diff --git a/site/zh-cn/beta/tutorials/load_data/numpy.ipynb b/site/zh-cn/beta/tutorials/load_data/numpy.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "numpy.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "private_outputs": true,
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.6.5"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "pixDvex9KBqt"
+      },
+      "source": [
+        "##### Copyright 2019 The TensorFlow Authors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "cellView": "form",
+        "colab_type": "code",
+        "id": "K16pBM8mKK7a",
+        "colab": {}
+      },
+      "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."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "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\" />在 Tensorflow.org 上查看</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\" />在 Google Colab 运行</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\" />在 Github 上查看源代码</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/blob/master/site/zh-cn/beta/tutorials/load_data/numpy.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\" />下载此 notebook</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GEe3i16tQPjo",
+        "colab_type": "text"
+      },
+      "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"
+      },
+      "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",
+      "metadata": {
+        "colab_type": "code",
+        "id": "D1gtCQrnNk6b",
+        "colab": {}
+      },
+      "source": [
+        "!pip install tensorflow==2.0.0-beta1"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "k6J3JzK5NxQ6",
+        "colab": {}
+      },
+      "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"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "G0yWiN8-cpDb"
+      },
+      "source": [
+        "### 从 `.npz` 文件中加载"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "GLHNrFM6RWoM",
+        "colab": {}
+      },
+      "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']"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "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",
+      "metadata": {
+        "colab_type": "code",
+        "id": "QN_8wwc5R7Qm",
+        "colab": {}
+      },
+      "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))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "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",
+      "metadata": {
+        "colab_type": "code",
+        "id": "GTXdRMPcSXZj",
+        "colab": {}
+      },
+      "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)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "w69Jl8k6lilg"
+      },
+      "source": [
+        "### 建立和训练模型"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "Uhxr8py4DkDN",
+        "colab": {}
+      },
+      "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()])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "XLDzlPGgOHBx",
+        "colab": {}
+      },
+      "source": [
+        "model.fit(train_dataset, epochs=10)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "2q82yN8mmKIE",
+        "colab": {}
+      },
+      "source": [
+        "model.evaluate(test_dataset)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file