site/zh-cn/beta/tutorials/load_data/numpy.ipynb

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   "source": [
    "##### Copyright 2019 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "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": {
    "colab_type": "text",
    "id": "TfRdquslKbO3"
   },
   "source": [
    "# 使用 tf.data 加载 NumPy 数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-uq3F0ggKlZb"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://tensorflow.google.cn/beta/tutorials/load_data/numpy\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\" />View on tensorflow.google.cn</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/zh-cn/beta/tutorials/load_data/numpy.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/zh-cn/beta/tutorials/load_data/numpy.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/r2/tutorials/load_data/numpy.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
      "cell_type": "markdown",
      "metadata": {
        "id": "GEe3i16tQPjo",
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      "source": [
        "Note: 我们的 TensorFlow 社区翻译了这些文档。因为社区翻译是尽力而为， 所以无法保证它们是最准确的，并且反映了最新的\n",
        "[官方英文文档](https://www.tensorflow.org/?hl=en)。如果您有改进此翻译的建议， 请提交 pull request 到\n",
        "[tensorflow/docs](https://github.com/tensorflow/docs) GitHub 仓库。要志愿地撰写或者审核译文，请加入\n",
        "[docs-zh-cn@tensorflow.org Google Group](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs-zh-cn)。"
      ]
    },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-0tqX8qkXZEj"
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   "source": [
    "本教程提供了将数据从 NumPy 数组加载到 `tf.data.Dataset`  的示例\n",
    "本示例从一个  `.npz` 文件中加载  MNIST 数据集。但是，本实例中 NumPy 数据的来源并不重要。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-Ze5IBx9clLB"
   },
   "source": [
    "## 安装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D1gtCQrnNk6b"
   },
   "outputs": [],
   "source": [
    "!pip install tensorflow==2.0.0-beta1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    " \n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorflow_datasets as tfds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G0yWiN8-cpDb"
   },
   "source": [
    "### 从 `.npz` 文件中加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GLHNrFM6RWoM"
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   "outputs": [],
   "source": [
    "DATA_URL = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz'\n",
    "\n",
    "path = tf.keras.utils.get_file('mnist.npz', DATA_URL)\n",
    "with np.load(path) as data:\n",
    "  train_examples = data['x_train']\n",
    "  train_labels = data['y_train']\n",
    "  test_examples = data['x_test']\n",
    "  test_labels = data['y_test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cCeCkvrDgCMM"
   },
   "source": [
    "## 使用 `tf.data.Dataset` 加载 NumPy 数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tslB0tJPgB-2"
   },
   "source": [
    "假设您有一个示例数组和相应的标签数组，请将两个数组作为元组传递给 `tf.data.Dataset.from_tensor_slices` 以创建 `tf.data.Dataset` 。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QN_8wwc5R7Qm"
   },
   "outputs": [],
   "source": [
    "train_dataset = tf.data.Dataset.from_tensor_slices((train_examples, train_labels))\n",
    "test_dataset = tf.data.Dataset.from_tensor_slices((test_examples, test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6Rco85bbkDfN"
   },
   "source": [
    "## 使用该数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0dvl1uUukc4K"
   },
   "source": [
    "### 打乱和批次化数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GTXdRMPcSXZj"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "SHUFFLE_BUFFER_SIZE = 100\n",
    "\n",
    "train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)\n",
    "test_dataset = test_dataset.batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w69Jl8k6lilg"
   },
   "source": [
    "### 建立和训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Uhxr8py4DkDN"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.RMSprop(),\n",
    "                loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "                metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XLDzlPGgOHBx"
   },
   "outputs": [],
   "source": [
    "model.fit(train_dataset, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2q82yN8mmKIE"
   },
   "outputs": [],
   "source": [
    "model.evaluate(test_dataset)"
   ]
  }
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