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...Learning_with_TensorFlow/1.4.0/Chapter07/.ipynb_checkpoints/7. 数据集基本使用方法-checkpoint.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,224 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import tempfile\n", | ||
| "import tensorflow as tf" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### 1. 从数组创建数据集。" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "1\n", | ||
| "4\n", | ||
| "9\n", | ||
| "25\n", | ||
| "64\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "input_data = [1, 2, 3, 5, 8]\n", | ||
| "dataset = tf.data.Dataset.from_tensor_slices(input_data)\n", | ||
| "\n", | ||
| "# 定义迭代器。\n", | ||
| "iterator = dataset.make_one_shot_iterator()\n", | ||
| "\n", | ||
| "# get_next() 返回代表一个输入数据的张量。\n", | ||
| "x = iterator.get_next()\n", | ||
| "y = x * x\n", | ||
| "\n", | ||
| "with tf.Session() as sess:\n", | ||
| " for i in range(len(input_data)):\n", | ||
| " print(sess.run(y))\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### 2. 读取文本文件里的数据。" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "File1, line1.\n", | ||
| "File1, line2.\n", | ||
| "File2, line1.\n", | ||
| "File2, line2.\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# 创建文本文件作为本例的输入。\n", | ||
| "with open(\"./test1.txt\", \"w\") as file:\n", | ||
| " file.write(\"File1, line1.\\n\") \n", | ||
| " file.write(\"File1, line2.\\n\")\n", | ||
| "with open(\"./test2.txt\", \"w\") as file:\n", | ||
| " file.write(\"File2, line1.\\n\") \n", | ||
| " file.write(\"File2, line2.\\n\")\n", | ||
| "\n", | ||
| "# 从文本文件创建数据集。这里可以提供多个文件。\n", | ||
| "input_files = [\"./test1.txt\", \"./test2.txt\"]\n", | ||
| "dataset = tf.data.TextLineDataset(input_files)\n", | ||
| "\n", | ||
| "# 定义迭代器。\n", | ||
| "iterator = dataset.make_one_shot_iterator()\n", | ||
| "\n", | ||
| "# 这里get_next()返回一个字符串类型的张量，代表文件中的一行。\n", | ||
| "x = iterator.get_next() \n", | ||
| "with tf.Session() as sess:\n", | ||
| " for i in range(4):\n", | ||
| " print(sess.run(x))\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### 3. 解析TFRecord文件里的数据。读取文件为本章第一节创建的文件。" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "7\n", | ||
| "3\n", | ||
| "4\n", | ||
| "6\n", | ||
| "1\n", | ||
| "8\n", | ||
| "1\n", | ||
| "0\n", | ||
| "9\n", | ||
| "8\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# 解析一个TFRecord的方法。\n", | ||
| "def parser(record):\n", | ||
| " features = tf.parse_single_example(\n", | ||
| " record,\n", | ||
| " features={\n", | ||
| " 'image_raw':tf.FixedLenFeature([],tf.string),\n", | ||
| " 'pixels':tf.FixedLenFeature([],tf.int64),\n", | ||
| " 'label':tf.FixedLenFeature([],tf.int64)\n", | ||
| " })\n", | ||
| " decoded_images = tf.decode_raw(features['image_raw'],tf.uint8)\n", | ||
| " retyped_images = tf.cast(decoded_images, tf.float32)\n", | ||
| " images = tf.reshape(retyped_images, [784])\n", | ||
| " labels = tf.cast(features['label'],tf.int32)\n", | ||
| " #pixels = tf.cast(features['pixels'],tf.int32)\n", | ||
| " return images, labels\n", | ||
| "\n", | ||
| "# 从TFRecord文件创建数据集。这里可以提供多个文件。\n", | ||
| "input_files = [\"output.tfrecords\"]\n", | ||
| "dataset = tf.data.TFRecordDataset(input_files)\n", | ||
| "\n", | ||
| "# map()函数表示对数据集中的每一条数据进行调用解析方法。\n", | ||
| "dataset = dataset.map(parser)\n", | ||
| "\n", | ||
| "# 定义遍历数据集的迭代器。\n", | ||
| "iterator = dataset.make_one_shot_iterator()\n", | ||
| "\n", | ||
| "# 读取数据，可用于进一步计算\n", | ||
| "image, label = iterator.get_next()\n", | ||
| "\n", | ||
| "with tf.Session() as sess:\n", | ||
| " for i in range(10):\n", | ||
| " x, y = sess.run([image, label]) \n", | ||
| " print(y)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### 4. 使用initializable_iterator来动态初始化数据集。" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# 从TFRecord文件创建数据集，具体文件路径是一个placeholder，稍后再提供具体路径。\n", | ||
| "input_files = tf.placeholder(tf.string)\n", | ||
| "dataset = tf.data.TFRecordDataset(input_files)\n", | ||
| "dataset = dataset.map(parser)\n", | ||
| "\n", | ||
| "# 定义遍历dataset的initializable_iterator。\n", | ||
| "iterator = dataset.make_initializable_iterator()\n", | ||
| "image, label = iterator.get_next()\n", | ||
| "\n", | ||
| "with tf.Session() as sess:\n", | ||
| " # 首先初始化iterator，并给出input_files的值。\n", | ||
| " sess.run(iterator.initializer,\n", | ||
| " feed_dict={input_files: [\"output.tfrecords\"]})\n", | ||
| " # 遍历所有数据一个epoch。当遍历结束时，程序会抛出OutOfRangeError。\n", | ||
| " while True:\n", | ||
| " try:\n", | ||
| " x, y = sess.run([image, label])\n", | ||
| " except tf.errors.OutOfRangeError:\n", | ||
| " break \n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 2", | ||
| "language": "python", | ||
| "name": "python2" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 2 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython2", | ||
| "version": "2.7.13" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 1 | ||
| } |
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