beginners/notebook-examples/chapter-5/mnist-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用 tf.contrib.learn 模块加载 MNIST 数据集（Deprecated）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-38-61750033177f>:6: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From /Users/Patient/tf-course/py3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From /Users/Patient/tf-course/py3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./mnist/dataset/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From /Users/Patient/tf-course/py3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./mnist/dataset/train-labels-idx1-ubyte.gz\n",
      "Extracting ./mnist/dataset/t10k-images-idx3-ubyte.gz\n",
      "Extracting ./mnist/dataset/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /Users/Patient/tf-course/py3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "# 导入数据\n",
    "mnist = input_data.read_data_sets('./mnist/dataset/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用 Keras 加载 MNIST 数据集\n",
    "\n",
    "\n",
    "**tf.kera.datasets.mnist.load_data(path=‘mnist.npz’)**\n",
    "\n",
    "\n",
    "**Arguments:**\n",
    "\n",
    "- **path:** 本地缓存 MNIST 数据集(mnist.npz)的相对路径（~/.keras/datasets）\n",
    "\n",
    "**Returns：**\n",
    "\n",
    "```\n",
    "Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n",
    "```\n",
    "\n",
    "#### 详情查看 [mnist.load_data](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist/load_data) API 文档"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data('mnist/mnist.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (60000,)\n",
      "(10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, y_train.shape)\n",
    "print(x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "for i in range(15):\n",
    "    plt.subplot(3,5,i+1) # 绘制前15个手写体数字，以3行5列子图形式展示\n",
    "    plt.tight_layout() # 自动适配子图尺寸\n",
    "    plt.imshow(x_train[i], cmap='Greys') # 使用灰色显示像素灰度值\n",
    "    plt.title(\"Label: {}\".format(y_train[i])) # 设置标签为子图标题\n",
    "    plt.xticks([]) # 删除x轴标记\n",
    "    plt.yticks([]) # 删除y轴标记"
   ]
  },
  {
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  {
   "cell_type": "code",
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   "outputs": [],
   "source": []
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