site/zh-cn/guide/migrate/evaluator.ipynb

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      "source": [
        "##### Copyright 2021 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "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": "77z2OchJTk0l"
      },
      "source": [
        "# 迁移评估\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>     <a target=\"_blank\" href=\"https://tensorflow.google.cn/guide/migrate/evaluator\"><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/guide/migrate/evaluator.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 运行</a>\n",
        "</td>\n",
        "  <td>     <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/guide/migrate/evaluator.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/guide/migrate/evaluator.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": "n4O6fPyYTxZv"
      },
      "source": [
        "评估是对模型进行衡量和基准测试的关键部分。\n",
        "\n",
        "本指南演示了如何将评估器任务从 TensorFlow 1 迁移到 TensorFlow 2。在 TensorFlow 1 中，当 API 以分布式方式运行时，此功能由 `tf.estimator.train_and_evaluate` 实现。在 Tensorflow 2 中，可以使用内置 `tf.keras.utils.SidecarEvaluator`，或在评估器任务上使用自定义评估循环。\n",
        "\n",
        "TensorFlow 1 (`tf.estimator.Estimator.evaluate`) 和 TensorFlow 2（`Model.fit(..., validation_data=(...))` 或 `Model.evaluate`）中都有简单的连续评估选项。当您不希望工作进程在训练和评估之间切换时，评估器任务更合适，而当您希望分布评估时，`Model.fit` 中的内置评估更合适。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pHJfmkCFUhQf"
      },
      "source": [
        "## 安装"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VXnPvQi8Ui1F"
      },
      "outputs": [],
      "source": [
        "import tensorflow.compat.v1 as tf1\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import tempfile\n",
        "import time\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tww-uIoiUlsT"
      },
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtlucRG_Uro_"
      },
      "source": [
        "## TensorFlow 1：使用 tf.estimator.train_and_evaluate 进行评估\n",
        "\n",
        "在 TensorFlow 1 中，可以配置 `tf.estimator` 以使用 `tf.estimator.train_and_evaluate` 评估 Estimator。\n",
        "\n",
        "在此示例中，首先定义 `tf.estimator.Estimator` 并指定训练和评估规范："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q8shCkV2jKcc"
      },
      "outputs": [],
      "source": [
        "feature_columns = [tf1.feature_column.numeric_column(\"x\", shape=[28, 28])]\n",
        "\n",
        "classifier = tf1.estimator.DNNClassifier(\n",
        "    feature_columns=feature_columns,\n",
        "    hidden_units=[256, 32],\n",
        "    optimizer=tf1.train.AdamOptimizer(0.001),\n",
        "    n_classes=10,\n",
        "    dropout=0.2\n",
        ")\n",
        "\n",
        "train_input_fn = tf1.estimator.inputs.numpy_input_fn(\n",
        "    x={\"x\": x_train},\n",
        "    y=y_train.astype(np.int32),\n",
        "    num_epochs=10,\n",
        "    batch_size=50,\n",
        "    shuffle=True,\n",
        ")\n",
        "\n",
        "test_input_fn = tf1.estimator.inputs.numpy_input_fn(\n",
        "    x={\"x\": x_test},\n",
        "    y=y_test.astype(np.int32),\n",
        "    num_epochs=10,\n",
        "    shuffle=False\n",
        ")\n",
        "\n",
        "train_spec = tf1.estimator.TrainSpec(input_fn=train_input_fn, max_steps=10)\n",
        "eval_spec = tf1.estimator.EvalSpec(input_fn=test_input_fn,\n",
        "                                   steps=10,\n",
        "                                   throttle_secs=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGP7Nyenk1gr"
      },
      "source": [
        "随后，训练和评估模型。评估在训练之间同步运行，因为它在此笔记本中被限制为本地运行，并且在训练和评估之间交替运行。但是，如果 Estimator 是以分布式方式使用的，则评估器将作为专用评估器任务运行。有关详情，请查看[分布式训练的迁移指南](https://tensorflow.google.cn/guide/migrate/multi_worker_cpu_gpu_training)。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xWKMsmt6jYSL"
      },
      "outputs": [],
      "source": [
        "tf1.estimator.train_and_evaluate(estimator=classifier,\n",
        "                                train_spec=train_spec,\n",
        "                                eval_spec=eval_spec)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T5LtVtmvYx7J"
      },
      "source": [
        "## TensorFlow 2：评估 Keras 模型\n",
        "\n",
        "在 TensorFlow 2 中，如果您使用 `Model.fit` API 进行训练，则可以使用 `tf.keras.utils.SidecarEvaluator` 评估模型。此外，还可以在 Tensorboard 中呈现评估指标，本指南中未介绍此功能。\n",
        "\n",
        "为了帮助演示这一点，我们首先定义和训练模型：\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ci3yB6A5lwJu"
      },
      "outputs": [],
      "source": [
        "def create_model():\n",
        "  return tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(10)\n",
        "  ])\n",
        "\n",
        "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "\n",
        "model = create_model()\n",
        "model.compile(optimizer='adam',\n",
        "              loss=loss,\n",
        "              metrics=['accuracy'],\n",
        "              steps_per_execution=10,\n",
        "              run_eagerly=True)\n",
        "\n",
        "log_dir = tempfile.mkdtemp()\n",
        "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath=os.path.join(log_dir, 'ckpt-{epoch}'),\n",
        "    save_weights_only=True)\n",
        "\n",
        "model.fit(x=x_train,\n",
        "          y=y_train,\n",
        "          epochs=1,\n",
        "          callbacks=[model_checkpoint])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AhU3VTYZoDh-"
      },
      "source": [
        "然后，使用 `tf.keras.utils.SidecarEvaluator` 评估模型。在实际训练中，建议使用单独的作业进行评估，以释放工作进程资源进行训练。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1VOQLDNkl2bl"
      },
      "outputs": [],
      "source": [
        "data = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n",
        "data = data.batch(64)\n",
        "\n",
        "tf.keras.utils.SidecarEvaluator(\n",
        "    model=model,\n",
        "    data=data,\n",
        "    checkpoint_dir=log_dir,\n",
        "    max_evaluations=1\n",
        ").start()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQUS8nO9FZlH"
      },
      "source": [
        "## 后续步骤\n",
        "\n",
        "- 要详细了解 sidecar 评估，请考虑阅读 `tf.keras.utils.SidecarEvaluator` API 文档。\n",
        "- 要考虑在 Keras 中交替进行训练和评估，请考虑阅读[其他内置方法](https://tensorflow.google.cn/guide/keras/train_and_evaluate)。"
      ]
    }
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