{"id":819,"date":"2021-01-21T21:48:18","date_gmt":"2021-01-21T13:48:18","guid":{"rendered":"http:\/\/www.mustenaka.cn\/?p=819"},"modified":"2021-02-03T10:57:26","modified_gmt":"2021-02-03T02:57:26","slug":"andrewngnotebook2","status":"publish","type":"post","link":"https:\/\/www.mustenaka.cn\/index.php\/2021\/01\/21\/andrewngnotebook2\/","title":{"rendered":"\u5434\u6069\u8fbe\u8bfe\u7a0b\u7b14\u8bb02"},"content":{"rendered":"

\u672c\u7ae0\u7b14\u8bb0\u6709\u5982\u4e0b\u5185\u5bb9\uff1a<\/p>\n

    \n
  1. \u4ecb\u7ecd\u5411\u91cf\u5316\uff1a\u63a5\u4e0a\u6587\u8865\u5145\u5feb\u901f\u8ba1\u7b97\u65b9\u6cd5<\/li>\n
  2. \u5411\u91cf\u5316\u5728\u903b\u8f91\u56de\u5f52\u4e2d\u7684\u5e94\u7528<\/li>\n
  3. \u5982\u4f55\u5411\u91cf\u5316\u903b\u8f91\u56de\u5f52<\/li>\n
  4. Python\u7684\u5e7f\u64ad\u7279\u6027(Boardcasting)<\/li>\n
  5. \u5728\u7f16\u5199\u4ee3\u7801\u65f6\u5bb9\u6613\u9047\u5230\u7684\u9519\u8bef\uff0c\u4ea7\u751f\u79e9\u4e3a1\u7684\u975e\u884c\u975e\u5217\u5411\u91cf\u7ec4\uff0c\u4ee5\u53ca\u907f\u514d\u65b9\u6cd5<\/li>\n<\/ol>\n

    1.\u5411\u91cf\u5316<\/h4>\n

    \u8ba9\u4ee3\u7801\u8fd0\u884c\u7684\u975e\u5e38\u5feb\u7684\u65b9\u6cd5<\/p><\/blockquote>\n

    \u5728\u903b\u8f91\u56de\u5f52\u4e2d\uff0c\u9700\u8981\u8ba1\u7b97Z=wT*x +b\uff08w\u548cx\u90fd\u662f\u5217\u5411\u91cf\uff0cw\u548cx\u90fd\u8f93\u5165nx\u7ef4\u5b9e\u6570\u96c6\uff09\uff0c\u56e0\u6b64\u5c31\u9700\u8981\u4e24\u5c42\u904d\u5386\uff0c\u6765\u4f9d\u6b21\u8fdb\u884c\u8ba1\u7b97\uff0c\u5c31\u5982\u540c\u7ebf\u6027\u4ee3\u6570\u5b66\u79d1\u53ef\u4ee5\u7b80\u5316\u6211\u4eec\u5bf9\u65b9\u7a0b\u6c42\u89e3\u4e00\u6837\uff0c\u5229\u7528\u7ebf\u6027\u4ee3\u6570\uff08Python\u4e2d\u4f7f\u7528numpy\u5e93\uff09\u6765\u7b80\u5316\u8ba1\u7b97\uff0c\u5229\u7528\u5f15\u5165\u7279\u5f81\u503c\u7279\u5f81\u5411\u91cf\u7b49\u7b49<\/p>\n

    PS\uff1anumpy\u662f\u771f\u7684\u5feb\uff0c\u5176\u672c\u8eab\u662f\u7ebf\u6027\u4ee3\u6570\u8ba1\u7b97\uff0c\u5728\u903b\u8f91\u4e0a\u4f18\u5316\u4e86\u4e0d\u5c11\u901f\u5ea6\uff0c\u800c\u4e14\u8fd8\u652f\u6301\u5e76\u884c\u8fd0\u7b97\uff0c\u6548\u7387\u771f\u7684\u5f88\u9ad8\u3002\u5bf9\u4e8e\u56fe\u50cf\u5904\u7406\uff0c\u5bf9\u4e8e\u4e00\u5f20\u56fe\u800c\u8a00\u4e24\u5c42for\u548cnumpy\u662f\u51e0\u79d2\u949f\u548c\u4e00\u77ac\u95f4\u7684\u5dee\u522b\u3002<\/p><\/blockquote>\n

    \u975e\u5411\u91cf\u5316\u5b9e\u73b0<\/p>\n

    z = 0\r\nfor i in range(n-x):\r\n    z += w[i] * x[i]\r\nz += b\r\n<\/code><\/pre>\n
    \u5411\u91cf\u5316\u5b9e\u73b0<\/p>\n
    z = np.dot(w,x) + b <\/code><\/pre>\n
    \u6211\u5728\u81ea\u5df1\u7684\u7535\u8111\u7528jupyter\u8dd1\u4e86\u4e00\u4e0b\uff0c\u5bf9\u6bd4\u4e86\u5dee\u522b\uff08for\u5faa\u73af\u57281000000\u7684\u7ea7\u522b\u4e0b\uff0c\u8fd0\u7b97\u901f\u5ea6\u6162\u4e86\u5dee\u4e0d\u591a300\u591a\u500d\uff09<\/p>\n
    \"\"<\/p>\n
    \u73b0\u5728\u7684\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\uff0c\u57fa\u672c\u4e0a\u5b9e\u5728GPU\u4e0a\u9762\u8dd1\uff08\u56e0\u4e3aGPU\u66f4\u52a0\u64c5\u957f\u5e76\u884c\uff09\uff0c\u65e0\u8bba\u662fGPU\u8fd8\u662fCPU\uff0c\u5176\u90fd\u542b\u6709SIMD\u6307\u4ee4\uff08\u5355\u6307\u4ee4\u6d41\u591a\u6570\u636e\u6d41\uff0csingle instruction multiple data\uff09\uff0cPython\u7684numpy\u5229\u7528\u4e86\u8fd9\u6837\u7684\u6307\u4ee4\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97\uff0c\u6240\u4ee5\u9020\u6210\u4e86\u8fd9\u6837\u7684\u6548\u679c\u3002<\/p>\n
    2.\u5411\u91cf\u5316\u5728\u903b\u8f91\u56de\u5f52\u4e2d\u7684\u5e94\u7528<\/h4>\n
    \u5bf9\u4e8e\u6574\u4e2a\u903b\u8f91\u56de\u5f52\u800c\u8a00\uff0c\u5c31\u53ef\u4ee5\u7528numpy\u7684\u5411\u91cf\u4ee3\u66ff\u5982\u4e0b\u5185\u5bb9\uff08\u7eff\u8272\u5b57\u8ff9\uff09<\/p>\n
    \"\"<\/p>\n
    3.\u5411\u91cf\u5316\u903b\u8f91\u56de\u5f52<\/h4>\n
    \u6ce8\u610f\u590d\u4e60\u4e00\u4e0b\u7ebf\u6027\u4ee3\u6570\uff0c\u77e9\u9635\u4e00\u7ae0\u7684\u5185\u5bb9<\/p><\/blockquote>\n
    \"\"<\/p>\n
    \u503c\u5f97\u6ce8\u610f\u7684\u5730\u65b9\u662f\uff0c\u6574\u4f53\u6765\u8bf4 Z = w\u8f6c\u7f6e * x + b\uff0cW\u662f\u4e00\u4e2am\u7ef4\u77e9\u9635\uff08\u8868\u793a\u6709m\u4e2a\u6570\u636e\uff09\uff0c\u800cx\u6709n\u4e2a\u7279\u5f81\uff0c\u4f46\u662fb\u53ea\u662f\u4e00\u4e2a\u5b9e\u6570\uff0c\u76f4\u63a5\u4f7f\u7528\u5982\u4e0b\u4ee3\u7801\u5c31\u53ef\u4ee5\u5b8c\u6210PPT\u4e2d\u516c\u5f0f\u7684\u76f8\u52a0<\/p>\n
    Z = np.dot(w.T , X) + b<\/code><\/pre>\n
    b\u4f5c\u4e3a\u4e00\u4e2a\u5b9e\u6570\uff08\u4e5f\u53ef\u4ee5\u7406\u89e3\u4e3a\u4e00\u4e2a1*1\u7ef4\u7684\u77e9\u9635\uff09\uff0c\u4f1a\u81ea\u52a8\u7684\u53d8\u6210\u6240\u6c42\u76841*m\u7ef4\u7684\u77e9\u9635\uff0c\u8fd9\u79cd\u8bbe\u8ba1\u601d\u7ef4\u5728Python\u4e2d\u79f0\u4e4b\u4e3a\u5e7f\u64ad\uff08Broadcasting\uff09\uff0c\u6700\u7ec8\u6c42\u51fa\u6765\u7684Z\u4e5f\u662f\u4e00\u4e2a1*m\u7ef4\u5ea6\u7684\u77e9\u9635\u3002\uff08z \u2208 Z \uff0cZ1\u2026\u2026Zm\uff09<\/p>\n
    \u90a3\u4e48\u5bf9\u4e8ea\u800c\u8a00\u53c8\u5982\u4f55\u5462\uff1fa\u901a\u8fc7sigmoid\u51fd\u6570\u5bf9\u6bcf\u4e00\u4e2az\u6c42\u5f97\uff0c\u5b9e\u9645\u4e0a\uff0c\u53ef\u4ee5\u76f4\u63a5\u628aZ\u4f5c\u4e3a\u4e00\u4e2a\u8f93\u5165\u53c2\u6570\u4e22\u8fdbsigmoid\u51fd\u6570\u4e2d\uff0c\u76f4\u63a5\u6c42\u51fa A\uff08a \u2208 A\uff0c a1 \u2026\u2026 am\uff09\u3002<\/p>\n
    \"\"<\/p>\n
    \u6838\u5fc3\u7684\u6838\u5fc3\u5c31\u662f\uff1a\u7528\u77e9\u9635\u4ee3\u66fffor\u5faa\u73af<\/p>\n
    \u63a5\u4e0b\u6765\u662f\u68af\u5ea6\u7684\u8f93\u51fa<\/p>\n
    \"\"<\/p>\n
    \u5bf9\u4e8e\u6574\u4e2a\u903b\u8f91\u56de\u5f52\u7684\u6838\u5fc3\u4ee3\u7801\u800c\u8a00\uff0c\u4e24\u4e2afor\u5faa\u73af\u90fd\u53ef\u4ee5\u6d88\u53bb\u4e86\uff0c\u6700\u540e\u7684\u6548\u679c\u4e3a<\/p>\n
    \"\"<\/p>\n
    \uff08\u8fd9\u91cc\u597d\u50cf\u6ca1\u6709\u8bb2\u5230\u6210\u672c\u51fd\u6570J\u7684\u63a8\u5bfc\uff09<\/p>\n
    4.Python\u7684\u5e7f\u64ad\uff08Boardcasting\uff09<\/h4>\n
    \u7ed9\u4f60\u4e00\u4e2a\u98df\u7269\u8868\u683c\u6570\u636e\uff0c\uff08\u6bcf100\u514b\u6709\uff09\u5206\u522b\u7531 \u78b3\u6c34\u5316\u5408\u7269\uff08Carbohydrates\uff09\uff0c\u86cb\u767d\u8d28\uff08proteins\uff09\uff0c\u4ee5\u53ca\u8102\u80aa\uff08fats\uff09\u4e09\u4e2a\u7279\u5f81\u7ec4\u6210\uff0c\u5176\u4e2d\u8f93\u5165\u7684\u98df\u7269\u79cd\u7c7b\u6709 \u82f9\u679c\uff08Apples\uff09\uff0c\u725b\u8089\uff08Beef\uff09\uff0c\u9e21\u86cb\uff08Eggs\uff09\uff0c\u548c\u571f\u8c46\uff08Potatoes\uff09\u7ec4\u6210<\/p>\n
    <\/div>
    <\/th><\/th><\/th><\/th><\/th><\/tr><\/thead>
    <\/td>apple <\/td>beef <\/td>eggs <\/td>potatoes <\/td><\/tr>
    carb <\/td>56.0 <\/td>0.0 <\/td>4.4 <\/td>68.0 <\/td><\/tr>
    protein <\/td>1.2 <\/td>104.0 <\/td>52.0 <\/td>8.0 <\/td><\/tr>
    fat <\/td>1.8 <\/td>135.0 <\/td>99.0 <\/td>0.9 <\/td><\/tr><\/tbody><\/table><\/div>Table Plugin<\/a>\n
    PS\uff1a\u5361\u8def\u91cc\u8ba1\u7b97\u516c\u5f0f\uff1a\u78b3\u6c34\u5316\u5408\u7269 + \u86cb\u767d\u8d28 + \u8102\u80aa<\/p><\/blockquote>\n
    \u5bf9\u4e8e\u82f9\u679c\u8fd9\u4e00\u5217\uff0c\u5176\u4e2d\u7684\u5361\u8def\u91cc\u542b\u91cf\u662f 56.0 + 1.2 + 1.8\u00a0 = 59 \u5361\u8def\u91cc\uff0c\u5176\u4e2d\uff0c\u6765\u81ea\u78b3\u6c34\u5316\u5408\u7269\u7684\u5361\u8def\u91cc\u6709 56\/59 = 94.9% \u2026\u2026<\/p>\n
    \u5bf9\u4e8e\u5361\u8def\u91cc\u800c\u8a00\uff0c\u6211\u4eec\u9700\u8981\u6c42\u89e3\u6bcf\u4e00\u4e2a\u98df\u7269\u79cd\u7c7b\u7684\u5361\u8def\u91cc\uff0c\u95ee\u9898\u662f\uff0c\u53ef\u4ee5\u4e0d\u4f7f\u7528\u663e\u793a\u5faa\u73af\uff08for-loop\uff09\u6765\u8fdb\u884c\u6b64\u9879\u64cd\u4f5c\u4e48\uff1f<\/p>\n
     <\/p>\n
    import numpy as np\r\n\r\nA = np.array([\r\n    [56.0 , 0.0 , 4.4 , 68.0],\r\n    [1.2 , 104.0 , 52.0 , 8.0],\r\n    [1.8 , 135.0 , 99.0 , 0.9]\r\n])\r\n\r\nprint(A)<\/code><\/pre>\n
    #output<\/p>\n
    [[ 56.    0.    4.4  68. ]\r\n [  1.2 104.   52.    8. ]\r\n [  1.8 135.   99.    0.9]]<\/pre>\n
    PS:\u5728numpy\u4e2darray\uff0c\u8fdb\u884c\u6570\u636e\u8f93\u51fa\u65f6\uff0c\u4f1a\u81ea\u52a8\u6d88\u53bb\u65e0\u610f\u4e49\u7684\u5c0f\u6570\u70b90\uff0c\u6240\u4ee5\u4f1a\u9020\u6210\u8f93\u51fa\u51fa\u73b0\u4e8656. \uff0c 0. \u8fd9\u79cd\u5947\u602a\u7684\u60c5\u51b5\uff0c\u5176\u5b9e\u5c31\u662f \u70b9 \u540e\u5168\u4e3a0\u800c\u5df2<\/p><\/blockquote>\n
    \u63a5\u4e0b\u6765\u8fdb\u884c\u9010\u5217\u76f8\u52a0<\/p>\n
    #\u8ba1\u7b97\u603b\u6570\uff0c\u603b\u7684\u5361\u8def\u91cc\r\ncal =  A.sum(axis=0)\r\n#\u6ce8\u610f\uff1aaxis = 0 - \u7ad6\u76f4\u76f8\u52a0 \uff0c1 - \u6c34\u5e73\u76f8\u52a0\r\n\r\nprint(cal)<\/code><\/pre>\n
    #output<\/p>\n
    [ 59.  239.  155.4  76.9]<\/pre>\n<\/blockquote>\n
    \u4e4b\u540e\u5c31\u8981\u5229\u7528\u5e7f\u64ad\u7279\u6027\uff0c\u76f4\u63a5\u8fdb\u884c\u4e0d\u540c\u89c4\u6a21\u5c3a\u5ea6\u7684\u77e9\u9635\u76f8\u4e92\u4e58\u9664<\/p>\n
    #\u8ba1\u7b97\u6bcf\u4e00\u79cd\u6210\u5206\u5728\u603b\u7684\u5361\u8def\u91cc\u4e2d\u5360\u6bd4\u591a\u5c11\r\npercentage = 100 * A\/cal.reshape(1,4)\r\n#\u6ce8\u610f\u6b64\u65f6cal\u5df2\u7ecf\u662f\u30101*4\u3011\u77e9\u9635\u4e86\uff0c\u4f7f\u7528reshape\u53ea\u662f\u786e\u4fdd\u8303\u56f4\u800c\u5df2\uff0c\u53ef\u4ee5\u53bb\u6389\uff0c\u4e0d\u8fc7\u5efa\u8bae\u90fd\u89c4\u8303\u4f7f\u7528\uff0c\u5176\u590d\u6742\u5ea6\u4e3aO\uff081\uff09\r\n#\u5229\u7528\u4e86\u5e7f\u64ad\u7279\u6027\uff0cA\uff083*4\u77e9\u9635\uff09\/ cal\uff081*4\u77e9\u9635\uff09\r\n\r\nprint(percentage)<\/code><\/pre>\n
    #output<\/p>\n
    [[94.91525424  0.          2.83140283 88.42652796]\r\n [ 2.03389831 43.51464435 33.46203346 10.40312094]\r\n [ 3.05084746 56.48535565 63.70656371  1.17035111]]<\/pre>\n<\/blockquote>\n
    \u5e7f\u64ad\uff08\u7528\u4e00\u4e9b\u56fe\u6765\u89e3\u91ca\uff0c\u4e09\u79cd\u53d8\u6362\u89c4\u5219\uff09<\/h6>\n
    \"\"<\/p>\n
    \u5e7f\u64ad\u7684\u6838\u5fc3\u89c4\u5219\uff1a\u5f53\u4e00\u4e2a(m*n)\u77e9\u9635\u8fdb\u884c\u3010+\uff0c-\uff0c*\uff0c\/\u3011\u5bf9(1\uff0cn)\uff0c\uff08m\uff0c1\uff09\uff0c\uff081\uff0c1\uff09\u56db\u5219\u8fd0\u7b97\u65f6\uff0c\u90fd\u4f1a\u81ea\u52a8\u4f7f\u7528\u590d\u5236\u7684\u65b9\u5f0f\u5c06\u5176\u53d8\u6210\u4e00\u4e2a\uff08m*n\uff09\u77e9\u9635\uff0c\u518d\u8fdb\u884c\u8fd0\u7b97\u5f97\u51fa\u7ed3\u679c\uff1b\u53cd\u8fc7\u6765\uff0c\u5982\u679c\u662f\u4e00\u4e2a(1\uff0cn)\uff0c\uff08m\uff0c1\uff09\uff0c\uff081\uff0c1\uff09\u77e9\u9635\u5bf9\u4e00\u4e2a\uff08m*n\uff09\u77e9\u9635\u8fdb\u884c\u8fd0\u7b97\u65f6\uff0c\u4e5f\u4f1a\u4f7f\u7528\u81ea\u52a8\u590d\u5236\u7684\u65b9\u5f0f\u5236\u9020\u4e3a\u4e00\u4e2a\u76f8\u7b49\u89c4\u6a21\u7684(m,n)\u77e9\u9635\u3002<\/p><\/blockquote>\n
    \u7f16\u5199\u4ee3\u7801\u6ce8\u610f<\/h3>\n
    \u6709\u7684\u65f6\u5019Python\u56e0\u4e3a\u4ed6\u7684\u4fbf\u5229\u6027\u8ba9\u5f88\u591a\u7684\u5f00\u53d1\u4eba\u5458\u559c\u6b22\uff0c\u5f53\u6211\u4eec\u5229\u7528\u5e7f\u64ad\u7279\u6027\u7684\u65f6\u5019\uff0c\u65e2\u4e0d\u4f1a\u62a5Type\u7c7b\u578b\u9519\u8bef\uff08\u884c\u5411\u91cf\u548c\u5217\u5411\u91cf\u7c7b\u578b\u4e0d\u4e00\u81f4\uff09\u4e5f\u4e0d\u4f1a\u62a5\u8d8a\u754c\u7b49\u9519\u8bef\uff0c\u800c\u662f\u76f4\u63a5\u76f8\u52a0\u6210\u529f\u4e86\uff0c\u8fd9\u79cd\u98ce\u683c\u5f88\u65b9\u4fbf\u7f16\u7801\uff0c\u4f46\u662f\u540c\u65f6\u4e5f\u4f1a\u9020\u6210\u5f88\u591a\u7ec6\u5fae\u7684\uff0c\u5f88\u96be\u4ee5\u8ba9\u4eba\u7422\u78e8\u7684BUG\u3002<\/p>\n
    \u6bd4\u5982\uff0c\u4f7f\u7528\u968f\u673a\uff0c\u751f\u62105\u4e2a\u9ad8\u65af\u53d8\u91cf\u5b58\u50a8\u518d\u6570\u7ec4a\u4e2d<\/p>\n
    import numpy as np\r\n\r\na = np.random.randn(5)\r\n\r\nprint(a)<\/code><\/pre>\n
    output\uff1a<\/p>\n
    [-1.1915006   1.14112716 -0.3530684   1.63963826  0.50971633]<\/pre>\n<\/blockquote>\n
    \u6b64\u65f6\u6211\u4eec\u518d\u8f93\u51fa\u4e00\u4e0ba\u7684shape<\/p>\n
    # \u6b64\u65f6\u751f\u6210\u7684 (5,) \u65e2\u4e0d\u662f\u884c\u5411\u91cf\uff0c\u4e5f\u4e0d\u662f\u5217\u5411\u91cf\uff0c\u5c31\u5f88\u5947\u602a\r\nprint(a.shape)<\/code><\/pre>\n
    output:<\/p>\n
    (5,)<\/pre>\n<\/blockquote>\n
    \u53ef\u4ee5\u53d1\u73b0\uff0c\u6b64\u65f6\u8f93\u51fa\u7684\uff085\uff0c\uff09\uff0c\u6240\u4ee5\u8fd9\u662f\u6240\u8c13\u7684Python\u79e9\u4e3a1\u7684\u6570\u7ec4\uff0c\u4f46\u662f\uff0c\u5b83\u65e2\u4e0d\u662f\u4e00\u4e2a\u884c\u5411\u91cf\uff0c\u4e5f\u4e0d\u662f\u4e00\u4e2a\u5217\u5411\u91cf\uff0c\u6240\u4ee5\u5f53\u6211\u4eec\u5bf9a\u8fdb\u884c\u8f6c\u7f6e\u7684\u65f6\u5019\uff08a.T\uff09\u4f1a\u53d1\u73b0\u548c\u539f\u6765\u7684\u6570\u7ec4\u8fd8\u662f\u4e00\u6837\u7684\u3002<\/p>\n
    PS1\uff1a\u8865\u5145\u4e00\u4e0b\u77e9\u9635\u7684\u8f6c\u7f6e\uff08\u7ffb\u7ebf\u6027\u4ee3\u6570\u8bfe\u672c\u53bb\uff09\uff1a<\/p>\n
    \u77e9\u9635\u8f6c\u7f6e\uff1a\u628a\u884c\uff0c\u6362\u6210\u540c\u5e8f\u6570\u7684\u5217\u5373\u53ef\uff0c\u4e00\u4e2a\u7279\u6b8a\u7684\u6982\u5ff5a.T = a\u65f6\u8bf4\u660ea\u662f\u4e00\u4e2a\u5bf9\u79f0\u77e9\u9635 \uff0c\u800c\u5f53a.T = -a \u7684\u65f6\u5019\u8bf4\u660ea\u4e3a\u53cd\u5bf9\u79f0\u77e9\u9635<\/p>\n
    PS1\uff1a\u8865\u5145\u4e00\u4e0b\u77e9\u9635\u7684\u79e9\uff0c\u6211\u63a8\u8350\u770b\u8fd9\u4e00\u7bc7\u77e5\u4e4e\u6587\u7ae0\uff0c\u53ef\u4ee5\u5feb\u901f\u7406\u89e3https:\/\/www.zhihu.com\/question\/21605094<\/a> \uff0c\u5f53\u7136\u6211\u66f4\u52a0\u63a8\u8350\u5543\u5b8c\u6574\u672c\u7ebf\u6027\u4ee3\u6570\u7684\u4e66\uff0c\u6216\u8005\u8ddf\u7740\u4e00\u4e2a\u8003\u7814\u8001\u5e08\u7cfb\u7edf\u5b66\u4e00\u904d<\/p>\n
    print(a.T)<\/code><\/pre>\n
    output\uff1a<\/p>\n
    [-1.1915006   1.14112716 -0.3530684   1.63963826  0.50971633]<\/pre>\n<\/blockquote>\n
    a.T\u4e0ea\u4e00\u6837\uff0c\u6b64\u65f6\u6211\u4eec\u518d\u8ba1\u7b97\u4e00\u4e0ba\u548ca.T\u7684\u5185\u79ef\uff0c\u4f60\u53ef\u80fd\u89c9\u5f97a\u4e58\u4ee5a.T\uff0c\u6216\u8005\u8bf4\u5916\u79ef\u53ef\u80fd\u4f1a\u5f97\u5230\u4e00\u4e2a\u77e9\u9635\uff0c\u4f46\u662f\u5982\u679c\u6211\u4eec\u76f4\u63a5\u8fd9\u6837\u505a\u7684\u8bdd\uff0c\u53ea\u4f1a\u5f97\u5230\u4e00\u4e2a\u6570\u5b57<\/p>\n
    print(np.dot(a,a.T))<\/code><\/pre>\n
    output:<\/p>\n
    5.79472651727104<\/pre>\n<\/blockquote>\n
    \u6240\u4ee5\u5efa\u8bae\u5728\u7f16\u5199\u795e\u7ecf\u7f51\u7edc\u7684\u65f6\u5019\uff0c\u5c31\u4e0d\u8981\u4f7f\u7528\u8fd9\u79cd\u6570\u636e\u7ed3\u6784\uff0c\u5176\u4e2d\u5f62\u72b6\u65f6(5,)\u6216\u8005(n,)\u8fd9\u79cd\u79e9\u4e3a1\u7684\u6570\u7ec4\uff0c\u76f8\u53cd\u5982\u679c\u4f60\u4ee4a\u8fd9\u79cd5*1\u77e9\u9635\u7684\u8bdd\uff0c\u90a3\u4e48\u5c31\u53ef\u4ee5\u5f97\u5230\u4ee4a\u53d8\u62105*1\u7684\u5217\u5411\u91cf<\/p>\n
    a = np.random.randn(5,1)\r\nprint(a)<\/code><\/pre>\n
    output<\/p>\n
    [[-0.34963724]\r\n [-1.13975557]\r\n [-0.60878052]\r\n [ 0.02157292]\r\n [ 1.30132785]]<\/pre>\n<\/blockquote>\n
    \u53ef\u4ee5\u53d1\u73b0\uff0c\u5728Python\u7684numpy\u4e2d\uff0c\uff085,1\uff09\u548c(5, )\u867d\u7136\u770b\u8d77\u6765\u4e00\u6837\uff0c\u800c\u4e14\u8fd9\u4e24\u4e2a\u77e9\u9635\u79e9\u90fd\u662f1\uff0c\u4f46\u662f\u5b9e\u9645\u6548\u679c\u548c\u4f5c\u7528\u7f3a\u5927\u76f8\u5f84\u5ead\uff0c\uff085,\uff09\u4eff\u4f5b\u4e0d\u662f\u4e00\u4e2a\u77e9\u9635\uff0c\u8fd9\u6837\u5f80\u5f80\u4f1a\u9020\u6210\u5f88\u591a\u4e0d\u53ef\u4ee5\u9884\u4f30\u7684BUG\u3002<\/p>\n
    \uff0c\u5f53\u6211\u4eec\u91cd\u65b0\u8f93\u51fa\u4e0a\u9762\u7684\u6c42\u89e3\uff0c\u4e00\u5207\u90fd\u53d8\u5f97\u6b63\u5e38\u4e86<\/p>\n
    print(a.T)<\/code><\/pre>\n
    output\uff1a<\/p>\n
    [[-0.34963724 -1.13975557 -0.60878052  0.02157292  1.30132785]]<\/pre>\n<\/blockquote>\n
    \u4ee5\u53ca\u4f7f\u7528np.dot\u6c42\u5916\u79ef<\/p>\n
    #\u8fd9\u6837\u53ef\u4ee5\u6b63\u786e\u6c42\u5f97\u77e9\u9635\u7684\u5916\u79ef\r\nprint(np.dot(a,a.T))<\/code><\/pre>\n
    output:<\/p>\n
    [[ 1.22246197e-01  3.98500987e-01  2.12852340e-01 -7.54269441e-03\r\n  -4.54992671e-01]\r\n [ 3.98500987e-01  1.29904276e+00  6.93860994e-01 -2.45878502e-02\r\n  -1.48319566e+00]\r\n [ 2.12852340e-01  6.93860994e-01  3.70613727e-01 -1.31331706e-02\r\n  -7.92223049e-01]\r\n [-7.54269441e-03 -2.45878502e-02 -1.31331706e-02  4.65390666e-04\r\n   2.80734352e-02]\r\n [-4.54992671e-01 -1.48319566e+00 -7.92223049e-01  2.80734352e-02\r\n   1.69345416e+00]]<\/pre>\n<\/blockquote>\n
    PS\uff1anp.dot() \u662f\u4e00\u79cd\u6bd4\u8f83\u7075\u6d3b\u7684\u6c42\u89e3\u65b9\u6cd5\uff0c\u5982\u679c\u4f60\u7684\u662f\u4e00\u7ef4\u77e9\u9635\uff0c\u6c42\u89e3\u5c31\u662f\u6211\u4eec\u719f\u6089\u7684\u5185\u79ef\uff0c\u5982\u679c\u662f\u4e8c\u7ef4\u77e9\u9635\uff0c\u5c31\u662f\u77e9\u9635\u76f8\u4e58<\/p>\n
    \"\"<\/p>\n
    \u7efc\u4e0a\uff0c\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff0c\u5728\u5b9e\u9645\u7f16\u5199\u4ee3\u7801\u4e2d\uff0c\u5e94\u8be5\u517b\u6210\u4e00\u4e2a\u4e60\u60ef\uff0c\u5c31\u662f\u5bf9\u4e8e\u81ea\u5df1\u521b\u9020\u7684\u6570\u636e\u7ed3\u6784\uff0c\u9700\u8981\u5bf9\u5176\u8fdb\u884c\u9a8c\u8bc1\uff0c\u4f7f\u7528\u8bf8\u5982 assert(a.shape == (5,1)) \u8fd9\u6837\u7684\u4ee3\u7801\u65b9\u6cd5\u6765\u786e\u8ba4\u662f\u5426\u6570\u636e\u7ed3\u6784\u6ee1\u8db3\u9884\u671f\uff0c\u6216\u8005\u5373\u65f6\u7684\u4f7f\u7528 a = a.reshape( (5,1) )\u6765\u8c03\u6574\u81ea\u5df1\u7684\u6570\u636e\u7ed3\u6784\uff0c\u4ee5\u907f\u514d\u51fa\u73b0\u79e9\u4e3a\u4e00\u7684\u975e\u884c\u975e\u5217\u77e9\u9635\u9020\u6210\u5947\u602a\u7684Bug\u3002<\/p>\n
     <\/p>\n","protected":false},"excerpt":{"rendered":"
    \u672c\u7ae0\u7b14\u8bb0\u6709\u5982\u4e0b\u5185\u5bb9\uff1a \u4ecb\u7ecd\u5411\u91cf\u5316\uff1a\u63a5\u4e0a\u6587\u8865\u5145\u5feb\u901f\u8ba1\u7b97\u65b9\u6cd5 \u5411\u91cf\u5316\u5728\u903b\u8f91\u56de\u5f52\u4e2d\u7684\u5e94\u7528 \u5982\u4f55\u5411\u91cf\u5316\u903b\u8f91\u56de […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5,30],"tags":[12,31,6],"class_list":["post-819","post","type-post","status-publish","format-standard","hentry","category-algorithm","category-python","tag-ai","tag-python","tag-algorithm"],"views":1069,"_links":{"self":[{"href":"https:\/\/www.mustenaka.cn\/index.php\/wp-json\/wp\/v2\/posts\/819","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.mustenaka.cn\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.mustenaka.cn\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.mustenaka.cn\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.mustenaka.cn\/index.php\/wp-json\/wp\/v2\/comments?post=819"}],"version-history":[{"count":15,"href":"https:\/\/www.mustenaka.cn\/index.php\/wp-json\/wp\/v2\/posts\/819\/revisions"}],"predecessor-version":[{"id":873,"href":"https:\/\/www.mustenaka.cn\/index.php\/wp-json\/wp\/v2\/posts\/819\/revisions\/873"}],"wp:attachment":[{"href":"https:\/\/www.mustenaka.cn\/index.php\/wp-json\/wp\/v2\/media?parent=819"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.mustenaka.cn\/index.php\/wp-json\/wp\/v2\/categories?post=819"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.mustenaka.cn\/index.php\/wp-json\/wp\/v2\/tags?post=819"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}