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zh-Cn: tutorials/load_data/numpy.ipynb #885
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1b3747a
init zh-cn numpy.ipynb
flyyuan f4fae99
Complete translation
flyyuan d03c30e
Update site/zh-cn/beta/tutorials/load_data/numpy.ipynb
flyyuan 07f8a79
'tensorflow.org' replace 'tensorflow.google.cn'
flyyuan adb5fe6
Update site/zh-cn/beta/tutorials/load_data/numpy.ipynb
flyyuan 3363d55
Update site/zh-cn/beta/tutorials/load_data/numpy.ipynb
flyyuan 9a90773
Update site/zh-cn/beta/tutorials/load_data/numpy.ipynb
flyyuan 0954fa7
翻译开头的四个按钮和修改对应链接
flyyuan 5310648
merge commit
flyyuan ebbdcf4
merge commit
flyyuan 9e63ad4
colab
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": { | ||
"colab_type": "text", | ||
"id": "pixDvex9KBqt" | ||
}, | ||
"source": [ | ||
"##### Copyright 2019 The TensorFlow Authors." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 0, | ||
"metadata": { | ||
"cellView": "form", | ||
"colab": {}, | ||
"colab_type": "code", | ||
"id": "K16pBM8mKK7a" | ||
}, | ||
"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": { | ||
"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>" | ||
] | ||
}, | ||
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||
{ | ||
"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", | ||
"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", | ||
"id": "k6J3JzK5NxQ6" | ||
}, | ||
"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" | ||
}, | ||
"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)" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"colab": { | ||
"collapsed_sections": [], | ||
"name": "numpy.ipynb", | ||
"private_outputs": true, | ||
"provenance": [], | ||
"toc_visible": true, | ||
"version": "0.3.2" | ||
}, | ||
"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" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 1 | ||
} |
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这四个按钮要翻译的
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而且需要把他们的链接修改下,请仔细阅读标准修改
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已按要求修改