手写数字识别.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "E:\\anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets,svm,metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 设置属性防止中文乱码\n",
    "mpl.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.,  0.,  5., ...,  1.,  0.,  0.],\n",
       "        [ 0.,  0., 13., ..., 15.,  5.,  0.],\n",
       "        [ 0.,  3., 15., ..., 11.,  8.,  0.],\n",
       "        ...,\n",
       "        [ 0.,  4., 11., ..., 12.,  7.,  0.],\n",
       "        [ 0.,  2., 14., ..., 12.,  0.,  0.],\n",
       "        [ 0.,  0.,  6., ...,  0.,  0.,  0.]],\n",
       "\n",
       "       [[ 0.,  0.,  0., ...,  5.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  9.,  0.,  0.],\n",
       "        [ 0.,  0.,  3., ...,  6.,  0.,  0.],\n",
       "        ...,\n",
       "        [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
       "        [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ..., 10.,  0.,  0.]],\n",
       "\n",
       "       [[ 0.,  0.,  0., ..., 12.,  0.,  0.],\n",
       "        [ 0.,  0.,  3., ..., 14.,  0.,  0.],\n",
       "        [ 0.,  0.,  8., ..., 16.,  0.,  0.],\n",
       "        ...,\n",
       "        [ 0.,  9., 16., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  3., 13., ..., 11.,  5.,  0.],\n",
       "        [ 0.,  0.,  0., ..., 16.,  9.,  0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 0.,  0.,  1., ...,  1.,  0.,  0.],\n",
       "        [ 0.,  0., 13., ...,  2.,  1.,  0.],\n",
       "        [ 0.,  0., 16., ..., 16.,  5.,  0.],\n",
       "        ...,\n",
       "        [ 0.,  0., 16., ..., 15.,  0.,  0.],\n",
       "        [ 0.,  0., 15., ..., 16.,  0.,  0.],\n",
       "        [ 0.,  0.,  2., ...,  6.,  0.,  0.]],\n",
       "\n",
       "       [[ 0.,  0.,  2., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0., 14., ..., 15.,  1.,  0.],\n",
       "        [ 0.,  4., 16., ..., 16.,  7.,  0.],\n",
       "        ...,\n",
       "        [ 0.,  0.,  0., ..., 16.,  2.,  0.],\n",
       "        [ 0.,  0.,  4., ..., 16.,  2.,  0.],\n",
       "        [ 0.,  0.,  5., ..., 12.,  0.,  0.]],\n",
       "\n",
       "       [[ 0.,  0., 10., ...,  1.,  0.,  0.],\n",
       "        [ 0.,  2., 16., ...,  1.,  0.,  0.],\n",
       "        [ 0.,  0., 15., ..., 15.,  0.,  0.],\n",
       "        ...,\n",
       "        [ 0.,  4., 16., ..., 16.,  6.,  0.],\n",
       "        [ 0.,  8., 16., ..., 16.,  8.,  0.],\n",
       "        [ 0.,  1.,  8., ..., 12.,  1.,  0.]]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#加载数字图片数据\n",
    "digits=datasets.load_digits()\n",
    "# digits\n",
    "digits.images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取样本数量，并将图片数据格式化\n",
    "#（所有图片大小，像素点都一致=》转换成的向量大小一致）\n",
    "n_samples=len(digits.images)\n",
    "data=digits.images.reshape((n_samples,-1))\n",
    "data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##模型构建\n",
    "classifier=svm.SVC(gamma=0.001) #默认是rbf\n",
    "\n",
    "#使用1/2的数据集进行模型训练\n",
    "#提取一半数据训练，后一半数据测试\n",
    "classifier.fit(data[:int(n_samples/2)],digits.target[:int(n_samples/2)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类器SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)的分类效果'：\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99        88\n",
      "           1       0.99      0.97      0.98        91\n",
      "           2       0.99      0.99      0.99        86\n",
      "           3       0.98      0.87      0.92        91\n",
      "           4       0.99      0.96      0.97        92\n",
      "           5       0.95      0.97      0.96        91\n",
      "           6       0.99      0.99      0.99        91\n",
      "           7       0.96      0.99      0.97        89\n",
      "           8       0.94      1.00      0.97        88\n",
      "           9       0.93      0.98      0.95        92\n",
      "\n",
      "   micro avg       0.97      0.97      0.97       899\n",
      "   macro avg       0.97      0.97      0.97       899\n",
      "weighted avg       0.97      0.97      0.97       899\n",
      "\n",
      "\n",
      "score_svm:\n",
      "0.968854\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(28,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#测试集数据部分实际值和预测值获取\n",
    "#后一半数据作为测试集\n",
    "expected=digits.target[int(n_samples/2):]  #y_test\n",
    "predicted=classifier.predict(data[int(n_sample/2):]) #y_predicted\n",
    "\n",
    "#生成一个分类报告classification_report\n",
    "print(\"分类器%s的分类效果'：\\n%s\\n\"%(classifier,metrics.classification_report(expected,predicted)))\n",
    "print(\"score_svm:\\n%f\"%classifier.score(data[int(n_samples/2):],digits.target[int(n_samples/2):]))\n",
    "\n",
    "\n",
    "# expected[expected!=predicted].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 进行图片展示\n",
    "plt.figure(facecolor='gray',figsize=(12,5))\n",
    "#先画出5个预测失败的\n",
    "#把预测错的值取出\n",
    "images_and_predictions=list(zip(digits.images[int(n_samples/2):][expected!=predicted],expected[expected!=predicted],predicted[expected!=predicted]))\n",
    "#通过enumerate，分别拿出前5个值\n",
    "for index,(image,expection,prediction) in enumerate(images_and_predictions[:5]):\n",
    "    plt.subplot(2, 5, index + 1)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(image,cmap=plt.cm.gray_r,interpolation='nearest') #把cmap中的灰度值与image矩阵对应并填充\n",
    "    plt.title(u'预测值/实际值:%i/%i' % (prediction, expection))\n",
    "\n",
    "#再画5个预测成功的\n",
    "images_and_predictions = list(zip(digits.images[int(n_samples / 2):][expected == predicted], expected[expected == predicted], predicted[expected == predicted]))\n",
    "for index, (image,expection, prediction) in enumerate(images_and_predictions[:5]):\n",
    "    plt.subplot(2, 5, index + 6)\n",
    "#     plt.axis('off')\n",
    "    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.title(u'预测值/实际值:%i/%i' % (prediction, expection))\n",
    "\n",
    "plt.subplots_adjust(.04, .02, .97, .94, .09, .2)\n",
    "plt.show()    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}