{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "cellView": "form",
        "id": "BZSlp3DAjdYf"
      },
      "source": [
        "````{admonition} Copyright 2019 The TensorFlow Authors.\n",
        "```\n",
        "#@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",
        "```\n",
        "````"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # 设置日志级别为ERROR，以减少警告信息\n",
        "# 禁用 Gemini 的底层库（gRPC 和 Abseil）在初始化日志警告\n",
        "os.environ[\"GRPC_VERBOSITY\"] = \"ERROR\"\n",
        "os.environ[\"GLOG_minloglevel\"] = \"3\"  # 0: INFO, 1: WARNING, 2: ERROR, 3: FATAL\n",
        "os.environ[\"GLOG_minloglevel\"] = \"true\"\n",
        "import logging\n",
        "import tensorflow as tf\n",
        "tf.get_logger().setLevel(logging.ERROR)\n",
        "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
        "!export TF_FORCE_GPU_ALLOW_GROWTH=true"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3wF5wszaj97Y"
      },
      "source": [
        "# 专家的 TensorFlow 2 快速入门"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUNzJc4jTj6G"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>     <a target=\"_blank\" href=\"https://tensorflow.google.cn/tutorials/quickstart/advanced\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">View on TensorFlow.org</a>\n",
        "</td>\n",
        "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/quickstart/advanced.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行</a></td>\n",
        "  <td>     <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/quickstart/advanced.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 上查看源代码</a>\n",
        "</td>\n",
        "  <td>     <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/quickstart/advanced.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a>   </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hiH7AC-NTniF"
      },
      "source": [
        "这是一个 [Google Colaboratory](https://colab.research.google.com/notebooks/welcome.ipynb) 笔记本。Python 程序可以直接在浏览器中运行，这是学习和使用 Tensorflow 的绝佳方式。要按照本教程操作，请点击此页面顶部的按钮，在 Google Colab 中运行笔记本。\n",
        "\n",
        "1. 在 Colab 中，连接到 Python 运行时：在菜单栏的右上方，选择 *CONNECT*。\n",
        "2. 运行所有笔记本代码单元：选择 *Runtime* &gt; *Run all*。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eOsVdx6GGHmU"
      },
      "source": [
        "下载并安装 TensorFlow 2。将 TensorFlow 导入您的程序：\n",
        "\n",
        "注：升级 `pip` 以安装 TensorFlow 2 软件包。请参阅[安装指南](https://tensorflow.google.cn/install)了解详细信息。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QS7DDTiZGRTo"
      },
      "source": [
        "将 TensorFlow 导入到您的程序："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "0trJmd6DjqBZ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TensorFlow version: 2.17.0\n"
          ]
        }
      ],
      "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": [
        "加载并准备 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "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": [
        "使用 `tf.data` 将数据集分批和重排："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "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": [
        "使用 Keras <a>模型子类化 API</a> 构建 <code>tf.keras</code> 模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "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": [
        "选择用于训练的优化器和损失函数： "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "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": [
        "选择指标来衡量模型的损失和准确率。这些指标在周期内累积值，然后打印总体结果。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "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": [
        "使用 `tf.GradientTape`训练模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "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": [
        "测试模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "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": 10,
      "metadata": {
        "id": "i-2pkctU_Ci7"
      },
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "'Mean' object has no attribute 'reset_states'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[10], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m EPOCHS \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(EPOCHS):\n\u001b[1;32m      4\u001b[0m   \u001b[38;5;66;03m# Reset the metrics at the start of the next epoch\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m   train_loss\u001b[38;5;241m.\u001b[39mreset_states()\n\u001b[1;32m      6\u001b[0m   train_accuracy\u001b[38;5;241m.\u001b[39mreset_states()\n\u001b[1;32m      7\u001b[0m   test_loss\u001b[38;5;241m.\u001b[39mreset_states()\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Mean' object has no attribute 'reset_states'"
          ]
        }
      ],
      "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": [
        "现在，经过训练，照片分类器在此数据集上的准确率约为 98%。要了解详情，请阅读 [TensorFlow 教程](https://tensorflow.google.cn/tutorials)。"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "advanced.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "xxx",
      "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": "3.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}