site/ja/tutorials/quickstart/advanced.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rX8mhOLljYeM"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "BZSlp3DAjdYf"
      },
      "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": {
        "id": "3wF5wszaj97Y"
      },
      "source": [
        "# エキスパートのための TensorFlow 2 クイックスタート"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUNzJc4jTj6G"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>     <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/quickstart/advanced\">     <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">     TensorFlow.org で表示</a>\n",
        "</td>\n",
        "  <td>     <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/tutorials/quickstart/advanced.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Google Colabで実行</a>\n",
        "</td>\n",
        "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/tutorials/quickstart/advanced.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">GitHubでソースを表示</a></td>\n",
        "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ja/tutorials/quickstart/advanced.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">ノートブックをダウンロード</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hiH7AC-NTniF"
      },
      "source": [
        "これは [Google Colaboratory](https://colab.research.google.com/notebooks/welcome.ipynb) のノートブックファイルです。Python プログラムはブラウザ上で直接実行されるため、TensorFlow を学んで使用するのに最適です。このチュートリアルを進めるには、このページの上部にあるボタンをクリックして Google Colab でノートブックを実行してください。\n",
        "\n",
        "1. Colab で、Python ランタイムに接続します。メニューバーの右上にある *CONNECT* を選択してください。\n",
        "2. ノートブックのコードセルをすべて実行します。*Runtime* &gt; *Run all* を選択してください。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eOsVdx6GGHmU"
      },
      "source": [
        "TensorFlow 2 をダウンロードしてインストールします。TensorFlow をプログラムにインポートします。\n",
        "\n",
        "注意: `pip` をアップグレードして TensorFlow 2 をインストールします。詳細は、[インストールガイド](https://www.tensorflow.org/install)をご覧ください。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QS7DDTiZGRTo"
      },
      "source": [
        "TensorFlow をプログラムにインポートします。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0trJmd6DjqBZ"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "print(\"TensorFlow version:\", tf.__version__)\n",
        "\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D\n",
        "from tensorflow.keras import Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7NAbSZiaoJ4z"
      },
      "source": [
        "[MNIST データセット](http://yann.lecun.com/exdb/mnist/)をロードして準備します。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JqFRS6K07jJs"
      },
      "outputs": [],
      "source": [
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "\n",
        "# Add a channels dimension\n",
        "x_train = x_train[..., tf.newaxis].astype(\"float32\")\n",
        "x_test = x_test[..., tf.newaxis].astype(\"float32\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k1Evqx0S22r_"
      },
      "source": [
        "`tf.data` を使用して、データセットをバッチ化してシャッフルします。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8Iu_quO024c2"
      },
      "outputs": [],
      "source": [
        "train_ds = tf.data.Dataset.from_tensor_slices(\n",
        "    (x_train, y_train)).shuffle(10000).batch(32)\n",
        "\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPZ68wASog_I"
      },
      "source": [
        "Keras の <a>model subclassing API</a> を使って <code>tf.keras</code> モデルを構築します。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h3IKyzTCDNGo"
      },
      "outputs": [],
      "source": [
        "class MyModel(Model):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv1 = Conv2D(32, 3, activation='relu')\n",
        "    self.flatten = Flatten()\n",
        "    self.d1 = Dense(128, activation='relu')\n",
        "    self.d2 = Dense(10)\n",
        "\n",
        "  def call(self, x):\n",
        "    x = self.conv1(x)\n",
        "    x = self.flatten(x)\n",
        "    x = self.d1(x)\n",
        "    return self.d2(x)\n",
        "\n",
        "# Create an instance of the model\n",
        "model = MyModel()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGih-c2LgbJu"
      },
      "source": [
        "トレーニングを実施するために、オプティマイザと損失関数を選択します。 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u48C9WQ774n4"
      },
      "outputs": [],
      "source": [
        "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JB6A1vcigsIe"
      },
      "source": [
        "モデルの損失と精度を測定するためのメトリクスを選択します。これらのメトリクスはエポックの値を集計し、最終結果を出力します。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N0MqHFb4F_qn"
      },
      "outputs": [],
      "source": [
        "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
        "train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n",
        "\n",
        "test_loss = tf.keras.metrics.Mean(name='test_loss')\n",
        "test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ix4mEL65on-w"
      },
      "source": [
        "`tf.GradientTape` を使ってモデルをトレーニングします。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OZACiVqA8KQV"
      },
      "outputs": [],
      "source": [
        "@tf.function\n",
        "def train_step(images, labels):\n",
        "  with tf.GradientTape() as tape:\n",
        "    # training=True is only needed if there are layers with different\n",
        "    # behavior during training versus inference (e.g. Dropout).\n",
        "    predictions = model(images, training=True)\n",
        "    loss = loss_object(labels, predictions)\n",
        "  gradients = tape.gradient(loss, model.trainable_variables)\n",
        "  optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
        "\n",
        "  train_loss(loss)\n",
        "  train_accuracy(labels, predictions)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8YT7UmFgpjV"
      },
      "source": [
        "モデルをテストします。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xIKdEzHAJGt7"
      },
      "outputs": [],
      "source": [
        "@tf.function\n",
        "def test_step(images, labels):\n",
        "  # training=False is only needed if there are layers with different\n",
        "  # behavior during training versus inference (e.g. Dropout).\n",
        "  predictions = model(images, training=False)\n",
        "  t_loss = loss_object(labels, predictions)\n",
        "\n",
        "  test_loss(t_loss)\n",
        "  test_accuracy(labels, predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i-2pkctU_Ci7"
      },
      "outputs": [],
      "source": [
        "EPOCHS = 5\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "  # Reset the metrics at the start of the next epoch\n",
        "  train_loss.reset_states()\n",
        "  train_accuracy.reset_states()\n",
        "  test_loss.reset_states()\n",
        "  test_accuracy.reset_states()\n",
        "\n",
        "  for images, labels in train_ds:\n",
        "    train_step(images, labels)\n",
        "\n",
        "  for test_images, test_labels in test_ds:\n",
        "    test_step(test_images, test_labels)\n",
        "\n",
        "  print(\n",
        "    f'Epoch {epoch + 1}, '\n",
        "    f'Loss: {train_loss.result()}, '\n",
        "    f'Accuracy: {train_accuracy.result() * 100}, '\n",
        "    f'Test Loss: {test_loss.result()}, '\n",
        "    f'Test Accuracy: {test_accuracy.result() * 100}'\n",
        "  )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4JfEh7kvx6m"
      },
      "source": [
        "この画像分類器は、このデータセットで約 98% の精度にトレーニングされました。さらに学習するには、[TensorFlow のチュートリアル](https://www.tensorflow.org/tutorials/) をご覧ください。"
      ]
    }
  ],
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    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "advanced.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
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  "nbformat_minor": 0
}