC_Qkt7A" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "img_tensor = tf.image.decode_image(img_raw)\n", - "\n", - "print(img_tensor.shape)\n", - "print(img_tensor.dtype)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "3k-Of2Tfmbeq" - }, - "source": [ - "モデルに合わせてリサイズします。" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "XFpz-3_vlJgp" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(192, 192, 3)\n", - "0.0\n", - "1.0\n" - ] - } - ], - "source": [ - "img_final = tf.image.resize_images(img_tensor, [192, 192])\n", - "img_final = img_final/255.0\n", - "print(img_final.shape)\n", - "print(img_final.numpy().min())\n", - "print(img_final.numpy().max())\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "aCsAa4Psl4AQ" - }, - "source": [ - "このあと使用するために、簡単な関数にまとめます。" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "HmUiZJNU73vA" - }, - "outputs": [], - "source": [ - "def preprocess_image(image):\n", - " image = tf.image.decode_jpeg(image, channels=3)\n", - " image = tf.image.resize_images(image, [192, 192])\n", - " image /= 255.0 # normalize to [0,1] range\n", - "\n", - " return image" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "einETrJnO-em" - }, - "outputs": [], - "source": [ - "def load_and_preprocess_image(path):\n", - " image = tf.read_file(path)\n", - " return preprocess_image(image)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "3brWQcdtz78y" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "image_path = all_image_paths[0]\n", - "label = all_image_labels[0]\n", - "\n", - "plt.imshow(load_and_preprocess_image(img_path))\n", - "plt.grid(False)\n", - "plt.xlabel(caption_image(img_path))\n", - "plt.title(label_names[label].title())\n", - "print()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "n2TCr1TQ8pA3" - }, - "source": [ - "## `tf.data.Dataset`の構築" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "6H9Z5Mq63nSH" - }, - "source": [ - "### 画像のデータセット" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "GN-s04s-6Luq" - }, - "source": [ - "`tf.data.Dataset`を構築する最も簡単な方法は、`from_tensor_slices`メソッドを使うことです。\n", - "\n", - "文字列の配列をスライスすると、文字列のデータセットが出来上がります。" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "6oRPG3Jz3ie_" - }, - "outputs": [], - "source": [ - "path_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "uML4JeMmIAvO" - }, - "source": [ - "`output_shapes`と`output_types`という2つのフィールドが、データセット中の要素の中身を示しています。この場合には、バイナリ文字列というスカラーのセットです。" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "mIsNflFbIK34" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape: TensorShape([])\n", - "type: \n", - "\n", - "\n" - ] - } - ], - "source": [ - "print('shape: ', repr(path_ds.output_shapes))\n", - "print('type: ', path_ds.output_types)\n", - "print()\n", - "print(path_ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "ZjyGcM8OwBJ2" - }, - "source": [ - "`preprocess_image`をファイルパスのデータセットにマップすることで、画像を実行時にロードし整形する新しいデータセットを作成します。" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "D1iba6f4khu-" - }, - "outputs": [], - "source": [ - "image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "JLUPs2a-lEEJ" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py:532: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Colocations handled automatically by placer.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" + }, + { + "metadata": { + "colab_type": "code", + "id": "ssUZ7Qh96UR3", + "colab": {} + }, + "cell_type": "code", + "source": [ + "label_names = sorted(item.name for item in data_root.glob('*/') if item.is_dir())\n", + "label_names" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "9l_JEBql2OzS" + }, + "cell_type": "markdown", + "source": [ + "ラベルにインデックスを割り当てます。" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.figure(figsize=(8,8))\n", - "for n,image in enumerate(image_ds.take(4)):\n", - " plt.subplot(2,2,n+1)\n", - " plt.imshow(image)\n", - " plt.grid(False)\n", - " plt.xticks([])\n", - " plt.yticks([])\n", - " plt.xlabel(caption_image(all_image_paths[n]))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "P6FNqPbxkbdx" - }, - "source": [ - "### `(image, label)`のペアのデータセット" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "YgvrWLKG67-x" - }, - "source": [ - "同じ`from_tensor_slices`メソッドを使ってラベルのデータセットを作ることができます。" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "AgBsAiV06udj" - }, - "outputs": [], - "source": [ - "label_ds = tf.data.Dataset.from_tensor_slices(tf.cast(all_image_labels, tf.int64))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "HEsk5nN0vyeX" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dandelion\n", - "dandelion\n", - "daisy\n", - "dandelion\n", - "tulips\n", - "tulips\n", - "roses\n", - "daisy\n", - "tulips\n", - "dandelion\n" - ] - } - ], - "source": [ - "for label in label_ds.take(10):\n", - " print(label_names[label.numpy()])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "jHjgrEeTxyYz" - }, - "source": [ - "これらのデータセットは同じ順番なので、zipすることで`(image, label)`というペアのデータセットができます。" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "AOEWNMdQwsbN" - }, - "outputs": [], - "source": [ - "image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "yA2F09SJLMuM" - }, - "source": [ - "新しいデータセットの`shapes`と`types`は、それぞれのフィールドを示すシェイプと型のタプルです。" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "DuVYNinrLL-N" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "image shape: (192, 192, 3)\n", - "label shape: ()\n", - "types: (tf.float32, tf.int64)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "print('image shape: ', image_label_ds.output_shapes[0])\n", - "print('label shape: ', image_label_ds.output_shapes[1])\n", - "print('types: ', image_label_ds.output_types)\n", - "print()\n", - "print(image_label_ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "2WYMikoPWOQX" - }, - "source": [ - "注：`all_image_labels`や`all_image_paths`の配列がある場合、`tf.data.dataset.Dataset.zip`メソッドの代わりとなるのは、配列のペアをスライスすることです。" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "HOFwZI-2WhzV" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "code", + "id": "Y8pCV46CzPlp", + "colab": {} + }, + "cell_type": "code", + "source": [ + "label_to_index = dict((name, index) for index,name in enumerate(label_names))\n", + "label_to_index" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "VkXsHg162T9F" + }, + "cell_type": "markdown", + "source": [ + "ファイルとラベルのインデックスの一覧を作成します。" ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = tf.data.Dataset.from_tensor_slices((all_image_paths, all_image_labels))\n", - "\n", - "# The tuples are unpacked into the positional arguments of the mapped function\n", - "# タプルは解体され、マップ関数の位置引数に割り当てられます\n", - "def load_and_preprocess_from_path_label(path, label):\n", - " return load_and_preprocess_image(path), label\n", - "\n", - "image_label_ds = ds.map(load_and_preprocess_from_path_label)\n", - "image_label_ds" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "vYGCgJuR_9Qp" - }, - "source": [ - "### 基本的な訓練手法" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "wwZavzgsIytz" - }, - "source": [ - "このデータセットを使ってモデルの訓練を行うには、データが\n", - "\n", - "* よくシャッフルされ\n", - "* バッチ化され\n", - "* 限りなく繰り返され\n", - "* バッチが出来るだけ早く利用できる\n", - "\n", - "ことが必要です。\n", - "\n", - "これらの特性は`tf.data`APIを使えば簡単に付け加えることができます。" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "uZmZJx8ePw_5" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "code", + "id": "q62i1RBP4Q02", + "colab": {} + }, + "cell_type": "code", + "source": [ + "all_image_labels = [label_to_index[pathlib.Path(path).parent.name]\n", + " for path in all_image_paths]\n", + "\n", + "print(\"First 10 labels indices: \", all_image_labels[:10])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "i5L09icm9iph" + }, + "cell_type": "markdown", + "source": [ + "### 画像の読み込みと整形" ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "BATCH_SIZE = 32\n", - "\n", - "# シャッフルバッファのサイズをデータセットと同じに設定することで、データが完全にシャッフルされる\n", - "# ようにできます。\n", - "ds = image_label_ds.shuffle(buffer_size=image_count)\n", - "ds = ds.repeat()\n", - "ds = ds.batch(BATCH_SIZE)\n", - "# `prefetch`を使うことで、モデルの訓練中にバックグラウンドでデータセットがバッチを取得できます。\n", - "ds = ds.prefetch(buffer_size=AUTOTUNE)\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "6JsM-xHiFCuW" - }, - "source": [ - "注意すべきことがいくつかあります。\n", - "\n", - "1. 順番が重要です。\n", - "\n", - " * `.repeat`の前に`.shuffle`すると、エポックの境界を越えて要素がシャッフルされます。（他の要素がすべて出現する前に2回出現する要素があるかもしれません）\n", - " * `.batch`の後に`.shuffle`すると、バッチの順番がシャッフルされますが、要素がバッチを越えてシャッフルされることはありません。\n", - "\n", - "1. 完全なシャッフルのため、`buffer_size`をデータセットと同じサイズに設定します。データセットのサイズ未満の場合、値が大きいほど良くランダム化されますが、より多くのメモリーを使用します。\n", - "\n", - "1. シャッフルバッファがいっぱいになってから要素が取り出されます。そのため、大きな`buffer_size`が`Dataset`を使い始める際の遅延の原因になります。\n", - "\n", - "1. シャッフルされたデータセットは、シャッフルバッファが完全に空になるまでデータセットが終わりであることを伝えません。`.repeat`によって`Dataset`が再起動されると、シャッフルバッファが一杯になるまでもう一つの待ち時間が発生します。\n", - "\n", - "最後の問題は、`tf.data.Dataset.apply`メソッドを、融合された`tf.data.experimental.shuffle_and_repeat`関数を使うことで対処できます。" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Ocr6PybXNDoO" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "text", + "id": "SbqqRUS79ooq" + }, + "cell_type": "markdown", + "source": [ + "TensorFlowには画像を読み込んで処理するために必要なツールが備わっています。" ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = image_label_ds.apply(\n", - " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", - "ds = ds.batch(BATCH_SIZE)\n", - "ds = ds.prefetch(buffer_size=AUTOTUNE)\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "GBBZMSuAmQVL" - }, - "source": [ - "### データセットをモデルにつなぐ\n", - "\n", - "`tf.keras.applications`からMobileNet v2のコピーを取得します。\n", - "\n", - "これを簡単な転移学習のサンプルに使用します。\n", - "\n", - "MobileNetの重みを訓練不可に設定します。" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "KbJrXn9omO_g" - }, - "outputs": [], - "source": [ - "mobile_net = tf.keras.applications.MobileNetV2(input_shape=(192, 192, 3), include_top=False)\n", - "mobile_net.trainable=False" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Y7NVWiLF3Vbf" - }, - "source": [ - "このモデルは、入力が`[-1,1]`の範囲に正規化されていることを想定しています。\n", - "\n", - "```\n", - "help(keras_applications.mobilenet_v2.preprocess_input)\n", - "```\n", - "\n", - "
\n",
-    "...\n",
-    "This function applies the \"Inception\" preprocessing which converts\n",
-    "the RGB values from [0, 255] to [-1, 1] \n",
-    "...\n",
-    "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "CboYya6LmdQI" - }, - "source": [ - "このため、データをMobileNetモデルに渡す前に、入力を`[0,1]`の範囲から`[-1,1]`の範囲に変換する必要があります。" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "SNOkHUGv3FYq" - }, - "outputs": [], - "source": [ - "def change_range(image,label):\n", - " return 2*image-1, label\n", - "\n", - "keras_ds = ds.map(change_range)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "QDzZ3Nye5Rpv" - }, - "source": [ - "MobileNetは画像ごとに`6x6`の特徴量の空間を返します。\n", - "\n", - "バッチを１つ渡してみましょう。" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "OzAhGkEK6WuE" - }, - "outputs": [], - "source": [ - "# シャッフルバッファがいっぱいになるまで、データセットは何秒かかかります。\n", - "image_batch, label_batch = next(iter(keras_ds))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "LcFdiWpO5WbV" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(32, 6, 6, 1280)\n" - ] - } - ], - "source": [ - "feature_map_batch = mobile_net(image_batch)\n", - "print(feature_map_batch.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "vrbjEvaC5XmU" - }, - "source": [ - "MobileNetをラップしたモデルを作り、出力層である`tf.keras.layers.Dense`の前に、`tf.keras.layers.GlobalAveragePooling2D`で空間の軸に沿って平均値を求めます。" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "X0ooIU9fNjPJ" - }, - "outputs": [], - "source": [ - "model = tf.keras.Sequential([\n", - " mobile_net,\n", - " tf.keras.layers.GlobalAveragePooling2D(),\n", - " tf.keras.layers.Dense(len(label_names))])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "foQYUJs97V4V" - }, - "source": [ - "期待したとおりのシェイプの出力が得られます。" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "1nwYxvpj7ZEf" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "min logit: -3.2900493\n", - "max logit: 2.763081\n", - "\n", - "Shape: (32, 5)\n" - ] - } - ], - "source": [ - "logit_batch = model(image_batch).numpy()\n", - "\n", - "print(\"min logit:\", logit_batch.min())\n", - "print(\"max logit:\", logit_batch.max())\n", - "print()\n", - "\n", - "print(\"Shape:\", logit_batch.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "pFc4I_J2nNOJ" - }, - "source": [ - "訓練手法を記述するためにモデルをコンパイルします。" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "ZWGqLEWYRNvv" - }, - "outputs": [], - "source": [ - "model.compile(optimizer=tf.train.AdamOptimizer(), \n", - " loss=tf.keras.losses.sparse_categorical_crossentropy,\n", - " metrics=[\"accuracy\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "tF1mO6haBOSd" - }, - "source": [ - "訓練可能な変数は2つ、全結合層の`weights`と`bias`です。" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "pPQ5yqyKBJMm" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2" + }, + { + "metadata": { + "colab_type": "code", + "id": "jQZdySHvksOu", + "colab": {} + }, + "cell_type": "code", + "source": [ + "img_path = all_image_paths[0]\n", + "img_path" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "2t2h2XCcmK1Y" + }, + "cell_type": "markdown", + "source": [ + "以下は生のデータです。" ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(model.trainable_variables) " - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "kug5Wg66UJjl" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "mobilenetv2_1.00_192 (Model) (None, 6, 6, 1280) 2257984 \n", - "_________________________________________________________________\n", - "global_average_pooling2d (Gl (None, 1280) 0 \n", - "_________________________________________________________________\n", - "dense (Dense) (None, 5) 6405 \n", - "=================================================================\n", - "Total params: 2,264,389\n", - "Trainable params: 6,405\n", - "Non-trainable params: 2,257,984\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "f_glpYZ-nYC_" - }, - "source": [ - "モデルを訓練します。\n", - "\n", - "普通は、エポックごとの本当のステップ数を指定しますが、ここではデモの目的なので3ステップだけとします。" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "AnXPRNWoTypI" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "115.0" + }, + { + "metadata": { + "colab_type": "code", + "id": "LJfkyC_Qkt7A", + "colab": {} + }, + "cell_type": "code", + "source": [ + "img_raw = tf.read_file(img_path)\n", + "print(repr(img_raw)[:100]+\"...\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "opN8AVc8mSbz" + }, + "cell_type": "markdown", + "source": [ + "画像のテンソルにデコードします。" ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "steps_per_epoch=tf.ceil(len(all_image_paths)/BATCH_SIZE).numpy()\n", - "steps_per_epoch" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "q_8sabaaSGAp" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3/3 [==============================] - 30s 10s/step - loss: 8.3349 - acc: 0.2812\n" - ] - }, - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "code", + "id": "Tm0tdrlfk0Bb", + "colab": {} + }, + "cell_type": "code", + "source": [ + "img_tensor = tf.image.decode_image(img_raw)\n", + "\n", + "print(img_tensor.shape)\n", + "print(img_tensor.dtype)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "3k-Of2Tfmbeq" + }, + "cell_type": "markdown", + "source": [ + "モデルに合わせてリサイズします。" ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit(ds, epochs=1, steps_per_epoch=3)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "UMVnoBcG_NlQ" - }, - "source": [ - "## 性能\n", - "\n", - "注：このセクションでは性能の向上に役立ちそうな簡単なトリックをいくつか紹介します。詳しくは、[Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets)を参照してください。\n", - "\n", - "上記の単純なパイプラインは、エポックごとにそれぞれのファイルを一つずつ読み込みます。これは、CPUを使ったローカルでの訓練では問題になりませんが、GPUを使った訓練では十分ではなく、いかなる分散訓練でも使うべきではありません。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "oNmQqgGhLWie" - }, - "source": [ - "調査のため、まず、データセットの性能をチェックする簡単な関数を定義します。" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "_gFVe1rp_MYr" - }, - "outputs": [], - "source": [ - "import time\n", - "\n", - "def timeit(ds, batches=2*steps_per_epoch+1):\n", - " overall_start = time.time()\n", - " # タイマーをスタートする前に、パイプラインの初期化の（シャッフルバッファを埋める）ため、\n", - " # バッチを１つ取得します\n", - " it = iter(ds.take(batches+1))\n", - " next(it)\n", - "\n", - " start = time.time()\n", - " for i,(images,labels) in enumerate(it):\n", - " if i%10 == 0:\n", - " print('.',end='')\n", - " print()\n", - " end = time.time()\n", - "\n", - " duration = end-start\n", - " print(\"{} batches: {} s\".format(batches, duration))\n", - " print(\"{:0.5f} Images/s\".format(BATCH_SIZE*batches/duration))\n", - " print(\"Total time: {}s\".format(end-overall_start))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "TYiOr4vdLcNX" - }, - "source": [ - "現在のデータセットの性能は次のとおりです。" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "ZDxLwMJOReVe" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "code", + "id": "XFpz-3_vlJgp", + "colab": {} + }, + "cell_type": "code", + "source": [ + "img_final = tf.image.resize_images(img_tensor, [192, 192])\n", + "img_final = img_final/255.0\n", + "print(img_final.shape)\n", + "print(img_final.numpy().min())\n", + "print(img_final.numpy().max())\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "aCsAa4Psl4AQ" + }, + "cell_type": "markdown", + "source": [ + "このあと使用するために、簡単な関数にまとめます。" ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = image_label_ds.apply(\n", - " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", - "ds = ds.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "IjouTJadRxyp" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................\n", - "231.0 batches: 25.843446016311646 s\n", - "286.02997 Images/s\n", - "Total time: 43.207932233810425s\n" - ] - } - ], - "source": [ - "timeit(ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "HsLlXMO7EWBR" - }, - "source": [ - "### キャッシュ" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "lV1NOn2zE2lR" - }, - "source": [ - "`tf.data.Dataset.cache`を使うと、エポックを越えて計算結果を簡単にキャッシュできます。特に、データがメモリに収まるときには効果的です。\n", - "\n", - "ここでは、画像が前処理（デコードとリサイズ）された後でキャッシュされます。" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "qj_U09xpDvOg" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "code", + "id": "HmUiZJNU73vA", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def preprocess_image(image):\n", + " image = tf.image.decode_jpeg(image, channels=3)\n", + " image = tf.image.resize_images(image, [192, 192])\n", + " image /= 255.0 # normalize to [0,1] range\n", + "\n", + " return image" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "einETrJnO-em", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def load_and_preprocess_image(path):\n", + " image = tf.read_file(path)\n", + " return preprocess_image(image)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "3brWQcdtz78y", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "image_path = all_image_paths[0]\n", + "label = all_image_labels[0]\n", + "\n", + "plt.imshow(load_and_preprocess_image(img_path))\n", + "plt.grid(False)\n", + "plt.xlabel(caption_image(img_path))\n", + "plt.title(label_names[label].title())\n", + "print()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "n2TCr1TQ8pA3" + }, + "cell_type": "markdown", + "source": [ + "## `tf.data.Dataset`の構築" ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = image_label_ds.cache()\n", - "ds = ds.apply(\n", - " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", - "ds = ds.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "rdxpvQ7VEo3y" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................\n", - "231.0 batches: 1.0587589740753174 s\n", - "6981.75900 Images/s\n", - "Total time: 14.936384201049805s\n" - ] - } - ], - "source": [ - "timeit(ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "usIv7MqqZQps" - }, - "source": [ - "メモリキャッシュを使う際の欠点のひとつは、実行の都度キャッシュを再構築しなければならないことです。このため、データセットがスタートするたびに同じだけ起動のための遅延が発生します。" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "eKX6ergKb_xd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................\n", - "231.0 batches: 1.0269150733947754 s\n", - "7198.25835 Images/s\n", - "Total time: 15.162395000457764s\n" - ] - } - ], - "source": [ - "timeit(ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "jUzpG4lYNkN-" - }, - "source": [ - "データがメモリに収まらない場合には、キャッシュファイルを使用します。" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "vIvF8K4GMq0g" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "text", + "id": "6H9Z5Mq63nSH" + }, + "cell_type": "markdown", + "source": [ + "### 画像のデータセット" ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = image_label_ds.cache(filename='./cache.tf-data')\n", - "ds = ds.apply(\n", - " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", - "ds = ds.batch(BATCH_SIZE).prefetch(1)\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "eTIj6IOmM4yA" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................\n", - "231.0 batches: 12.766232967376709 s\n", - "579.02750 Images/s\n", - "Total time: 33.048365116119385s\n" - ] - } - ], - "source": [ - "timeit(ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "qqo3dyB0Z4t2" - }, - "source": [ - "キャッシュファイルには、キャッシュを再構築することなくデータセットを再起動できるという利点もあります。2回めがどれほど早いか見てみましょう。" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "hZhVdR8MbaUj" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................\n", - "231.0 batches: 9.893965005874634 s\n", - "747.12211 Images/s\n", - "Total time: 14.534404039382935s\n" - ] - } - ], - "source": [ - "timeit(ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "WqOVlf8tFrDU" - }, - "source": [ - "### TFRecord ファイル" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "y1llOTwWFzmR" - }, - "source": [ - "#### 生の画像データ\n", - "\n", - "TFRecordファイルは、バイナリの大きなオブジェクトのシーケンスを保存するための単純なフォーマットです。複数のサンプルを同じファイルに詰め込むことで、TensorFlowは複数のサンプルを一度に読み込むことができます。これは、特にGCSのようなリモートストレージサービスを使用する際の性能にとって重要です。\n", - "\n", - "最初に、生の画像データからTFRecordファイルを構築します。" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "EqtARqKuHQLu" - }, - "outputs": [], - "source": [ - "image_ds = tf.data.Dataset.from_tensor_slices(all_image_paths).map(tf.read_file)\n", - "tfrec = tf.data.experimental.TFRecordWriter('images.tfrec')\n", - "tfrec.write(image_ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "flR2GXWFKcO1" - }, - "source": [ - "次に、TFRecordファイルを読み込み、以前定義した`preprocess_image`関数を使って画像のデコード/リフォーマットを行うデータセットを構築します。" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "j9PVUL2SFufn" - }, - "outputs": [], - "source": [ - "image_ds = tf.data.TFRecordDataset('images.tfrec').map(preprocess_image)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "cRp1eZDRKzyN" - }, - "source": [ - "これを、前に定義済みのラベルデータセットとzipし、予定される`(image,label)`のペアを得ます。" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "7XI_nDU2KuhS" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "text", + "id": "GN-s04s-6Luq" + }, + "cell_type": "markdown", + "source": [ + "`tf.data.Dataset`を構築する最も簡単な方法は、`from_tensor_slices`メソッドを使うことです。\n", + "\n", + "文字列の配列をスライスすると、文字列のデータセットが出来上がります。" ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = tf.data.Dataset.zip((image_ds, label_ds))\n", - "ds = ds.apply(\n", - " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", - "ds=ds.batch(BATCH_SIZE).prefetch(AUTOTUNE)\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "3ReSapoPK22E" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................\n", - "231.0 batches: 25.64651608467102 s\n", - "288.22628 Images/s\n", - "Total time: 38.50603103637695s\n" - ] - } - ], - "source": [ - "timeit(ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "wb7VyoKNOMms" - }, - "source": [ - "これは、`cache`バージョンよりも低速です。前処理をキャッシュしていないからです。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "NF9W-CTKkM-f" - }, - "source": [ - "#### シリアライズしたテンソル" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "J9HzljSPkxt0" - }, - "source": [ - "前処理をTFRecordファイルに保存するには、前やったように前処理した画像のデータセットを作ります。" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "OzS0Azukkjyw" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "code", + "id": "6oRPG3Jz3ie_", + "colab": {} + }, + "cell_type": "code", + "source": [ + "path_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "uML4JeMmIAvO" + }, + "cell_type": "markdown", + "source": [ + "`output_shapes`と`output_types`という2つのフィールドが、データセット中の要素の中身を示しています。この場合には、バイナリ文字列というスカラーのセットです。" ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "paths_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)\n", - "image_ds = paths_ds.map(load_and_preprocess_image)\n", - "image_ds" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "onWOwLpYlzJQ" - }, - "source": [ - "`.jpeg`文字列のデータセットではなく、これはテンソルのデータセットです。\n", - "\n", - "これをTFRecordファイルにシリアライズするには、まず、テンソルのデータセットを文字列のデータセットに変換します。" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "xxZSwnRllyf0" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "code", + "id": "mIsNflFbIK34", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print('shape: ', repr(path_ds.output_shapes))\n", + "print('type: ', path_ds.output_types)\n", + "print()\n", + "print(path_ds)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "ZjyGcM8OwBJ2" + }, + "cell_type": "markdown", + "source": [ + "`preprocess_image`をファイルパスのデータセットにマップすることで、画像を実行時にロードし整形する新しいデータセットを作成します。" ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = image_ds.map(tf.serialize_tensor)\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "w9N6hJWAkKPC" - }, - "outputs": [], - "source": [ - "tfrec = tf.data.experimental.TFRecordWriter('images.tfrec')\n", - "tfrec.write(ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "OlFc9dJSmcx0" - }, - "source": [ - "前処理をキャッシュしたことにより、データはTFRecordファイルから非常に効率的にロードできます。テンソルを使用する前にデシリアライズすることを忘れないでください。" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "BsqFyTBFmSCZ" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "code", + "id": "D1iba6f4khu-", + "colab": {} + }, + "cell_type": "code", + "source": [ + "image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "JLUPs2a-lEEJ", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(8,8))\n", + "for n,image in enumerate(image_ds.take(4)):\n", + " plt.subplot(2,2,n+1)\n", + " plt.imshow(image)\n", + " plt.grid(False)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.xlabel(caption_image(all_image_paths[n]))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "P6FNqPbxkbdx" + }, + "cell_type": "markdown", + "source": [ + "### `(image, label)`のペアのデータセット" ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "RESTORE_TYPE = image_ds.output_types\n", - "RESTORE_SHAPE = image_ds.output_shapes\n", - "\n", - "ds = tf.data.TFRecordDataset('images.tfrec')\n", - "\n", - "def parse(x):\n", - " result = tf.parse_tensor(x, out_type=RESTORE_TYPE)\n", - " result = tf.reshape(result, RESTORE_SHAPE)\n", - " return result\n", - "\n", - "ds = ds.map(parse, num_parallel_calls=AUTOTUNE)\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "OPs_sLV9pQg5" - }, - "source": [ - "次にラベルを追加し、以前と同じような標準的な処理を適用します。" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "XYxBwaLYnGop" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" + }, + { + "metadata": { + "colab_type": "text", + "id": "YgvrWLKG67-x" + }, + "cell_type": "markdown", + "source": [ + "同じ`from_tensor_slices`メソッドを使ってラベルのデータセットを作ることができます。" ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = tf.data.Dataset.zip((ds, label_ds))\n", - "ds = ds.apply(\n", - " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", - "ds=ds.batch(BATCH_SIZE).prefetch(AUTOTUNE)\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "W8X6RmGan1-P" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................\n", - "231.0 batches: 9.515093088150024 s\n", - "776.87101 Images/s\n", - "Total time: 13.438390016555786s\n" - ] - } - ], - "source": [ - "timeit(ds)" - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "name": "images.ipynb", - "private_outputs": true, - "provenance": [], - "toc_visible": true, - "version": "0.3.2" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "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": "3.6.5" - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " + }, + { + "metadata": { + "colab_type": "code", + "id": "AgBsAiV06udj", + "colab": {} + }, + "cell_type": "code", + "source": [ + "label_ds = tf.data.Dataset.from_tensor_slices(tf.cast(all_image_labels, tf.int64))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "HEsk5nN0vyeX", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for label in label_ds.take(10):\n", + " print(label_names[label.numpy()])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "jHjgrEeTxyYz" + }, + "cell_type": "markdown", + "source": [ + "これらのデータセットは同じ順番なので、zipすることで`(image, label)`というペアのデータセットができます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "AOEWNMdQwsbN", + "colab": {} + }, + "cell_type": "code", + "source": [ + "image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "yA2F09SJLMuM" + }, + "cell_type": "markdown", + "source": [ + "新しいデータセットの`shapes`と`types`は、それぞれのフィールドを示すシェイプと型のタプルです。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "DuVYNinrLL-N", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print('image shape: ', image_label_ds.output_shapes[0])\n", + "print('label shape: ', image_label_ds.output_shapes[1])\n", + "print('types: ', image_label_ds.output_types)\n", + "print()\n", + "print(image_label_ds)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "2WYMikoPWOQX" + }, + "cell_type": "markdown", + "source": [ + "注：`all_image_labels`や`all_image_paths`のような配列がある場合、`tf.data.dataset.Dataset.zip`メソッドの代わりとなるのは、配列のペアをスライスすることです。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "HOFwZI-2WhzV", + "colab": {} + }, + "cell_type": "code", + "source": [ + "ds = tf.data.Dataset.from_tensor_slices((all_image_paths, all_image_labels))\n", + "\n", + "# The tuples are unpacked into the positional arguments of the mapped function\n", + "# タプルは展開され、マップ関数の位置引数に割り当てられます\n", + "def load_and_preprocess_from_path_label(path, label):\n", + " return load_and_preprocess_image(path), label\n", + "\n", + "image_label_ds = ds.map(load_and_preprocess_from_path_label)\n", + "image_label_ds" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "vYGCgJuR_9Qp" + }, + "cell_type": "markdown", + "source": [ + "### 基本的な訓練手法" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "wwZavzgsIytz" + }, + "cell_type": "markdown", + "source": [ + "このデータセットを使ってモデルの訓練を行うには、データが\n", + "\n", + "* よくシャッフルされ\n", + "* バッチ化され\n", + "* 限りなく繰り返され\n", + "* バッチが出来るだけ早く利用できる\n", + "\n", + "ことが必要です。\n", + "\n", + "これらの特性は`tf.data`APIを使えば簡単に付け加えることができます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "uZmZJx8ePw_5", + "colab": {} + }, + "cell_type": "code", + "source": [ + "BATCH_SIZE = 32\n", + "\n", + "# シャッフルバッファのサイズをデータセットと同じに設定することで、データが完全にシャッフルされる\n", + "# ようにできます。\n", + "ds = image_label_ds.shuffle(buffer_size=image_count)\n", + "ds = ds.repeat()\n", + "ds = ds.batch(BATCH_SIZE)\n", + "# `prefetch`を使うことで、モデルの訓練中にバックグラウンドでデータセットがバッチを取得できます。\n", + "ds = ds.prefetch(buffer_size=AUTOTUNE)\n", + "ds" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "6JsM-xHiFCuW" + }, + "cell_type": "markdown", + "source": [ + "注意すべきことがいくつかあります。\n", + "\n", + "1. 順番が重要です。\n", + "\n", + " * `.repeat`の前に`.shuffle`すると、エポックの境界を越えて要素がシャッフルされます。（他の要素がすべて出現する前に2回出現する要素があるかもしれません）\n", + " * `.batch`の後に`.shuffle`すると、バッチの順番がシャッフルされますが、要素がバッチを越えてシャッフルされることはありません。\n", + "\n", + "1. 完全なシャッフルのため、`buffer_size`をデータセットと同じサイズに設定しています。データセットのサイズ未満の場合、値が大きいほど良くランダム化されますが、より多くのメモリーを使用します。\n", + "\n", + "1. シャッフルバッファがいっぱいになってから要素が取り出されます。そのため、大きな`buffer_size`が`Dataset`を使い始める際の遅延の原因になります。\n", + "\n", + "1. シャッフルされたデータセットは、シャッフルバッファが完全に空になるまでデータセットが終わりであることを伝えません。`.repeat`によって`Dataset`が再起動されると、シャッフルバッファが一杯になるまでもう一つの待ち時間が発生します。\n", + "\n", + "最後の問題は、`tf.data.Dataset.apply`メソッドを、融合された`tf.data.experimental.shuffle_and_repeat`関数と組み合わせることで対処できます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ocr6PybXNDoO", + "colab": {} + }, + "cell_type": "code", + "source": [ + "ds = image_label_ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds = ds.batch(BATCH_SIZE)\n", + "ds = ds.prefetch(buffer_size=AUTOTUNE)\n", + "ds" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "GBBZMSuAmQVL" + }, + "cell_type": "markdown", + "source": [ + "### データセットをモデルにつなぐ\n", + "\n", + "`tf.keras.applications`からMobileNet v2のコピーを取得します。\n", + "\n", + "これを簡単な転移学習のサンプルに使用します。\n", + "\n", + "MobileNetの重みを訓練不可に設定します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "KbJrXn9omO_g", + "colab": {} + }, + "cell_type": "code", + "source": [ + "mobile_net = tf.keras.applications.MobileNetV2(input_shape=(192, 192, 3), include_top=False)\n", + "mobile_net.trainable=False" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "Y7NVWiLF3Vbf" + }, + "cell_type": "markdown", + "source": [ + "このモデルは、入力が`[-1,1]`の範囲に正規化されていることを想定しています。\n", + "\n", + "```\n", + "help(keras_applications.mobilenet_v2.preprocess_input)\n", + "```\n", + "\n", + "
\n",
+        "...\n",
+        "This function applies the \"Inception\" preprocessing which converts\n",
+        "the RGB values from [0, 255] to [-1, 1] \n",
+        "...\n",
+        "
" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "CboYya6LmdQI" + }, + "cell_type": "markdown", + "source": [ + "このため、データをMobileNetモデルに渡す前に、入力を`[0,1]`の範囲から`[-1,1]`の範囲に変換する必要があります。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "SNOkHUGv3FYq", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def change_range(image,label):\n", + " return 2*image-1, label\n", + "\n", + "keras_ds = ds.map(change_range)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "QDzZ3Nye5Rpv" + }, + "cell_type": "markdown", + "source": [ + "MobileNetは画像ごとに`6x6`の特徴量の空間を返します。\n", + "\n", + "バッチを１つ渡してみましょう。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "OzAhGkEK6WuE", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# シャッフルバッファがいっぱいになるまで、データセットは何秒かかかります。\n", + "image_batch, label_batch = next(iter(keras_ds))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "LcFdiWpO5WbV", + "colab": {} + }, + "cell_type": "code", + "source": [ + "feature_map_batch = mobile_net(image_batch)\n", + "print(feature_map_batch.shape)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "vrbjEvaC5XmU" + }, + "cell_type": "markdown", + "source": [ + "MobileNetをラップしたモデルを作り、出力層である`tf.keras.layers.Dense`の前に、`tf.keras.layers.GlobalAveragePooling2D`で空間の軸に沿って平均値を求めます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "X0ooIU9fNjPJ", + "colab": {} + }, + "cell_type": "code", + "source": [ + "model = tf.keras.Sequential([\n", + " mobile_net,\n", + " tf.keras.layers.GlobalAveragePooling2D(),\n", + " tf.keras.layers.Dense(len(label_names))])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "foQYUJs97V4V" + }, + "cell_type": "markdown", + "source": [ + "期待したとおりの形状の出力が得られます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "1nwYxvpj7ZEf", + "colab": {} + }, + "cell_type": "code", + "source": [ + "logit_batch = model(image_batch).numpy()\n", + "\n", + "print(\"min logit:\", logit_batch.min())\n", + "print(\"max logit:\", logit_batch.max())\n", + "print()\n", + "\n", + "print(\"Shape:\", logit_batch.shape)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "pFc4I_J2nNOJ" + }, + "cell_type": "markdown", + "source": [ + "訓練手法を記述するためにモデルをコンパイルします。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ZWGqLEWYRNvv", + "colab": {} + }, + "cell_type": "code", + "source": [ + "model.compile(optimizer=tf.train.AdamOptimizer(), \n", + " loss=tf.keras.losses.sparse_categorical_crossentropy,\n", + " metrics=[\"accuracy\"])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "tF1mO6haBOSd" + }, + "cell_type": "markdown", + "source": [ + "訓練可能な変数は2つ、全結合層の`weights`と`bias`です。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "pPQ5yqyKBJMm", + "colab": {} + }, + "cell_type": "code", + "source": [ + "len(model.trainable_variables) " + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "kug5Wg66UJjl", + "colab": {} + }, + "cell_type": "code", + "source": [ + "model.summary()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "f_glpYZ-nYC_" + }, + "cell_type": "markdown", + "source": [ + "モデルを訓練します。\n", + "\n", + "普通は、エポックごとの本当のステップ数を指定しますが、ここではデモの目的なので3ステップだけとします。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "AnXPRNWoTypI", + "colab": {} + }, + "cell_type": "code", + "source": [ + "steps_per_epoch=tf.ceil(len(all_image_paths)/BATCH_SIZE).numpy()\n", + "steps_per_epoch" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "q_8sabaaSGAp", + "colab": {} + }, + "cell_type": "code", + "source": [ + "model.fit(ds, epochs=1, steps_per_epoch=3)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "UMVnoBcG_NlQ" + }, + "cell_type": "markdown", + "source": [ + "## 性能\n", + "\n", + "注：このセクションでは性能の向上に役立ちそうな簡単なトリックをいくつか紹介します。詳しくは、[Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets)を参照してください。\n", + "\n", + "上記の単純なパイプラインは、エポックごとにそれぞれのファイルを一つずつ読み込みます。これは、CPUを使ったローカルでの訓練では問題になりませんが、GPUを使った訓練では十分ではなく、いかなる分散訓練でも使うべきではありません。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "oNmQqgGhLWie" + }, + "cell_type": "markdown", + "source": [ + "調査のため、まず、データセットの性能をチェックする簡単な関数を定義します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "_gFVe1rp_MYr", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import time\n", + "\n", + "def timeit(ds, batches=2*steps_per_epoch+1):\n", + " overall_start = time.time()\n", + " # タイマーをスタートする前に、パイプラインの初期化の（シャッフルバッファを埋める）ため、\n", + " # バッチを１つ取得します\n", + " it = iter(ds.take(batches+1))\n", + " next(it)\n", + "\n", + " start = time.time()\n", + " for i,(images,labels) in enumerate(it):\n", + " if i%10 == 0:\n", + " print('.',end='')\n", + " print()\n", + " end = time.time()\n", + "\n", + " duration = end-start\n", + " print(\"{} batches: {} s\".format(batches, duration))\n", + " print(\"{:0.5f} Images/s\".format(BATCH_SIZE*batches/duration))\n", + " print(\"Total time: {}s\".format(end-overall_start))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "TYiOr4vdLcNX" + }, + "cell_type": "markdown", + "source": [ + "現在のデータセットの性能は次のとおりです。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ZDxLwMJOReVe", + "colab": {} + }, + "cell_type": "code", + "source": [ + "ds = image_label_ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds = ds.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n", + "ds" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "IjouTJadRxyp", + "colab": {} + }, + "cell_type": "code", + "source": [ + "timeit(ds)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "HsLlXMO7EWBR" + }, + "cell_type": "markdown", + "source": [ + "### キャッシュ" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "lV1NOn2zE2lR" + }, + "cell_type": "markdown", + "source": [ + "`tf.data.Dataset.cache`を使うと、エポックを越えて計算結果を簡単にキャッシュできます。特に、データがメモリに収まるときには効果的です。\n", + "\n", + "ここでは、画像が前処理（デコードとリサイズ）された後でキャッシュされます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "qj_U09xpDvOg", + "colab": {} + }, + "cell_type": "code", + "source": [ + "ds = image_label_ds.cache()\n", + "ds = ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds = ds.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n", + "ds" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "rdxpvQ7VEo3y", + "colab": {} + }, + "cell_type": "code", + "source": [ + "timeit(ds)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "usIv7MqqZQps" + }, + "cell_type": "markdown", + "source": [ + "メモリキャッシュを使う際の欠点のひとつは、実行の都度キャッシュを再構築しなければならないことです。このため、データセットがスタートするたびに同じだけ起動のための遅延が発生します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "eKX6ergKb_xd", + "colab": {} + }, + "cell_type": "code", + "source": [ + "timeit(ds)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "jUzpG4lYNkN-" + }, + "cell_type": "markdown", + "source": [ + "データがメモリに収まらない場合には、キャッシュファイルを使用します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "vIvF8K4GMq0g", + "colab": {} + }, + "cell_type": "code", + "source": [ + "ds = image_label_ds.cache(filename='./cache.tf-data')\n", + "ds = ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds = ds.batch(BATCH_SIZE).prefetch(1)\n", + "ds" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "eTIj6IOmM4yA", + "colab": {} + }, + "cell_type": "code", + "source": [ + "timeit(ds)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "qqo3dyB0Z4t2" + }, + "cell_type": "markdown", + "source": [ + "キャッシュファイルには、キャッシュを再構築することなくデータセットを再起動できるという利点もあります。2回めがどれほど早いか見てみましょう。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "hZhVdR8MbaUj", + "colab": {} + }, + "cell_type": "code", + "source": [ + "timeit(ds)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "WqOVlf8tFrDU" + }, + "cell_type": "markdown", + "source": [ + "### TFRecord ファイル" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "y1llOTwWFzmR" + }, + "cell_type": "markdown", + "source": [ + "#### 生の画像データ\n", + "\n", + "TFRecordファイルは、バイナリの大きなオブジェクトのシーケンスを保存するための単純なフォーマットです。複数のサンプルを同じファイルに詰め込むことで、TensorFlowは複数のサンプルを一度に読み込むことができます。これは、特にGCSのようなリモートストレージサービスを使用する際の性能にとって重要です。\n", + "\n", + "最初に、生の画像データからTFRecordファイルを構築します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "EqtARqKuHQLu", + "colab": {} + }, + "cell_type": "code", + "source": [ + "image_ds = tf.data.Dataset.from_tensor_slices(all_image_paths).map(tf.read_file)\n", + "tfrec = tf.data.experimental.TFRecordWriter('images.tfrec')\n", + "tfrec.write(image_ds)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "flR2GXWFKcO1" + }, + "cell_type": "markdown", + "source": [ + "次に、TFRecordファイルを読み込み、以前定義した`preprocess_image`関数を使って画像のデコード/リフォーマットを行うデータセットを構築します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "j9PVUL2SFufn", + "colab": {} + }, + "cell_type": "code", + "source": [ + "image_ds = tf.data.TFRecordDataset('images.tfrec').map(preprocess_image)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "cRp1eZDRKzyN" + }, + "cell_type": "markdown", + "source": [ + "これを、前に定義済みのラベルデータセットとzipし、期待通りの`(image,label)`のペアを得ます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "7XI_nDU2KuhS", + "colab": {} + }, + "cell_type": "code", + "source": [ + "ds = tf.data.Dataset.zip((image_ds, label_ds))\n", + "ds = ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds=ds.batch(BATCH_SIZE).prefetch(AUTOTUNE)\n", + "ds" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "3ReSapoPK22E", + "colab": {} + }, + "cell_type": "code", + "source": [ + "timeit(ds)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "wb7VyoKNOMms" + }, + "cell_type": "markdown", + "source": [ + "これは、`cache`バージョンよりも低速です。前処理をキャッシュしていないからです。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "NF9W-CTKkM-f" + }, + "cell_type": "markdown", + "source": [ + "#### シリアライズしたテンソル" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "J9HzljSPkxt0" + }, + "cell_type": "markdown", + "source": [ + "前処理をTFRecordファイルに保存するには、前やったように前処理した画像のデータセットを作ります。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "OzS0Azukkjyw", + "colab": {} + }, + "cell_type": "code", + "source": [ + "paths_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)\n", + "image_ds = paths_ds.map(load_and_preprocess_image)\n", + "image_ds" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "onWOwLpYlzJQ" + }, + "cell_type": "markdown", + "source": [ + "`.jpeg`文字列のデータセットではなく、これはテンソルのデータセットです。\n", + "\n", + "これをTFRecordファイルにシリアライズするには、まず、テンソルのデータセットを文字列のデータセットに変換します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "xxZSwnRllyf0", + "colab": {} + }, + "cell_type": "code", + "source": [ + "ds = image_ds.map(tf.serialize_tensor)\n", + "ds" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "w9N6hJWAkKPC", + "colab": {} + }, + "cell_type": "code", + "source": [ + "tfrec = tf.data.experimental.TFRecordWriter('images.tfrec')\n", + "tfrec.write(ds)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "OlFc9dJSmcx0" + }, + "cell_type": "markdown", + "source": [ + "前処理をキャッシュしたことにより、データはTFRecordファイルから非常に効率的にロードできます。テンソルを使用する前にデシリアライズすることを忘れないでください。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "BsqFyTBFmSCZ", + "colab": {} + }, + "cell_type": "code", + "source": [ + "RESTORE_TYPE = image_ds.output_types\n", + "RESTORE_SHAPE = image_ds.output_shapes\n", + "\n", + "ds = tf.data.TFRecordDataset('images.tfrec')\n", + "\n", + "def parse(x):\n", + " result = tf.parse_tensor(x, out_type=RESTORE_TYPE)\n", + " result = tf.reshape(result, RESTORE_SHAPE)\n", + " return result\n", + "\n", + "ds = ds.map(parse, num_parallel_calls=AUTOTUNE)\n", + "ds" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "OPs_sLV9pQg5" + }, + "cell_type": "markdown", + "source": [ + "次にラベルを追加し、以前と同じような標準的な処理を適用します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XYxBwaLYnGop", + "colab": {} + }, + "cell_type": "code", + "source": [ + "ds = tf.data.Dataset.zip((ds, label_ds))\n", + "ds = ds.apply(\n", + " tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))\n", + "ds=ds.batch(BATCH_SIZE).prefetch(AUTOTUNE)\n", + "ds" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "W8X6RmGan1-P", + "colab": {} + }, + "cell_type": "code", + "source": [ + "timeit(ds)" + ], + "execution_count": 0, + "outputs": [] } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} + ] +} \ No newline at end of file From dd794ca8ce903bb9bcbef04972ebddc6646c5017 Mon Sep 17 00:00:00 2001 From: masa-ita Date: Fri, 5 Apr 2019 22:44:04 +0900 Subject: [PATCH 3/6] Changed after ohtaman's review. --- site/ja/tutorials/load_data/tf_records.ipynb | 1233 ++++++++++++++++++ 1 file changed, 1233 insertions(+) create mode 100644 site/ja/tutorials/load_data/tf_records.ipynb diff --git a/site/ja/tutorials/load_data/tf_records.ipynb b/site/ja/tutorials/load_data/tf_records.ipynb new file mode 100644 index 00000000000..2a5475ba6fa --- /dev/null +++ b/site/ja/tutorials/load_data/tf_records.ipynb @@ -0,0 +1,1233 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "tf-records.ipynb", + "version": "0.3.2", + "provenance": [], + "private_outputs": true, + "collapsed_sections": [ + "pL--_KGdYoBz" + ], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "cells": [ + { + "metadata": { + "colab_type": "text", + "id": "pL--_KGdYoBz" + }, + "cell_type": "markdown", + "source": [ + "##### Copyright 2018 The TensorFlow Authors." + ] + }, + { + "metadata": { + "cellView": "both", + "colab_type": "code", + "id": "uBDvXpYzYnGj", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "HQzaEQuJiW_d" + }, + "cell_type": "markdown", + "source": [ + "# TFRecords と `tf.Example` の使用法\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "
\n", + " View on TensorFlow.org\n", + " \n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "3pkUd_9IZCFO" + }, + "cell_type": "markdown", + "source": [ + "データの読み込みを効率的にするには、データをシリアライズし、連続的に読み込めるファイルのセット（各ファイルは100-200MB）に保存することが有効です。データをネットワーク経由で流そうとする場合には、特にそうです。また、データの前処理をキャッシュする際にも役立ちます。\n", + "\n", + "TFRecord形式は、バイナリレコードの系列を保存するための単純な形式です。\n", + "\n", + "[プロトコルバッファ](https://developers.google.com/protocol-buffers/) は、構造化データを効率的にシリアライズする、プラットフォームや言語に依存しないライブラリです。\n", + "\n", + "プロトコルメッセージは`.proto`という拡張子のファイルで定義されます。メッセージ型を識別する最も簡単な方法です。\n", + "\n", + "`tf.Example`メッセージ（あるいはプロトコルバッファ）は、`{\"string\": value}`形式のマッピングを表現する柔軟なメッセージ型です。これは、TensorFlow用に設計され、[TFX](https://www.tensorflow.org/tfx/)のような上位レベルのAPIで共通に使用されています。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Ac83J0QxjhFt" + }, + "cell_type": "markdown", + "source": [ + "このノートブックでは、`tf.Example`メッセージの作成、パースと使用法をデモし、その後、`tf.Example`メッセージをパースして、`.tfrecord`に書き出し、その後読み取る方法を示します。\n", + "\n", + "注：こうした構造は有用ですが必ずそうしなければならなというものではありません。[`tf.data`](https://www.tensorflow.org/guide/datasets) を使っていて、それでもなおデータの読み込みが訓練のボトルネックである場合でなければ、既存のコードをTFRecordsを使用するために変更する必要はありません。データセットの性能改善のヒントは、 [Data Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets)を参照ください。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "WkRreBf1eDVc" + }, + "cell_type": "markdown", + "source": [ + "## 設定" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ja7sezsmnXph", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import absolute_import\n", + "from __future__ import division\n", + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "tf.enable_eager_execution()\n", + "\n", + "import numpy as np\n", + "import IPython.display as display" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "e5Kq88ccUWQV" + }, + "cell_type": "markdown", + "source": [ + "## `tf.Example`" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "VrdQHgvNijTi" + }, + "cell_type": "markdown", + "source": [ + "### `tf.Example`用のデータ型" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "lZw57Qrn4CTE" + }, + "cell_type": "markdown", + "source": [ + "基本的には`tf.Example`は`{\"string\": tf.train.Feature}`というマッピングです。\n", + "\n", + "`tf.train.Feature`メッセージ型は次の3つの型のうち1つをとることができます（[.proto file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto)を参照）。一般的なデータ型の多くは、これらの型のいずれかに強制的に変換することができます。\n", + "\n", + "1. `tf.train.BytesList` (次の型のデータを扱うことが可能)\n", + " - `string`\n", + " - `byte` \n", + "1. `tf.train.FloatList` (次の型のデータを扱うことが可能)\n", + " - `float` (`float32`)\n", + " - `double` (`float64`) \n", + "1. `tf.train.Int64List` (次の型のデータを扱うことが可能)\n", + " - `bool`\n", + " - `enum`\n", + " - `int32`\n", + " - `uint32`\n", + " - `int64`\n", + " - `uint64`" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "_e3g9ExathXP" + }, + "cell_type": "markdown", + "source": [ + "通常のTensorFlowの型を`tf.Example`互換の `tf.train.Feature`に変換するには、次のショートカット関数を使うことができます。\n", + "\n", + "どの関数も、1個のスカラー値を入力とし、上記の3つの`list`型のうちの一つを含む`tf.train.Feature`を返します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mbsPOUpVtYxA", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# 下記の関数を使うと値を tf.Exampleと互換性の有る型に変換できる\n", + "\n", + "def _bytes_feature(value):\n", + " \"\"\"string / byte 型から byte_listを返す\"\"\"\n", + " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n", + "\n", + "def _float_feature(value):\n", + " \"\"\"float / double 型から float_listを返す\"\"\"\n", + " return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n", + "\n", + "def _int64_feature(value):\n", + " \"\"\"bool / enum / int / uint 型から Int64_listを返す\"\"\"\n", + " return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "Wst0v9O8hgzy" + }, + "cell_type": "markdown", + "source": [ + "注：単純化のため、このサンプルではスカラー値の入力のみを扱っています。スカラー値ではない特徴を扱う最も簡単な方法は、`tf.serialize_tensor`を使ってテンソルをバイナリ文字列に変換する方法です。TensorFlowでは文字列はスカラー値として扱います。バイナリ文字列をテンソルに戻すには、`tf.parse_tensor`を使用します。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "vsMbkkC8xxtB" + }, + "cell_type": "markdown", + "source": [ + "上記の関数の使用例を下記に示します。入力が様々な型であるのに対して、出力が標準化されていることに注目してください。入力が、強制変換できない型であった場合、例外が発生します。（例：`_int64_feature(1.0)`はエラーとなります。`1.0`が浮動小数点数であるためで、代わりに`_float_feature`関数を使用すべきです）" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "hZzyLGr0u73y", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(_bytes_feature(b'test_string'))\n", + "print(_bytes_feature(u'test_bytes'.encode('utf-8')))\n", + "\n", + "print(_float_feature(np.exp(1)))\n", + "\n", + "print(_int64_feature(True))\n", + "print(_int64_feature(1))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "nj1qpfQU5qmi" + }, + "cell_type": "markdown", + "source": [ + "メッセージはすべて`.SerializeToString` を使ってバイナリ文字列にシリアライズすることができます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "5afZkORT5pjm", + "colab": {} + }, + "cell_type": "code", + "source": [ + "feature = _float_feature(np.exp(1))\n", + "\n", + "feature.SerializeToString()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "laKnw9F3hL-W" + }, + "cell_type": "markdown", + "source": [ + "### `tf.Example` メッセージの作成" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "b_MEnhxchQPC" + }, + "cell_type": "markdown", + "source": [ + "既存のデータから`tf.Example`を作成したいとします。実際には、データセットの出処はどこでも良いのですが、1件の観測記録から`tf.Example`メッセージを作る手順は同じです。\n", + "\n", + "1. 観測記録それぞれにおいて、各値は上記の関数を使って3種類の互換性のある型をからなる`tf.train.Feature`に変換する必要があります。\n", + "\n", + "1. 次に、特徴の名前を表す文字列と、#1で作ったエンコード済みの特徴量を対応させたマップ（ディクショナリ）を作成します。\n", + "\n", + "1. #2で作成したマップを[特徴量メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)に変換します。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "4EgFQ2uHtchc" + }, + "cell_type": "markdown", + "source": [ + "このノートブックでは、NumPyを使ってデータセットを作成します。\n", + "\n", + "このデータセットには4つの特徴量があります。\n", + "- `False` または `True`を表す論理値。出現確率は等しいものとします。\n", + "- ランダムなバイト値。全体において一様であるとします。\n", + "- `[-10000, 10000)`の範囲から一様にサンプリングした整数値。\n", + "- 標準正規分布からサンプリングした浮動小数点数。\n", + "\n", + "サンプルは上記の分布から独立して同じ様に分布した10,000件の観測記録からなるものとします。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "CnrguFAy3YQv", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# データセットに含まれる観測結果の件数\n", + "n_observations = int(1e4)\n", + "\n", + "# ブール特徴量 FalseまたはTrueとしてエンコードされている\n", + "feature0 = np.random.choice([False, True], n_observations)\n", + "\n", + "# 整数特徴量 -10000 から 10000 の間の乱数\n", + "feature1 = np.random.randint(0, 5, n_observations)\n", + "\n", + "# バイト特徴量\n", + "strings = np.array([b'cat', b'dog', b'chicken', b'horse', b'goat'])\n", + "feature2 = strings[feature1]\n", + "\n", + "# 浮動小数点数特徴量 標準正規分布から発生\n", + "feature3 = np.random.randn(n_observations)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "aGrscehJr7Jd" + }, + "cell_type": "markdown", + "source": [ + "これらの特徴量は、`_bytes_feature`, `_float_feature`, `_int64_feature`のいずれかを使って`tf.Example`互換の型に強制変換されます。その後、エンコード済みの特徴量から`tf.Example`メッセージを作成できます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "RTCS49Ij_kUw", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def serialize_example(feature0, feature1, feature2, feature3):\n", + " \"\"\"\n", + " Creates a tf.Example message ready to be written to a file.\n", + " ファイル出力可能なtf.Exampleメッセージを作成する\n", + " \"\"\"\n", + "\n", + " # 特徴量名とtf.Example互換データ型との対応ディクショナリを作成\n", + "\n", + " feature = {\n", + " 'feature0': _int64_feature(feature0),\n", + " 'feature1': _int64_feature(feature1),\n", + " 'feature2': _bytes_feature(feature2),\n", + " 'feature3': _float_feature(feature3),\n", + " }\n", + "\n", + " # tf.train.Exampleを用いて特徴メッセージを作成\n", + "\n", + " example_proto = tf.train.Example(features=tf.train.Features(feature=feature))\n", + " return example_proto.SerializeToString()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "XftzX9CN_uGT" + }, + "cell_type": "markdown", + "source": [ + "例えば、データセットに`[False, 4, bytes('goat'), 0.9876]`という1つの観測記録があるとします。`create_message()`を使うとこの観測記録から`tf.Example`メッセージを作成し印字できます。上記のように、観測記録一つ一つが`Features`メッセージとして書かれています。`tf.Example` [メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88)は、この`Features` メッセージを包むラッパーに過ぎないことに注意してください。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "N8BtSx2RjYcb", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# データセットからの観測記録の例\n", + "\n", + "example_observation = []\n", + "\n", + "serialized_example = serialize_example(False, 4, b'goat', 0.9876)\n", + "serialized_example" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "_pbGATlG6u-4" + }, + "cell_type": "markdown", + "source": [ + "メッセージをデコードするには、`tf.train.Example.FromString`メソッドを使用します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "dGim-mEm6vit", + "colab": {} + }, + "cell_type": "code", + "source": [ + "example_proto = tf.train.Example.FromString(serialized_example)\n", + "example_proto" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "y-Hjmee-fbLH" + }, + "cell_type": "markdown", + "source": [ + "## `tf.data`を使用したTFRecordファイル" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "GmehkCCT81Ez" + }, + "cell_type": "markdown", + "source": [ + "`tf.data`モジュールには、TensorFlowでデータを読み書きするツールが含まれます。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "1FISEuz8ubu3" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの書き出し\n", + "\n", + "データをデータセットにする最も簡単な方法は`from_tensor_slices`メソッドです。\n", + "\n", + "配列に適用すると、このメソッドはスカラー値のデータセットを返します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mXeaukvwu5_-", + "colab": {} + }, + "cell_type": "code", + "source": [ + "tf.data.Dataset.from_tensor_slices(feature1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "f-q0VKyZvcad" + }, + "cell_type": "markdown", + "source": [ + "配列のタプルに適用すると、タプルのデータセットが返されます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "H5sWyu1kxnvg", + "colab": {} + }, + "cell_type": "code", + "source": [ + "features_dataset = tf.data.Dataset.from_tensor_slices((feature0, feature1, feature2, feature3))\n", + "features_dataset" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "m1C-t71Nywze", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# データセットから1つのサンプルだけを取り出すには`take(1)` を使います。\n", + "for f0,f1,f2,f3 in features_dataset.take(1):\n", + " print(f0)\n", + " print(f1)\n", + " print(f2)\n", + " print(f3)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "mhIe63awyZYd" + }, + "cell_type": "markdown", + "source": [ + "`Dataset`のそれぞれの要素に関数を適用するには、`tf.data.Dataset.map`メソッドを使用します。\n", + "\n", + "マップされる関数はTensorFlowのグラフモードで動作する必要があります。関数は`tf.Tensors`を処理し、返す必要があります。`create_example`のような非テンソル関数は、互換性のため`tf.py_func`でラップすることができます。\n", + "\n", + "`tf.py_func`を使用する際には、シェイプと型は取得できないため、指定する必要があります。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "apB5KYrJzjPI", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def tf_serialize_example(f0,f1,f2,f3):\n", + " tf_string = tf.py_func(\n", + " serialize_example, \n", + " (f0,f1,f2,f3), # pass these args to the above function.\n", + " tf.string) # the return type is `tf.string`.\n", + " return tf.reshape(tf_string, ()) # The result is a scalar" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "CrFZ9avE3HUF" + }, + "cell_type": "markdown", + "source": [ + "この関数をデータセットのそれぞれの要素に適用します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "VDeqYVbW3ww9", + "colab": {} + }, + "cell_type": "code", + "source": [ + "serialized_features_dataset = features_dataset.map(tf_serialize_example)\n", + "serialized_features_dataset" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "p6lw5VYpjZZC" + }, + "cell_type": "markdown", + "source": [ + "TFRecordファイルに書き出します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "vP1VgTO44UIE", + "colab": {} + }, + "cell_type": "code", + "source": [ + "filename = 'test.tfrecord'\n", + "writer = tf.data.experimental.TFRecordWriter(filename)\n", + "writer.write(serialized_features_dataset)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "6aV0GQhV8tmp" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの読み込み" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "o3J5D4gcSy8N" + }, + "cell_type": "markdown", + "source": [ + "`tf.data.TFRecordDataset`クラスを使ってTFRecordファイルを読み込むこともできます。\n", + "\n", + "`tf.data`を使ってTFRecordファイルを取り扱う際の詳細については、[こちら](https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data)を参照ください。 \n", + "\n", + "`TFRecordDataset`を使うことは、入力データを標準化し、パフォーマンスを最適化するのに有用です。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "6OjX6UZl-bHC", + "colab": {} + }, + "cell_type": "code", + "source": [ + "filenames = [filename]\n", + "raw_dataset = tf.data.TFRecordDataset(filenames)\n", + "raw_dataset" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "6_EQ9i2E_-Fz" + }, + "cell_type": "markdown", + "source": [ + "この時点で、データセットにはシリアライズされた`tf.train.Example`メッセージが含まれています。データセットをイテレートすると、スカラーの文字列テンソルが返ってきます。\n", + "\n", + "`.take`メソッドを使って最初の10レコードだけを表示します。\n", + "\n", + "注：`tf.data.Dataset`をイテレートできるのは、Eager Executionが有効になっている場合のみです。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "hxVXpLz_AJlm", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for raw_record in raw_dataset.take(10):\n", + " print(repr(raw_record))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "W-6oNzM4luFQ" + }, + "cell_type": "markdown", + "source": [ + "これらのテンソルは下記の関数でパースできます。\n", + "\n", + "注：ここでは、`feature_description`が必要です。データセットはグラフ実行を使用するため、この記述を使ってシェイプと型を構築するのです。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "zQjbIR1nleiy", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# 特徴の記述\n", + "feature_description = {\n", + " 'feature0': tf.FixedLenFeature([], tf.int64, default_value=0),\n", + " 'feature1': tf.FixedLenFeature([], tf.int64, default_value=0),\n", + " 'feature2': tf.FixedLenFeature([], tf.string, default_value=''),\n", + " 'feature3': tf.FixedLenFeature([], tf.float32, default_value=0.0),\n", + "}\n", + "\n", + "def _parse_function(example_proto):\n", + " # 上記の記述を使って入力のtf.Exampleを処理\n", + " return tf.parse_single_example(example_proto, feature_description)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "gWETjUqhEQZf" + }, + "cell_type": "markdown", + "source": [ + "あるいは、`tf.parse example`を使ってバッチ全体を一度にパースします。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "AH73hav6Bnmg" + }, + "cell_type": "markdown", + "source": [ + "`tf.data.Dataset.map`メソッドを使って、データセットの各アイテムにこの関数を適用します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "6Ob7D-zmBm1w", + "colab": {} + }, + "cell_type": "code", + "source": [ + "parsed_dataset = raw_dataset.map(_parse_function)\n", + "parsed_dataset " + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "sNV-XclGnOvn" + }, + "cell_type": "markdown", + "source": [ + "Eager Execution を使ってデータセット中の観測記録を表示します。このデータセットには10,000件の観測記録がありますが、最初の10個だけ表示します。 \n", + "データは特徴量のディクショナリの形で表示されます。それぞれの項目は`tf.Tensor`であり、このテンソルの`numpy` 要素は特徴量を表します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "x2LT2JCqhoD_", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for parsed_record in parsed_dataset.take(10):\n", + " print(repr(raw_record))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "Cig9EodTlDmg" + }, + "cell_type": "markdown", + "source": [ + "ここでは、`tf.parse_example` が`tf.Example`のフィールドを通常のテンソルに展開しています。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "jyg1g3gU7DNn" + }, + "cell_type": "markdown", + "source": [ + "## tf.python_ioを使ったTFRecordファイル" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "3FXG3miA7Kf1" + }, + "cell_type": "markdown", + "source": [ + "`tf.python_io`モジュールには、TFRecordファイルの読み書きのための純粋なPython関数も含まれています。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "CKn5uql2lAaN" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの書き出し" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "LNW_FA-GQWXs" + }, + "cell_type": "markdown", + "source": [ + "次にこの10,000件の観測記録を`test.tfrecords`ファイルに出力します。観測記録はそれぞれ`tf.Example`メッセージに変換され、ファイルに出力されます。その後、`test.tfrecords`ファイルが作成されたことを確認することができます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "MKPHzoGv7q44", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# `tf.Example`観測記録をファイルに出力\n", + "with tf.python_io.TFRecordWriter(filename) as writer:\n", + " for i in range(n_observations):\n", + " example = serialize_example(feature0[i], feature1[i], feature2[i], feature3[i])\n", + " writer.write(example)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "EjdFHHJMpUUo", + "colab": {} + }, + "cell_type": "code", + "source": [ + "!ls" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "wtQ7k0YWQ1cz" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの読み込み" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "utkozytkQ-2K" + }, + "cell_type": "markdown", + "source": [ + "モデルに入力にするため、このデータを読み込みたいとしましょう。\n", + "\n", + "次の例では、データをそのまま、`tf.Example`メッセージとしてインポートします。これは、ファイルが期待されるデータを含んでいるかを確認するのに役に立ちます。これは、また、入力データがTFRecordとして保存されているが、[この](https://www.tensorflow.org/guide/datasets#consuming_numpy_arrays)例のようにNumPyデータ（またはそれ以外のデータ型）として入力したい場合に有用です。このコーディング例では値そのものを読み取れるからです。\n", + "\n", + "入力ファイルの中のTFRecordをイテレートして、`tf.Example`メッセージを取り出し、その中の値を読み取って保存できます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "36ltP9B8OezA", + "colab": {} + }, + "cell_type": "code", + "source": [ + "record_iterator = tf.python_io.tf_record_iterator(path=filename)\n", + "\n", + "for string_record in record_iterator:\n", + " example = tf.train.Example()\n", + " example.ParseFromString(string_record)\n", + " \n", + " print(example)\n", + " \n", + " # Exit after 1 iteration as this is purely demonstrative.\n", + " # 純粋にデモであるため、イテレーションの1回目で終了\n", + " break" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "i3uquiiGTZTK" + }, + "cell_type": "markdown", + "source": [ + "（上記で作成した`tf.Example`型の）`example`オブジェクトの特徴量は（他のプロトコルバッファメッセージと同様に）ゲッターを使ってアクセス可能です。`example.features`は`repeated feature`メッセージを返し、`feature`メッセージをを取得すると（Pythonのディクショナリとして保存された）特徴量の名前と特徴量の値のマッピングが得られます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "-UNzS7vsUBs0", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(dict(example.features.feature))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "u1M-WrbqUUVW" + }, + "cell_type": "markdown", + "source": [ + "このディクショナリから、指定した値をディクショナリとして得ることができます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "2yCBu70IUb2H", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(example.features.feature['feature3'])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "4dw6_OI9UiNZ" + }, + "cell_type": "markdown", + "source": [ + "次に、ゲッターを使って値にアクセスできます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "BdDYjDnDUlFe", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(example.features.feature['feature3'].float_list.value)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "S0tFDrwdoj3q" + }, + "cell_type": "markdown", + "source": [ + "## ウォークスルー： 画像データの読み書き" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "rjN2LFxFpcR9" + }, + "cell_type": "markdown", + "source": [ + "以下は、TFRecordを使って画像データを読み書きする方法の例です。この例の目的は、データ（この場合は画像）を入力し、そのデータをTFRecordファイルに書き込んで、再びそのファイルを読み込み、画像を表示するという手順を最初から最後まで示すことです。\n", + "\n", + "これは、例えば、同じ入力データセットを使って複数のモデルを構築するといった場合に役立ちます。画像データをそのまま保存する代わりに、TFRecord形式に前処理しておき、その後の処理やモデル構築に使用することができます。\n", + "\n", + "まずは、雪の中の猫の[画像](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg)と、ニューヨーク市にあるウイリアムズバーグ橋の [写真](https://upload.wikimedia.org/wikipedia/commons/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg)をダウンロードしましょう。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "5Lk2qrKvN0yu" + }, + "cell_type": "markdown", + "source": [ + "### 画像の取得" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "3a0fmwg8lHdF", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cat_in_snow = tf.keras.utils.get_file('320px-Felis_catus-cat_on_snow.jpg', 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/Felis_catus-cat_on_snow.jpg/320px-Felis_catus-cat_on_snow.jpg')\n", + "williamsburg_bridge = tf.keras.utils.get_file('194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg','https://upload.wikimedia.org/wikipedia/commons/thumb/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "7aJJh7vENeE4", + "colab": {} + }, + "cell_type": "code", + "source": [ + "display.display(display.Image(filename=cat_in_snow))\n", + "display.display(display.HTML('Image cc-by: Von.grzanka'))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "KkW0uuhcXZqA", + "colab": {} + }, + "cell_type": "code", + "source": [ + "display.display(display.Image(filename=williamsburg_bridge))\n", + "display.display(display.HTML('source'))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "VSOgJSwoN5TQ" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの書き出し" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Azx83ryQEU6T" + }, + "cell_type": "markdown", + "source": [ + "上記で行ったように、この特徴量を`tf.Example`と互換のデータ型にエンコードできます。この場合には、生の画像文字列を特徴として保存するだけではなく、縦、横のサイズにチャネル数、更に画像を保存する際に猫の画像と橋の画像を区別するための`label`特徴量を付け加えます。猫の画像には`0`を、橋の画像には`1`を使うことにしましょう。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "kC4TS1ZEONHr", + "colab": {} + }, + "cell_type": "code", + "source": [ + "image_labels = {\n", + " cat_in_snow : 0,\n", + " williamsburg_bridge : 1,\n", + "}" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "c5njMSYNEhNZ", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# 猫の画像を使った例\n", + "image_string = open(cat_in_snow, 'rb').read()\n", + "\n", + "label = image_labels[cat_in_snow]\n", + "\n", + "# 関連する特徴量のディクショナリを作成\n", + "def image_example(image_string, label):\n", + " image_shape = tf.image.decode_jpeg(image_string).shape\n", + "\n", + " feature = {\n", + " 'height': _int64_feature(image_shape[0]),\n", + " 'width': _int64_feature(image_shape[1]),\n", + " 'depth': _int64_feature(image_shape[2]),\n", + " 'label': _int64_feature(label),\n", + " 'image_raw': _bytes_feature(image_string),\n", + " }\n", + "\n", + " return tf.train.Example(features=tf.train.Features(feature=feature))\n", + "\n", + "for line in str(image_example(image_string, label)).split('\\n')[:15]:\n", + " print(line)\n", + "print('...')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "2G_o3O9MN0Qx" + }, + "cell_type": "markdown", + "source": [ + "ご覧のように、すべての特徴量が`tf.Example`メッセージに保存されました。上記のコードを関数化し、このサンプルメッセージを`images.tfrecords`ファイルに書き込みます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "qcw06lQCOCZU", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# 生の画像をimages.tfrecordsファイルに書き出す\n", + "# まず、2つの画像をtf.Exampleメッセージに変換し、\n", + "# 次に.tfrecordsファイルに書き出す\n", + "with tf.python_io.TFRecordWriter('images.tfrecords') as writer:\n", + " for filename, label in image_labels.items():\n", + " image_string = open(filename, 'rb').read()\n", + " tf_example = image_example(image_string, label)\n", + " writer.write(tf_example.SerializeToString())" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "yJrTe6tHPCfs", + "colab": {} + }, + "cell_type": "code", + "source": [ + "!ls" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "jJSsCkZLPH6K" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの読み込み\n", + "\n", + "これで、`images.tfrecords`ファイルができました。このファイルの中のレコードをイテレートし、書き込んだものを読み出します。このユースケースでは、画像を復元するだけなので、必要なのは生画像の文字列だけです。上記のゲッター、すなわち、`example.features.feature['image_raw'].bytes_list.value[0]`を使って抽出することができます。猫と橋のどちらであるかを決めるため、ラベルも使用します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "M6Cnfd3cTKHN", + "colab": {} + }, + "cell_type": "code", + "source": [ + "raw_image_dataset = tf.data.TFRecordDataset('images.tfrecords')\n", + "\n", + "# 特徴量を記述するディクショナリを作成\n", + "image_feature_description = {\n", + " 'height': tf.FixedLenFeature([], tf.int64),\n", + " 'width': tf.FixedLenFeature([], tf.int64),\n", + " 'depth': tf.FixedLenFeature([], tf.int64),\n", + " 'label': tf.FixedLenFeature([], tf.int64),\n", + " 'image_raw': tf.FixedLenFeature([], tf.string),\n", + "}\n", + "\n", + "def _parse_image_function(example_proto):\n", + " # 入力のtf.Exampleのプロトコルバッファを上記のディクショナリを使って解釈\n", + " return tf.parse_single_example(example_proto, image_feature_description)\n", + "\n", + "parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n", + "parsed_image_dataset" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "0PEEFPk4NEg1" + }, + "cell_type": "markdown", + "source": [ + "TFRecordファイルから画像を復元しましょう。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yZf8jOyEIjSF", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for image_features in parsed_image_dataset:\n", + " image_raw = image_features['image_raw'].numpy()\n", + " display.display(display.Image(data=image_raw))" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 63c72320fb879839cf377548104a22f2a9c46323 Mon Sep 17 00:00:00 2001 From: Masatoshi Itagaki Date: Sat, 13 Apr 2019 23:33:22 +0900 Subject: [PATCH 4/6] renamed tf-record.ipynb to tf_record.ipynb --- site/ja/tutorials/load_data/tf-records.ipynb | 1687 ------------ site/ja/tutorials/load_data/tf_records.ipynb | 2503 +++++++++--------- 2 files changed, 1271 insertions(+), 2919 deletions(-) delete mode 100644 site/ja/tutorials/load_data/tf-records.ipynb diff --git a/site/ja/tutorials/load_data/tf-records.ipynb b/site/ja/tutorials/load_data/tf-records.ipynb deleted file mode 100644 index 238da53524c..00000000000 --- a/site/ja/tutorials/load_data/tf-records.ipynb +++ /dev/null @@ -1,1687 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "pL--_KGdYoBz" - }, - "source": [ - "##### Copyright 2018 The TensorFlow Authors." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "cellView": "both", - "colab": {}, - "colab_type": "code", - "id": "uBDvXpYzYnGj" - }, - "outputs": [], - "source": [ - "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "HQzaEQuJiW_d" - }, - "source": [ - "# TFRecords と `tf.Example` の使用法\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " View on TensorFlow.org\n", - " \n", - " Run in Google Colab\n", - " \n", - " View source on GitHub\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "3pkUd_9IZCFO" - }, - "source": [ - "データの読み込みを効率的にするには、データをシリアライズし、連続的に読み込めるファイルのセット（各ファイルは100-200MB）に保存することが有効です。データをネットワーク経由で流そうとする場合には、特にそうです。また、データの前処理をキャッシングする際にも役立ちます。\n", - "\n", - "TFRecord形式は、バイナリレコードの系列を保存するための単純な形式です。\n", - "\n", - "[プロトコルバッファ](https://developers.google.com/protocol-buffers/) は、構造化データを効率的にシリアライズする、プラットフォームや言語に依存しないライブラリです。\n", - "\n", - "プロトコルメッセージは`.proto`という拡張子のファイルで表されます。メッセージの型を識別する最も簡単な方法です。\n", - "\n", - "`tf.Example`メッセージ（あるいはプロトコルバッファ）は、`{\"string\": value}`形式のマッピングを表現する柔軟なメッセージタイプです。これは、TensorFlow用に設計され、[TFX](https://www.tensorflow.org/tfx/)のような上位レベルのAPIで共通に使用されています。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Ac83J0QxjhFt" - }, - "source": [ - "このノートブックでは、`tf.Example`の作成、解析と使用法をデモし、その後、`tf.Example`メッセージを`.tfrecord`に書き出し、読み取る方法を示します。\n", - "\n", - "注：こうした構造は有用ですが必ずそうしなければならなというものではありません。[`tf.data`](https://www.tensorflow.org/guide/datasets) を使っていて、データの読み込みが訓練のボトルネックである場合でなければ、既存のコードをTFRecordsを使用するために変更する必要はありません。データセットの性能改善のヒントは、 [Data Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets)を参照ください。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "WkRreBf1eDVc" - }, - "source": [ - "## 設定" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Ja7sezsmnXph" - }, - "outputs": [], - "source": [ - "from __future__ import absolute_import\n", - "from __future__ import division\n", - "from __future__ import print_function\n", - "\n", - "import tensorflow as tf\n", - "tf.enable_eager_execution()\n", - "\n", - "import numpy as np\n", - "import IPython.display as display" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "e5Kq88ccUWQV" - }, - "source": [ - "## `tf.Example`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "VrdQHgvNijTi" - }, - "source": [ - "### `tf.Example`用のデータ型" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "lZw57Qrn4CTE" - }, - "source": [ - "基本的には`tf.Example`は`{\"string\": tf.train.Feature}`というマッピングです。\n", - "\n", - "`tf.train.Feature`メッセージ型は次の3つの型のうち1つをとることができます。（[.proto file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto)を参照）この他の一般的なデータ型のほとんどは、強制的にこれらのうちの1つにすること可能です。\n", - "\n", - "1. `tf.train.BytesList` (次の型のデータを扱うことが可能)\n", - " - `string`\n", - " - `byte` \n", - "1. `tf.train.FloatList` (次の型のデータを扱うことが可能)\n", - " - `float` (`float32`)\n", - " - `double` (`float64`) \n", - "1. `tf.train.Int64List` (次の型のデータを扱うことが可能)\n", - " - `bool`\n", - " - `enum`\n", - " - `int32`\n", - " - `uint32`\n", - " - `int64`\n", - " - `uint64`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "_e3g9ExathXP" - }, - "source": [ - "通常のTensorFlowの型を`tf.Example`互換の `tf.train.Feature`に変換するには、次のショートカット関数を使うことができます。\n", - "\n", - "どの関数も、1個のスカラー値を入力とし、上記の3つの`list`型のうちの一つを含む`tf.train.Feature`を返します。" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "mbsPOUpVtYxA" - }, - "outputs": [], - "source": [ - "# 下記の関数を使うと値を tf.Exampleと互換性の有る型に変換できる\n", - "\n", - "def _bytes_feature(value):\n", - " \"\"\"string / byte 型から byte_listを返す\"\"\"\n", - " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n", - "\n", - "def _float_feature(value):\n", - " \"\"\"float / double 型から float_listを返す\"\"\"\n", - " return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n", - "\n", - "def _int64_feature(value):\n", - " \"\"\"bool / enum / int / uint 型から Int64_listを返す\"\"\"\n", - " return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Wst0v9O8hgzy" - }, - "source": [ - "注：単純化のため、このサンプルではスカラー値の入力のみを扱っています。スカラー値ではない特徴を扱う最も簡単な方法は、`tf.serialize_tensor`を使ってテンソルをバイナリ文字列に変換する方法です。TensorFlowでは文字列はスカラー値として扱います。バイナリ文字列をテンソルに戻すには、`tf.parse_tensor`を使用します。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "vsMbkkC8xxtB" - }, - "source": [ - "上記の関数の使用例を下記に示します。入力が様々な型であるのに対して、出力が標準化されていることに注目してください。入力が、強制変換できない型であった場合、例外が発生します。（例：`_int64_feature(1.0)`はエラーとなります。`1.0`が浮動小数点数であるためで、代わりに`_float_feature`関数を使用すべきです）" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "hZzyLGr0u73y" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bytes_list {\n", - " value: \"test_string\"\n", - "}\n", - "\n", - "bytes_list {\n", - " value: \"test_bytes\"\n", - "}\n", - "\n", - "float_list {\n", - " value: 2.7182817459106445\n", - "}\n", - "\n", - "int64_list {\n", - " value: 1\n", - "}\n", - "\n", - "int64_list {\n", - " value: 1\n", - "}\n", - "\n" - ] - } - ], - "source": [ - "print(_bytes_feature(b'test_string'))\n", - "print(_bytes_feature(u'test_bytes'.encode('utf-8')))\n", - "\n", - "print(_float_feature(np.exp(1)))\n", - "\n", - "print(_int64_feature(True))\n", - "print(_int64_feature(1))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "nj1qpfQU5qmi" - }, - "source": [ - "主要なメッセージはすべて`.SerializeToString` を使ってバイナリ文字列にシリアライズすることができます。" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "5afZkORT5pjm" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "b'\\x12\\x06\\n\\x04T\\xf8-@'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "feature = _float_feature(np.exp(1))\n", - "\n", - "feature.SerializeToString()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "laKnw9F3hL-W" - }, - "source": [ - "### `tf.Example` メッセージの作成" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "b_MEnhxchQPC" - }, - "source": [ - "既存のデータから`tf.Example`を作成したいとします。実際には、データセットの出処はどこでも良いのですが、1件の観測記録から`tf.Example`メッセージを作る手順は同じです。\n", - "\n", - "1. 観測記録それぞれにおいて、各値は上記の関数を使って3種類の互換性のある型をからなる`tf.train.Feature`に変換する必要があります。\n", - "\n", - "1. 次に、特徴の名前を表す文字列と、#1で作ったエンコード済みの特徴量を対応させたマップ（ディクショナリ）を作成します。\n", - "\n", - "1. #2で作成したマップを[特徴量メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)に変換します。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4EgFQ2uHtchc" - }, - "source": [ - "このノートブックでは、NumPyを使ってデータセットを作成します。\n", - "\n", - "このデータセットには4つの特徴量があります。\n", - "- `False` または `True`を表す論理値。出現確率は等しいものとします。\n", - "- ランダムなバイト値。全体において一様であるとします。\n", - "- `[-10000, 10000)`の範囲から一様にサンプリングした整数値。\n", - "- 標準正規分布からサンプリングした浮動小数点数。\n", - "\n", - "サンプルは上記の分布から独立して同じ様に分布した10,000件の観測記録からなるものとします。" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "CnrguFAy3YQv" - }, - "outputs": [], - "source": [ - "# データセットに含まれる観測結果の件数\n", - "n_observations = int(1e4)\n", - "\n", - "# ブール特徴量 FalseまたはTrueとしてエンコードされている\n", - "feature0 = np.random.choice([False, True], n_observations)\n", - "\n", - "# 整数特徴量 -10000 から 10000 の間の乱数\n", - "feature1 = np.random.randint(0, 5, n_observations)\n", - "\n", - "# バイト特徴量\n", - "strings = np.array([b'cat', b'dog', b'chicken', b'horse', b'goat'])\n", - "feature2 = strings[feature1]\n", - "\n", - "# 浮動小数点数特徴量 標準正規分布から発生\n", - "feature3 = np.random.randn(n_observations)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "aGrscehJr7Jd" - }, - "source": [ - "これらの特徴量は、`_bytes_feature`, `_float_feature`, `_int64_feature`のいずれかを使って`tf.Example`互換の型に強制変換されます。その後、エンコード済みの特徴量から`tf.Example`メッセージを作成できます。" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "RTCS49Ij_kUw" - }, - "outputs": [], - "source": [ - "def serialize_example(feature0, feature1, feature2, feature3):\n", - " \"\"\"\n", - " Creates a tf.Example message ready to be written to a file.\n", - " ファイル出力可能なtf.Exampleメッセージを作成する\n", - " \"\"\"\n", - "\n", - " # 特徴量名とtf.Example互換データ型との対応ディクショナリを作成\n", - "\n", - " feature = {\n", - " 'feature0': _int64_feature(feature0),\n", - " 'feature1': _int64_feature(feature1),\n", - " 'feature2': _bytes_feature(feature2),\n", - " 'feature3': _float_feature(feature3),\n", - " }\n", - "\n", - " # tf.train.Exampleを用いて特徴メッセージを作成\n", - "\n", - " example_proto = tf.train.Example(features=tf.train.Features(feature=feature))\n", - " return example_proto.SerializeToString()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "XftzX9CN_uGT" - }, - "source": [ - "例えば、データセットに`[False, 4, bytes('goat'), 0.9876]`という1つの観測記録があるとします。`create_message()`を使うとこの観測記録から`tf.Example`メッセージを作成し印字できます。上記のように、観測記録一つ一つが`Features`メッセージとして書かれています。`tf.Example` [メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88)は、この`Features` メッセージを包むラッパーに過ぎないことに注意してください。" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "N8BtSx2RjYcb" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "b'\\nR\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04[\\xd3|?\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x04\\n\\x14\\n\\x08feature2\\x12\\x08\\n\\x06\\n\\x04goat'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# データセットからの観測記録の例\n", - "\n", - "example_observation = []\n", - "\n", - "serialized_example = serialize_example(False, 4, b'goat', 0.9876)\n", - "serialized_example" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "_pbGATlG6u-4" - }, - "source": [ - "メッセージをデコードするには、`tf.train.Example.FromString`メソッドを使用します。" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "dGim-mEm6vit" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "features {\n", - " feature {\n", - " key: \"feature0\"\n", - " value {\n", - " int64_list {\n", - " value: 0\n", - " }\n", - " }\n", - " }\n", - " feature {\n", - " key: \"feature1\"\n", - " value {\n", - " int64_list {\n", - " value: 4\n", - " }\n", - " }\n", - " }\n", - " feature {\n", - " key: \"feature2\"\n", - " value {\n", - " bytes_list {\n", - " value: \"goat\"\n", - " }\n", - " }\n", - " }\n", - " feature {\n", - " key: \"feature3\"\n", - " value {\n", - " float_list {\n", - " value: 0.9876000285148621\n", - " }\n", - " }\n", - " }\n", - "}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "example_proto = tf.train.Example.FromString(serialized_example)\n", - "example_proto" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "y-Hjmee-fbLH" - }, - "source": [ - "## `tf.data`を使用したTFRecordファイル" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "GmehkCCT81Ez" - }, - "source": [ - "`tf.data`モジュールには、TensorFlowでデータを読み書きするツールが含まれます。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "1FISEuz8ubu3" - }, - "source": [ - "### TFRecordファイルの書き出し\n", - "\n", - "データをデータセットにする最も簡単な方法は`from_tensor_slices`メソッドです。\n", - "\n", - "配列に適用すると、このメソッドはスカラー値のデータセットを返します。" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "mXeaukvwu5_-" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tf.data.Dataset.from_tensor_slices(feature1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "f-q0VKyZvcad" - }, - "source": [ - "配列のタプルに適用すると、タプルのデータセットが返されます。" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "H5sWyu1kxnvg" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "features_dataset = tf.data.Dataset.from_tensor_slices((feature0, feature1, feature2, feature3))\n", - "features_dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "m1C-t71Nywze" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /Users/masatoshi/pyenvs/tfenv/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py:532: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Colocations handled automatically by placer.\n", - "tf.Tensor(False, shape=(), dtype=bool)\n", - "tf.Tensor(4, shape=(), dtype=int64)\n", - "tf.Tensor(b'goat', shape=(), dtype=string)\n", - "tf.Tensor(-0.2768728503385437, shape=(), dtype=float64)\n" - ] - } - ], - "source": [ - "# データセットから1つのサンプルだけを取り出すには`take(1)` を使います。\n", - "for f0,f1,f2,f3 in features_dataset.take(1):\n", - " print(f0)\n", - " print(f1)\n", - " print(f2)\n", - " print(f3)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "mhIe63awyZYd" - }, - "source": [ - "`Dataset`のそれぞれの要素に関数を適用するには、`tf.data.Dataset.map`メソッドを使用します。\n", - "\n", - "マップされる関数はTensorFlowのグラフモードで動作する必要があります。関数は`tf.Tensors`を処理し、返す必要があります。`create_example`のような非テンソル関数は、互換性のため`tf.py_func`でラップすることができます。\n", - "\n", - "`tf.py_func`を使用する際には、シェイプと型は取得できないため、指定する必要があります。" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "apB5KYrJzjPI" - }, - "outputs": [], - "source": [ - "def tf_serialize_example(f0,f1,f2,f3):\n", - " tf_string = tf.py_func(\n", - " serialize_example, \n", - " (f0,f1,f2,f3), # pass these args to the above function.\n", - " tf.string) # the return type is `tf.string`.\n", - " return tf.reshape(tf_string, ()) # The result is a scalar" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "CrFZ9avE3HUF" - }, - "source": [ - "この関数をデータセットのそれぞれの要素に適用します。" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "VDeqYVbW3ww9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From :5: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "tf.py_func is deprecated in TF V2. Instead, use\n", - " tf.py_function, which takes a python function which manipulates tf eager\n", - " tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n", - " an ndarray (just call tensor.numpy()) but having access to eager tensors\n", - " means `tf.py_function`s can use accelerators such as GPUs as well as\n", - " being differentiable using a gradient tape.\n", - " \n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "serialized_features_dataset = features_dataset.map(tf_serialize_example)\n", - "serialized_features_dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "p6lw5VYpjZZC" - }, - "source": [ - "TFRecordファイルに書き出します。" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "vP1VgTO44UIE" - }, - "outputs": [], - "source": [ - "filename = 'test.tfrecord'\n", - "writer = tf.data.experimental.TFRecordWriter(filename)\n", - "writer.write(serialized_features_dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "6aV0GQhV8tmp" - }, - "source": [ - "### TFRecordファイルの読み込み" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "o3J5D4gcSy8N" - }, - "source": [ - "`tf.data.TFRecordDataset`クラスを使ってTFRecordファイルを読み込むこともできます。\n", - "\n", - "`tf.data`を使ってTFRecordファイルを取り扱う際の詳細については、[こちら](https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data)を参照ください。 \n", - "\n", - "`TFRecordDataset`を使うことは、入力データを標準化し、パフォーマンスを最適化するのに有用です。" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "6OjX6UZl-bHC" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "filenames = [filename]\n", - "raw_dataset = tf.data.TFRecordDataset(filenames)\n", - "raw_dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "6_EQ9i2E_-Fz" - }, - "source": [ - "この時点で、データセットにはシリアライズされた`tf.train.Example`メッセージが含まれています。データセットをイテレートすると、スカラーの文字列テンソルが返ってきます。\n", - "\n", - "`.take`メソッドを使って最初の10レコードだけを表示します。\n", - "\n", - "注：`tf.data.Dataset`をイテレートできるのは、Eager Executionが有効になっている場合のみです。" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "hxVXpLz_AJlm" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x03'>\n", - "\n", - "\n", - "\n", - "'>\n", - "'>\n", - "\\xbf'>\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for raw_record in raw_dataset.take(10):\n", - " print(repr(raw_record))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "W-6oNzM4luFQ" - }, - "source": [ - "これらのテンソルは下記の関数でパースできます。\n", - "\n", - "注：ここでは、`feature_description`が必要です。データセットはグラフ実行を使用するため、この記述を使ってシェイプと型を構築するのです。" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "zQjbIR1nleiy" - }, - "outputs": [], - "source": [ - "# 特徴の記述\n", - "feature_description = {\n", - " 'feature0': tf.FixedLenFeature([], tf.int64, default_value=0),\n", - " 'feature1': tf.FixedLenFeature([], tf.int64, default_value=0),\n", - " 'feature2': tf.FixedLenFeature([], tf.string, default_value=''),\n", - " 'feature3': tf.FixedLenFeature([], tf.float32, default_value=0.0),\n", - "}\n", - "\n", - "def _parse_function(example_proto):\n", - " # 上記の記述を使って入力のtf.Exampleを処理\n", - " return tf.parse_single_example(example_proto, feature_description)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "gWETjUqhEQZf" - }, - "source": [ - "あるいは、`tf.parse example`を使ってバッチ全体を一度にパースします。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "AH73hav6Bnmg" - }, - "source": [ - "`tf.data.Dataset.map`メソッドを使って、データセットの各アイテムにこの関数を適用します。" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "6Ob7D-zmBm1w" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parsed_dataset = raw_dataset.map(_parse_function)\n", - "parsed_dataset " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "sNV-XclGnOvn" - }, - "source": [ - "Eager Execution を使ってデータセット中の観測記録を表示します。このデータセットには10,000件の観測記録がありますが、最初の10個だけ表示します。 \n", - "データは特徴量のディクショナリの形で表示されます。それぞれの項目は`tf.Tensor`であり、このテンソルの`numpy` 要素は特徴量を表します。" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "x2LT2JCqhoD_" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for parsed_record in parsed_dataset.take(10):\n", - " print(repr(raw_record))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Cig9EodTlDmg" - }, - "source": [ - "ここでは、`tf.parse_example` が`tf.Example`のフィールドを通常のテンソルに展開しています。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "jyg1g3gU7DNn" - }, - "source": [ - "## tf.python_ioを使ったTFRecordファイル" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "3FXG3miA7Kf1" - }, - "source": [ - "`tf.python_io`モジュールには、TFRecordファイルの読み書きのための純粋なPython関数も含まれています。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "CKn5uql2lAaN" - }, - "source": [ - "### TFRecordファイルの書き出し" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "LNW_FA-GQWXs" - }, - "source": [ - "次にこの10,000件の観測記録を`test.tfrecords`ファイルに出力します。観測記録はそれぞれ`tf.Example`メッセージに変換され、ファイルに出力されます。その後、`test.tfrecords`ファイルが作成されたことを確認することができます。" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "MKPHzoGv7q44" - }, - "outputs": [], - "source": [ - "# `tf.Example`観測記録をファイルに出力\n", - "with tf.python_io.TFRecordWriter(filename) as writer:\n", - " for i in range(n_observations):\n", - " example = serialize_example(feature0[i], feature1[i], feature2[i], feature3[i])\n", - " writer.write(example)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "EjdFHHJMpUUo" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "images.ipynb images.tfrecords test.tfrecord tf-records.ipynb\r\n" - ] - } - ], - "source": [ - "!ls" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "wtQ7k0YWQ1cz" - }, - "source": [ - "### TFRecordファイルの読み込み" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "utkozytkQ-2K" - }, - "source": [ - "モデルに入力にするため、このデータを読み込みたいとしましょう。\n", - "\n", - "次の例では、データをそのまま、`tf.Example`メッセージとしてインポートします。これは、ファイルが期待されるデータを含んでいるかを確認するのに役に立ちます。これは、また、入力データがTFRecordとして保存されているが、[この](https://www.tensorflow.org/guide/datasets#consuming_numpy_arrays)例のようにNumPyデータ（またはそれ以外のデータ型）として入力したい場合に有用です。このコーディング例では値そのものを読み取れるからです。\n", - "\n", - "入力ファイルの中のTFRecordをイテレートして、`tf.Example`メッセージを取り出し、その中の値を読み取って保存できます。" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "36ltP9B8OezA" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From :1: 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", - "features {\n", - " feature {\n", - " key: \"feature0\"\n", - " value {\n", - " int64_list {\n", - " value: 0\n", - " }\n", - " }\n", - " }\n", - " feature {\n", - " key: \"feature1\"\n", - " value {\n", - " int64_list {\n", - " value: 4\n", - " }\n", - " }\n", - " }\n", - " feature {\n", - " key: \"feature2\"\n", - " value {\n", - " bytes_list {\n", - " value: \"goat\"\n", - " }\n", - " }\n", - " }\n", - " feature {\n", - " key: \"feature3\"\n", - " value {\n", - " float_list {\n", - " value: -0.276872843503952\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n" - ] - } - ], - "source": [ - "record_iterator = tf.python_io.tf_record_iterator(path=filename)\n", - "\n", - "for string_record in record_iterator:\n", - " example = tf.train.Example()\n", - " example.ParseFromString(string_record)\n", - " \n", - " print(example)\n", - " \n", - " # Exit after 1 iteration as this is purely demonstrative.\n", - " # 純粋にデモであるため、イテレーションの1回目で終了\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "i3uquiiGTZTK" - }, - "source": [ - "（上記で作成した`tf.Example`型の）`example`オブジェクトの特徴量は（他のプロトコルバッファメッセージと同様に）ゲッターを使ってアクセス可能です。`example.features`は`repeated feature`メッセージを返し、`feature`メッセージをを取得すると（Pythonのディクショナリとして保存された）特徴量の名前と特徴量の値のマッピングが得られます。" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "-UNzS7vsUBs0" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'feature0': int64_list {\n", - " value: 0\n", - "}\n", - ", 'feature1': int64_list {\n", - " value: 4\n", - "}\n", - ", 'feature2': bytes_list {\n", - " value: \"goat\"\n", - "}\n", - ", 'feature3': float_list {\n", - " value: -0.276872843503952\n", - "}\n", - "}\n" - ] - } - ], - "source": [ - "print(dict(example.features.feature))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "u1M-WrbqUUVW" - }, - "source": [ - "このディクショナリから、指定した値をディクショナリとして得ることができます。" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "2yCBu70IUb2H" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "float_list {\n", - " value: -0.276872843503952\n", - "}\n", - "\n" - ] - } - ], - "source": [ - "print(example.features.feature['feature3'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4dw6_OI9UiNZ" - }, - "source": [ - "次に、ゲッターを使って値にアクセスできます。" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "BdDYjDnDUlFe" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.276872843503952]\n" - ] - } - ], - "source": [ - "print(example.features.feature['feature3'].float_list.value)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "S0tFDrwdoj3q" - }, - "source": [ - "## ウォークスルー： 画像データの読み書き" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "rjN2LFxFpcR9" - }, - "source": [ - "以下は、TFRecordを使って画像データを読み書きする方法の例です。この例の目的は、データ（この場合は画像）を入力し、そのデータをTFRecordファイルに書き込んで、再びそのファイルを読み込み、画像を表示するという手順を最初から最後まで示すことです。\n", - "\n", - "これは、例えば、同じ入力データセットを使って複数のモデルを構築するといった場合に役立ちます。画像データをそのまま保存する代わりに、TFRecord形式に前処理しておき、その後の処理やモデル構築に使用することができます。\n", - "\n", - "まずは、雪の中の猫の[画像](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg)と、ニューヨーク市にあるウイリアムズバーグ橋の [写真](https://upload.wikimedia.org/wikipedia/commons/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg)をダウンロードしましょう。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "5Lk2qrKvN0yu" - }, - "source": [ - "### 画像の取得" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "3a0fmwg8lHdF" - }, - "outputs": [], - "source": [ - "cat_in_snow = tf.keras.utils.get_file('320px-Felis_catus-cat_on_snow.jpg', 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/Felis_catus-cat_on_snow.jpg/320px-Felis_catus-cat_on_snow.jpg')\n", - "williamsburg_bridge = tf.keras.utils.get_file('194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg','https://upload.wikimedia.org/wikipedia/commons/thumb/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "7aJJh7vENeE4" - }, - "outputs": [ - { - "data": { - "image/jpeg": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "source" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display.display(display.Image(filename=williamsburg_bridge))\n", - "display.display(display.HTML('source'))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "VSOgJSwoN5TQ" - }, - "source": [ - "### TFRecordファイルの書き出し" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Azx83ryQEU6T" - }, - "source": [ - "上記で行ったように、この特徴量を`tf.Example`と互換のデータ型にエンコードできます。この場合には、生の画像文字列を特徴として保存するだけではなく、縦、横のサイズにチャネル数、更に画像を保存する際に猫の画像と橋の画像を区別するための`label`特徴量を付け加えます。猫の画像には`0`を、橋の画像には`1`を使うことにしましょう。" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "kC4TS1ZEONHr" - }, - "outputs": [], - "source": [ - "image_labels = {\n", - " cat_in_snow : 0,\n", - " williamsburg_bridge : 1,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "c5njMSYNEhNZ" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "features {\n", - " feature {\n", - " key: \"depth\"\n", - " value {\n", - " int64_list {\n", - " value: 3\n", - " }\n", - " }\n", - " }\n", - " feature {\n", - " key: \"height\"\n", - " value {\n", - " int64_list {\n", - " value: 213\n", - " }\n", - "...\n" - ] - } - ], - "source": [ - "# 猫の画像を使った例\n", - "image_string = open(cat_in_snow, 'rb').read()\n", - "\n", - "label = image_labels[cat_in_snow]\n", - "\n", - "# 関連する特徴量のディクショナリを作成\n", - "def image_example(image_string, label):\n", - " image_shape = tf.image.decode_jpeg(image_string).shape\n", - "\n", - " feature = {\n", - " 'height': _int64_feature(image_shape[0]),\n", - " 'width': _int64_feature(image_shape[1]),\n", - " 'depth': _int64_feature(image_shape[2]),\n", - " 'label': _int64_feature(label),\n", - " 'image_raw': _bytes_feature(image_string),\n", - " }\n", - "\n", - " return tf.train.Example(features=tf.train.Features(feature=feature))\n", - "\n", - "for line in str(image_example(image_string, label)).split('\\n')[:15]:\n", - " print(line)\n", - "print('...')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "2G_o3O9MN0Qx" - }, - "source": [ - "ご覧のように、すべての特徴量が`tf.Example`メッセージに保存されました。上記のコードを関数化し、このサンプルメッセージを`images.tfrecords`ファイルに書き込みます。" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "qcw06lQCOCZU" - }, - "outputs": [], - "source": [ - "# 生の画像をimages.tfrecordsファイルに書き出す\n", - "# まず、2つの画像をtf.Exampleメッセージに変換し、\n", - "# 次に.tfrecordsファイルに書き出す\n", - "with tf.python_io.TFRecordWriter('images.tfrecords') as writer:\n", - " for filename, label in image_labels.items():\n", - " image_string = open(filename, 'rb').read()\n", - " tf_example = image_example(image_string, label)\n", - " writer.write(tf_example.SerializeToString())" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "yJrTe6tHPCfs" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "images.ipynb images.tfrecords test.tfrecord tf-records.ipynb\r\n" - ] - } - ], - "source": [ - "!ls" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "jJSsCkZLPH6K" - }, - "source": [ - "### TFRecordファイルの読み込み\n", - "\n", - "これで、`images.tfrecords`ファイルができました。このファイルの中のレコードをイテレートし、書き込んだものを読み出します。このユースケースでは、画像を復元するだけなので、必要なのは生画像の文字列だけです。上記のゲッター、すなわち、`example.features.feature['image_raw'].bytes_list.value[0]`を使って抽出することができます。猫と橋のどちらであるかを決めるため、ラベルも使用します。" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "M6Cnfd3cTKHN" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raw_image_dataset = tf.data.TFRecordDataset('images.tfrecords')\n", - "\n", - "# 特徴量を記述するディクショナリを作成\n", - "image_feature_description = {\n", - " 'height': tf.FixedLenFeature([], tf.int64),\n", - " 'width': tf.FixedLenFeature([], tf.int64),\n", - " 'depth': tf.FixedLenFeature([], tf.int64),\n", - " 'label': tf.FixedLenFeature([], tf.int64),\n", - " 'image_raw': tf.FixedLenFeature([], tf.string),\n", - "}\n", - "\n", - "def _parse_image_function(example_proto):\n", - " # 入力のtf.Exampleのプロトコルバッファを上記のディクショナリを使って解釈\n", - " return tf.parse_single_example(example_proto, image_feature_description)\n", - "\n", - "parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n", - "parsed_image_dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "0PEEFPk4NEg1" - }, - "source": [ - "TFRecordファイルから画像を復元しましょう。" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "yZf8jOyEIjSF" - }, - "outputs": [ - { - "data": { - "image/jpeg": 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GIQdpjL+kgszkrkcsDWMnDkildNFuLh9ph5rSS8tL3/H4oprvU3iRIYZ96W8cYO1RICC8jFmLEengDNJpRiop2EW+16FZ/DN/oeluBqSppgRp1DsTNH13Jv8A8vzjNO+zTaHlYOI6f4z1PTdWe806fbFECIopDlM/5se9erBuCSOKaUm2Xk/2meMLyAB9RManBykYVR26+3Wm+V+xKCrRU6jretagq3F9qN5NvGOZSFVPoOAen8qn8jb2NwRqt7+/mCs5kLHG52JJJPX/AL1omZ0WMREEBtYgQxBO4E8fP9+9ZPLs0WAtzLlYoU3BgMAL1A7/ANaaQN+haeJbSQKedoy3qyQfbNO7FoYMUbw7pAEXHAzyam2iqTEhYr5JcZRc9WPJFX3I6DWj3Wr+Gr9dR0O8uLO6To8eMkY5ypyCPqKa5EwcJI719lH7QVsiiz8eTXKTHhb7ZuQk/wCYKMgfODWvYikzur30Ws2UV3YXMU9nJhkkjbKuPg961jSMpJ+RxSn3bbCMNjk0vIeAaXH3YKE5bvTqw0WIvGEW6Q/lU9SrMx3nmqdvNHULF5pTGCwXmmkSVs0r7DIzk/FXQMErCXgj1fSnoWyl1XRZnJeE8ntWkeT2ZS434AaPY3kBcSIAuaJyTHBNG0WUnkkOeNo6Vi8mqY6uomVCMZqeo7FbjUPLjYvgCqUbE3RWaddedclnGFz0q3GkTF2B+7lQHTOaLJotNLkZQGfOfmokiosuxeKUweDWfU0sBJtk5zgU9CF51XyiAOaoQAsRb4x6qBGdMRg5Mnc0SBFjqRBtDj2qFst6NDukWTekgyrZBBFdNWjDRyjxN4XmsL2BDepDo0j52FMHHXYW+vTIrg5eLrL6OuHJ2X2JXXhpZdQ/w8G4aJx5kU0mPTnnp8cc4rN8dOkV3xbKDS59SsEXw59xlXWEmMHkYw7EjIbB6qeu7PSuLm+PfJk9Di5UuHvZ2m38HyWVvDp95I8l1JEgmZVLKJCoG4t8Yx7HFc645QfUUuRTXYDq+g6lolzHJpl9Zyyhdr/eY2G9sclAuf5DJq5cfUiM09o0b7VdeOmaE6tOp1GeMxRrECoTPUjPT8/eq4oXJBOWDg8MXn3EEU4VpG/E+cE//L5/nxXc5YbOZR0jZNQgVYIYUJC5VckEMf8AjmueLt2dElgDrkam3WCHhcAMQcEADP55GKvjeSJrBqoVQscjE53GRhj8IHGPk11HMWSysbcMq7VJHPc49v8AtUqORtg7IiKYFxmRhuY/9PtVMSHDIrweZ5aksAAxXOAD7e9SkOwcsMb24klMhBG4ntS80PxZGSVVVdsY3AjOB/Sl1bKUkhuAfeoAxU89MKTWbfU0rsip1CyVDuwykdDjIP51rHksylx0bJ9n32g694Luh9yuZJLEt++s3YFJB8ccHp0rVS66Irthnr/wV4s0nxR4fTUdGuUmAAE0YyGhfHKsCOP9a3TUtGUouOx+3k82Z2BzVtUiFkO2+ZwD0pDLiwiVIiCOcVEmUirurzNyYUGatLBNmVtw+C36UWFBobcBzxSbAMYgAc0rHQvDGz5OOAadgZu4VMZxw2KExFNFOYSyfxVdWSmJ6pdAxlSfVnpVwjkmbBaYxUbz0FVMUTb47FRGpYVzdjWiLoqqdoAAoATeUtkDqKoRODe7DOcUmA+8WEzSsYBBufBximAWQpGuR1pAJPOzZDfhqqARS1Sa4yV4FVdIirZYz6Tb3Vm8MsYaNhyp9+1ZtlpHMtX0k+HrlYJXLQknynBJYDuST8np8Vl/JpsS07TFOvwa3b6sw1CC3a3QSIpHllt2D36/NZyp5KVm6xa7FdJmYo9yn4tgOOOuCevviueSjdmqbqjVfFPiC30/zZZnUXXAjkPKtu6ce2Pbn9K5ZtWdEE6OFeN7mXU9UJvztlB3SoTwoA4IPcY6VfHjI5o1y3hluL/zIozljkrjt2OO9aydRyZxVsevpFkCjfsQTBVJOQfn8+azii5MDq0v7q4RT+Ecs3O4nn++1awRnN4KG9tttpGoVSW2qm0+3UV0JnO0HClbq3iAA2rk4HBpgAuIijzSDgBcBemP9/rQB8wcQRxhiWYAscgkD2x+nzQIF98ciSJhlRgKp/l/P+lFeQssLFFEcpnO0KdrMD1PcD498f0qOTWDTjXsvdLhX7kJpUKJv2hcEcdOv+v9K5J3Z1Rqiv1RmFw0GQQTk5Pf61pB4siWcGv3UCxysICElGSBng57D3/Ot1L2YSj6Ng8EeJ77RNVW8024+7zYxIu4hXHHDDoc9qabhlBiSpnrvwJq0GvaTFqNpKrxvw4HVH7qR2/Ou1TUlaOVxcXRuKRBIw+OamxmLm68qNmzjihIG6K6yiPmNI/LNzmrfolIcDgEgnmpGPW6kpkDmpZSGHjUL80rHRXmTy2ZQfTVE2LPMJOFPNOgKSdXSd5GGABxWq1RkynZWmmLHlc1rpGZcWkIkQbfwrWUmapG2PKQMdq5zUTkfcCKskWijJkJxRYh62AzgUmMncyYwi0JDYvnac0xA2JdvigCEo5AoEM2UA37j0pNjSHZp44xheoFSlZV0al4xs3v9LmMcYkmj/eIv+bHaiUbjSCLp5Of6bPGpaYyqW53enGMDoPjr+tcTfs6Cqv/ABEn+KPbWpIDYQOcFPUMA/JBx+mPauScrdI6IwxbNU1O8+/TNBM4K2b7k3ndujbufocfI3Gs/DNVtGnzyxXk7ysR5SHywWPXAyFB/nWqTikiG7tgbOBY2JZfL85eSeMqDx+p6CnKVglQKRB5GwsPMR92GOOc9v76U1sGVV/LukVjtZmyDjOeB1+tdMVRzSdgbELKLaNgCqguMjGTxVkE4woklmOMseOM8Y68f6UAwUOwxSFhuDOWUEZzjp/r8UxCKMB5hcnDEKABk59l9v8AemIJDEEaSNtvmMAd3J2gg9uu7jA+tDYIsIbaGzEkczkxxvlyycs2eVUZ5/hzz/rWbdmkcF0oyiFtq4XAwDhFOcAe/HT6VzyRvF+ANxFBawnIzcDsy7vnB/v3ojkcmUGpoXkWfaDt5RSeGHuO/XPb3+lbxeKMZLNi01rFJ6o1kV8cqxxz1789KpNoTSeUb99jPjOTwv4pgiuZQmn3ZEU27pnsTnvz1rXjl1ZEl2VM9kC7V4FAxyOOa3owsSuYzIVPO0GqToQzkJtUd6QApkMb7veiwLS0mCw89alotMhd3BEW5T+dCQmyr8wlSfxZ71dEg7eFlkZ15+Pam2CMajD58BUdaIumKStFJa2siSFHB2561q5Gaiy8toVSLatYt2apUh4SE8GpoYLH7zNMQYcdKQGQ208UAYY4bcaABsdxyOaYGVHIoAzOmAMUkwMrNhcDiigIE5JJpgDl3NhccUCOU+PYLfw9rUQjkWFL0F4iRwrA+ofQZzXJzwSydHE2zQJdVspJlVJI2ZJA/o4A28kk/kPn9a89xzZ2J4o1u4uZlsdQuio8u5LKDjBA3AgcdPw/rRGNsqTpFVHBJLboHDLFnexJyHPye3firdJkLKLeWyhjTe8mZVXK55GD0+n0rNOzR4Nb1wyQh2iY7WI9Pv8Al7V0caTZjySaRRJJv3MyqwGcAD4/r/tXTRz3ZYx7Y5pXwMJDkZHbv/zU7wMkIGS0WMfix6z3APOB8/7UWIjIoVmUBQkQ9ee3HSqQhSUBbdZGiG8/+mnGAPp70woYmg+6G3uy0atCqttJx1HGfnjpU3Y0EtUuby4gBICIC+CoIRc9AOmSP1pPCKRsVhbQvcPKXKhSWRWyA2MDHJHX88DHHOBzytm8aRWXkYvpUkMkaQAhi7EZzjcRjOM9sdeABVRVbJk7YW1i+8LG0EVvFFkjkcFSSeuOwbAYdjUylRUY2VU0OJ13ZbYFR++Mk8HHtjk1SlaCqYskcXmRiaAyRSBv3UcoRg5BAGSCOuDjHI44zmtYszkj079gPiKfXPDcdhqF0ZrvTsRs0h9bJ/CT746Zrr4p9onNyRp2dmMMbQ8AfFOyaKrUPMimi2qTngVaJY22EhMk/GBSBmvm7ubq62xHZDnr8VpSiiE22XL3KGD7tH6j/Eais2aX4BXCGOJQvpHehCGNOQzDEY496JYGshmtGMpDdBU2FGGtkQHcOTRYADEI1LKelMRCJT1agA20sMjigCYdEQ7qQCb3H734qqFZJmaU4Xp70hhVQxr9aACRlQOetAH0jhgQBzSoACRMzZY8e1MQcwrjrikMUluCJfKAzjvVUK/BzL7XLFb/AFyxiuEzD93IVs9CW5rn5VZrB0jncvhTTIEIgMsMcmQSpJduCWP8gPqa8/kdbOyGdGveJSrCDT4l8vYNpOegz+I/yohjI3qiutlMsi2ihkfdgFu4A56fGf1pSwuw45wT1R1ktwVBQwjB5OOPcduKnjWSpukatd3YfeJDwWwM/wBDx/fNdkY0cspWV1m4+8zKrYVlJ4HUd8j++laMhFzaRqY8yjBZUBAO7qeT9agocuifMKrklcHcOnHAB980kDAXMQKrbkKuB5kpB4Yjtn+/inYhTzSs+ZFXdI+5OM4x3pgNabF97s91yoWIOCqHOSBkhQe/Tp9MVLZSD6pGxszGIsMMZJU5fIyM8ntjv70IBqBUFje2aySTTNGpjw4cjJUDjrkjIHtUNFpgJ9v3C22H9zETkBcbpByQDjkH2+D2xT0F2yx+8zWdkzJJIvqEaqg3mNSOTk9s7j+WBWTVs0TwLSxBmLKhLllSPC7mkYdCe+CRzzjke1Kh2JXlmQgaG3YHjgsd4OTgA/JB69AKuONkSydB+wbWDZeJYreYHyblDANxUtGxORzxkZ4P1FdPBKpV7MuRXGz08sz27Ks/pB6AmurejmLKRRPAjKBuWp0x7BSJFcwOknToaMpg1Zq96q20hETEKDjFbRd7MngtLBolhBA9R7mobtmiQ3H5UtrKJcF+wpWG0Rs5Bbw+o7cdqHkA33sMNynJpUFg5riDyBJPMB8A069AyuXVklLQxwOd3RyOMUU7DwWKqFJ3dRSsQKScbtqnFMBa4k6AdKaEyDqGAJHAo0GxmBlEfXBpDJmQMvJoATknaOQgAkU0iWR+9tngH86KHZiO8kz7/SgD6S5kmbYmQaEqC7CLbyJF5h5J/WhsKrJr/i/SZNSsBOqt59t6kPx3/lUyVoqLOW6jLJboJI4wHHpUnkKvfJPfHPHxXncsHZ18cjn1xBcuJrySRWFw5cOBgAbsAY7cCs1jBo85JHbDdh4VbftPlg/p/pUvKoqOHZSX+pylQZU4LYXn3+lbcfHRlOdlLexL92kMBYyRjPBzkfH0rdGJU6bIv35QMeWcrk9Bn3pyWBLZfQekyxNhMAFGPHBOMf0qPBbH23BTMzMSWAGADk/1465oELXBN1ZSsFAZ2Kh2PUDv+n9aNAzEb27H7zlGUSeVGxGd2DgZH0GfyxToLAO1xdefDbHbGCEU7Op/07jAoryF+B+wuN8dwPMdXWRYfVjdICpx8YyBz2OKGsgtGbeLyZZJlRlnm8p9o9IEYzuPTPxn5PfFJjWSwtbVDY3lnb7cwRB1m9OQoH4QR05PzzWMpXk2iqClI553RwrJaoCY9jESck4B6kAbj0BwDQAKdblwkhZkYhSk3LYxgjntjpge546mpKEb2B3szcW8s9tPFJhk2kD3Oe3YHr9KcbTrwJ01Yx4anMerfepLX7sUx5Mg/eBGBHA6Hkd8nBx75q3+tUyUeuNO1l9U0+zubiItIyBiT3OOtejHKtHFLDDre3DSsC/lqP4RVUibYU3biILnaCfzpUMrLuAzXqsWOwdapOlRLVuyxlnRoxHEuFUdazUadstu9AorwRNtBOaqrEBvL6GwU3eqXMcVp7u2AKTaSBLJrT/aX4W82NF1WJVlbai4OW+fgfJpKaK6su7F7fVJfNguUe2XvG2Qa0vGCOrvJZeZHkJEMk8CkkAe5ebO0fiNQhmbaJiQJeD3psBnUZLVQgGAR1qY2N0J+YhTOcrVCPnnjdML1FFALzCTYGU8e1ABUEojMkm0KO1AC8ErXU+xUwpPLVTjRKlbG7qNIFxERnvSWStH1hCQvmz429qGJBnu0J2orbfelQ7IXe7yPR6if4RQgZoninwy13E91bKu8As8Z6N/ftWfJxqWioTo5d4nWG0sJ/OfYSMhQm1V+B844rhnCmdUJWc8uNUOUkSJxNGm1WUEhsUKA3LGBVZU1CJDKzCdfWo75Hz+n8q0qjO7KycX9tK0zQ7ldvUK0VEFdbDzL2RwuY2GcZx3x+XNN6BGyaeGknyjBoyMqdo4PHJB7dP5VBRbTQr5UokOEQFRtHq56ge5NJgVRgmur8oX2xxoF2qDsUEcjPdumew6/FO6DYtKbOAebFFPdPE4G4EABPYAcde/NJNsdE3uJor+2QiNI0JLscgnGSAQOnbPuR8VdkBHCtKEkVpLNpEkC/OO5xnJB6YqdZKLqWP96LtAJDHhSeq7h6lBJ75wDj3z9M26waJFmFL3dqEmha4uJFHlOCAckl1bPDcgnIPBPesdGiB6mw/xBoEeQGVwXYnaMKrcHHB5bGR/OhaDyHu4AlgqIWFvCGV07MQPyyOe3c0IGUrpKtwDCFSYbNwBIXgEdPYqOo9xzVr7E/orIbm6tL4SgI8eeHA3Ng9QV689c1dJqiU2mepPs41K31HwlYfdQMopDAe+fbt/3rs4ZXE5+VVI3FLeKIkyZZiPwitLZlgHBa/vgbjGzqE7079BRiSNQ+wLyT+gosCOoG3srYyzyLGoxkk4+P8AWlY6OQeO/tj0/SM2mjot1cyEhXGNqqDjP1ODj9ahz9DUTjnjbxFrfiiRZ9TvHECnKWseQkSk9h3PyaycrZfWjTrfd5nLBR1Ungn2I+KGxo9R/s7WkieGbiS7LCORwUBBxj4zx+lbQ0Lk9HU2SMO7xHCr0+tWZBYo5HmeY5VR0JpBtkI0nuJdyk7c8k09C2Ykga7k2qPQp5b3oToHnBM2LtN5cIIjH4mPejsFeEQktN0vlxDpwW7Udga9Gb0CARwL6n7n2oi7yDxgmkIk2x7iT35pN1kKIusUN3HFGcJ/Ef8AShO1YPGENS2yTyAk7YB1PvSuhtWSZoZm27wlunH1oysgTaGKfb5XphXueM0Wwr0ElSOKLZAC0hPLe1Kx0DihgGc4du9O2FHPvH/ge28Q2s7KqCZf4Rxkd8Y71nOCkVGXU4lqWiRWEU1qsJRoPSVJ5BrjknFnRFpo0m40+GW6SaOQAcF17Ee4q1LAmic86xo6ttyuOh+Ohpogo7byfvsgibKOmFz/AAtu6VpQiy0jdG6xnjYwUE/yP9/FSxo3O6t/L0sPCTvPHPqwx7/NKWAWTUQsly06I4gs41ClsjJ+BnuT3NIYzHbpHYG3s5ZFCbgu0Zfkg4H1HxUt5spaE7ospWGRpZVQMA7sAo74xjg9CT1yfbFNAxzSHMcWzKLG6FnnYkFVxk4J6Ejj/wC3xRLLCKL6O3CxyrdJJ5Qk3CPLMn4cc49jhh35I4zWMmaRQ2S8kBBRLlo/3sOGJdTgsin8ieO2SKzbo0ooJEu73VriUvHlweG5APTBx2AAH0x71phRJV2bbHA0mj2kccqzzAFWKj1bwAdxHtnr/wA0rTdiarBrN+p3XrTKcKjJD6fSnJwVU8kY3HH0z0NWvBNlBrCW8iSG2hlDRtlZEDD4IP8AkJ4GDxWkbsmR1f8AZ58bWttqp0u5mfz7rEUXnZCs3YewPOBjriteNdZU9MmT7RtbR6GW4e0kciLzZXPU9BXTVnPdFjCES3eWQK8xGfzqG80VWLNL8U+OtE8KrF9+lEuoTybBbocsuBk59uw/MUpPwCRxbxf4t1HxPf26zgrazI06QxnCoMggnHcAY+euKxcrNFGtnIfEGjPZ6gpibc+0SSqqkLHnkKCeTxjn3OKaeBVQ9p9wl1bmF2ZznG0rgZ+e/wCZpMpZOn/Zn9kp1aYahrcrW2lqdw7mX4AI61pGF7FJqJ6J0jRLPTLJViEVpYqMIgPatbrCMnnLCS2a3R3RSeXbk4Vs9aLFQSTUd8oQpthHJ9zT6+RWGuL4NB+5AjTp80lH2O/Qt98JQCIhAOvuadCsanuDNaDy5RH7461KWR3aFh53khUIC+/c1QhdOSwdgHHU96YHzkgqImwT2FAArxWGA52KB1oQmN2l03kBNu9B3NJoEwaopuQzjdk4AHQU/AeT68a7F9HHFIDFjJCihVQndjr3Epj2qMDoc9ami7At5YX1OU9wDTEfQqpZiDtHb5oYHPPtL8PJJZXWpwYVo4yZFxy2O9Y8sOys0hKnR5Na6uDJIqxP5PmFQ+Dlc9visqVF3kzeWzWpDNdEBupLZPvgc007EyvmljEjm2PXn6fPxVoRcaHeRz3sC/h81thUnOGPAz8dKmSGjrsPhy/g0qY3seFByBj8IH9/pRKDoFJWcturW4uLuPTbVxEItzzMOBuPX88VC1ZTD/drWyhSGGaSW5l3AJFgvJ7tu+vGD7ce9J5BMakFpcW8nnTmKQeoxRpvQIOqk/Ujv2PxWeUabDwIYMsLqCWFkEqsigjcSV4zwQOPYZ9zScilEvrRGE9vFKVkhaQbpBnCsygkdgRk8Z9vyrBy2aqIaeC4aG1igkdYyXUeW2Cgzwp6gjG3nrj6mknY9AJYDDkqi/PPvVuyVQHw7fCC9uEn9RcbYyOijnJxjnn+eKqKwKWRa4tA12st1GI1tX4Rs4kYYyBn3x/U1qsIxspbqWfUGmngMhMjfvZckDLc+r3GSfV3wM06rYjWLwLC6i2lEz4J3pkEDjrzWyzszf0dq+z77f3stNj07xjZ3d7JbIFS/tcNI6jp5qkjJA/iBye4J5rZT9ktWWfiX7dpLnTb1/CunyxRrGcXd4ByThRtRT7+5544qJTEonnm81a+vtYkv76eSW7kkMjs553H+/5UqK0dR0+5Gp29gnm7jHgOiDBdiBtJb2AGMH3rB7Ni5sNJGty+UfMm3ZCq5wvyR2+mMY96EFHQ/Bf2R6ZZXEd/qkUDTp6ooiMBB2z7/SuqEKyzGU6wjpLRboQcF0j4BA9IArUyYxBAl26rPIWXrk9BRoNjRWIyBY3ztOBjoKQCaMqM6lc/NMQtcqzKdjVSEyFhEYGLSncxpydiiqDtcrIphYYz7Uq8jswrPGAm47T0opMNAzHmTJUkimIO5yBHHw4GeO1IYWOPdb5mBdhzipe8AtZPovMETEKAp6ChjC29tIpdlPqIobBIzE/kjeFy/SkAM+Y0o3th27DoKYH0tqIZfUxlJ5oUhURSWaGYnZ5gPAHZaMNBmzSvtl11NJ0CO2aaNJ74kMSQAka9f1JA/WseWXVUjWCt2zhdlbafJpN3c22GlLGafHII6AiuV20brZz7VPu9/eNhiVUY2j+GtI2kRLZSyR+TPtjwvPDH+VaEmw/Z/avc+KdLjjCEy3KjLjcu4f8AcdD1zTA9oNp0NxbyWuVdyoQ+w4x+lbtYyY3k8y/at4fuvDGp3EcRKvcuyxN0DhsZ5+mRXI49XRunaNMWKPTWjjBEMhCiWZ2A6jk568fHfpRdj0WkmrabbNKryzyxl8GRAUJOAMlfkZ6HPfvWLhJmqkkXmnXUNsUjMb/d5HZjuIjPvlQx45I69ckdK55RbNoySL+302IQNLG/mWdxL5pyPcjj8vUMfSs4pydFyaSsGJEslAXkszMQ3fIHf8q6Ix6oycnJlNd3BuFRmbiR8nHGM/8ANIdCtlF5l2gBIVztJzxnuP8AX61ajZDkbb9oXheaxtoroBmtn8tpW255HBJ+P04Jrolx9TBSs5/qK/4jrkdlEfLtUQKzrgDaWPXPGOM5981K1Y/opdfcedHZWsSJI3AKuT6QTj6EjB+h7VUfZLKjVYHtxDarFsc/i4wWOehPfmrsRuen26QaRbozxqsSMW3MpwxGMHpyecdc5GKybyaVgp9R8KSRAvEzy5GIgq7c4IGOmcgduuOeaa5ET1oL4dTULPUkiljTazZ6KUU++B2+mKGlLRUW0em/AuhRx6bFdzpHLLIAcR8R+/51tx8SWWZz5PCOhxRw3EWLlDEcgKue1bN1oz3sdl+72eUkJ8g/hUd6m2weBi3ubRLPapRfak7Hiij1qX7nDHNA+XJ/CB1NaRzhmcnWUElkijZvMYD2ApFAdglG6N8k9Fp3RNWRjim3klN2O1Fjo+EG5uIiH+aLCkNQ6dcoFlbB56Gl2TCjFyZzMMqqGhDFzG25mUnd3agBmBJCMqSe3vmkBO8gliQFty8cAULIMHppllkPmkqo446mqaSJTbGZlRQW3YUdeaRVmIZEL5IyooaFdkzdQtkIoBFKgsgsiLGwjA5Oc0DOFfbs8N3rKW86rIkduow3QZJNcnyZVJHRwq0zhemyLY6vHbmRfukpMUgAYYVuOOcEjNEf2QpKmJavpN1Z39xHZxMsCHAYcEimpWsiaEVtbl32zegYzuP9f5/zq7Ebj9j1nKftI0JInRiJtzK3RgATn6+w+KcctCej2C5lLHIC564HWuijI1zxx4XtPFWnx22pAs0TeZDIvBRsdf6UpQUkNSaZ5m8UeF7mx8avaalbsscUe+MtzHKAcE5rjknBUdCalkqVgvNkS2qRtOymUnOwIu4kDJPXjjB5AqG15Gr8BbZLOKGL77sJ2eaWcByc/PX8vntUO3/JpGl/Ru+g3NmPD90LI4jiKk7VOPV0wOgrKKkpZLk1WCuvtQQwSMPWFTJIPBPXGf771TjYk6Kq+WW1jXEkWY2wpUnDp03KTyRlWz8cVURSbH/DIdtStDFAZYPNDFFbBfJxj69MVakk0ierZ6ouNKstS8NSQThN8icCTja2OOB84rtavBy6ycI8afZzcWGoyT6fH58EqgnaDhdpyAPf3IrGXG4lxkmc5vbI2+u4WJjNPCUjdjgRnPfv8dRxUJ4KrJnxH4fTUPMuLWTZLEod1GSFwoB47nOORxzn3qVOhuN5DeE7s3Ajs9Qt7aOeH0208ahTgjpkcH8/c0pryi4u9m7ad4YZbjfGn49rKMFiBng8d+uT1+tRd4Ko6j4V+zGxtGW/uLdDNwd4cgZznIxjmuvjhWWYTl4R0NrSKAqB6Yjgknk1tZkLXKvPIZLdsIOAxpr7E/oLdSebHFHEAzgdX6mhIHklKsSW6xkDzG6vjofakBJtMhhjFxKw3AcAnv70dvA68lNNctPsSNM/OK0SohuyVraSeZveUg9gD0obEo+yxRRG2fPOfrUFhw5BB8wUgCPduy7TMMD2pUOwHnKHBLBj80xGTMOcqB8UAfRShPwLj6GgCcs7P1Ut9TQAN3KkeWBnHNAAXSRzlgCD2p2FH2yXcB0FFgGlt0XmFz85pWAIQNg+rFAHnv7brgW/ijUCZARGqKBnphR+tcfPmdHTxOo2cGkuUkuCZC6xu4O/3XPJH/FapUZtm3a8x0+GOe4vvvMc8aSwYHLKQcH+WPyrNr9qRSeLZqP32aaSSRQIoud21evwauqJOh/s92dzN9ptldQp50EEckkzDH7oFcZwfkgcfrVw2KWj1k78gnJroMgMkrgYCEjtRgWSu1jRrLV7Yw6hbJMCpXJHK59j2pSSkqZStHAfE3hoeD9Z+5BJJ7UW4lgdlB3YOMcnqMniuDng0dPFI554hn8+8lKD059GRyo6jPseaXGqQ5u2b39kmntrMPiCxEb4eySVT1y6OCR+eSKaj2lX0DdRv7KsLc6kx+5whLSBjGzMPxHIzj6YI/OsZNR2aQTlkFeaXE8j3TykxCcqg5y65wMDtgZ/XNQuSsRL6J5Za+FrZpNVsY7e+gZC2djgDaM9xxnpt9uacXclaG1SwendPtkSCEOD6VGQW3YNeusI815ZYTyxyJsMalcEYx8YpUBqmt+C9F1SUSTWyCRQQGXgkE5P86l8aZSk0a/H9mFpvVhcP5iPlW6ek4yDjvkde9YvgRa5aJ2v2R6D5gmuPMaUMWyDjOeoPuPrTjxVsb5PRvulaXpumW6xwxocfxba1UEtIhzb2PyXUZ24JwOnsKqiAEt4mCXJbPvQkFgLIGefbFGEibkux4pvAIvdO0u1+8eb5qvMBjg9KhydFJLZbLpFu6nem4nqajuPqiB0G0JywY9+WJo7h1RzhXmI9JC10mNBV3Yy7t+VLAwqS7TwuT7mkMOZsj1HFIZISx455pAFjki7Ak/SgAyevnac/SkBMwqeCMUASWAHgZoAkLUZ5BoAmIMng9PmgCYtRkZJP50ASWOEHBIJ+tAExFEWG4DHwaQHkj9o+EweOtRVmKwSMspDZyVKggD865n/AORm6/hHJl0nUJWsgluXe9z5Kq4JbB5yM+nr3xxz0rSiLOmeM9Dhk8O+DbmcBpTosfpB6kO3X3wCBU8lxa+yoZs0O4WJV8pwDgnCL6QP1/rSQM61+zvpsH+NS3cWVMULEOq5BJIHX2q+PMiZ/wAnoYSomOc10UZWSFyh425NFDsIsoJ4iNKgs539uUUcnheCVkfek2AqpuyCpzkDr/3rDn/k04tnmW8Zmcjq6kqzAnHuMn36/l9Kxias7h+zRZOLnWbtwFVIkgA98nJ+nQ1XDnkf0h8uONfbM+NbGOx1vVLOCMQxs29PYBueP51x/LVTNvju4nPNQjWzt5oHZ32tvyTjGQR/U1EH20azVbLLwLp8cviLR0SKYr95UbBLucnp1B9utaxbc0jNpdWeoo7SfzBGmHHXg9PivVs86jEsTxSlJFOfinYURRuc7RQB9v8ATx1oAiHJXkgjpigCG5y+1Bk4yc9qYENsjAkqQKAMiLzFG/8ABRdBRMWqM2WYsF7dgKOwqGLJIrZG2yupJyccUm7GlQ9HqWzkTTke26pcUOz6XXJS52NIMds0dBWVMsEECFnGW7AVoskt0ZtvMkg8tvRATyo6n86GkhJsfeG3SIDylIx1PWoLK+a39X7tBk+/aqQmx63toYlJnG4YqWBGRRI+2BNkf+duv6U69hd6CBNq4DEkdzSGTVX/AM3P0ooCRVwhLvgD2FFILPrWPzizksyj3NDwJFtDFD5QCglj2CVDKFJ9LluAdpeJO/qpqSQqsPb6YsKeogn6UnIdDf3ONEyUycdalyHR4z/aP8y7+1HUraWeKG3aeOIOSWEaqigkgZPGaxSubZq8RRze3eQSK/3lJEgBniEzhXZcgAYycscfhz05zg1qQegvtN8O3GnfZF4W1S9jjQ2tvbx3SxpjyFlTjH/xO0Yo5P2ivoIYbPOt9EYJmaCQTyOwXcTkYP8AfWoQ2d2/ZzCS6nfWcgYyJaLIzKuFTL9PzyPir4sNsU8o9Ax2UZAVQuT71tZnRCS1EJ/hJ+KfYVGQrnhQAPegZzj7edsHhazWWQ/vLgkDdj8KHnr9K5vkv9UacX9Hm6/yLqRZGYRqcjuG6Ege5II5x7VhHRs9nfP2bLUReFdQnVDNPc3ARCpznHOSfqa2+OrlJ/4TzyqMV/prP2k6qJfGl8wljEUbGElG4IUYznucg1yfJfaTo2+OqVnNfFN+l9bJFEGDD1s4HyeCf75qeDjcXbL5p2qOn/szaEb3W73Wpy8i2KeTESuAZ5B6iT7qn/8AfxXdwwy5HNOVRr2eh5LUZ4dkI/ymtznFpLYk8yMT7d6YEDEc84GPcUAYwGIBkG48YXvTAyIAjbejdM0gMiPaCVwSO9AEfLkIxjApgSMBA5Ix1+lFgR4BIHTpQBHBI9hQB8y7fxfpQAKRgqjJBzxTEQKsXbMcjHdgkjgH2p2KgqmYsGC7Yx2xUjGBcQuArMu72HJNSMVfUrSIlC0zlc52xMapJsm0M6Zcw3aNLOJrePO2MOuC5+lKVrQ1T2T80tcOqK2FIyW6Y70h+QiSAquxd5PJfI29aLAYjEfl72njBJIG3+H60uwUKzyRPgLcxOTjKn04/wB6pNixobspnWHYmw98gcc9PpzUsaAx+I0hljA9TEhWXZ37jilVjsvYdbjklSBYQ0jjIVX5I9+ajqOwseo2jyMkhdGHQFCfy470UwslLqNuo4V3i7uBwB+dS0x2eTv2ofDtinii08Q208zadfuBdrGATFKBg4zx6gAeTjg0q6u35LvssFH9kXg6fx740tr7VLWO38K2RUXO44jYJykSsRmRmYZY+xPI9IrTLyRo9V+PLGy8T+C9c0uW4j23lrIqt1wQNwIHwVpNYBPJ4Hv7b7s5NsuxUKlgF/ET9TyBWcXZbVHav2XpTLqfiFUcvOYLf0oMKBvbjH6VrAiR6DAij5nuFVccguAM1paIJWzW7NuhlSUf9DbqE09ANGSKIhZGCuezcUAedP2rdaW2vdEtIDumktmkOeiqX4/XFYci7SRpB0cJj1CWRWZwI2cY3H+Iew98kH+xUdaLs9J+DvEKeF/srtk05ozf3ZIizyETaN8nHyeM9/pWXFyOMZe7NeaPZxX0cn1KOfU74w6fHLcXihikEZDSSsFLHC8EnAJwOTg8Vgk5S/01xGJrGj293r2q21jYwl7ueRYo4wp5YnAOPjvn2NdUY1hGF9nbPZXgDw5beDfDltpVlvkEeZJZnHqllb8T47fHwBXYkkqOeUrZtqKTDvyBu/iPagQswG5iv9aYiC+UdwLqzY6e1FgSt4lllZYNpKjJ5A/nQ2CRiaJ45cSKVI6ZFCYBABtCoBjvzmgDDRK2DyXPVs0ADmjDKFOFA9upoQC4tmDk7i0fPA4qgBta7s5Zs+4c8UCJm3LCNT6Qo7D/AFoAxDAkLSZIYuMerkj4A7UN2CNkmswofAYbhzjoR81imWUf3eFQEkBDluQTlVHatKJskunQgMY1ijaQcsCQ2KWQwfX1gPKCGLqQRI3Uf7Uk2NpUL3bXHnAOdmwD1FPjr37U0AvI/mRHzpY2DHHrYgqKSEAtbiysJFliQ+jjjLr9QDVNOWBYjkPaztqk5fDBWY7pQo4+g7fWjr1QdrC6pZRpDHHNGpySzSqDnGejGkm7G1aE02RALaukag7vKjLbD9fmhu9hVaHpMyDEDLHgg9AFY+5zS1sZP7w0Rc+e7yY5ZcLx7CirAClxIZ/U8u1lJyuTtx2/4pNAZ2vcIyKXZCQpDkkfPAphRLUbSxYbJxDcwjACzwbunTjHvU234HoDfyOLLyXnW2t8ekMQFJ7gAirWXZLwUOv+LNO0LR7q5fVLY3v3V1gt3cCSVipAAXqMms5ySVMcc6PIPiJJI3ZojkY2knPbvjp0HxWMDWR0z9l6dLfxTrNvPAN8+nrMGfvskH8jkHn2FaReaRMliz0FPcxSFkZ0e4YgKuzBY+wJFa0vJnfouNLhgiRJLhmDFsshChQvdgByTnqahtL+Sv8ATUvGnjux8ExHzriKd7oyPChUGR1HBYscBFzx3+KJySWVkEn4Z5G+1DxLdeMPFE2szpiIosEezJRFXICg/wB5696hW8suq0axB6A55zjGKGB2Tw9dTJ4asoL+NoJ7eFkMbghk9WcsMZGeK4eX9W0jrj+1P6NF8UXcb3kaW8gEq/vGkVRuD9sMDwTx9MfNacMWssz5ZJukdx/Zc8LqFn8Yaqs+yQyWunhlyJT/AO7MCef+nPvu+a64xezFypV7O/LfWgcxbnjtgvCoBlz8nuat2Z4J7oJbj0XUVvEi8GYhm5/6en59aMhg+kS0uLZbfTZoLqVG9UjrhBzk7j/tRbWWGHhBl02Muwea0igUZMcABJP+lH5A6i9nprTl5gkzw/8AtbVXJ9+ppudYBR8k76ymtYo3MJlc5Cxphm6fNCkmJpoSW0v7vBkt5osDqwC7R9arskFNkUhv1McccJbPAkRWZRj++tFoKZFVnSVxc7w0eC25dgGew96LVYCgri48rzoYSYxx6hgA/JNFrTCmJS3V9Exb/DJZEXA37wA2f8vPP5Va6+yG5LwG0q4fVJZoHtbi0dMZWZSjOD3GcYFKS65sat7RsVvYxptBWOMYwMEc1m5F0VV7qE8xIiGz2B5qkktiZSTWkruXnuGxjnjFaqSWkZ9fbHbe/giJEZ3A4HPPA+TUOLY7SHYtQRyUiidoz/mOc1PVoqxsaq2NjWkeQMHLdKXUdiczpIy/+XQFehzz+tNIRhYISxY28ak9h0pgWFk9urqsq7F+Omal2NF2kCugKsrL271lZVFJeeHYixKJkHoAcY/3q1ITRXtoUkSqAuCT25/4ppoVAPuE8eRi3ePeSzvlHUewHIPPxTEA1ewvrez8/TZ47p0IMsWxt4UfiZAPxHHbGe/PSiLTdMbTrBrsup317O40YvfQoB5kc1v5ZHwTgH9K0UEv7wZuTf8AI81/qk1kmbW3srsFMlZJHJXPIIIO3Ax71PSKe7RVyayB1Cy1zUtHuYvvelxXUisscjrIxQkH1cL1Gfb564qZJV+o1fk5ZN9i+tNh0v8AT2lB3K370/r6c1z/AIpXho3U4lbqX2HeLLm4BXUdFht3YuwcTk4z1/8AT7jPGf8AenHirbE5rwbn9nH2T3HhXWL3U7/U4bm6mh+7xiNdiJHkHkEk7uABjgD3q4xURSl2OlRQSQxBYvu7oWyS0hLNj3z/AKUUTZhXaB55ttusj4BfJYqOwH/FOmFnN/tZ+zT/AMcXljfDVUs7mGDyUVE8xW9W4bh1GCT+VHWwTPvAv2Wz6PNex3d1BfaRdmNZrNdOEcJZUK4G+Rl9WRk4zwMEZNLrjY7zY1B9jnhO2Bkg0BjMvmfvpLh2EYZdvAY4G3qp7Nz8VLj9j7fRr+ufY3ol5NLJFreq200q7SseydRjg54U8jGSWyTlicmj8SH+Qt/Bf2b+FvD+iy2Oo6BZeILh3Zmv7q22SMrdBt3HZj3U/PBqnF+Bd/RudrJHZafb2FjpohtIYRBFFJOSsaDhQBjij9hOmOJcL5cUccaQyKAHJLNvx1J9s005eROjPmujyfdp0cSNtxMGOF65Jz/T9aeWLCPvNd7uIuw2gbNqgBB/1Y/l/vRkeBq3NxGpeS5S1WYFSYogSfb4Ix+YpZCgcs91DM6C7mlj24C7GQD2GM8fWnV+BCkkYa9BuVmleT1Mzs3pwBjkH25xj4+apXWBNItlubxrVULb0K7d/mbGRR0GOhP15qKyU2BnubgQARzXgKAejzzg/H54p0IB9+1NpswWTl5CN8rTejj3zk88cimo+2H+F3Hb396haPf0AUXMu4LwMnaCQe9ThDoWutOWd4hPetC9s+VwqgH5zzgd8flVJixsObgvKkVzLFfW0eWDxpkrx1x1z/LmlXlBfsBfmOWQmBCJOAkaqV3KP4vamrWxMJOLhkIhjC56k8k1aryIqbywu58mV1jU9cnOa0jKKJcWxNbERygTTCRB0RBVd70T0otbWWUKQkYUHgZ7Cs2UhiGKXkk5J+KTGhmOCQ8k8/SpGFW0f6596LAkbSbtgUrGFhjuIMmNip+DSdMB2HUmHoulI/6lqOvoqxvNvPypD0ZAGLeIZO3gn45p2IG8cAUs64HuGxQBWTXFuf3MDygg8kDOPrVdWKxO5WP1PJGbgAerD4GPp1poQSLzGtVjtLWLyySQijgfPNH+jBGzmDSCSNUkf1BmGAvxgf1owGQVjZ3lzcSKzegcLuUhc59wemKHSQKwjWEsEZe4gVZA5LKr7l2/GeaVrwANJbfzSkUU1vcYyZgw6f5en8qbixWgE9nJdMAskkb4xlT/AL5xQsD2F+5wi7aaW2lYsArhpN2eCDgduvPei3QUHa+mQCK3jaK2BA2AAHA7Z/2/Wl1vLCyV1cwTQENA5Bbo5DcY/v3pdXY7K42czyrLBEVgBBaMRhg3/wBvaqX2LJKex3YbyYIsk5V59xH1osBY28Hmui+tgvJ83I/MY/1pgShsyxchcrGM7whwaAAJNFE8vIyPxexP0p0xWT3hgcRiYkdEAz/M0UAbRN1yZfMga1ii/CpIJJI7YOBSnFIcWOsHcZR8sBgArgdM4JpVQ7MRFmGHhVnxgFnx27Y6UUKxmO3jVlMgUIqkhVUEfnk80DEyIfvax2sqyXTcKr5AzjOD78c06YvOCamKLJugQqnaREgwD7cfX+dKn4HY47WCjYY51/zAhUqaY7RX3yRXCqsUUionKrJLyD71UU0S8icsV9I6mCeGJVUBWjGCCM8+5PNVjyLIrPaapO8iy3sxj/EYWkOw++4dD7+1NOK8EuLOhRxLgcVjZofS20bqdyg/lRYynm0+FJTtGM/FWpMTVE47VOKLENR26ZpWNIZjhUUrCgyxKKQwqxikB8YV70WAN7aN1IYD2osCruIjaShoXINVdiYLz5XJV3Y/Q4p0hMG+DlzuJ/CctnNOxEPKRXBI3KeNvQZ96dgQuG8rCADDAk8ULImxWDU5mv1jT0Z4yKbiqsFIflnlPDSEjpz+lTRRGSSW3LRrKdo5wvApUnkBC5mJIZtzHqMtwD1qkkIjbo10yhnKDGfSKbwAWW4WO5FuqHOAN+7mlV5Cy1SE4lDuW2bSO3BqCqI6hBGLBpUUBtuRnnn3oi8gxKwst9x92aVi2CxkI549varbrIki6i0mJohI7uzMMZNZORfUrpdJji8wrI2FOAABVqRLQCQRRhUaPcVG4tnGadiE7p97LGqIqOOvJYEnrknH8qaQgCP5cjSIqbugyMgY9vrVAV+pXbwKx2xmSUgbgoG0e3H9aqMUyW6LOJY1WCNIkVWIU4HPp7596zbyWsobubgxLtRF6ZyfgUkFgrBnuEYs+0g/wihug2MpEvnIMvnfjIPxSsYd4Y0jDIGBUnBzz+R7UKTChbyAtwj7iWOQaq/Aq8g76JINzct5eGIz+Ie30oTE8ZGLBGvtMW9DeUnOIQAwGD7mlJ9XQ0sWDtnLjzW5JwTmmwQWONbiVwcgEHvmlpCP/9k=\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for image_features in parsed_image_dataset:\n", - " image_raw = image_features['image_raw'].numpy()\n", - " display.display(display.Image(data=image_raw))" - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [ - "pL--_KGdYoBz" - ], - "name": "tf-records.ipynb", - "private_outputs": true, - "provenance": [], - "toc_visible": true, - "version": "0.3.2" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "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": "3.6.5" - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/site/ja/tutorials/load_data/tf_records.ipynb b/site/ja/tutorials/load_data/tf_records.ipynb index 2a5475ba6fa..20cd8400230 100644 --- a/site/ja/tutorials/load_data/tf_records.ipynb +++ b/site/ja/tutorials/load_data/tf_records.ipynb @@ -1,1233 +1,1272 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "tf-records.ipynb", - "version": "0.3.2", - "provenance": [], - "private_outputs": true, - "collapsed_sections": [ - "pL--_KGdYoBz" - ], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - } - }, - "cells": [ - { - "metadata": { - "colab_type": "text", - "id": "pL--_KGdYoBz" - }, - "cell_type": "markdown", - "source": [ - "##### Copyright 2018 The TensorFlow Authors." - ] - }, - { - "metadata": { - "cellView": "both", - "colab_type": "code", - "id": "uBDvXpYzYnGj", - "colab": {} - }, - "cell_type": "code", - "source": [ - "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "HQzaEQuJiW_d" - }, - "cell_type": "markdown", - "source": [ - "# TFRecords と `tf.Example` の使用法\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " View on TensorFlow.org\n", - " \n", - " Run in Google Colab\n", - " \n", - " View source on GitHub\n", - "
" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "3pkUd_9IZCFO" - }, - "cell_type": "markdown", - "source": [ - "データの読み込みを効率的にするには、データをシリアライズし、連続的に読み込めるファイルのセット（各ファイルは100-200MB）に保存することが有効です。データをネットワーク経由で流そうとする場合には、特にそうです。また、データの前処理をキャッシュする際にも役立ちます。\n", - "\n", - "TFRecord形式は、バイナリレコードの系列を保存するための単純な形式です。\n", - "\n", - "[プロトコルバッファ](https://developers.google.com/protocol-buffers/) は、構造化データを効率的にシリアライズする、プラットフォームや言語に依存しないライブラリです。\n", - "\n", - "プロトコルメッセージは`.proto`という拡張子のファイルで定義されます。メッセージ型を識別する最も簡単な方法です。\n", - "\n", - "`tf.Example`メッセージ（あるいはプロトコルバッファ）は、`{\"string\": value}`形式のマッピングを表現する柔軟なメッセージ型です。これは、TensorFlow用に設計され、[TFX](https://www.tensorflow.org/tfx/)のような上位レベルのAPIで共通に使用されています。" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "Ac83J0QxjhFt" - }, - "cell_type": "markdown", - "source": [ - "このノートブックでは、`tf.Example`メッセージの作成、パースと使用法をデモし、その後、`tf.Example`メッセージをパースして、`.tfrecord`に書き出し、その後読み取る方法を示します。\n", - "\n", - "注：こうした構造は有用ですが必ずそうしなければならなというものではありません。[`tf.data`](https://www.tensorflow.org/guide/datasets) を使っていて、それでもなおデータの読み込みが訓練のボトルネックである場合でなければ、既存のコードをTFRecordsを使用するために変更する必要はありません。データセットの性能改善のヒントは、 [Data Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets)を参照ください。" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "WkRreBf1eDVc" - }, - "cell_type": "markdown", - "source": [ - "## 設定" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "Ja7sezsmnXph", - "colab": {} - }, - "cell_type": "code", - "source": [ - "from __future__ import absolute_import\n", - "from __future__ import division\n", - "from __future__ import print_function\n", - "\n", - "import tensorflow as tf\n", - "tf.enable_eager_execution()\n", - "\n", - "import numpy as np\n", - "import IPython.display as display" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "e5Kq88ccUWQV" - }, - "cell_type": "markdown", - "source": [ - "## `tf.Example`" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "VrdQHgvNijTi" - }, - "cell_type": "markdown", - "source": [ - "### `tf.Example`用のデータ型" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "lZw57Qrn4CTE" - }, - "cell_type": "markdown", - "source": [ - "基本的には`tf.Example`は`{\"string\": tf.train.Feature}`というマッピングです。\n", - "\n", - "`tf.train.Feature`メッセージ型は次の3つの型のうち1つをとることができます（[.proto file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto)を参照）。一般的なデータ型の多くは、これらの型のいずれかに強制的に変換することができます。\n", - "\n", - "1. `tf.train.BytesList` (次の型のデータを扱うことが可能)\n", - " - `string`\n", - " - `byte` \n", - "1. `tf.train.FloatList` (次の型のデータを扱うことが可能)\n", - " - `float` (`float32`)\n", - " - `double` (`float64`) \n", - "1. `tf.train.Int64List` (次の型のデータを扱うことが可能)\n", - " - `bool`\n", - " - `enum`\n", - " - `int32`\n", - " - `uint32`\n", - " - `int64`\n", - " - `uint64`" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "_e3g9ExathXP" - }, - "cell_type": "markdown", - "source": [ - "通常のTensorFlowの型を`tf.Example`互換の `tf.train.Feature`に変換するには、次のショートカット関数を使うことができます。\n", - "\n", - "どの関数も、1個のスカラー値を入力とし、上記の3つの`list`型のうちの一つを含む`tf.train.Feature`を返します。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "mbsPOUpVtYxA", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# 下記の関数を使うと値を tf.Exampleと互換性の有る型に変換できる\n", - "\n", - "def _bytes_feature(value):\n", - " \"\"\"string / byte 型から byte_listを返す\"\"\"\n", - " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n", - "\n", - "def _float_feature(value):\n", - " \"\"\"float / double 型から float_listを返す\"\"\"\n", - " return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n", - "\n", - "def _int64_feature(value):\n", - " \"\"\"bool / enum / int / uint 型から Int64_listを返す\"\"\"\n", - " return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "Wst0v9O8hgzy" - }, - "cell_type": "markdown", - "source": [ - "注：単純化のため、このサンプルではスカラー値の入力のみを扱っています。スカラー値ではない特徴を扱う最も簡単な方法は、`tf.serialize_tensor`を使ってテンソルをバイナリ文字列に変換する方法です。TensorFlowでは文字列はスカラー値として扱います。バイナリ文字列をテンソルに戻すには、`tf.parse_tensor`を使用します。" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "vsMbkkC8xxtB" - }, - "cell_type": "markdown", - "source": [ - "上記の関数の使用例を下記に示します。入力が様々な型であるのに対して、出力が標準化されていることに注目してください。入力が、強制変換できない型であった場合、例外が発生します。（例：`_int64_feature(1.0)`はエラーとなります。`1.0`が浮動小数点数であるためで、代わりに`_float_feature`関数を使用すべきです）" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "hZzyLGr0u73y", - "colab": {} - }, - "cell_type": "code", - "source": [ - "print(_bytes_feature(b'test_string'))\n", - "print(_bytes_feature(u'test_bytes'.encode('utf-8')))\n", - "\n", - "print(_float_feature(np.exp(1)))\n", - "\n", - "print(_int64_feature(True))\n", - "print(_int64_feature(1))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "nj1qpfQU5qmi" - }, - "cell_type": "markdown", - "source": [ - "メッセージはすべて`.SerializeToString` を使ってバイナリ文字列にシリアライズすることができます。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "5afZkORT5pjm", - "colab": {} - }, - "cell_type": "code", - "source": [ - "feature = _float_feature(np.exp(1))\n", - "\n", - "feature.SerializeToString()" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "laKnw9F3hL-W" - }, - "cell_type": "markdown", - "source": [ - "### `tf.Example` メッセージの作成" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "b_MEnhxchQPC" - }, - "cell_type": "markdown", - "source": [ - "既存のデータから`tf.Example`を作成したいとします。実際には、データセットの出処はどこでも良いのですが、1件の観測記録から`tf.Example`メッセージを作る手順は同じです。\n", - "\n", - "1. 観測記録それぞれにおいて、各値は上記の関数を使って3種類の互換性のある型をからなる`tf.train.Feature`に変換する必要があります。\n", - "\n", - "1. 次に、特徴の名前を表す文字列と、#1で作ったエンコード済みの特徴量を対応させたマップ（ディクショナリ）を作成します。\n", - "\n", - "1. #2で作成したマップを[特徴量メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)に変換します。" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "4EgFQ2uHtchc" - }, - "cell_type": "markdown", - "source": [ - "このノートブックでは、NumPyを使ってデータセットを作成します。\n", - "\n", - "このデータセットには4つの特徴量があります。\n", - "- `False` または `True`を表す論理値。出現確率は等しいものとします。\n", - "- ランダムなバイト値。全体において一様であるとします。\n", - "- `[-10000, 10000)`の範囲から一様にサンプリングした整数値。\n", - "- 標準正規分布からサンプリングした浮動小数点数。\n", - "\n", - "サンプルは上記の分布から独立して同じ様に分布した10,000件の観測記録からなるものとします。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "CnrguFAy3YQv", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# データセットに含まれる観測結果の件数\n", - "n_observations = int(1e4)\n", - "\n", - "# ブール特徴量 FalseまたはTrueとしてエンコードされている\n", - "feature0 = np.random.choice([False, True], n_observations)\n", - "\n", - "# 整数特徴量 -10000 から 10000 の間の乱数\n", - "feature1 = np.random.randint(0, 5, n_observations)\n", - "\n", - "# バイト特徴量\n", - "strings = np.array([b'cat', b'dog', b'chicken', b'horse', b'goat'])\n", - "feature2 = strings[feature1]\n", - "\n", - "# 浮動小数点数特徴量 標準正規分布から発生\n", - "feature3 = np.random.randn(n_observations)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "aGrscehJr7Jd" - }, - "cell_type": "markdown", - "source": [ - "これらの特徴量は、`_bytes_feature`, `_float_feature`, `_int64_feature`のいずれかを使って`tf.Example`互換の型に強制変換されます。その後、エンコード済みの特徴量から`tf.Example`メッセージを作成できます。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "RTCS49Ij_kUw", - "colab": {} - }, - "cell_type": "code", - "source": [ - "def serialize_example(feature0, feature1, feature2, feature3):\n", - " \"\"\"\n", - " Creates a tf.Example message ready to be written to a file.\n", - " ファイル出力可能なtf.Exampleメッセージを作成する\n", - " \"\"\"\n", - "\n", - " # 特徴量名とtf.Example互換データ型との対応ディクショナリを作成\n", - "\n", - " feature = {\n", - " 'feature0': _int64_feature(feature0),\n", - " 'feature1': _int64_feature(feature1),\n", - " 'feature2': _bytes_feature(feature2),\n", - " 'feature3': _float_feature(feature3),\n", - " }\n", - "\n", - " # tf.train.Exampleを用いて特徴メッセージを作成\n", - "\n", - " example_proto = tf.train.Example(features=tf.train.Features(feature=feature))\n", - " return example_proto.SerializeToString()" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "XftzX9CN_uGT" - }, - "cell_type": "markdown", - "source": [ - "例えば、データセットに`[False, 4, bytes('goat'), 0.9876]`という1つの観測記録があるとします。`create_message()`を使うとこの観測記録から`tf.Example`メッセージを作成し印字できます。上記のように、観測記録一つ一つが`Features`メッセージとして書かれています。`tf.Example` [メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88)は、この`Features` メッセージを包むラッパーに過ぎないことに注意してください。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "N8BtSx2RjYcb", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# データセットからの観測記録の例\n", - "\n", - "example_observation = []\n", - "\n", - "serialized_example = serialize_example(False, 4, b'goat', 0.9876)\n", - "serialized_example" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "_pbGATlG6u-4" - }, - "cell_type": "markdown", - "source": [ - "メッセージをデコードするには、`tf.train.Example.FromString`メソッドを使用します。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "dGim-mEm6vit", - "colab": {} - }, - "cell_type": "code", - "source": [ - "example_proto = tf.train.Example.FromString(serialized_example)\n", - "example_proto" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "y-Hjmee-fbLH" - }, - "cell_type": "markdown", - "source": [ - "## `tf.data`を使用したTFRecordファイル" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "GmehkCCT81Ez" - }, - "cell_type": "markdown", - "source": [ - "`tf.data`モジュールには、TensorFlowでデータを読み書きするツールが含まれます。" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "1FISEuz8ubu3" - }, - "cell_type": "markdown", - "source": [ - "### TFRecordファイルの書き出し\n", - "\n", - "データをデータセットにする最も簡単な方法は`from_tensor_slices`メソッドです。\n", - "\n", - "配列に適用すると、このメソッドはスカラー値のデータセットを返します。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "mXeaukvwu5_-", - "colab": {} - }, - "cell_type": "code", - "source": [ - "tf.data.Dataset.from_tensor_slices(feature1)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "f-q0VKyZvcad" - }, - "cell_type": "markdown", - "source": [ - "配列のタプルに適用すると、タプルのデータセットが返されます。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "H5sWyu1kxnvg", - "colab": {} - }, - "cell_type": "code", - "source": [ - "features_dataset = tf.data.Dataset.from_tensor_slices((feature0, feature1, feature2, feature3))\n", - "features_dataset" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "code", - "id": "m1C-t71Nywze", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# データセットから1つのサンプルだけを取り出すには`take(1)` を使います。\n", - "for f0,f1,f2,f3 in features_dataset.take(1):\n", - " print(f0)\n", - " print(f1)\n", - " print(f2)\n", - " print(f3)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "mhIe63awyZYd" - }, - "cell_type": "markdown", - "source": [ - "`Dataset`のそれぞれの要素に関数を適用するには、`tf.data.Dataset.map`メソッドを使用します。\n", - "\n", - "マップされる関数はTensorFlowのグラフモードで動作する必要があります。関数は`tf.Tensors`を処理し、返す必要があります。`create_example`のような非テンソル関数は、互換性のため`tf.py_func`でラップすることができます。\n", - "\n", - "`tf.py_func`を使用する際には、シェイプと型は取得できないため、指定する必要があります。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "apB5KYrJzjPI", - "colab": {} - }, - "cell_type": "code", - "source": [ - "def tf_serialize_example(f0,f1,f2,f3):\n", - " tf_string = tf.py_func(\n", - " serialize_example, \n", - " (f0,f1,f2,f3), # pass these args to the above function.\n", - " tf.string) # the return type is `tf.string`.\n", - " return tf.reshape(tf_string, ()) # The result is a scalar" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "CrFZ9avE3HUF" - }, - "cell_type": "markdown", - "source": [ - "この関数をデータセットのそれぞれの要素に適用します。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "VDeqYVbW3ww9", - "colab": {} - }, - "cell_type": "code", - "source": [ - "serialized_features_dataset = features_dataset.map(tf_serialize_example)\n", - "serialized_features_dataset" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "p6lw5VYpjZZC" - }, - "cell_type": "markdown", - "source": [ - "TFRecordファイルに書き出します。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "vP1VgTO44UIE", - "colab": {} - }, - "cell_type": "code", - "source": [ - "filename = 'test.tfrecord'\n", - "writer = tf.data.experimental.TFRecordWriter(filename)\n", - "writer.write(serialized_features_dataset)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "6aV0GQhV8tmp" - }, - "cell_type": "markdown", - "source": [ - "### TFRecordファイルの読み込み" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "o3J5D4gcSy8N" - }, - "cell_type": "markdown", - "source": [ - "`tf.data.TFRecordDataset`クラスを使ってTFRecordファイルを読み込むこともできます。\n", - "\n", - "`tf.data`を使ってTFRecordファイルを取り扱う際の詳細については、[こちら](https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data)を参照ください。 \n", - "\n", - "`TFRecordDataset`を使うことは、入力データを標準化し、パフォーマンスを最適化するのに有用です。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "6OjX6UZl-bHC", - "colab": {} - }, - "cell_type": "code", - "source": [ - "filenames = [filename]\n", - "raw_dataset = tf.data.TFRecordDataset(filenames)\n", - "raw_dataset" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "6_EQ9i2E_-Fz" - }, - "cell_type": "markdown", - "source": [ - "この時点で、データセットにはシリアライズされた`tf.train.Example`メッセージが含まれています。データセットをイテレートすると、スカラーの文字列テンソルが返ってきます。\n", - "\n", - "`.take`メソッドを使って最初の10レコードだけを表示します。\n", - "\n", - "注：`tf.data.Dataset`をイテレートできるのは、Eager Executionが有効になっている場合のみです。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "hxVXpLz_AJlm", - "colab": {} - }, - "cell_type": "code", - "source": [ - "for raw_record in raw_dataset.take(10):\n", - " print(repr(raw_record))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "W-6oNzM4luFQ" - }, - "cell_type": "markdown", - "source": [ - "これらのテンソルは下記の関数でパースできます。\n", - "\n", - "注：ここでは、`feature_description`が必要です。データセットはグラフ実行を使用するため、この記述を使ってシェイプと型を構築するのです。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "zQjbIR1nleiy", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# 特徴の記述\n", - "feature_description = {\n", - " 'feature0': tf.FixedLenFeature([], tf.int64, default_value=0),\n", - " 'feature1': tf.FixedLenFeature([], tf.int64, default_value=0),\n", - " 'feature2': tf.FixedLenFeature([], tf.string, default_value=''),\n", - " 'feature3': tf.FixedLenFeature([], tf.float32, default_value=0.0),\n", - "}\n", - "\n", - "def _parse_function(example_proto):\n", - " # 上記の記述を使って入力のtf.Exampleを処理\n", - " return tf.parse_single_example(example_proto, feature_description)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "gWETjUqhEQZf" - }, - "cell_type": "markdown", - "source": [ - "あるいは、`tf.parse example`を使ってバッチ全体を一度にパースします。" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "AH73hav6Bnmg" - }, - "cell_type": "markdown", - "source": [ - "`tf.data.Dataset.map`メソッドを使って、データセットの各アイテムにこの関数を適用します。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "6Ob7D-zmBm1w", - "colab": {} - }, - "cell_type": "code", - "source": [ - "parsed_dataset = raw_dataset.map(_parse_function)\n", - "parsed_dataset " - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "sNV-XclGnOvn" - }, - "cell_type": "markdown", - "source": [ - "Eager Execution を使ってデータセット中の観測記録を表示します。このデータセットには10,000件の観測記録がありますが、最初の10個だけ表示します。 \n", - "データは特徴量のディクショナリの形で表示されます。それぞれの項目は`tf.Tensor`であり、このテンソルの`numpy` 要素は特徴量を表します。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "x2LT2JCqhoD_", - "colab": {} - }, - "cell_type": "code", - "source": [ - "for parsed_record in parsed_dataset.take(10):\n", - " print(repr(raw_record))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "Cig9EodTlDmg" - }, - "cell_type": "markdown", - "source": [ - "ここでは、`tf.parse_example` が`tf.Example`のフィールドを通常のテンソルに展開しています。" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "jyg1g3gU7DNn" - }, - "cell_type": "markdown", - "source": [ - "## tf.python_ioを使ったTFRecordファイル" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "3FXG3miA7Kf1" - }, - "cell_type": "markdown", - "source": [ - "`tf.python_io`モジュールには、TFRecordファイルの読み書きのための純粋なPython関数も含まれています。" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "CKn5uql2lAaN" - }, - "cell_type": "markdown", - "source": [ - "### TFRecordファイルの書き出し" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "LNW_FA-GQWXs" - }, - "cell_type": "markdown", - "source": [ - "次にこの10,000件の観測記録を`test.tfrecords`ファイルに出力します。観測記録はそれぞれ`tf.Example`メッセージに変換され、ファイルに出力されます。その後、`test.tfrecords`ファイルが作成されたことを確認することができます。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "MKPHzoGv7q44", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# `tf.Example`観測記録をファイルに出力\n", - "with tf.python_io.TFRecordWriter(filename) as writer:\n", - " for i in range(n_observations):\n", - " example = serialize_example(feature0[i], feature1[i], feature2[i], feature3[i])\n", - " writer.write(example)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "code", - "id": "EjdFHHJMpUUo", - "colab": {} - }, - "cell_type": "code", - "source": [ - "!ls" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "wtQ7k0YWQ1cz" - }, - "cell_type": "markdown", - "source": [ - "### TFRecordファイルの読み込み" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "utkozytkQ-2K" - }, - "cell_type": "markdown", - "source": [ - "モデルに入力にするため、このデータを読み込みたいとしましょう。\n", - "\n", - "次の例では、データをそのまま、`tf.Example`メッセージとしてインポートします。これは、ファイルが期待されるデータを含んでいるかを確認するのに役に立ちます。これは、また、入力データがTFRecordとして保存されているが、[この](https://www.tensorflow.org/guide/datasets#consuming_numpy_arrays)例のようにNumPyデータ（またはそれ以外のデータ型）として入力したい場合に有用です。このコーディング例では値そのものを読み取れるからです。\n", - "\n", - "入力ファイルの中のTFRecordをイテレートして、`tf.Example`メッセージを取り出し、その中の値を読み取って保存できます。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "36ltP9B8OezA", - "colab": {} - }, - "cell_type": "code", - "source": [ - "record_iterator = tf.python_io.tf_record_iterator(path=filename)\n", - "\n", - "for string_record in record_iterator:\n", - " example = tf.train.Example()\n", - " example.ParseFromString(string_record)\n", - " \n", - " print(example)\n", - " \n", - " # Exit after 1 iteration as this is purely demonstrative.\n", - " # 純粋にデモであるため、イテレーションの1回目で終了\n", - " break" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "i3uquiiGTZTK" - }, - "cell_type": "markdown", - "source": [ - "（上記で作成した`tf.Example`型の）`example`オブジェクトの特徴量は（他のプロトコルバッファメッセージと同様に）ゲッターを使ってアクセス可能です。`example.features`は`repeated feature`メッセージを返し、`feature`メッセージをを取得すると（Pythonのディクショナリとして保存された）特徴量の名前と特徴量の値のマッピングが得られます。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "-UNzS7vsUBs0", - "colab": {} - }, - "cell_type": "code", - "source": [ - "print(dict(example.features.feature))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "u1M-WrbqUUVW" - }, - "cell_type": "markdown", - "source": [ - "このディクショナリから、指定した値をディクショナリとして得ることができます。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "2yCBu70IUb2H", - "colab": {} - }, - "cell_type": "code", - "source": [ - "print(example.features.feature['feature3'])" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "4dw6_OI9UiNZ" - }, - "cell_type": "markdown", - "source": [ - "次に、ゲッターを使って値にアクセスできます。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "BdDYjDnDUlFe", - "colab": {} - }, - "cell_type": "code", - "source": [ - "print(example.features.feature['feature3'].float_list.value)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "S0tFDrwdoj3q" - }, - "cell_type": "markdown", - "source": [ - "## ウォークスルー： 画像データの読み書き" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "rjN2LFxFpcR9" - }, - "cell_type": "markdown", - "source": [ - "以下は、TFRecordを使って画像データを読み書きする方法の例です。この例の目的は、データ（この場合は画像）を入力し、そのデータをTFRecordファイルに書き込んで、再びそのファイルを読み込み、画像を表示するという手順を最初から最後まで示すことです。\n", - "\n", - "これは、例えば、同じ入力データセットを使って複数のモデルを構築するといった場合に役立ちます。画像データをそのまま保存する代わりに、TFRecord形式に前処理しておき、その後の処理やモデル構築に使用することができます。\n", - "\n", - "まずは、雪の中の猫の[画像](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg)と、ニューヨーク市にあるウイリアムズバーグ橋の [写真](https://upload.wikimedia.org/wikipedia/commons/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg)をダウンロードしましょう。" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "5Lk2qrKvN0yu" - }, - "cell_type": "markdown", - "source": [ - "### 画像の取得" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "3a0fmwg8lHdF", - "colab": {} - }, - "cell_type": "code", - "source": [ - "cat_in_snow = tf.keras.utils.get_file('320px-Felis_catus-cat_on_snow.jpg', 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/Felis_catus-cat_on_snow.jpg/320px-Felis_catus-cat_on_snow.jpg')\n", - "williamsburg_bridge = tf.keras.utils.get_file('194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg','https://upload.wikimedia.org/wikipedia/commons/thumb/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "code", - "id": "7aJJh7vENeE4", - "colab": {} - }, - "cell_type": "code", - "source": [ - "display.display(display.Image(filename=cat_in_snow))\n", - "display.display(display.HTML('Image cc-by: Von.grzanka'))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "code", - "id": "KkW0uuhcXZqA", - "colab": {} - }, - "cell_type": "code", - "source": [ - "display.display(display.Image(filename=williamsburg_bridge))\n", - "display.display(display.HTML('source'))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "VSOgJSwoN5TQ" - }, - "cell_type": "markdown", - "source": [ - "### TFRecordファイルの書き出し" - ] - }, - { - "metadata": { - "colab_type": "text", - "id": "Azx83ryQEU6T" - }, - "cell_type": "markdown", - "source": [ - "上記で行ったように、この特徴量を`tf.Example`と互換のデータ型にエンコードできます。この場合には、生の画像文字列を特徴として保存するだけではなく、縦、横のサイズにチャネル数、更に画像を保存する際に猫の画像と橋の画像を区別するための`label`特徴量を付け加えます。猫の画像には`0`を、橋の画像には`1`を使うことにしましょう。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "kC4TS1ZEONHr", - "colab": {} - }, - "cell_type": "code", - "source": [ - "image_labels = {\n", - " cat_in_snow : 0,\n", - " williamsburg_bridge : 1,\n", - "}" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "code", - "id": "c5njMSYNEhNZ", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# 猫の画像を使った例\n", - "image_string = open(cat_in_snow, 'rb').read()\n", - "\n", - "label = image_labels[cat_in_snow]\n", - "\n", - "# 関連する特徴量のディクショナリを作成\n", - "def image_example(image_string, label):\n", - " image_shape = tf.image.decode_jpeg(image_string).shape\n", - "\n", - " feature = {\n", - " 'height': _int64_feature(image_shape[0]),\n", - " 'width': _int64_feature(image_shape[1]),\n", - " 'depth': _int64_feature(image_shape[2]),\n", - " 'label': _int64_feature(label),\n", - " 'image_raw': _bytes_feature(image_string),\n", - " }\n", - "\n", - " return tf.train.Example(features=tf.train.Features(feature=feature))\n", - "\n", - "for line in str(image_example(image_string, label)).split('\\n')[:15]:\n", - " print(line)\n", - "print('...')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "2G_o3O9MN0Qx" - }, - "cell_type": "markdown", - "source": [ - "ご覧のように、すべての特徴量が`tf.Example`メッセージに保存されました。上記のコードを関数化し、このサンプルメッセージを`images.tfrecords`ファイルに書き込みます。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "qcw06lQCOCZU", - "colab": {} - }, - "cell_type": "code", - "source": [ - "# 生の画像をimages.tfrecordsファイルに書き出す\n", - "# まず、2つの画像をtf.Exampleメッセージに変換し、\n", - "# 次に.tfrecordsファイルに書き出す\n", - "with tf.python_io.TFRecordWriter('images.tfrecords') as writer:\n", - " for filename, label in image_labels.items():\n", - " image_string = open(filename, 'rb').read()\n", - " tf_example = image_example(image_string, label)\n", - " writer.write(tf_example.SerializeToString())" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "code", - "id": "yJrTe6tHPCfs", - "colab": {} - }, - "cell_type": "code", - "source": [ - "!ls" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "jJSsCkZLPH6K" - }, - "cell_type": "markdown", - "source": [ - "### TFRecordファイルの読み込み\n", - "\n", - "これで、`images.tfrecords`ファイルができました。このファイルの中のレコードをイテレートし、書き込んだものを読み出します。このユースケースでは、画像を復元するだけなので、必要なのは生画像の文字列だけです。上記のゲッター、すなわち、`example.features.feature['image_raw'].bytes_list.value[0]`を使って抽出することができます。猫と橋のどちらであるかを決めるため、ラベルも使用します。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "M6Cnfd3cTKHN", - "colab": {} - }, - "cell_type": "code", - "source": [ - "raw_image_dataset = tf.data.TFRecordDataset('images.tfrecords')\n", - "\n", - "# 特徴量を記述するディクショナリを作成\n", - "image_feature_description = {\n", - " 'height': tf.FixedLenFeature([], tf.int64),\n", - " 'width': tf.FixedLenFeature([], tf.int64),\n", - " 'depth': tf.FixedLenFeature([], tf.int64),\n", - " 'label': tf.FixedLenFeature([], tf.int64),\n", - " 'image_raw': tf.FixedLenFeature([], tf.string),\n", - "}\n", - "\n", - "def _parse_image_function(example_proto):\n", - " # 入力のtf.Exampleのプロトコルバッファを上記のディクショナリを使って解釈\n", - " return tf.parse_single_example(example_proto, image_feature_description)\n", - "\n", - "parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n", - "parsed_image_dataset" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "colab_type": "text", - "id": "0PEEFPk4NEg1" - }, - "cell_type": "markdown", - "source": [ - "TFRecordファイルから画像を復元しましょう。" - ] - }, - { - "metadata": { - "colab_type": "code", - "id": "yZf8jOyEIjSF", - "colab": {} - }, - "cell_type": "code", - "source": [ - "for image_features in parsed_image_dataset:\n", - " image_raw = image_features['image_raw'].numpy()\n", - " display.display(display.Image(data=image_raw))" - ], - "execution_count": 0, - "outputs": [] - } - ] -} \ No newline at end of file + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pL--_KGdYoBz" + }, + "source": [ + "##### Copyright 2018 The TensorFlow Authors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "both", + "colab": {}, + "colab_type": "code", + "id": "uBDvXpYzYnGj" + }, + "outputs": [], + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HQzaEQuJiW_d" + }, + "source": [ + "# TFRecords と `tf.Example` の使用法\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "
\n", + " View on TensorFlow.org\n", + " \n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3pkUd_9IZCFO" + }, + "source": [ + "データの読み込みを効率的にするには、データをシリアライズし、連続的に読み込めるファイルのセット（各ファイルは100-200MB）に保存することが有効です。データをネットワーク経由で流そうとする場合には、特にそうです。また、データの前処理をキャッシュする際にも役立ちます。\n", + "\n", + "TFRecord形式は、バイナリレコードの系列を保存するための単純な形式です。\n", + "\n", + "[プロトコルバッファ](https://developers.google.com/protocol-buffers/) は、構造化データを効率的にシリアライズする、プラットフォームや言語に依存しないライブラリです。\n", + "\n", + "プロトコルメッセージは`.proto`という拡張子のファイルで定義されます。メッセージ型を識別する最も簡単な方法です。\n", + "\n", + "`tf.Example`メッセージ（あるいはプロトコルバッファ）は、`{\"string\": value}`形式のマッピングを表現する柔軟なメッセージ型です。これは、TensorFlow用に設計され、[TFX](https://www.tensorflow.org/tfx/)のような上位レベルのAPIで共通に使用されています。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Ac83J0QxjhFt" + }, + "source": [ + "このノートブックでは、`tf.Example`メッセージの作成、パースと使用法をデモし、その後、`tf.Example`メッセージをパースして、`.tfrecord`に書き出し、その後読み取る方法を示します。\n", + "\n", + "注：こうした構造は有用ですが必ずそうしなければならなというものではありません。[`tf.data`](https://www.tensorflow.org/guide/datasets) を使っていて、それでもなおデータの読み込みが訓練のボトルネックである場合でなければ、既存のコードをTFRecordsを使用するために変更する必要はありません。データセットの性能改善のヒントは、 [Data Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets)を参照ください。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "WkRreBf1eDVc" + }, + "source": [ + "## 設定" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Ja7sezsmnXph" + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import\n", + "from __future__ import division\n", + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "tf.enable_eager_execution()\n", + "\n", + "import numpy as np\n", + "import IPython.display as display" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "e5Kq88ccUWQV" + }, + "source": [ + "## `tf.Example`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VrdQHgvNijTi" + }, + "source": [ + "### `tf.Example`用のデータ型" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "lZw57Qrn4CTE" + }, + "source": [ + "基本的には`tf.Example`は`{\"string\": tf.train.Feature}`というマッピングです。\n", + "\n", + "`tf.train.Feature`メッセージ型は次の3つの型のうち1つをとることができます（[.proto file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto)を参照）。一般的なデータ型の多くは、これらの型のいずれかに強制的に変換することができます。\n", + "\n", + "1. `tf.train.BytesList` (次の型のデータを扱うことが可能)\n", + " - `string`\n", + " - `byte` \n", + "1. `tf.train.FloatList` (次の型のデータを扱うことが可能)\n", + " - `float` (`float32`)\n", + " - `double` (`float64`) \n", + "1. `tf.train.Int64List` (次の型のデータを扱うことが可能)\n", + " - `bool`\n", + " - `enum`\n", + " - `int32`\n", + " - `uint32`\n", + " - `int64`\n", + " - `uint64`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_e3g9ExathXP" + }, + "source": [ + "通常のTensorFlowの型を`tf.Example`互換の `tf.train.Feature`に変換するには、次のショートカット関数を使うことができます。\n", + "\n", + "どの関数も、1個のスカラー値を入力とし、上記の3つの`list`型のうちの一つを含む`tf.train.Feature`を返します。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "mbsPOUpVtYxA" + }, + "outputs": [], + "source": [ + "# 下記の関数を使うと値を tf.Exampleと互換性の有る型に変換できる\n", + "\n", + "def _bytes_feature(value):\n", + " \"\"\"string / byte 型から byte_listを返す\"\"\"\n", + " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n", + "\n", + "def _float_feature(value):\n", + " \"\"\"float / double 型から float_listを返す\"\"\"\n", + " return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n", + "\n", + "def _int64_feature(value):\n", + " \"\"\"bool / enum / int / uint 型から Int64_listを返す\"\"\"\n", + " return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Wst0v9O8hgzy" + }, + "source": [ + "注：単純化のため、このサンプルではスカラー値の入力のみを扱っています。スカラー値ではない特徴を扱う最も簡単な方法は、`tf.serialize_tensor`を使ってテンソルをバイナリ文字列に変換する方法です。TensorFlowでは文字列はスカラー値として扱います。バイナリ文字列をテンソルに戻すには、`tf.parse_tensor`を使用します。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vsMbkkC8xxtB" + }, + "source": [ + "上記の関数の使用例を下記に示します。入力が様々な型であるのに対して、出力が標準化されていることに注目してください。入力が、強制変換できない型であった場合、例外が発生します。（例：`_int64_feature(1.0)`はエラーとなります。`1.0`が浮動小数点数であるためで、代わりに`_float_feature`関数を使用すべきです）" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "hZzyLGr0u73y" + }, + "outputs": [], + "source": [ + "print(_bytes_feature(b'test_string'))\n", + "print(_bytes_feature(u'test_bytes'.encode('utf-8')))\n", + "\n", + "print(_float_feature(np.exp(1)))\n", + "\n", + "print(_int64_feature(True))\n", + "print(_int64_feature(1))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nj1qpfQU5qmi" + }, + "source": [ + "メッセージはすべて`.SerializeToString` を使ってバイナリ文字列にシリアライズすることができます。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "5afZkORT5pjm" + }, + "outputs": [], + "source": [ + "feature = _float_feature(np.exp(1))\n", + "\n", + "feature.SerializeToString()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "laKnw9F3hL-W" + }, + "source": [ + "### `tf.Example` メッセージの作成" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b_MEnhxchQPC" + }, + "source": [ + "既存のデータから`tf.Example`を作成したいとします。実際には、データセットの出処はどこでも良いのですが、1件の観測記録から`tf.Example`メッセージを作る手順は同じです。\n", + "\n", + "1. 観測記録それぞれにおいて、各値は上記の関数を使って3種類の互換性のある型のうち1つだけを含む`tf.train.Feature`に変換する必要があります。\n", + "\n", + "1. 次に、特徴の名前を表す文字列と、#1で作ったエンコード済みの特徴量を対応させたマップ（ディクショナリ）を作成します。\n", + "\n", + "1. #2で作成したマップを[Featuresメッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)に変換します。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4EgFQ2uHtchc" + }, + "source": [ + "このノートブックでは、NumPyを使ってデータセットを作成します。\n", + "\n", + "このデータセットには4つの特徴量があります。\n", + "- `False` または `True`を表す論理値。出現確率は等しいものとします。\n", + "- `[0,5)`の範囲から一様にサンプリングした整数値。\n", + "- 整数特徴量をインデックスとした文字列テーブルを使って生成した文字列特徴量\n", + "- 標準正規分布からサンプリングした浮動小数点数。\n", + "\n", + "サンプルは上記の分布から独立して同じ様に分布した10,000件の観測記録からなるものとします。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "CnrguFAy3YQv" + }, + "outputs": [], + "source": [ + "# データセットに含まれる観測結果の件数\n", + "n_observations = int(1e4)\n", + "\n", + "# ブール特徴量 FalseまたはTrueとしてエンコードされている\n", + "feature0 = np.random.choice([False, True], n_observations)\n", + "\n", + "# 整数特徴量 0以上 5未満の乱数\n", + "feature1 = np.random.randint(0, 5, n_observations)\n", + "\n", + "# バイト文字列特徴量\n", + "strings = np.array([b'cat', b'dog', b'chicken', b'horse', b'goat'])\n", + "feature2 = strings[feature1]\n", + "\n", + "# 浮動小数点数特徴量 標準正規分布から発生\n", + "feature3 = np.random.randn(n_observations)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aGrscehJr7Jd" + }, + "source": [ + "これらの特徴量は、`_bytes_feature`, `_float_feature`, `_int64_feature`のいずれかを使って`tf.Example`互換の型に強制変換されます。その後、エンコード済みの特徴量から`tf.Example`メッセージを作成できます。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "RTCS49Ij_kUw" + }, + "outputs": [], + "source": [ + "def serialize_example(feature0, feature1, feature2, feature3):\n", + " \"\"\"\n", + " Creates a tf.Example message ready to be written to a file.\n", + " ファイル出力可能なtf.Exampleメッセージを作成する\n", + " \"\"\"\n", + "\n", + " # 特徴量名とtf.Example互換データ型との対応ディクショナリを作成\n", + "\n", + " feature = {\n", + " 'feature0': _int64_feature(feature0),\n", + " 'feature1': _int64_feature(feature1),\n", + " 'feature2': _bytes_feature(feature2),\n", + " 'feature3': _float_feature(feature3),\n", + " }\n", + "\n", + " # tf.train.Exampleを用いて特徴メッセージを作成\n", + "\n", + " example_proto = tf.train.Example(features=tf.train.Features(feature=feature))\n", + " return example_proto.SerializeToString()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XftzX9CN_uGT" + }, + "source": [ + "例えば、データセットに`[False, 4, bytes('goat'), 0.9876]`という1つの観測記録があるとします。`create_message()`を使うとこの観測記録から`tf.Example`メッセージを作成し印字できます。上記のように、観測記録一つ一つが`Features`メッセージとして書かれています。`tf.Example` [メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88)は、この`Features` メッセージを包むラッパーに過ぎないことに注意してください。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "N8BtSx2RjYcb" + }, + "outputs": [], + "source": [ + "# データセットからの観測記録の例\n", + "\n", + "example_observation = []\n", + "\n", + "serialized_example = serialize_example(False, 4, b'goat', 0.9876)\n", + "serialized_example" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_pbGATlG6u-4" + }, + "source": [ + "メッセージをデコードするには、`tf.train.Example.FromString`メソッドを使用します。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "dGim-mEm6vit" + }, + "outputs": [], + "source": [ + "example_proto = tf.train.Example.FromString(serialized_example)\n", + "example_proto" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TFRecords Format Details\n", + "\n", + "A TFRecord file contains a sequence of records. The file can only be read sequentially.\n", + "\n", + "Each record contains a byte-string, for the data-payload, plus the data-length, and CRC32C (32-bit CRC using the Castagnoli polynomial) hashes for integrity checking. \n", + "\n", + "Each record has the format\n", + "\n", + " uint64 length\n", + " uint32 masked_crc32_of_length\n", + " byte data[length]\n", + " uint32 masked_crc32_of_data\n", + "\n", + "The records are concatenated together to produce the file. CRCs are\n", + "[described here](https://en.wikipedia.org/wiki/Cyclic_redundancy_check), and\n", + "the mask of a CRC is\n", + "\n", + " masked_crc = ((crc >> 15) | (crc << 17)) + 0xa282ead8ul\n", + " \n", + "Note: There is no requirement to use `tf.Example` in TFRecord files. `tf.Example` is just a method of serializing dictionaries to byte-strings. Lines of text, encoded image data, or serialized tensors (using `tf.io.serialize_tensor`, and\n", + "`tf.io.parse_tensor` when loading). See the `tf.io` module for more options. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "y-Hjmee-fbLH" + }, + "source": [ + "## `tf.data`を使用したTFRecordファイル" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GmehkCCT81Ez" + }, + "source": [ + "`tf.data`モジュールには、TensorFlowでデータを読み書きするツールが含まれます。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1FISEuz8ubu3" + }, + "source": [ + "### TFRecordファイルの書き出し\n", + "\n", + "データをデータセットにする最も簡単な方法は`from_tensor_slices`メソッドです。\n", + "\n", + "配列に適用すると、このメソッドはスカラー値のデータセットを返します。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "mXeaukvwu5_-" + }, + "outputs": [], + "source": [ + "tf.data.Dataset.from_tensor_slices(feature1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "f-q0VKyZvcad" + }, + "source": [ + "配列のタプルに適用すると、タプルのデータセットが返されます。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "H5sWyu1kxnvg" + }, + "outputs": [], + "source": [ + "features_dataset = tf.data.Dataset.from_tensor_slices((feature0, feature1, feature2, feature3))\n", + "features_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "m1C-t71Nywze" + }, + "outputs": [], + "source": [ + "# データセットから1つのサンプルだけを取り出すには`take(1)` を使います。\n", + "for f0,f1,f2,f3 in features_dataset.take(1):\n", + " print(f0)\n", + " print(f1)\n", + " print(f2)\n", + " print(f3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mhIe63awyZYd" + }, + "source": [ + "`Dataset`のそれぞれの要素に関数を適用するには、`tf.data.Dataset.map`メソッドを使用します。\n", + "\n", + "マップされる関数はTensorFlowのグラフモードで動作する必要があります。関数は`tf.Tensors`を処理し、返す必要があります。`create_example`のような非テンソル関数は、互換性のため`tf.py_func`でラップすることができます。\n", + "\n", + "`tf.py_func`を使用する際には、シェイプと型は取得できないため、指定する必要があります。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "apB5KYrJzjPI" + }, + "outputs": [], + "source": [ + "def tf_serialize_example(f0,f1,f2,f3):\n", + " tf_string = tf.py_func(\n", + " serialize_example, \n", + " (f0,f1,f2,f3), # pass these args to the above function.\n", + " tf.string) # the return type is `tf.string`.\n", + " return tf.reshape(tf_string, ()) # The result is a scalar" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CrFZ9avE3HUF" + }, + "source": [ + "この関数をデータセットのそれぞれの要素に適用します。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VDeqYVbW3ww9" + }, + "outputs": [], + "source": [ + "serialized_features_dataset = features_dataset.map(tf_serialize_example)\n", + "serialized_features_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "p6lw5VYpjZZC" + }, + "source": [ + "TFRecordファイルに書き出します。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vP1VgTO44UIE" + }, + "outputs": [], + "source": [ + "filename = 'test.tfrecord'\n", + "writer = tf.data.experimental.TFRecordWriter(filename)\n", + "writer.write(serialized_features_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6aV0GQhV8tmp" + }, + "source": [ + "### TFRecordファイルの読み込み" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "o3J5D4gcSy8N" + }, + "source": [ + "`tf.data.TFRecordDataset`クラスを使ってTFRecordファイルを読み込むこともできます。\n", + "\n", + "`tf.data`を使ってTFRecordファイルを取り扱う際の詳細については、[こちら](https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data)を参照ください。 \n", + "\n", + "`TFRecordDataset`を使うことは、入力データを標準化し、パフォーマンスを最適化するのに有用です。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "6OjX6UZl-bHC" + }, + "outputs": [], + "source": [ + "filenames = [filename]\n", + "raw_dataset = tf.data.TFRecordDataset(filenames)\n", + "raw_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6_EQ9i2E_-Fz" + }, + "source": [ + "この時点で、データセットにはシリアライズされた`tf.train.Example`メッセージが含まれています。データセットをイテレートすると、スカラーの文字列テンソルが返ってきます。\n", + "\n", + "`.take`メソッドを使って最初の10レコードだけを表示します。\n", + "\n", + "注：`tf.data.Dataset`をイテレートできるのは、Eager Executionが有効になっている場合のみです。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "hxVXpLz_AJlm" + }, + "outputs": [], + "source": [ + "for raw_record in raw_dataset.take(10):\n", + " print(repr(raw_record))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W-6oNzM4luFQ" + }, + "source": [ + "これらのテンソルは下記の関数でパースできます。\n", + "\n", + "注：ここでは、`feature_description`が必要です。データセットはグラフ実行を使用するため、この記述を使ってシェイプと型を構築するのです。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "zQjbIR1nleiy" + }, + "outputs": [], + "source": [ + "# 特徴の記述\n", + "feature_description = {\n", + " 'feature0': tf.FixedLenFeature([], tf.int64, default_value=0),\n", + " 'feature1': tf.FixedLenFeature([], tf.int64, default_value=0),\n", + " 'feature2': tf.FixedLenFeature([], tf.string, default_value=''),\n", + " 'feature3': tf.FixedLenFeature([], tf.float32, default_value=0.0),\n", + "}\n", + "\n", + "def _parse_function(example_proto):\n", + " # 上記の記述を使って入力のtf.Exampleを処理\n", + " return tf.parse_single_example(example_proto, feature_description)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "gWETjUqhEQZf" + }, + "source": [ + "あるいは、`tf.parse example`を使ってバッチ全体を一度にパースします。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AH73hav6Bnmg" + }, + "source": [ + "`tf.data.Dataset.map`メソッドを使って、データセットの各アイテムにこの関数を適用します。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "6Ob7D-zmBm1w" + }, + "outputs": [], + "source": [ + "parsed_dataset = raw_dataset.map(_parse_function)\n", + "parsed_dataset " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "sNV-XclGnOvn" + }, + "source": [ + "Eager Execution を使ってデータセット中の観測記録を表示します。このデータセットには10,000件の観測記録がありますが、最初の10個だけ表示します。 \n", + "データは特徴量のディクショナリの形で表示されます。それぞれの項目は`tf.Tensor`であり、このテンソルの`numpy` 要素は特徴量を表します。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x2LT2JCqhoD_" + }, + "outputs": [], + "source": [ + "for parsed_record in parsed_dataset.take(10):\n", + " print(repr(raw_record))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Cig9EodTlDmg" + }, + "source": [ + "ここでは、`tf.parse_example` が`tf.Example`のフィールドを通常のテンソルに展開しています。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jyg1g3gU7DNn" + }, + "source": [ + "## tf.python_ioを使ったTFRecordファイル" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3FXG3miA7Kf1" + }, + "source": [ + "`tf.python_io`モジュールには、TFRecordファイルの読み書きのための純粋なPython関数も含まれています。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CKn5uql2lAaN" + }, + "source": [ + "### TFRecordファイルの書き出し" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LNW_FA-GQWXs" + }, + "source": [ + "次にこの10,000件の観測記録を`test.tfrecords`ファイルに出力します。観測記録はそれぞれ`tf.Example`メッセージに変換され、ファイルに出力されます。その後、`test.tfrecords`ファイルが作成されたことを確認することができます。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "MKPHzoGv7q44" + }, + "outputs": [], + "source": [ + "# `tf.Example`観測記録をファイルに出力\n", + "with tf.python_io.TFRecordWriter(filename) as writer:\n", + " for i in range(n_observations):\n", + " example = serialize_example(feature0[i], feature1[i], feature2[i], feature3[i])\n", + " writer.write(example)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EjdFHHJMpUUo" + }, + "outputs": [], + "source": [ + "!ls" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wtQ7k0YWQ1cz" + }, + "source": [ + "### TFRecordファイルの読み込み" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "utkozytkQ-2K" + }, + "source": [ + "モデルに入力にするため、このデータを読み込みたいとしましょう。\n", + "\n", + "次の例では、データをそのまま、`tf.Example`メッセージとしてインポートします。これは、ファイルが期待されるデータを含んでいるかを確認するのに役に立ちます。これは、また、入力データがTFRecordとして保存されているが、[この](https://www.tensorflow.org/guide/datasets#consuming_numpy_arrays)例のようにNumPyデータ（またはそれ以外のデータ型）として入力したい場合に有用です。このコーディング例では値そのものを読み取れるからです。\n", + "\n", + "入力ファイルの中のTFRecordをイテレートして、`tf.Example`メッセージを取り出し、その中の値を読み取って保存できます。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "36ltP9B8OezA" + }, + "outputs": [], + "source": [ + "record_iterator = tf.python_io.tf_record_iterator(path=filename)\n", + "\n", + "for string_record in record_iterator:\n", + " example = tf.train.Example()\n", + " example.ParseFromString(string_record)\n", + " \n", + " print(example)\n", + " \n", + " # Exit after 1 iteration as this is purely demonstrative.\n", + " # 純粋にデモであるため、イテレーションの1回目で終了\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "i3uquiiGTZTK" + }, + "source": [ + "（上記で作成した`tf.Example`型の）`example`オブジェクトの特徴量は（他のプロトコルバッファメッセージと同様に）ゲッターを使ってアクセス可能です。`example.features`は`repeated feature`メッセージを返し、`feature`メッセージをを取得すると（Pythonのディクショナリとして保存された）特徴量の名前と特徴量の値のマッピングが得られます。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "-UNzS7vsUBs0" + }, + "outputs": [], + "source": [ + "print(dict(example.features.feature))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "u1M-WrbqUUVW" + }, + "source": [ + "このディクショナリから、指定した値をディクショナリとして得ることができます。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "2yCBu70IUb2H" + }, + "outputs": [], + "source": [ + "print(example.features.feature['feature3'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4dw6_OI9UiNZ" + }, + "source": [ + "次に、ゲッターを使って値にアクセスできます。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "BdDYjDnDUlFe" + }, + "outputs": [], + "source": [ + "print(example.features.feature['feature3'].float_list.value)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "S0tFDrwdoj3q" + }, + "source": [ + "## ウォークスルー： 画像データの読み書き" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rjN2LFxFpcR9" + }, + "source": [ + "以下は、TFRecordを使って画像データを読み書きする方法の例です。この例の目的は、データ（この場合は画像）を入力し、そのデータをTFRecordファイルに書き込んで、再びそのファイルを読み込み、画像を表示するという手順を最初から最後まで示すことです。\n", + "\n", + "これは、例えば、同じ入力データセットを使って複数のモデルを構築するといった場合に役立ちます。画像データをそのまま保存する代わりに、TFRecord形式に前処理しておき、その後の処理やモデル構築に使用することができます。\n", + "\n", + "まずは、雪の中の猫の[画像](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg)と、ニューヨーク市にあるウイリアムズバーグ橋の [写真](https://upload.wikimedia.org/wikipedia/commons/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg)をダウンロードしましょう。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5Lk2qrKvN0yu" + }, + "source": [ + "### 画像の取得" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "3a0fmwg8lHdF" + }, + "outputs": [], + "source": [ + "cat_in_snow = tf.keras.utils.get_file('320px-Felis_catus-cat_on_snow.jpg', 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/Felis_catus-cat_on_snow.jpg/320px-Felis_catus-cat_on_snow.jpg')\n", + "williamsburg_bridge = tf.keras.utils.get_file('194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg','https://upload.wikimedia.org/wikipedia/commons/thumb/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "7aJJh7vENeE4" + }, + "outputs": [], + "source": [ + "display.display(display.Image(filename=cat_in_snow))\n", + "display.display(display.HTML('Image cc-by: Von.grzanka'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KkW0uuhcXZqA" + }, + "outputs": [], + "source": [ + "display.display(display.Image(filename=williamsburg_bridge))\n", + "display.display(display.HTML('source'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VSOgJSwoN5TQ" + }, + "source": [ + "### TFRecordファイルの書き出し" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Azx83ryQEU6T" + }, + "source": [ + "上記で行ったように、この特徴量を`tf.Example`と互換のデータ型にエンコードできます。この場合には、生の画像文字列を特徴として保存するだけではなく、縦、横のサイズにチャネル数、更に画像を保存する際に猫の画像と橋の画像を区別するための`label`特徴量を付け加えます。猫の画像には`0`を、橋の画像には`1`を使うことにしましょう。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "kC4TS1ZEONHr" + }, + "outputs": [], + "source": [ + "image_labels = {\n", + " cat_in_snow : 0,\n", + " williamsburg_bridge : 1,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "c5njMSYNEhNZ" + }, + "outputs": [], + "source": [ + "# 猫の画像を使った例\n", + "image_string = open(cat_in_snow, 'rb').read()\n", + "\n", + "label = image_labels[cat_in_snow]\n", + "\n", + "# 関連する特徴量のディクショナリを作成\n", + "def image_example(image_string, label):\n", + " image_shape = tf.image.decode_jpeg(image_string).shape\n", + "\n", + " feature = {\n", + " 'height': _int64_feature(image_shape[0]),\n", + " 'width': _int64_feature(image_shape[1]),\n", + " 'depth': _int64_feature(image_shape[2]),\n", + " 'label': _int64_feature(label),\n", + " 'image_raw': _bytes_feature(image_string),\n", + " }\n", + "\n", + " return tf.train.Example(features=tf.train.Features(feature=feature))\n", + "\n", + "for line in str(image_example(image_string, label)).split('\\n')[:15]:\n", + " print(line)\n", + "print('...')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2G_o3O9MN0Qx" + }, + "source": [ + "ご覧のように、すべての特徴量が`tf.Example`メッセージに保存されました。上記のコードを関数化し、このサンプルメッセージを`images.tfrecords`ファイルに書き込みます。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "qcw06lQCOCZU" + }, + "outputs": [], + "source": [ + "# 生の画像をimages.tfrecordsファイルに書き出す\n", + "# まず、2つの画像をtf.Exampleメッセージに変換し、\n", + "# 次に.tfrecordsファイルに書き出す\n", + "with tf.python_io.TFRecordWriter('images.tfrecords') as writer:\n", + " for filename, label in image_labels.items():\n", + " image_string = open(filename, 'rb').read()\n", + " tf_example = image_example(image_string, label)\n", + " writer.write(tf_example.SerializeToString())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "yJrTe6tHPCfs" + }, + "outputs": [], + "source": [ + "!ls" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jJSsCkZLPH6K" + }, + "source": [ + "### TFRecordファイルの読み込み\n", + "\n", + "これで、`images.tfrecords`ファイルができました。このファイルの中のレコードをイテレートし、書き込んだものを読み出します。このユースケースでは、画像を復元するだけなので、必要なのは生画像の文字列だけです。上記のゲッター、すなわち、`example.features.feature['image_raw'].bytes_list.value[0]`を使って抽出することができます。猫と橋のどちらであるかを決めるため、ラベルも使用します。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "M6Cnfd3cTKHN" + }, + "outputs": [], + "source": [ + "raw_image_dataset = tf.data.TFRecordDataset('images.tfrecords')\n", + "\n", + "# 特徴量を記述するディクショナリを作成\n", + "image_feature_description = {\n", + " 'height': tf.FixedLenFeature([], tf.int64),\n", + " 'width': tf.FixedLenFeature([], tf.int64),\n", + " 'depth': tf.FixedLenFeature([], tf.int64),\n", + " 'label': tf.FixedLenFeature([], tf.int64),\n", + " 'image_raw': tf.FixedLenFeature([], tf.string),\n", + "}\n", + "\n", + "def _parse_image_function(example_proto):\n", + " # 入力のtf.Exampleのプロトコルバッファを上記のディクショナリを使って解釈\n", + " return tf.parse_single_example(example_proto, image_feature_description)\n", + "\n", + "parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n", + "parsed_image_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "0PEEFPk4NEg1" + }, + "source": [ + "TFRecordファイルから画像を復元しましょう。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "yZf8jOyEIjSF" + }, + "outputs": [], + "source": [ + "for image_features in parsed_image_dataset:\n", + " image_raw = image_features['image_raw'].numpy()\n", + " display.display(display.Image(data=image_raw))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "pL--_KGdYoBz" + ], + "name": "tf-records.ipynb", + "private_outputs": true, + "provenance": [], + "toc_visible": true, + "version": "0.3.2" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 1f01b90f4756c2caf393f15384ab224c83dde6fa Mon Sep 17 00:00:00 2001 From: Masatoshi Itagaki Date: Sun, 14 Apr 2019 00:05:45 +0900 Subject: [PATCH 5/6] chenged after ohtaman's review --- site/ja/tutorials/load_data/tf_records.ipynb | 25 +++++++++----------- 1 file changed, 11 insertions(+), 14 deletions(-) diff --git a/site/ja/tutorials/load_data/tf_records.ipynb b/site/ja/tutorials/load_data/tf_records.ipynb index 20cd8400230..99800c832d7 100644 --- a/site/ja/tutorials/load_data/tf_records.ipynb +++ b/site/ja/tutorials/load_data/tf_records.ipynb @@ -432,27 +432,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## TFRecords Format Details\n", + "## TFRecordフォーマットの詳細\n", "\n", - "A TFRecord file contains a sequence of records. The file can only be read sequentially.\n", + "TFRecordファイルにはレコードのシーケンスが含まれます。このファイルはシーケンシャル読み取りのみが可能です。\n", "\n", - "Each record contains a byte-string, for the data-payload, plus the data-length, and CRC32C (32-bit CRC using the Castagnoli polynomial) hashes for integrity checking. \n", + "それぞれのレコードには、データを格納するためのバイト文字列とデータ長、そして整合性チェックのためのCRC32C（Castagnoli多項式を使った32ビットのCRC）ハッシュ値が含まれます。\n", "\n", - "Each record has the format\n", + "各レコードのフォーマットは下記の通りです。\n", "\n", - " uint64 length\n", - " uint32 masked_crc32_of_length\n", - " byte data[length]\n", - " uint32 masked_crc32_of_data\n", + " uint64 長さ\n", + " uint32 長さのマスク済みcrc32ハッシュ値\n", + " byte data[長さ]\n", + " uint32 データのマスク済みcrc32ハッシュ値\n", "\n", - "The records are concatenated together to produce the file. CRCs are\n", - "[described here](https://en.wikipedia.org/wiki/Cyclic_redundancy_check), and\n", - "the mask of a CRC is\n", + "複数のレコードが結合されてファイルを構成します。CRCについては[ここ](https://en.wikipedia.org/wiki/Cyclic_redundancy_check)に說明があります。CRCのマスクは下記のとおりです。\n", "\n", " masked_crc = ((crc >> 15) | (crc << 17)) + 0xa282ead8ul\n", " \n", - "Note: There is no requirement to use `tf.Example` in TFRecord files. `tf.Example` is just a method of serializing dictionaries to byte-strings. Lines of text, encoded image data, or serialized tensors (using `tf.io.serialize_tensor`, and\n", - "`tf.io.parse_tensor` when loading). See the `tf.io` module for more options. " + "注：TFRecordファイルを作るのに、`tf.Example`を使わなければならないということはありません。`tf.Example`は、ディクショナリをバイト文字列にシリアライズする方法の1つです。エンコードされた画像データや、（`tf.io.serialize_tensor`を使ってシリアライズされ、`tf.io.parse_tensor`で読み込まれる）シリアライズされたテンソルもあります。その他のオプションについては、`tf.io`モジュールを参照してください。" ] }, { @@ -1075,7 +1072,7 @@ "id": "Azx83ryQEU6T" }, "source": [ - "上記で行ったように、この特徴量を`tf.Example`と互換のデータ型にエンコードできます。この場合には、生の画像文字列を特徴として保存するだけではなく、縦、横のサイズにチャネル数、更に画像を保存する際に猫の画像と橋の画像を区別するための`label`特徴量を付け加えます。猫の画像には`0`を、橋の画像には`1`を使うことにしましょう。" + "上記で行ったように、この特徴量を`tf.Example`と互換のデータ型にエンコードできます。この場合には、生画像のバイト文字列を特徴として保存するだけではなく、縦、横のサイズにチャネル数、更に画像を保存する際に猫の画像と橋の画像を区別するための`label`特徴量を付け加えます。猫の画像には`0`を、橋の画像には`1`を使うことにしましょう。" ] }, { From faea3f1113db6c51358181418845e9e24925c305 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Mon, 22 Apr 2019 10:40:44 -0700 Subject: [PATCH 6/6] Formatting --- site/ja/tutorials/load_data/tf_records.ipynb | 2539 +++++++++--------- 1 file changed, 1271 insertions(+), 1268 deletions(-) diff --git a/site/ja/tutorials/load_data/tf_records.ipynb b/site/ja/tutorials/load_data/tf_records.ipynb index 99800c832d7..792c23847c3 100644 --- a/site/ja/tutorials/load_data/tf_records.ipynb +++ b/site/ja/tutorials/load_data/tf_records.ipynb @@ -1,1269 +1,1272 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "pL--_KGdYoBz" - }, - "source": [ - "##### Copyright 2018 The TensorFlow Authors." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "both", - "colab": {}, - "colab_type": "code", - "id": "uBDvXpYzYnGj" - }, - "outputs": [], - "source": [ - "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "HQzaEQuJiW_d" - }, - "source": [ - "# TFRecords と `tf.Example` の使用法\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "
\n", - " View on TensorFlow.org\n", - " \n", - " Run in Google Colab\n", - " \n", - " View source on GitHub\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "3pkUd_9IZCFO" - }, - "source": [ - "データの読み込みを効率的にするには、データをシリアライズし、連続的に読み込めるファイルのセット（各ファイルは100-200MB）に保存することが有効です。データをネットワーク経由で流そうとする場合には、特にそうです。また、データの前処理をキャッシュする際にも役立ちます。\n", - "\n", - "TFRecord形式は、バイナリレコードの系列を保存するための単純な形式です。\n", - "\n", - "[プロトコルバッファ](https://developers.google.com/protocol-buffers/) は、構造化データを効率的にシリアライズする、プラットフォームや言語に依存しないライブラリです。\n", - "\n", - "プロトコルメッセージは`.proto`という拡張子のファイルで定義されます。メッセージ型を識別する最も簡単な方法です。\n", - "\n", - "`tf.Example`メッセージ（あるいはプロトコルバッファ）は、`{\"string\": value}`形式のマッピングを表現する柔軟なメッセージ型です。これは、TensorFlow用に設計され、[TFX](https://www.tensorflow.org/tfx/)のような上位レベルのAPIで共通に使用されています。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Ac83J0QxjhFt" - }, - "source": [ - "このノートブックでは、`tf.Example`メッセージの作成、パースと使用法をデモし、その後、`tf.Example`メッセージをパースして、`.tfrecord`に書き出し、その後読み取る方法を示します。\n", - "\n", - "注：こうした構造は有用ですが必ずそうしなければならなというものではありません。[`tf.data`](https://www.tensorflow.org/guide/datasets) を使っていて、それでもなおデータの読み込みが訓練のボトルネックである場合でなければ、既存のコードをTFRecordsを使用するために変更する必要はありません。データセットの性能改善のヒントは、 [Data Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets)を参照ください。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "WkRreBf1eDVc" - }, - "source": [ - "## 設定" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Ja7sezsmnXph" - }, - "outputs": [], - "source": [ - "from __future__ import absolute_import\n", - "from __future__ import division\n", - "from __future__ import print_function\n", - "\n", - "import tensorflow as tf\n", - "tf.enable_eager_execution()\n", - "\n", - "import numpy as np\n", - "import IPython.display as display" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "e5Kq88ccUWQV" - }, - "source": [ - "## `tf.Example`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "VrdQHgvNijTi" - }, - "source": [ - "### `tf.Example`用のデータ型" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "lZw57Qrn4CTE" - }, - "source": [ - "基本的には`tf.Example`は`{\"string\": tf.train.Feature}`というマッピングです。\n", - "\n", - "`tf.train.Feature`メッセージ型は次の3つの型のうち1つをとることができます（[.proto file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto)を参照）。一般的なデータ型の多くは、これらの型のいずれかに強制的に変換することができます。\n", - "\n", - "1. `tf.train.BytesList` (次の型のデータを扱うことが可能)\n", - " - `string`\n", - " - `byte` \n", - "1. `tf.train.FloatList` (次の型のデータを扱うことが可能)\n", - " - `float` (`float32`)\n", - " - `double` (`float64`) \n", - "1. `tf.train.Int64List` (次の型のデータを扱うことが可能)\n", - " - `bool`\n", - " - `enum`\n", - " - `int32`\n", - " - `uint32`\n", - " - `int64`\n", - " - `uint64`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "_e3g9ExathXP" - }, - "source": [ - "通常のTensorFlowの型を`tf.Example`互換の `tf.train.Feature`に変換するには、次のショートカット関数を使うことができます。\n", - "\n", - "どの関数も、1個のスカラー値を入力とし、上記の3つの`list`型のうちの一つを含む`tf.train.Feature`を返します。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "mbsPOUpVtYxA" - }, - "outputs": [], - "source": [ - "# 下記の関数を使うと値を tf.Exampleと互換性の有る型に変換できる\n", - "\n", - "def _bytes_feature(value):\n", - " \"\"\"string / byte 型から byte_listを返す\"\"\"\n", - " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n", - "\n", - "def _float_feature(value):\n", - " \"\"\"float / double 型から float_listを返す\"\"\"\n", - " return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n", - "\n", - "def _int64_feature(value):\n", - " \"\"\"bool / enum / int / uint 型から Int64_listを返す\"\"\"\n", - " return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Wst0v9O8hgzy" - }, - "source": [ - "注：単純化のため、このサンプルではスカラー値の入力のみを扱っています。スカラー値ではない特徴を扱う最も簡単な方法は、`tf.serialize_tensor`を使ってテンソルをバイナリ文字列に変換する方法です。TensorFlowでは文字列はスカラー値として扱います。バイナリ文字列をテンソルに戻すには、`tf.parse_tensor`を使用します。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "vsMbkkC8xxtB" - }, - "source": [ - "上記の関数の使用例を下記に示します。入力が様々な型であるのに対して、出力が標準化されていることに注目してください。入力が、強制変換できない型であった場合、例外が発生します。（例：`_int64_feature(1.0)`はエラーとなります。`1.0`が浮動小数点数であるためで、代わりに`_float_feature`関数を使用すべきです）" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "hZzyLGr0u73y" - }, - "outputs": [], - "source": [ - "print(_bytes_feature(b'test_string'))\n", - "print(_bytes_feature(u'test_bytes'.encode('utf-8')))\n", - "\n", - "print(_float_feature(np.exp(1)))\n", - "\n", - "print(_int64_feature(True))\n", - "print(_int64_feature(1))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "nj1qpfQU5qmi" - }, - "source": [ - "メッセージはすべて`.SerializeToString` を使ってバイナリ文字列にシリアライズすることができます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "5afZkORT5pjm" - }, - "outputs": [], - "source": [ - "feature = _float_feature(np.exp(1))\n", - "\n", - "feature.SerializeToString()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "laKnw9F3hL-W" - }, - "source": [ - "### `tf.Example` メッセージの作成" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "b_MEnhxchQPC" - }, - "source": [ - "既存のデータから`tf.Example`を作成したいとします。実際には、データセットの出処はどこでも良いのですが、1件の観測記録から`tf.Example`メッセージを作る手順は同じです。\n", - "\n", - "1. 観測記録それぞれにおいて、各値は上記の関数を使って3種類の互換性のある型のうち1つだけを含む`tf.train.Feature`に変換する必要があります。\n", - "\n", - "1. 次に、特徴の名前を表す文字列と、#1で作ったエンコード済みの特徴量を対応させたマップ（ディクショナリ）を作成します。\n", - "\n", - "1. #2で作成したマップを[Featuresメッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)に変換します。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4EgFQ2uHtchc" - }, - "source": [ - "このノートブックでは、NumPyを使ってデータセットを作成します。\n", - "\n", - "このデータセットには4つの特徴量があります。\n", - "- `False` または `True`を表す論理値。出現確率は等しいものとします。\n", - "- `[0,5)`の範囲から一様にサンプリングした整数値。\n", - "- 整数特徴量をインデックスとした文字列テーブルを使って生成した文字列特徴量\n", - "- 標準正規分布からサンプリングした浮動小数点数。\n", - "\n", - "サンプルは上記の分布から独立して同じ様に分布した10,000件の観測記録からなるものとします。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "CnrguFAy3YQv" - }, - "outputs": [], - "source": [ - "# データセットに含まれる観測結果の件数\n", - "n_observations = int(1e4)\n", - "\n", - "# ブール特徴量 FalseまたはTrueとしてエンコードされている\n", - "feature0 = np.random.choice([False, True], n_observations)\n", - "\n", - "# 整数特徴量 0以上 5未満の乱数\n", - "feature1 = np.random.randint(0, 5, n_observations)\n", - "\n", - "# バイト文字列特徴量\n", - "strings = np.array([b'cat', b'dog', b'chicken', b'horse', b'goat'])\n", - "feature2 = strings[feature1]\n", - "\n", - "# 浮動小数点数特徴量 標準正規分布から発生\n", - "feature3 = np.random.randn(n_observations)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "aGrscehJr7Jd" - }, - "source": [ - "これらの特徴量は、`_bytes_feature`, `_float_feature`, `_int64_feature`のいずれかを使って`tf.Example`互換の型に強制変換されます。その後、エンコード済みの特徴量から`tf.Example`メッセージを作成できます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "RTCS49Ij_kUw" - }, - "outputs": [], - "source": [ - "def serialize_example(feature0, feature1, feature2, feature3):\n", - " \"\"\"\n", - " Creates a tf.Example message ready to be written to a file.\n", - " ファイル出力可能なtf.Exampleメッセージを作成する\n", - " \"\"\"\n", - "\n", - " # 特徴量名とtf.Example互換データ型との対応ディクショナリを作成\n", - "\n", - " feature = {\n", - " 'feature0': _int64_feature(feature0),\n", - " 'feature1': _int64_feature(feature1),\n", - " 'feature2': _bytes_feature(feature2),\n", - " 'feature3': _float_feature(feature3),\n", - " }\n", - "\n", - " # tf.train.Exampleを用いて特徴メッセージを作成\n", - "\n", - " example_proto = tf.train.Example(features=tf.train.Features(feature=feature))\n", - " return example_proto.SerializeToString()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "XftzX9CN_uGT" - }, - "source": [ - "例えば、データセットに`[False, 4, bytes('goat'), 0.9876]`という1つの観測記録があるとします。`create_message()`を使うとこの観測記録から`tf.Example`メッセージを作成し印字できます。上記のように、観測記録一つ一つが`Features`メッセージとして書かれています。`tf.Example` [メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88)は、この`Features` メッセージを包むラッパーに過ぎないことに注意してください。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "N8BtSx2RjYcb" - }, - "outputs": [], - "source": [ - "# データセットからの観測記録の例\n", - "\n", - "example_observation = []\n", - "\n", - "serialized_example = serialize_example(False, 4, b'goat', 0.9876)\n", - "serialized_example" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "_pbGATlG6u-4" - }, - "source": [ - "メッセージをデコードするには、`tf.train.Example.FromString`メソッドを使用します。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "dGim-mEm6vit" - }, - "outputs": [], - "source": [ - "example_proto = tf.train.Example.FromString(serialized_example)\n", - "example_proto" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## TFRecordフォーマットの詳細\n", - "\n", - "TFRecordファイルにはレコードのシーケンスが含まれます。このファイルはシーケンシャル読み取りのみが可能です。\n", - "\n", - "それぞれのレコードには、データを格納するためのバイト文字列とデータ長、そして整合性チェックのためのCRC32C（Castagnoli多項式を使った32ビットのCRC）ハッシュ値が含まれます。\n", - "\n", - "各レコードのフォーマットは下記の通りです。\n", - "\n", - " uint64 長さ\n", - " uint32 長さのマスク済みcrc32ハッシュ値\n", - " byte data[長さ]\n", - " uint32 データのマスク済みcrc32ハッシュ値\n", - "\n", - "複数のレコードが結合されてファイルを構成します。CRCについては[ここ](https://en.wikipedia.org/wiki/Cyclic_redundancy_check)に說明があります。CRCのマスクは下記のとおりです。\n", - "\n", - " masked_crc = ((crc >> 15) | (crc << 17)) + 0xa282ead8ul\n", - " \n", - "注：TFRecordファイルを作るのに、`tf.Example`を使わなければならないということはありません。`tf.Example`は、ディクショナリをバイト文字列にシリアライズする方法の1つです。エンコードされた画像データや、（`tf.io.serialize_tensor`を使ってシリアライズされ、`tf.io.parse_tensor`で読み込まれる）シリアライズされたテンソルもあります。その他のオプションについては、`tf.io`モジュールを参照してください。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "y-Hjmee-fbLH" - }, - "source": [ - "## `tf.data`を使用したTFRecordファイル" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "GmehkCCT81Ez" - }, - "source": [ - "`tf.data`モジュールには、TensorFlowでデータを読み書きするツールが含まれます。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "1FISEuz8ubu3" - }, - "source": [ - "### TFRecordファイルの書き出し\n", - "\n", - "データをデータセットにする最も簡単な方法は`from_tensor_slices`メソッドです。\n", - "\n", - "配列に適用すると、このメソッドはスカラー値のデータセットを返します。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "mXeaukvwu5_-" - }, - "outputs": [], - "source": [ - "tf.data.Dataset.from_tensor_slices(feature1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "f-q0VKyZvcad" - }, - "source": [ - "配列のタプルに適用すると、タプルのデータセットが返されます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "H5sWyu1kxnvg" - }, - "outputs": [], - "source": [ - "features_dataset = tf.data.Dataset.from_tensor_slices((feature0, feature1, feature2, feature3))\n", - "features_dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "m1C-t71Nywze" - }, - "outputs": [], - "source": [ - "# データセットから1つのサンプルだけを取り出すには`take(1)` を使います。\n", - "for f0,f1,f2,f3 in features_dataset.take(1):\n", - " print(f0)\n", - " print(f1)\n", - " print(f2)\n", - " print(f3)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "mhIe63awyZYd" - }, - "source": [ - "`Dataset`のそれぞれの要素に関数を適用するには、`tf.data.Dataset.map`メソッドを使用します。\n", - "\n", - "マップされる関数はTensorFlowのグラフモードで動作する必要があります。関数は`tf.Tensors`を処理し、返す必要があります。`create_example`のような非テンソル関数は、互換性のため`tf.py_func`でラップすることができます。\n", - "\n", - "`tf.py_func`を使用する際には、シェイプと型は取得できないため、指定する必要があります。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "apB5KYrJzjPI" - }, - "outputs": [], - "source": [ - "def tf_serialize_example(f0,f1,f2,f3):\n", - " tf_string = tf.py_func(\n", - " serialize_example, \n", - " (f0,f1,f2,f3), # pass these args to the above function.\n", - " tf.string) # the return type is `tf.string`.\n", - " return tf.reshape(tf_string, ()) # The result is a scalar" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "CrFZ9avE3HUF" - }, - "source": [ - "この関数をデータセットのそれぞれの要素に適用します。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "VDeqYVbW3ww9" - }, - "outputs": [], - "source": [ - "serialized_features_dataset = features_dataset.map(tf_serialize_example)\n", - "serialized_features_dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "p6lw5VYpjZZC" - }, - "source": [ - "TFRecordファイルに書き出します。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "vP1VgTO44UIE" - }, - "outputs": [], - "source": [ - "filename = 'test.tfrecord'\n", - "writer = tf.data.experimental.TFRecordWriter(filename)\n", - "writer.write(serialized_features_dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "6aV0GQhV8tmp" - }, - "source": [ - "### TFRecordファイルの読み込み" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "o3J5D4gcSy8N" - }, - "source": [ - "`tf.data.TFRecordDataset`クラスを使ってTFRecordファイルを読み込むこともできます。\n", - "\n", - "`tf.data`を使ってTFRecordファイルを取り扱う際の詳細については、[こちら](https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data)を参照ください。 \n", - "\n", - "`TFRecordDataset`を使うことは、入力データを標準化し、パフォーマンスを最適化するのに有用です。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "6OjX6UZl-bHC" - }, - "outputs": [], - "source": [ - "filenames = [filename]\n", - "raw_dataset = tf.data.TFRecordDataset(filenames)\n", - "raw_dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "6_EQ9i2E_-Fz" - }, - "source": [ - "この時点で、データセットにはシリアライズされた`tf.train.Example`メッセージが含まれています。データセットをイテレートすると、スカラーの文字列テンソルが返ってきます。\n", - "\n", - "`.take`メソッドを使って最初の10レコードだけを表示します。\n", - "\n", - "注：`tf.data.Dataset`をイテレートできるのは、Eager Executionが有効になっている場合のみです。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "hxVXpLz_AJlm" - }, - "outputs": [], - "source": [ - "for raw_record in raw_dataset.take(10):\n", - " print(repr(raw_record))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "W-6oNzM4luFQ" - }, - "source": [ - "これらのテンソルは下記の関数でパースできます。\n", - "\n", - "注：ここでは、`feature_description`が必要です。データセットはグラフ実行を使用するため、この記述を使ってシェイプと型を構築するのです。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "zQjbIR1nleiy" - }, - "outputs": [], - "source": [ - "# 特徴の記述\n", - "feature_description = {\n", - " 'feature0': tf.FixedLenFeature([], tf.int64, default_value=0),\n", - " 'feature1': tf.FixedLenFeature([], tf.int64, default_value=0),\n", - " 'feature2': tf.FixedLenFeature([], tf.string, default_value=''),\n", - " 'feature3': tf.FixedLenFeature([], tf.float32, default_value=0.0),\n", - "}\n", - "\n", - "def _parse_function(example_proto):\n", - " # 上記の記述を使って入力のtf.Exampleを処理\n", - " return tf.parse_single_example(example_proto, feature_description)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "gWETjUqhEQZf" - }, - "source": [ - "あるいは、`tf.parse example`を使ってバッチ全体を一度にパースします。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "AH73hav6Bnmg" - }, - "source": [ - "`tf.data.Dataset.map`メソッドを使って、データセットの各アイテムにこの関数を適用します。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "6Ob7D-zmBm1w" - }, - "outputs": [], - "source": [ - "parsed_dataset = raw_dataset.map(_parse_function)\n", - "parsed_dataset " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "sNV-XclGnOvn" - }, - "source": [ - "Eager Execution を使ってデータセット中の観測記録を表示します。このデータセットには10,000件の観測記録がありますが、最初の10個だけ表示します。 \n", - "データは特徴量のディクショナリの形で表示されます。それぞれの項目は`tf.Tensor`であり、このテンソルの`numpy` 要素は特徴量を表します。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "x2LT2JCqhoD_" - }, - "outputs": [], - "source": [ - "for parsed_record in parsed_dataset.take(10):\n", - " print(repr(raw_record))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Cig9EodTlDmg" - }, - "source": [ - "ここでは、`tf.parse_example` が`tf.Example`のフィールドを通常のテンソルに展開しています。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "jyg1g3gU7DNn" - }, - "source": [ - "## tf.python_ioを使ったTFRecordファイル" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "3FXG3miA7Kf1" - }, - "source": [ - "`tf.python_io`モジュールには、TFRecordファイルの読み書きのための純粋なPython関数も含まれています。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "CKn5uql2lAaN" - }, - "source": [ - "### TFRecordファイルの書き出し" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "LNW_FA-GQWXs" - }, - "source": [ - "次にこの10,000件の観測記録を`test.tfrecords`ファイルに出力します。観測記録はそれぞれ`tf.Example`メッセージに変換され、ファイルに出力されます。その後、`test.tfrecords`ファイルが作成されたことを確認することができます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "MKPHzoGv7q44" - }, - "outputs": [], - "source": [ - "# `tf.Example`観測記録をファイルに出力\n", - "with tf.python_io.TFRecordWriter(filename) as writer:\n", - " for i in range(n_observations):\n", - " example = serialize_example(feature0[i], feature1[i], feature2[i], feature3[i])\n", - " writer.write(example)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "EjdFHHJMpUUo" - }, - "outputs": [], - "source": [ - "!ls" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "wtQ7k0YWQ1cz" - }, - "source": [ - "### TFRecordファイルの読み込み" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "utkozytkQ-2K" - }, - "source": [ - "モデルに入力にするため、このデータを読み込みたいとしましょう。\n", - "\n", - "次の例では、データをそのまま、`tf.Example`メッセージとしてインポートします。これは、ファイルが期待されるデータを含んでいるかを確認するのに役に立ちます。これは、また、入力データがTFRecordとして保存されているが、[この](https://www.tensorflow.org/guide/datasets#consuming_numpy_arrays)例のようにNumPyデータ（またはそれ以外のデータ型）として入力したい場合に有用です。このコーディング例では値そのものを読み取れるからです。\n", - "\n", - "入力ファイルの中のTFRecordをイテレートして、`tf.Example`メッセージを取り出し、その中の値を読み取って保存できます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "36ltP9B8OezA" - }, - "outputs": [], - "source": [ - "record_iterator = tf.python_io.tf_record_iterator(path=filename)\n", - "\n", - "for string_record in record_iterator:\n", - " example = tf.train.Example()\n", - " example.ParseFromString(string_record)\n", - " \n", - " print(example)\n", - " \n", - " # Exit after 1 iteration as this is purely demonstrative.\n", - " # 純粋にデモであるため、イテレーションの1回目で終了\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "i3uquiiGTZTK" - }, - "source": [ - "（上記で作成した`tf.Example`型の）`example`オブジェクトの特徴量は（他のプロトコルバッファメッセージと同様に）ゲッターを使ってアクセス可能です。`example.features`は`repeated feature`メッセージを返し、`feature`メッセージをを取得すると（Pythonのディクショナリとして保存された）特徴量の名前と特徴量の値のマッピングが得られます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "-UNzS7vsUBs0" - }, - "outputs": [], - "source": [ - "print(dict(example.features.feature))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "u1M-WrbqUUVW" - }, - "source": [ - "このディクショナリから、指定した値をディクショナリとして得ることができます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "2yCBu70IUb2H" - }, - "outputs": [], - "source": [ - "print(example.features.feature['feature3'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4dw6_OI9UiNZ" - }, - "source": [ - "次に、ゲッターを使って値にアクセスできます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "BdDYjDnDUlFe" - }, - "outputs": [], - "source": [ - "print(example.features.feature['feature3'].float_list.value)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "S0tFDrwdoj3q" - }, - "source": [ - "## ウォークスルー： 画像データの読み書き" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "rjN2LFxFpcR9" - }, - "source": [ - "以下は、TFRecordを使って画像データを読み書きする方法の例です。この例の目的は、データ（この場合は画像）を入力し、そのデータをTFRecordファイルに書き込んで、再びそのファイルを読み込み、画像を表示するという手順を最初から最後まで示すことです。\n", - "\n", - "これは、例えば、同じ入力データセットを使って複数のモデルを構築するといった場合に役立ちます。画像データをそのまま保存する代わりに、TFRecord形式に前処理しておき、その後の処理やモデル構築に使用することができます。\n", - "\n", - "まずは、雪の中の猫の[画像](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg)と、ニューヨーク市にあるウイリアムズバーグ橋の [写真](https://upload.wikimedia.org/wikipedia/commons/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg)をダウンロードしましょう。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "5Lk2qrKvN0yu" - }, - "source": [ - "### 画像の取得" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "3a0fmwg8lHdF" - }, - "outputs": [], - "source": [ - "cat_in_snow = tf.keras.utils.get_file('320px-Felis_catus-cat_on_snow.jpg', 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/Felis_catus-cat_on_snow.jpg/320px-Felis_catus-cat_on_snow.jpg')\n", - "williamsburg_bridge = tf.keras.utils.get_file('194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg','https://upload.wikimedia.org/wikipedia/commons/thumb/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "7aJJh7vENeE4" - }, - "outputs": [], - "source": [ - "display.display(display.Image(filename=cat_in_snow))\n", - "display.display(display.HTML('Image cc-by: Von.grzanka'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "KkW0uuhcXZqA" - }, - "outputs": [], - "source": [ - "display.display(display.Image(filename=williamsburg_bridge))\n", - "display.display(display.HTML('source'))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "VSOgJSwoN5TQ" - }, - "source": [ - "### TFRecordファイルの書き出し" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Azx83ryQEU6T" - }, - "source": [ - "上記で行ったように、この特徴量を`tf.Example`と互換のデータ型にエンコードできます。この場合には、生画像のバイト文字列を特徴として保存するだけではなく、縦、横のサイズにチャネル数、更に画像を保存する際に猫の画像と橋の画像を区別するための`label`特徴量を付け加えます。猫の画像には`0`を、橋の画像には`1`を使うことにしましょう。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "kC4TS1ZEONHr" - }, - "outputs": [], - "source": [ - "image_labels = {\n", - " cat_in_snow : 0,\n", - " williamsburg_bridge : 1,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "c5njMSYNEhNZ" - }, - "outputs": [], - "source": [ - "# 猫の画像を使った例\n", - "image_string = open(cat_in_snow, 'rb').read()\n", - "\n", - "label = image_labels[cat_in_snow]\n", - "\n", - "# 関連する特徴量のディクショナリを作成\n", - "def image_example(image_string, label):\n", - " image_shape = tf.image.decode_jpeg(image_string).shape\n", - "\n", - " feature = {\n", - " 'height': _int64_feature(image_shape[0]),\n", - " 'width': _int64_feature(image_shape[1]),\n", - " 'depth': _int64_feature(image_shape[2]),\n", - " 'label': _int64_feature(label),\n", - " 'image_raw': _bytes_feature(image_string),\n", - " }\n", - "\n", - " return tf.train.Example(features=tf.train.Features(feature=feature))\n", - "\n", - "for line in str(image_example(image_string, label)).split('\\n')[:15]:\n", - " print(line)\n", - "print('...')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "2G_o3O9MN0Qx" - }, - "source": [ - "ご覧のように、すべての特徴量が`tf.Example`メッセージに保存されました。上記のコードを関数化し、このサンプルメッセージを`images.tfrecords`ファイルに書き込みます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "qcw06lQCOCZU" - }, - "outputs": [], - "source": [ - "# 生の画像をimages.tfrecordsファイルに書き出す\n", - "# まず、2つの画像をtf.Exampleメッセージに変換し、\n", - "# 次に.tfrecordsファイルに書き出す\n", - "with tf.python_io.TFRecordWriter('images.tfrecords') as writer:\n", - " for filename, label in image_labels.items():\n", - " image_string = open(filename, 'rb').read()\n", - " tf_example = image_example(image_string, label)\n", - " writer.write(tf_example.SerializeToString())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "yJrTe6tHPCfs" - }, - "outputs": [], - "source": [ - "!ls" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "jJSsCkZLPH6K" - }, - "source": [ - "### TFRecordファイルの読み込み\n", - "\n", - "これで、`images.tfrecords`ファイルができました。このファイルの中のレコードをイテレートし、書き込んだものを読み出します。このユースケースでは、画像を復元するだけなので、必要なのは生画像の文字列だけです。上記のゲッター、すなわち、`example.features.feature['image_raw'].bytes_list.value[0]`を使って抽出することができます。猫と橋のどちらであるかを決めるため、ラベルも使用します。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "M6Cnfd3cTKHN" - }, - "outputs": [], - "source": [ - "raw_image_dataset = tf.data.TFRecordDataset('images.tfrecords')\n", - "\n", - "# 特徴量を記述するディクショナリを作成\n", - "image_feature_description = {\n", - " 'height': tf.FixedLenFeature([], tf.int64),\n", - " 'width': tf.FixedLenFeature([], tf.int64),\n", - " 'depth': tf.FixedLenFeature([], tf.int64),\n", - " 'label': tf.FixedLenFeature([], tf.int64),\n", - " 'image_raw': tf.FixedLenFeature([], tf.string),\n", - "}\n", - "\n", - "def _parse_image_function(example_proto):\n", - " # 入力のtf.Exampleのプロトコルバッファを上記のディクショナリを使って解釈\n", - " return tf.parse_single_example(example_proto, image_feature_description)\n", - "\n", - "parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n", - "parsed_image_dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "0PEEFPk4NEg1" - }, - "source": [ - "TFRecordファイルから画像を復元しましょう。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "yZf8jOyEIjSF" - }, - "outputs": [], - "source": [ - "for image_features in parsed_image_dataset:\n", - " image_raw = image_features['image_raw'].numpy()\n", - " display.display(display.Image(data=image_raw))" - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [ - "pL--_KGdYoBz" - ], - "name": "tf-records.ipynb", - "private_outputs": true, - "provenance": [], - "toc_visible": true, - "version": "0.3.2" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "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": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "tf_records.ipynb", + "version": "0.3.2", + "provenance": [], + "private_outputs": true, + "collapsed_sections": [ + "pL--_KGdYoBz" + ], + "toc_visible": true + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "cells": [ + { + "metadata": { + "colab_type": "text", + "id": "pL--_KGdYoBz" + }, + "cell_type": "markdown", + "source": [ + "##### Copyright 2018 The TensorFlow Authors." + ] + }, + { + "metadata": { + "cellView": "both", + "colab_type": "code", + "id": "uBDvXpYzYnGj", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "HQzaEQuJiW_d" + }, + "cell_type": "markdown", + "source": [ + "# TFRecords と `tf.Example` の使用法\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "
\n", + " View on TensorFlow.org\n", + " \n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "3pkUd_9IZCFO" + }, + "cell_type": "markdown", + "source": [ + "データの読み込みを効率的にするには、データをシリアライズし、連続的に読み込めるファイルのセット（各ファイルは100-200MB）に保存することが有効です。データをネットワーク経由で流そうとする場合には、特にそうです。また、データの前処理をキャッシュする際にも役立ちます。\n", + "\n", + "TFRecord形式は、バイナリレコードの系列を保存するための単純な形式です。\n", + "\n", + "[プロトコルバッファ](https://developers.google.com/protocol-buffers/) は、構造化データを効率的にシリアライズする、プラットフォームや言語に依存しないライブラリです。\n", + "\n", + "プロトコルメッセージは`.proto`という拡張子のファイルで定義されます。メッセージ型を識別する最も簡単な方法です。\n", + "\n", + "`tf.Example`メッセージ（あるいはプロトコルバッファ）は、`{\"string\": value}`形式のマッピングを表現する柔軟なメッセージ型です。これは、TensorFlow用に設計され、[TFX](https://www.tensorflow.org/tfx/)のような上位レベルのAPIで共通に使用されています。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Ac83J0QxjhFt" + }, + "cell_type": "markdown", + "source": [ + "このノートブックでは、`tf.Example`メッセージの作成、パースと使用法をデモし、その後、`tf.Example`メッセージをパースして、`.tfrecord`に書き出し、その後読み取る方法を示します。\n", + "\n", + "注：こうした構造は有用ですが必ずそうしなければならなというものではありません。[`tf.data`](https://www.tensorflow.org/guide/datasets) を使っていて、それでもなおデータの読み込みが訓練のボトルネックである場合でなければ、既存のコードをTFRecordsを使用するために変更する必要はありません。データセットの性能改善のヒントは、 [Data Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets)を参照ください。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "WkRreBf1eDVc" + }, + "cell_type": "markdown", + "source": [ + "## 設定" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ja7sezsmnXph", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import absolute_import\n", + "from __future__ import division\n", + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "tf.enable_eager_execution()\n", + "\n", + "import numpy as np\n", + "import IPython.display as display" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "e5Kq88ccUWQV" + }, + "cell_type": "markdown", + "source": [ + "## `tf.Example`" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "VrdQHgvNijTi" + }, + "cell_type": "markdown", + "source": [ + "### `tf.Example`用のデータ型" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "lZw57Qrn4CTE" + }, + "cell_type": "markdown", + "source": [ + "基本的には`tf.Example`は`{\"string\": tf.train.Feature}`というマッピングです。\n", + "\n", + "`tf.train.Feature`メッセージ型は次の3つの型のうち1つをとることができます（[.proto file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto)を参照）。一般的なデータ型の多くは、これらの型のいずれかに強制的に変換することができます。\n", + "\n", + "1. `tf.train.BytesList` (次の型のデータを扱うことが可能)\n", + " - `string`\n", + " - `byte` \n", + "1. `tf.train.FloatList` (次の型のデータを扱うことが可能)\n", + " - `float` (`float32`)\n", + " - `double` (`float64`) \n", + "1. `tf.train.Int64List` (次の型のデータを扱うことが可能)\n", + " - `bool`\n", + " - `enum`\n", + " - `int32`\n", + " - `uint32`\n", + " - `int64`\n", + " - `uint64`" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "_e3g9ExathXP" + }, + "cell_type": "markdown", + "source": [ + "通常のTensorFlowの型を`tf.Example`互換の `tf.train.Feature`に変換するには、次のショートカット関数を使うことができます。\n", + "\n", + "どの関数も、1個のスカラー値を入力とし、上記の3つの`list`型のうちの一つを含む`tf.train.Feature`を返します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mbsPOUpVtYxA", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# 下記の関数を使うと値を tf.Exampleと互換性の有る型に変換できる\n", + "\n", + "def _bytes_feature(value):\n", + " \"\"\"string / byte 型から byte_listを返す\"\"\"\n", + " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n", + "\n", + "def _float_feature(value):\n", + " \"\"\"float / double 型から float_listを返す\"\"\"\n", + " return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n", + "\n", + "def _int64_feature(value):\n", + " \"\"\"bool / enum / int / uint 型から Int64_listを返す\"\"\"\n", + " return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "Wst0v9O8hgzy" + }, + "cell_type": "markdown", + "source": [ + "注：単純化のため、このサンプルではスカラー値の入力のみを扱っています。スカラー値ではない特徴を扱う最も簡単な方法は、`tf.serialize_tensor`を使ってテンソルをバイナリ文字列に変換する方法です。TensorFlowでは文字列はスカラー値として扱います。バイナリ文字列をテンソルに戻すには、`tf.parse_tensor`を使用します。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "vsMbkkC8xxtB" + }, + "cell_type": "markdown", + "source": [ + "上記の関数の使用例を下記に示します。入力が様々な型であるのに対して、出力が標準化されていることに注目してください。入力が、強制変換できない型であった場合、例外が発生します。（例：`_int64_feature(1.0)`はエラーとなります。`1.0`が浮動小数点数であるためで、代わりに`_float_feature`関数を使用すべきです）" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "hZzyLGr0u73y", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(_bytes_feature(b'test_string'))\n", + "print(_bytes_feature(u'test_bytes'.encode('utf-8')))\n", + "\n", + "print(_float_feature(np.exp(1)))\n", + "\n", + "print(_int64_feature(True))\n", + "print(_int64_feature(1))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "nj1qpfQU5qmi" + }, + "cell_type": "markdown", + "source": [ + "メッセージはすべて`.SerializeToString` を使ってバイナリ文字列にシリアライズすることができます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "5afZkORT5pjm", + "colab": {} + }, + "cell_type": "code", + "source": [ + "feature = _float_feature(np.exp(1))\n", + "\n", + "feature.SerializeToString()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "laKnw9F3hL-W" + }, + "cell_type": "markdown", + "source": [ + "### `tf.Example` メッセージの作成" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "b_MEnhxchQPC" + }, + "cell_type": "markdown", + "source": [ + "既存のデータから`tf.Example`を作成したいとします。実際には、データセットの出処はどこでも良いのですが、1件の観測記録から`tf.Example`メッセージを作る手順は同じです。\n", + "\n", + "1. 観測記録それぞれにおいて、各値は上記の関数を使って3種類の互換性のある型のうち1つだけを含む`tf.train.Feature`に変換する必要があります。\n", + "\n", + "1. 次に、特徴の名前を表す文字列と、#1で作ったエンコード済みの特徴量を対応させたマップ（ディクショナリ）を作成します。\n", + "\n", + "1. #2で作成したマップを[Featuresメッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)に変換します。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "4EgFQ2uHtchc" + }, + "cell_type": "markdown", + "source": [ + "このノートブックでは、NumPyを使ってデータセットを作成します。\n", + "\n", + "このデータセットには4つの特徴量があります。\n", + "- `False` または `True`を表す論理値。出現確率は等しいものとします。\n", + "- `[0,5)`の範囲から一様にサンプリングした整数値。\n", + "- 整数特徴量をインデックスとした文字列テーブルを使って生成した文字列特徴量\n", + "- 標準正規分布からサンプリングした浮動小数点数。\n", + "\n", + "サンプルは上記の分布から独立して同じ様に分布した10,000件の観測記録からなるものとします。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "CnrguFAy3YQv", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# データセットに含まれる観測結果の件数\n", + "n_observations = int(1e4)\n", + "\n", + "# ブール特徴量 FalseまたはTrueとしてエンコードされている\n", + "feature0 = np.random.choice([False, True], n_observations)\n", + "\n", + "# 整数特徴量 0以上 5未満の乱数\n", + "feature1 = np.random.randint(0, 5, n_observations)\n", + "\n", + "# バイト文字列特徴量\n", + "strings = np.array([b'cat', b'dog', b'chicken', b'horse', b'goat'])\n", + "feature2 = strings[feature1]\n", + "\n", + "# 浮動小数点数特徴量 標準正規分布から発生\n", + "feature3 = np.random.randn(n_observations)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "aGrscehJr7Jd" + }, + "cell_type": "markdown", + "source": [ + "これらの特徴量は、`_bytes_feature`, `_float_feature`, `_int64_feature`のいずれかを使って`tf.Example`互換の型に強制変換されます。その後、エンコード済みの特徴量から`tf.Example`メッセージを作成できます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "RTCS49Ij_kUw", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def serialize_example(feature0, feature1, feature2, feature3):\n", + " \"\"\"\n", + " Creates a tf.Example message ready to be written to a file.\n", + " ファイル出力可能なtf.Exampleメッセージを作成する\n", + " \"\"\"\n", + "\n", + " # 特徴量名とtf.Example互換データ型との対応ディクショナリを作成\n", + "\n", + " feature = {\n", + " 'feature0': _int64_feature(feature0),\n", + " 'feature1': _int64_feature(feature1),\n", + " 'feature2': _bytes_feature(feature2),\n", + " 'feature3': _float_feature(feature3),\n", + " }\n", + "\n", + " # tf.train.Exampleを用いて特徴メッセージを作成\n", + "\n", + " example_proto = tf.train.Example(features=tf.train.Features(feature=feature))\n", + " return example_proto.SerializeToString()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "XftzX9CN_uGT" + }, + "cell_type": "markdown", + "source": [ + "例えば、データセットに`[False, 4, bytes('goat'), 0.9876]`という1つの観測記録があるとします。`create_message()`を使うとこの観測記録から`tf.Example`メッセージを作成し印字できます。上記のように、観測記録一つ一つが`Features`メッセージとして書かれています。`tf.Example` [メッセージ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88)は、この`Features` メッセージを包むラッパーに過ぎないことに注意してください。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "N8BtSx2RjYcb", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# データセットからの観測記録の例\n", + "\n", + "example_observation = []\n", + "\n", + "serialized_example = serialize_example(False, 4, b'goat', 0.9876)\n", + "serialized_example" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "_pbGATlG6u-4" + }, + "cell_type": "markdown", + "source": [ + "メッセージをデコードするには、`tf.train.Example.FromString`メソッドを使用します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "dGim-mEm6vit", + "colab": {} + }, + "cell_type": "code", + "source": [ + "example_proto = tf.train.Example.FromString(serialized_example)\n", + "example_proto" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ZHV-ff6YHtDG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## TFRecordフォーマットの詳細\n", + "\n", + "TFRecordファイルにはレコードのシーケンスが含まれます。このファイルはシーケンシャル読み取りのみが可能です。\n", + "\n", + "それぞれのレコードには、データを格納するためのバイト文字列とデータ長、そして整合性チェックのためのCRC32C（Castagnoli多項式を使った32ビットのCRC）ハッシュ値が含まれます。\n", + "\n", + "各レコードのフォーマットは下記の通りです。\n", + "\n", + " uint64 長さ\n", + " uint32 長さのマスク済みcrc32ハッシュ値\n", + " byte data[長さ]\n", + " uint32 データのマスク済みcrc32ハッシュ値\n", + "\n", + "複数のレコードが結合されてファイルを構成します。CRCについては[ここ](https://en.wikipedia.org/wiki/Cyclic_redundancy_check)に說明があります。CRCのマスクは下記のとおりです。\n", + "\n", + " masked_crc = ((crc >> 15) | (crc << 17)) + 0xa282ead8ul\n", + " \n", + "注：TFRecordファイルを作るのに、`tf.Example`を使わなければならないということはありません。`tf.Example`は、ディクショナリをバイト文字列にシリアライズする方法の1つです。エンコードされた画像データや、（`tf.io.serialize_tensor`を使ってシリアライズされ、`tf.io.parse_tensor`で読み込まれる）シリアライズされたテンソルもあります。その他のオプションについては、`tf.io`モジュールを参照してください。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "y-Hjmee-fbLH" + }, + "cell_type": "markdown", + "source": [ + "## `tf.data`を使用したTFRecordファイル" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "GmehkCCT81Ez" + }, + "cell_type": "markdown", + "source": [ + "`tf.data`モジュールには、TensorFlowでデータを読み書きするツールが含まれます。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "1FISEuz8ubu3" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの書き出し\n", + "\n", + "データをデータセットにする最も簡単な方法は`from_tensor_slices`メソッドです。\n", + "\n", + "配列に適用すると、このメソッドはスカラー値のデータセットを返します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "mXeaukvwu5_-", + "colab": {} + }, + "cell_type": "code", + "source": [ + "tf.data.Dataset.from_tensor_slices(feature1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "f-q0VKyZvcad" + }, + "cell_type": "markdown", + "source": [ + "配列のタプルに適用すると、タプルのデータセットが返されます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "H5sWyu1kxnvg", + "colab": {} + }, + "cell_type": "code", + "source": [ + "features_dataset = tf.data.Dataset.from_tensor_slices((feature0, feature1, feature2, feature3))\n", + "features_dataset" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "m1C-t71Nywze", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# データセットから1つのサンプルだけを取り出すには`take(1)` を使います。\n", + "for f0,f1,f2,f3 in features_dataset.take(1):\n", + " print(f0)\n", + " print(f1)\n", + " print(f2)\n", + " print(f3)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "mhIe63awyZYd" + }, + "cell_type": "markdown", + "source": [ + "`Dataset`のそれぞれの要素に関数を適用するには、`tf.data.Dataset.map`メソッドを使用します。\n", + "\n", + "マップされる関数はTensorFlowのグラフモードで動作する必要があります。関数は`tf.Tensors`を処理し、返す必要があります。`create_example`のような非テンソル関数は、互換性のため`tf.py_func`でラップすることができます。\n", + "\n", + "`tf.py_func`を使用する際には、シェイプと型は取得できないため、指定する必要があります。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "apB5KYrJzjPI", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def tf_serialize_example(f0,f1,f2,f3):\n", + " tf_string = tf.py_func(\n", + " serialize_example, \n", + " (f0,f1,f2,f3), # pass these args to the above function.\n", + " tf.string) # the return type is `tf.string`.\n", + " return tf.reshape(tf_string, ()) # The result is a scalar" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "CrFZ9avE3HUF" + }, + "cell_type": "markdown", + "source": [ + "この関数をデータセットのそれぞれの要素に適用します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "VDeqYVbW3ww9", + "colab": {} + }, + "cell_type": "code", + "source": [ + "serialized_features_dataset = features_dataset.map(tf_serialize_example)\n", + "serialized_features_dataset" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "p6lw5VYpjZZC" + }, + "cell_type": "markdown", + "source": [ + "TFRecordファイルに書き出します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "vP1VgTO44UIE", + "colab": {} + }, + "cell_type": "code", + "source": [ + "filename = 'test.tfrecord'\n", + "writer = tf.data.experimental.TFRecordWriter(filename)\n", + "writer.write(serialized_features_dataset)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "6aV0GQhV8tmp" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの読み込み" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "o3J5D4gcSy8N" + }, + "cell_type": "markdown", + "source": [ + "`tf.data.TFRecordDataset`クラスを使ってTFRecordファイルを読み込むこともできます。\n", + "\n", + "`tf.data`を使ってTFRecordファイルを取り扱う際の詳細については、[こちら](https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data)を参照ください。 \n", + "\n", + "`TFRecordDataset`を使うことは、入力データを標準化し、パフォーマンスを最適化するのに有用です。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "6OjX6UZl-bHC", + "colab": {} + }, + "cell_type": "code", + "source": [ + "filenames = [filename]\n", + "raw_dataset = tf.data.TFRecordDataset(filenames)\n", + "raw_dataset" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "6_EQ9i2E_-Fz" + }, + "cell_type": "markdown", + "source": [ + "この時点で、データセットにはシリアライズされた`tf.train.Example`メッセージが含まれています。データセットをイテレートすると、スカラーの文字列テンソルが返ってきます。\n", + "\n", + "`.take`メソッドを使って最初の10レコードだけを表示します。\n", + "\n", + "注：`tf.data.Dataset`をイテレートできるのは、Eager Executionが有効になっている場合のみです。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "hxVXpLz_AJlm", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for raw_record in raw_dataset.take(10):\n", + " print(repr(raw_record))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "W-6oNzM4luFQ" + }, + "cell_type": "markdown", + "source": [ + "これらのテンソルは下記の関数でパースできます。\n", + "\n", + "注：ここでは、`feature_description`が必要です。データセットはグラフ実行を使用するため、この記述を使ってシェイプと型を構築するのです。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "zQjbIR1nleiy", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# 特徴の記述\n", + "feature_description = {\n", + " 'feature0': tf.FixedLenFeature([], tf.int64, default_value=0),\n", + " 'feature1': tf.FixedLenFeature([], tf.int64, default_value=0),\n", + " 'feature2': tf.FixedLenFeature([], tf.string, default_value=''),\n", + " 'feature3': tf.FixedLenFeature([], tf.float32, default_value=0.0),\n", + "}\n", + "\n", + "def _parse_function(example_proto):\n", + " # 上記の記述を使って入力のtf.Exampleを処理\n", + " return tf.parse_single_example(example_proto, feature_description)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "gWETjUqhEQZf" + }, + "cell_type": "markdown", + "source": [ + "あるいは、`tf.parse example`を使ってバッチ全体を一度にパースします。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "AH73hav6Bnmg" + }, + "cell_type": "markdown", + "source": [ + "`tf.data.Dataset.map`メソッドを使って、データセットの各アイテムにこの関数を適用します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "6Ob7D-zmBm1w", + "colab": {} + }, + "cell_type": "code", + "source": [ + "parsed_dataset = raw_dataset.map(_parse_function)\n", + "parsed_dataset " + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "sNV-XclGnOvn" + }, + "cell_type": "markdown", + "source": [ + "Eager Execution を使ってデータセット中の観測記録を表示します。このデータセットには10,000件の観測記録がありますが、最初の10個だけ表示します。 \n", + "データは特徴量のディクショナリの形で表示されます。それぞれの項目は`tf.Tensor`であり、このテンソルの`numpy` 要素は特徴量を表します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "x2LT2JCqhoD_", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for parsed_record in parsed_dataset.take(10):\n", + " print(repr(raw_record))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "Cig9EodTlDmg" + }, + "cell_type": "markdown", + "source": [ + "ここでは、`tf.parse_example` が`tf.Example`のフィールドを通常のテンソルに展開しています。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "jyg1g3gU7DNn" + }, + "cell_type": "markdown", + "source": [ + "## tf.python_ioを使ったTFRecordファイル" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "3FXG3miA7Kf1" + }, + "cell_type": "markdown", + "source": [ + "`tf.python_io`モジュールには、TFRecordファイルの読み書きのための純粋なPython関数も含まれています。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "CKn5uql2lAaN" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの書き出し" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "LNW_FA-GQWXs" + }, + "cell_type": "markdown", + "source": [ + "次にこの10,000件の観測記録を`test.tfrecords`ファイルに出力します。観測記録はそれぞれ`tf.Example`メッセージに変換され、ファイルに出力されます。その後、`test.tfrecords`ファイルが作成されたことを確認することができます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "MKPHzoGv7q44", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# `tf.Example`観測記録をファイルに出力\n", + "with tf.python_io.TFRecordWriter(filename) as writer:\n", + " for i in range(n_observations):\n", + " example = serialize_example(feature0[i], feature1[i], feature2[i], feature3[i])\n", + " writer.write(example)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "EjdFHHJMpUUo", + "colab": {} + }, + "cell_type": "code", + "source": [ + "!ls" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "wtQ7k0YWQ1cz" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの読み込み" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "utkozytkQ-2K" + }, + "cell_type": "markdown", + "source": [ + "モデルに入力にするため、このデータを読み込みたいとしましょう。\n", + "\n", + "次の例では、データをそのまま、`tf.Example`メッセージとしてインポートします。これは、ファイルが期待されるデータを含んでいるかを確認するのに役に立ちます。これは、また、入力データがTFRecordとして保存されているが、[この](https://www.tensorflow.org/guide/datasets#consuming_numpy_arrays)例のようにNumPyデータ（またはそれ以外のデータ型）として入力したい場合に有用です。このコーディング例では値そのものを読み取れるからです。\n", + "\n", + "入力ファイルの中のTFRecordをイテレートして、`tf.Example`メッセージを取り出し、その中の値を読み取って保存できます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "36ltP9B8OezA", + "colab": {} + }, + "cell_type": "code", + "source": [ + "record_iterator = tf.python_io.tf_record_iterator(path=filename)\n", + "\n", + "for string_record in record_iterator:\n", + " example = tf.train.Example()\n", + " example.ParseFromString(string_record)\n", + " \n", + " print(example)\n", + " \n", + " # Exit after 1 iteration as this is purely demonstrative.\n", + " # 純粋にデモであるため、イテレーションの1回目で終了\n", + " break" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "i3uquiiGTZTK" + }, + "cell_type": "markdown", + "source": [ + "（上記で作成した`tf.Example`型の）`example`オブジェクトの特徴量は（他のプロトコルバッファメッセージと同様に）ゲッターを使ってアクセス可能です。`example.features`は`repeated feature`メッセージを返し、`feature`メッセージをを取得すると（Pythonのディクショナリとして保存された）特徴量の名前と特徴量の値のマッピングが得られます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "-UNzS7vsUBs0", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(dict(example.features.feature))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "u1M-WrbqUUVW" + }, + "cell_type": "markdown", + "source": [ + "このディクショナリから、指定した値をディクショナリとして得ることができます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "2yCBu70IUb2H", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(example.features.feature['feature3'])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "4dw6_OI9UiNZ" + }, + "cell_type": "markdown", + "source": [ + "次に、ゲッターを使って値にアクセスできます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "BdDYjDnDUlFe", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(example.features.feature['feature3'].float_list.value)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "S0tFDrwdoj3q" + }, + "cell_type": "markdown", + "source": [ + "## ウォークスルー： 画像データの読み書き" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "rjN2LFxFpcR9" + }, + "cell_type": "markdown", + "source": [ + "以下は、TFRecordを使って画像データを読み書きする方法の例です。この例の目的は、データ（この場合は画像）を入力し、そのデータをTFRecordファイルに書き込んで、再びそのファイルを読み込み、画像を表示するという手順を最初から最後まで示すことです。\n", + "\n", + "これは、例えば、同じ入力データセットを使って複数のモデルを構築するといった場合に役立ちます。画像データをそのまま保存する代わりに、TFRecord形式に前処理しておき、その後の処理やモデル構築に使用することができます。\n", + "\n", + "まずは、雪の中の猫の[画像](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg)と、ニューヨーク市にあるウイリアムズバーグ橋の [写真](https://upload.wikimedia.org/wikipedia/commons/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg)をダウンロードしましょう。" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "5Lk2qrKvN0yu" + }, + "cell_type": "markdown", + "source": [ + "### 画像の取得" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "3a0fmwg8lHdF", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cat_in_snow = tf.keras.utils.get_file('320px-Felis_catus-cat_on_snow.jpg', 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/Felis_catus-cat_on_snow.jpg/320px-Felis_catus-cat_on_snow.jpg')\n", + "williamsburg_bridge = tf.keras.utils.get_file('194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg','https://upload.wikimedia.org/wikipedia/commons/thumb/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "7aJJh7vENeE4", + "colab": {} + }, + "cell_type": "code", + "source": [ + "display.display(display.Image(filename=cat_in_snow))\n", + "display.display(display.HTML('Image cc-by: Von.grzanka'))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "KkW0uuhcXZqA", + "colab": {} + }, + "cell_type": "code", + "source": [ + "display.display(display.Image(filename=williamsburg_bridge))\n", + "display.display(display.HTML('source'))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "VSOgJSwoN5TQ" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの書き出し" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Azx83ryQEU6T" + }, + "cell_type": "markdown", + "source": [ + "上記で行ったように、この特徴量を`tf.Example`と互換のデータ型にエンコードできます。この場合には、生画像のバイト文字列を特徴として保存するだけではなく、縦、横のサイズにチャネル数、更に画像を保存する際に猫の画像と橋の画像を区別するための`label`特徴量を付け加えます。猫の画像には`0`を、橋の画像には`1`を使うことにしましょう。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "kC4TS1ZEONHr", + "colab": {} + }, + "cell_type": "code", + "source": [ + "image_labels = {\n", + " cat_in_snow : 0,\n", + " williamsburg_bridge : 1,\n", + "}" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "c5njMSYNEhNZ", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# 猫の画像を使った例\n", + "image_string = open(cat_in_snow, 'rb').read()\n", + "\n", + "label = image_labels[cat_in_snow]\n", + "\n", + "# 関連する特徴量のディクショナリを作成\n", + "def image_example(image_string, label):\n", + " image_shape = tf.image.decode_jpeg(image_string).shape\n", + "\n", + " feature = {\n", + " 'height': _int64_feature(image_shape[0]),\n", + " 'width': _int64_feature(image_shape[1]),\n", + " 'depth': _int64_feature(image_shape[2]),\n", + " 'label': _int64_feature(label),\n", + " 'image_raw': _bytes_feature(image_string),\n", + " }\n", + "\n", + " return tf.train.Example(features=tf.train.Features(feature=feature))\n", + "\n", + "for line in str(image_example(image_string, label)).split('\\n')[:15]:\n", + " print(line)\n", + "print('...')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "2G_o3O9MN0Qx" + }, + "cell_type": "markdown", + "source": [ + "ご覧のように、すべての特徴量が`tf.Example`メッセージに保存されました。上記のコードを関数化し、このサンプルメッセージを`images.tfrecords`ファイルに書き込みます。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "qcw06lQCOCZU", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# 生の画像をimages.tfrecordsファイルに書き出す\n", + "# まず、2つの画像をtf.Exampleメッセージに変換し、\n", + "# 次に.tfrecordsファイルに書き出す\n", + "with tf.python_io.TFRecordWriter('images.tfrecords') as writer:\n", + " for filename, label in image_labels.items():\n", + " image_string = open(filename, 'rb').read()\n", + " tf_example = image_example(image_string, label)\n", + " writer.write(tf_example.SerializeToString())" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "yJrTe6tHPCfs", + "colab": {} + }, + "cell_type": "code", + "source": [ + "!ls" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "jJSsCkZLPH6K" + }, + "cell_type": "markdown", + "source": [ + "### TFRecordファイルの読み込み\n", + "\n", + "これで、`images.tfrecords`ファイルができました。このファイルの中のレコードをイテレートし、書き込んだものを読み出します。このユースケースでは、画像を復元するだけなので、必要なのは生画像の文字列だけです。上記のゲッター、すなわち、`example.features.feature['image_raw'].bytes_list.value[0]`を使って抽出することができます。猫と橋のどちらであるかを決めるため、ラベルも使用します。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "M6Cnfd3cTKHN", + "colab": {} + }, + "cell_type": "code", + "source": [ + "raw_image_dataset = tf.data.TFRecordDataset('images.tfrecords')\n", + "\n", + "# 特徴量を記述するディクショナリを作成\n", + "image_feature_description = {\n", + " 'height': tf.FixedLenFeature([], tf.int64),\n", + " 'width': tf.FixedLenFeature([], tf.int64),\n", + " 'depth': tf.FixedLenFeature([], tf.int64),\n", + " 'label': tf.FixedLenFeature([], tf.int64),\n", + " 'image_raw': tf.FixedLenFeature([], tf.string),\n", + "}\n", + "\n", + "def _parse_image_function(example_proto):\n", + " # 入力のtf.Exampleのプロトコルバッファを上記のディクショナリを使って解釈\n", + " return tf.parse_single_example(example_proto, image_feature_description)\n", + "\n", + "parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n", + "parsed_image_dataset" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "0PEEFPk4NEg1" + }, + "cell_type": "markdown", + "source": [ + "TFRecordファイルから画像を復元しましょう。" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yZf8jOyEIjSF", + "colab": {} + }, + "cell_type": "code", + "source": [ + "for image_features in parsed_image_dataset:\n", + " image_raw = image_features['image_raw'].numpy()\n", + " display.display(display.Image(data=image_raw))" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file