00_keras_mnist_intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 准备 mnist 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 查看 mnist 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train data: 60000\n",
      "x_test data: 10000\n"
     ]
    }
   ],
   "source": [
    "print('x_train data:', len(x_train))\n",
    "print('x_test data:', len(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28)\n",
      "y_train shape: (60000,)\n",
      "x_test shape: (10000, 28, 28)\n",
      "y_test shape: (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('x_train shape:', x_train.shape)\n",
    "print('y_train shape:', y_train.shape)\n",
    "print('x_test shape:', x_test.shape)\n",
    "print('y_test shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 定义 plot_image 函数显示数字图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train[0]: 5\n"
     ]
    }
   ],
   "source": [
    "def plot_image(image):\n",
    "    fig = plt.gcf()\n",
    "    fig.set_size_inches(2, 2)\n",
    "    plt.imshow(image, cmap='binary')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_image(x_train[0])\n",
    "print('y_train[0]:', y_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 定义 plot_images_labels_prediction 函数以查看数字图形、真实的数字与预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_images_labels_prediction(images, labels, predictions, idx, num=10):\n",
    "    \"\"\"\n",
    "    images: 数字图像数组\n",
    "    labels: 真实值数组\n",
    "    predictions: 预测结果数据\n",
    "    idx: 开始显示的数据index\n",
    "    num: 要显示的数据项数, 默认为10, 不超过25\n",
    "    \"\"\"\n",
    "    fig = plt.gcf()\n",
    "    fig.set_size_inches(12, 14)\n",
    "    if num > 25:\n",
    "        num = 25\n",
    "    for i in range(0, num):\n",
    "        ax = plt.subplot(5, 5, i+1)\n",
    "        ax.imshow(images[idx], cmap='binary')\n",
    "        title = 'lable=' + str(labels[idx])\n",
    "        if len(predictions) > 0:\n",
    "            title += ',predict=' + str(predictions[idx])\n",
    "        ax.set_title(title, fontsize=10)\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "        idx += 1\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images_labels_prediction(x_train, y_train, [], 0, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images_labels_prediction(x_test, y_test, [], 0, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. features 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 查看处理前的 shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28)\n",
      "y_train shape: (60000,)\n"
     ]
    }
   ],
   "source": [
    "print('x_train shape:', x_train.shape)\n",
    "print('y_train shape:', y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2 将 image 以 reshape 转换为一维的向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape(x_train.shape[0], 784).astype('float32')\n",
    "x_test = x_test.reshape(x_test.shape[0], 784).astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3 查看转换为一维向量的 shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 784)\n",
      "x_test shape: (10000, 784)\n"
     ]
    }
   ],
   "source": [
    "print('x_train shape:', x_train.shape)\n",
    "print('x_test shape:', x_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.4 查看 images 第0项的内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        18.,  18., 126., 136., 175.,  26., 166., 255., 247., 127.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "        30.,  36.,  94., 154., 170., 253., 253., 253., 253., 253., 225.,\n",
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       "         0.,   0.,   0.,  14.,   1., 154., 253.,  90.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "       139., 253., 190.,   2.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,  11., 190., 253.,  70.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,  35., 241., 225., 160., 108.,   1.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,  81., 240.,\n",
       "       253., 253., 119.,  25.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,  45., 186., 253., 253., 150.,  27.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,  16.,  93., 252., 253., 187.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 249., 253.,\n",
       "       249.,  64.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,  46., 130., 183., 253., 253., 207.,   2.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,  39., 148., 229., 253., 253., 253.,\n",
       "       250., 182.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,  24., 114.,\n",
       "       221., 253., 253., 253., 253., 201.,  78.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,  23.,  66., 213., 253., 253., 253., 253., 198.,  81.,\n",
       "         2.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,  18., 171., 219., 253., 253.,\n",
       "       253., 253., 195.,  80.,   9.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,  55.,\n",
       "       172., 226., 253., 253., 253., 253., 244., 133.,  11.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0., 136., 253., 253., 253., 212., 135.,\n",
       "       132.,  16.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "         0.,   0.,   0.], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.5 将 images 中的数字标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_normalize = x_train / 255.0\n",
    "x_test_normalize = x_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "       0.8862745 , 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
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       "       0.83137256, 0.5294118 , 0.5176471 , 0.0627451 , 0.        ,\n",
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       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        ], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_normalize[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.  label 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1 查看原来的 label 标签字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, 1, 9], dtype=uint8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.2 将 label 标签字段进行 one-hot 编码转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_one_hot = tf.keras.utils.to_categorical(y_train)\n",
    "y_test_one_hot = tf.keras.utils.to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.3 查看进行 one-hot 编码转换之后的 label 标签字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_one_hot[:5]"
   ]
  }
 ],
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