site/zh-cn/tutorials/distribute/save_and_load.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1l8bWGmIJuQa"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "CPSnXS88KFEo"
      },
      "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": "89xNCIO5hiCj"
      },
      "source": [
        "# 使用分布策略保存和加载模型"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Ejs4QVxIdAm"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td><a target=\"_blank\" href=\"https://tensorflow.google.cn/tutorials/distribute/save_and_load\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看</a></td>\n",
        "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/distribute/save_and_load.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/distribute/save_and_load.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 Github 上查看源代码</a></td>\n",
        "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/distribute/save_and_load.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": "A0lG6qgThxAS"
      },
      "source": [
        "## 概述\n",
        "\n",
        "本教程演示了如何在训练期间或训练之后使用 `tf.distribute.Strategy` 以 SavedModel 格式保存和加载模型。有两种用于保存和加载 Keras 模型的 API：高级（`tf.keras.Model.save` 和 `tf.keras.models.load_model`）和低级（`tf.saved_model.save` 和 `tf.saved_model.load`）。\n",
        "\n",
        "要全面了解 SavedModel 和序列化，请阅读[已保存模型指南](../../guide/saved_model.ipynb)和 [Keras 模型序列化指南](https://tensorflow.google.cn/guide/keras/save_and_serialize)。我们从一个简单的示例开始。\n",
        "\n",
        "小心：TensorFlow 模型是代码，对于不受信任的代码，一定要小心。请参阅[安全使用 TensorFlow](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 以了解详情。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FITHltVKQ4eZ"
      },
      "source": [
        "导入依赖项："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RWG5HchAiOrZ"
      },
      "outputs": [],
      "source": [
        "import tensorflow_datasets as tfds\n",
        "\n",
        "import tensorflow as tf\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqapWj98ptNV"
      },
      "source": [
        "使用 TensorFlow Datasets 和 `tf.data` 加载和准备数据，并使用 `tf.distribute.MirroredStrategy` 创建模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yrYiAf_ziRyw"
      },
      "outputs": [],
      "source": [
        "mirrored_strategy = tf.distribute.MirroredStrategy()\n",
        "\n",
        "def get_data():\n",
        "  datasets = tfds.load(name='mnist', as_supervised=True)\n",
        "  mnist_train, mnist_test = datasets['train'], datasets['test']\n",
        "\n",
        "  BUFFER_SIZE = 10000\n",
        "\n",
        "  BATCH_SIZE_PER_REPLICA = 64\n",
        "  BATCH_SIZE = BATCH_SIZE_PER_REPLICA * mirrored_strategy.num_replicas_in_sync\n",
        "\n",
        "  def scale(image, label):\n",
        "    image = tf.cast(image, tf.float32)\n",
        "    image /= 255\n",
        "\n",
        "    return image, label\n",
        "\n",
        "  train_dataset = mnist_train.map(scale).cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
        "  eval_dataset = mnist_test.map(scale).batch(BATCH_SIZE)\n",
        "\n",
        "  return train_dataset, eval_dataset\n",
        "\n",
        "def get_model():\n",
        "  with mirrored_strategy.scope():\n",
        "    model = tf.keras.Sequential([\n",
        "        tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),\n",
        "        tf.keras.layers.MaxPooling2D(),\n",
        "        tf.keras.layers.Flatten(),\n",
        "        tf.keras.layers.Dense(64, activation='relu'),\n",
        "        tf.keras.layers.Dense(10)\n",
        "    ])\n",
        "\n",
        "    model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                  optimizer=tf.keras.optimizers.Adam(),\n",
        "                  metrics=[tf.metrics.SparseCategoricalAccuracy()])\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qmU4Y3feS9Na"
      },
      "source": [
        "使用 `tf.keras.Model.fit` 训练模型： "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zmGurbJmS_vN"
      },
      "outputs": [],
      "source": [
        "model = get_model()\n",
        "train_dataset, eval_dataset = get_data()\n",
        "model.fit(train_dataset, epochs=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L01wjgvRizHS"
      },
      "source": [
        "## 保存和加载模型\n",
        "\n",
        "现在，您已经有一个简单的模型可供使用，让我们探索保存/加载 API。有两种可用的 API：\n",
        "\n",
        "- 高级 (Keras)：`Model.save` 和 `tf.keras.models.load_model`（`.keras` zip 存档格式）\n",
        "- 低级：`tf.saved_model.save` 和 `tf.saved_model.load`（TF SavedModel 格式）\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FX_IF2F1tvFs"
      },
      "source": [
        "### Keras API"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O8xfceg4Z3H_"
      },
      "source": [
        "以下为使用 Keras API 保存和加载模型的示例："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LYOStjV5knTQ"
      },
      "outputs": [],
      "source": [
        "keras_model_path = '/tmp/keras_save.keras'\n",
        "model.save(keras_model_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yvQIdQp3zNMp"
      },
      "source": [
        "恢复无 `tf.distribute.Strategy` 的模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WrXAAVtrzRgv"
      },
      "outputs": [],
      "source": [
        "restored_keras_model = tf.keras.models.load_model(keras_model_path)\n",
        "restored_keras_model.fit(train_dataset, epochs=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gYAnskzorda-"
      },
      "source": [
        "恢复模型后，您可以继在它上面续训练，甚至不需要再次调用 `Model.compile`，因为它在保存之前已经编译。模型以 Keras zip 存档格式保存，由 `.keras` 扩展名标记。有关详情，请参阅 [Keras 保存指南](https://tensorflow.google.cn/guide/keras/save_and_serialize)。\n",
        "\n",
        "现在，恢复模型并使用 `tf.distribute.Strategy` 对其进行训练："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wROPrJaAqBQz"
      },
      "outputs": [],
      "source": [
        "another_strategy = tf.distribute.OneDeviceStrategy('/cpu:0')\n",
        "with another_strategy.scope():\n",
        "  restored_keras_model_ds = tf.keras.models.load_model(keras_model_path)\n",
        "  restored_keras_model_ds.fit(train_dataset, epochs=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdiiPmL5tQk5"
      },
      "source": [
        "正如 `Model.fit` 输出所示，`tf.distribute.Strategy` 可以按预期进行加载。此处使用的策略不必与保存前所用策略相同。 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CrXIbmFt0f6"
      },
      "source": [
        "### `tf.saved_model` API"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HtGzPp6et4Em"
      },
      "source": [
        "使用较低级别的 API 保存模型类似于 Keras API："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4y6T31APuCqK"
      },
      "outputs": [],
      "source": [
        "model = get_model()  # get a fresh model\n",
        "saved_model_path = '/tmp/tf_save'\n",
        "tf.saved_model.save(model, saved_model_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1QNRYcwuRll"
      },
      "source": [
        "可以使用 `tf.saved_model.load` 进行加载。但是，由于它是一个较低级别的 API（因此用例范围更广泛），不会返回 Keras 模型。相反，它会返回一个对象，其中包含可用于进行推断的函数。例如："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aaEKqBSPwAuM"
      },
      "outputs": [],
      "source": [
        "DEFAULT_FUNCTION_KEY = 'serving_default'\n",
        "loaded = tf.saved_model.load(saved_model_path)\n",
        "inference_func = loaded.signatures[DEFAULT_FUNCTION_KEY]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x65l7AaHUZCA"
      },
      "source": [
        "加载的对象可能包含多个函数，每个函数与一个键关联。`\"serving_default\"` 键是使用已保存的 Keras 模型的推断函数的默认键。要使用此函数进行推断，请运行以下代码： "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5Ore5q8-UjW1"
      },
      "outputs": [],
      "source": [
        "predict_dataset = eval_dataset.map(lambda image, label: image)\n",
        "for batch in predict_dataset.take(1):\n",
        "  print(inference_func(batch))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osB1LY8WwUJZ"
      },
      "source": [
        "您还可以采用分布式方式加载和进行推断："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iDYvu12zYTmT"
      },
      "outputs": [],
      "source": [
        "another_strategy = tf.distribute.MirroredStrategy()\n",
        "with another_strategy.scope():\n",
        "  loaded = tf.saved_model.load(saved_model_path)\n",
        "  inference_func = loaded.signatures[DEFAULT_FUNCTION_KEY]\n",
        "\n",
        "  dist_predict_dataset = another_strategy.experimental_distribute_dataset(\n",
        "      predict_dataset)\n",
        "\n",
        "  # Calling the function in a distributed manner\n",
        "  for batch in dist_predict_dataset:\n",
        "    result = another_strategy.run(inference_func, args=(batch,))\n",
        "    print(result)\n",
        "    break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hWGSukoyw3fF"
      },
      "source": [
        "调用已恢复的函数只是基于已保存模型的前向传递 (`tf.keras.Model.predict`)。如果您想继续训练加载的函数，或者将加载的函数嵌入到更大的模型中，应如何操作？通常的做法是将此加载对象封装到 Keras 层以实现此目的。幸运的是，[TF Hub](https://tensorflow.google.cn/hub) 为此提供了 [`hub.KerasLayer`](https://github.com/tensorflow/hub/blob/master/tensorflow_hub/keras_layer.py)，如下所示："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "clfk3hQoyKu6"
      },
      "outputs": [],
      "source": [
        "import tensorflow_hub as hub\n",
        "\n",
        "def build_model(loaded):\n",
        "  x = tf.keras.layers.Input(shape=(28, 28, 1), name='input_x')\n",
        "  # Wrap what's loaded to a KerasLayer\n",
        "  keras_layer = hub.KerasLayer(loaded, trainable=True)(x)\n",
        "  model = tf.keras.Model(x, keras_layer)\n",
        "  return model\n",
        "\n",
        "another_strategy = tf.distribute.MirroredStrategy()\n",
        "with another_strategy.scope():\n",
        "  loaded = tf.saved_model.load(saved_model_path)\n",
        "  model = build_model(loaded)\n",
        "\n",
        "  model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[tf.metrics.SparseCategoricalAccuracy()])\n",
        "  model.fit(train_dataset, epochs=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oe1z_OtSJlu2"
      },
      "source": [
        "在上面的示例中，TensorFlow Hub 的 `hub.KerasLayer` 可将从 `tf.saved_model.load` 加载回的结果封装到可用于构建其他模型的 Keras 层。这对于迁移学习非常实用。 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFDOZpK5Wa3W"
      },
      "source": [
        "### 我应使用哪种 API？"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GC6GQ9HDLxD6"
      },
      "source": [
        "对于保存，如果您使用的是 Keras 模型，请使用 Keras `Model.save` API，除非您需要低级 API 允许的额外控制。如果您保存的不是 Keras 模型，那么您只能选择使用较低级的 API `tf.saved_model.save`。\n",
        "\n",
        "对于加载，您的 API 选择取决于您要从加载 API 中获得什么。如果您无法（或不想）获取 Keras 模型，请使用 `tf.saved_model.load`。否则，请使用 `tf.keras.models.load_model`。请注意，只有保存 Keras 模型后，才能恢复 Keras 模型。\n",
        "\n",
        "可以搭配使用 API。您可以使用 `model.save` 保存 Keras 模型，并使用低级 API `tf.saved_model.load` 加载非 Keras 模型。 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ktwg2GwnXE8v"
      },
      "outputs": [],
      "source": [
        "model = get_model()\n",
        "\n",
        "# Saving the model using Keras `Model.save`\n",
        "model.save(saved_model_path)\n",
        "\n",
        "another_strategy = tf.distribute.MirroredStrategy()\n",
        "# Loading the model using the lower-level API\n",
        "with another_strategy.scope():\n",
        "  loaded = tf.saved_model.load(saved_model_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Z7lSj8nZiW5"
      },
      "source": [
        "### 从本地设备保存/加载"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVAjWcosZodw"
      },
      "source": [
        "在远程设备上训练的过程中从本地 I/O 设备保存和加载时（例如，使用 Cloud TPU 时），必须使用 `tf.saved_model.SaveOptions` 和 `tf.saved_model.LoadOptions` 中的选项 `experimental_io_device` 将 I/O 设备设置为 `localhost`。例如："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jFcuzsI94bNA"
      },
      "outputs": [],
      "source": [
        "model = get_model()\n",
        "\n",
        "# Saving the model to a path on localhost.\n",
        "saved_model_path = '/tmp/tf_save'\n",
        "save_options = tf.saved_model.SaveOptions(experimental_io_device='/job:localhost')\n",
        "model.save(saved_model_path, options=save_options)\n",
        "\n",
        "# Loading the model from a path on localhost.\n",
        "another_strategy = tf.distribute.MirroredStrategy()\n",
        "with another_strategy.scope():\n",
        "  load_options = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost')\n",
        "  loaded = tf.keras.models.load_model(saved_model_path, options=load_options)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJTWOnC9iuA3"
      },
      "source": [
        "### 警告"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2cCSZrD7VCxe"
      },
      "source": [
        "一种特殊情况是当您以某种方式创建 Keras 模型，然后在训练之前保存它们。例如："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gurSIbDFjOBc"
      },
      "outputs": [],
      "source": [
        "class SubclassedModel(tf.keras.Model):\n",
        "  \"\"\"Example model defined by subclassing `tf.keras.Model`.\"\"\"\n",
        "\n",
        "  output_name = 'output_layer'\n",
        "\n",
        "  def __init__(self):\n",
        "    super(SubclassedModel, self).__init__()\n",
        "    self._dense_layer = tf.keras.layers.Dense(\n",
        "        5, dtype=tf.dtypes.float32, name=self.output_name)\n",
        "\n",
        "  def call(self, inputs):\n",
        "    return self._dense_layer(inputs)\n",
        "\n",
        "my_model = SubclassedModel()\n",
        "try:\n",
        "  my_model.save(saved_model_path)\n",
        "except ValueError as e:\n",
        "  print(f'{type(e).__name__}: ', *e.args)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4qMyXFDSPDO"
      },
      "source": [
        "SavedModel 保存跟踪 `tf.function` 时生成的 `tf.types.experimental.ConcreteFunction` 对象（请查看[计算图和 tf.function 简介](../../guide/intro_to_graphs.ipynb)指南中的*函数何时执行跟踪？*了解更多信息）。如果您收到像这样的 `ValueError`，那是因为 `Model.save` 无法找到或创建跟踪的 `ConcreteFunction`。\n",
        "\n",
        "**小心：**您不应在一个 `ConcreteFunction` 都没有的情况下保存模型，因为如果这样做，低级 API 将生成一个没有 `ConcreteFunction` 签名的 SavedModel（[详细了解](../../guide/saved_model.ipynb) SavedModel 格式）。例如："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "064SE47mYDj8"
      },
      "outputs": [],
      "source": [
        "tf.saved_model.save(my_model, saved_model_path)\n",
        "x = tf.saved_model.load(saved_model_path)\n",
        "x.signatures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRTxlASJX-cY"
      },
      "source": [
        "一般而言，模型的前向传递（`call` 方法）会在第一次调用模型时被自动跟踪，通常是通过 Keras `Model.fit` 方法。如果您设置了输入形状，例如通过将第一层设为 `tf.keras.layers.InputLayer` 或其他层类型，并将 `input_shape` 关键字参数传递给它，Keras [序贯](https://tensorflow.google.cn/guide/keras/sequential_model)和[函数式](https://tensorflow.google.cn/guide/keras/functional) API 也可以生成 `ConcreteFunction`。\n",
        "\n",
        "要验证您的模型是否有任何跟踪的 `ConcreteFunction`，请检查 `Model.save_spec` 是否为 `None`："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xAXise4eR0YJ"
      },
      "outputs": [],
      "source": [
        "print(my_model.save_spec() is None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G2G_FQrWJAO3"
      },
      "source": [
        "我们使用 `tf.keras.Model.fit` 来训练模型，可以注意到，`save_spec` 被定义并且模型保存将生效："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cv5LTi0zDkKS"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE_PER_REPLICA = 4\n",
        "BATCH_SIZE = BATCH_SIZE_PER_REPLICA * mirrored_strategy.num_replicas_in_sync\n",
        "\n",
        "dataset_size = 100\n",
        "dataset = tf.data.Dataset.from_tensors(\n",
        "    (tf.range(5, dtype=tf.float32), tf.range(5, dtype=tf.float32))\n",
        "    ).repeat(dataset_size).batch(BATCH_SIZE)\n",
        "\n",
        "my_model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "my_model.fit(dataset, epochs=2)\n",
        "\n",
        "print(my_model.save_spec() is None)\n",
        "my_model.save(saved_model_path)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "save_and_load.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}