{"id":73068,"date":"2018-09-18T23:24:41","date_gmt":"2018-09-18T14:24:41","guid":{"rendered":"https:\/\/www.sejuku.net\/blog\/?p=73068"},"modified":"2024-05-06T11:51:56","modified_gmt":"2024-05-06T02:51:56","slug":"%e3%80%90numpy%e5%85%a5%e9%96%80-np-save%e3%80%91%e9%85%8d%e5%88%97%e3%82%92%e3%83%95%e3%82%a1%e3%82%a4%e3%83%ab%e3%81%ab%e4%bf%9d%e5%ad%98%e3%81%99%e3%82%8bnp-save%e3%82%92%e4%bd%bf%e3%81%84%e3%81%93","status":"publish","type":"post","link":"https:\/\/www.sejuku.net\/blog\/73068","title":{"rendered":"\u3010NumPy\u5165\u9580 np.save\u3011\u914d\u5217\u3092\u30d5\u30a1\u30a4\u30eb\u306b\u4fdd\u5b58\u3059\u308bnp.save\u3092\u4f7f\u3044\u3053\u306a\u3059"},"content":{"rendered":"<p>\u3053\u3093\u306b\u3061\u306f\u3001<a href=\"https:\/\/lp.sejuku.net\/lp1_blog_01\/?cid=ai_btn1_73068\">\u30a4\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\u30fc\u306e\u30d5\u30af\u30ed\u30a6<\/a>\u3067\u3059\uff01<\/p>\n<p>\u7686\u3055\u3093\u306f\u6a5f\u68b0\u5b66\u7fd2\uff08\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306a\u3069\uff09\u306e\u5b66\u7fd2\u304c\u7d42\u308f\u3063\u305f\u5f8c\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u4fdd\u5b58\u3001\u3069\u3046\u3057\u3066\u3044\u307e\u3059\u304b\uff1f<\/p>\n<p>\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306a\u3069\u3092\u5909\u3048\u3066\u4f55\u5ea6\u3082\u7e70\u308a\u8fd4\u3057\u3066\u5b66\u7fd2\u3092\u3059\u308b\u969b\u3084\u3001\u826f\u3044\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u898b\u3064\u3051\u305f\u3068\u304d\u306a\u3069\u306b\u306f\u3001\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u4fdd\u5b58\u3057\u3066\u304a\u304d\u305f\u3044\u3068\u601d\u3046\u306f\u305a\u3002<\/p>\n<p>\u305d\u3093\u306a\u3068\u304d\u3001<span style=\"color: #008000;\"><strong>np.save<\/strong><\/span>\u3068\u3044\u3046\u95a2\u6570\u3067\u3001<span style=\"color: #0000ff;\"><strong>\u7c21\u5358\u306bNumPy\u306e\u914d\u5217\u3092\u4fdd\u5b58\u3059\u308b<\/strong><\/span>\u3053\u3068\u304c\u3067\u304d\u308b\u3093\u3067\u3059\uff01<\/p>\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u306f<\/p>\n<ul>\n<li><span style=\"color: #0000ff;\"><strong>NumPy\u306endarray\u3092\u30c6\u30ad\u30b9\u30c8\u30d5\u30a1\u30a4\u30eb\u3068\u3057\u3066\u4fdd\u5b58\u3059\u308b\u95a2\u6570<\/strong><\/span><span style=\"color: #008000;\"><strong>\u300cnp.save\u300d<\/strong><\/span><\/li>\n<li><strong><span style=\"color: #0000ff;\">\u4fdd\u5b58\u3057\u305f\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u8fbc\u3080<\/span><span style=\"color: #008000;\">\u300cnp.load\u300d<\/span><\/strong><\/li>\n<\/ul>\n<p>\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3088\uff01\u5fc5\u898b\u3067\u3059\uff01<\/p>\n<h2>np.save\u306e\u30d1\u30e9\u30e1\u30fc\u30bf<\/h2>\n<p><span style=\"color: #008000;\"><strong>np.save<\/strong><\/span>\u306f\u3001<span style=\"color: #0000ff;\"><strong>np.array\u5f62\u5f0f\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u30d5\u30a1\u30a4\u30eb\u3068\u3057\u3066\u4fdd\u5b58\u3059\u308b\u95a2\u6570<\/strong><\/span>\u3067\u3059\u3002<\/p>\n<p>np.save\u306e\u5f15\u6570\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3002<\/p>\n<pre class=\"lang:python decode:true\">np.save(\n    file, # \u30c7\u30fc\u30bf\u3092\u4fdd\u5b58\u3059\u308b\u30d5\u30a1\u30a4\u30eb\u540d\n    arr,  # \u914d\u5217\u578b\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\uff08list\u3084np.array)\n    fix_imports=True # Python2\u3067\u3082\u4f7f\u3048\u308b\u3088\u3046\u306b\u3059\u308b\u304b\u3069\u3046\u304b\n)<\/pre>\n<p>\u57fa\u672c\u7684\u306b\u306f<span style=\"color: #0000ff;\"><strong>fix_imports\u306f\u6c17\u306b\u3057\u306a\u304f\u3066\u5927\u4e08\u592b<\/strong><\/span>\u3067\u3059\u3002<\/p>\n<p><span style=\"color: #0000ff;\"><strong>\u7b2c\u4e00\u5f15\u6570\u306b\u30d5\u30a1\u30a4\u30eb\u540d<\/strong><\/span>\u3001<span style=\"color: #0000ff;\"><strong>\u7b2c\u4e8c\u5f15\u6570\u306b\u4fdd\u5b58\u3057\u305f\u3044\u30aa\u30d6\u30b8\u30a7\u30af\u30c8<\/strong><\/span>\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002<\/p>\n<p>\u30d5\u30a1\u30a4\u30eb\u540d\u306f\u81ea\u52d5\u7684\u306b<span style=\"color: #0000ff;\"><strong>npy\u304bnpz\u3068\u3044\u3046\u62e1\u5f35\u5b50<\/strong><\/span>\u304c\u3064\u3044\u305f\u5f62\u306b\u306a\u308b\u306e\u3067\u3001\u5f8c\u304b\u3089\u8aad\u307f\u8fbc\u3093\u3067\u4f7f\u3046\u3068\u304d\u306b\u306f\u3053\u306e\u62e1\u5f35\u5b50\u3092\u76ee\u5370\u306b\u3059\u308b\u3068\u3044\u3044\u3067\u3059\u3088\u3002<\/p>\n<h2>np.load\u306e\u30d1\u30e9\u30e1\u30fc\u30bf<\/h2>\n<p><span style=\"color: #008000;\"><strong>np.load<\/strong><\/span>\u306f<span style=\"color: #0000ff;\"><strong>npy\/npz\u5f62\u5f0f\u306e\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u8fbc\u3080\u305f\u3081\u306e\u95a2\u6570<\/strong><\/span>\u3067\u3059\u3002<\/p>\n<p>np.load\u306e\u5f15\u6570\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3002<\/p>\n<pre class=\"lang:python decode:true\">np.load(\n    file=\"tset.npy\",        # npy\u304bnpz\u62e1\u5f35\u5b50\u306e\u30d5\u30a1\u30a4\u30eb\u3092\u6307\u5b9a\n    allow_pickle=True,      # npy\/npz\u3067\u4fdd\u5b58\u3055\u308c\u305fpickle\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u8fbc\u3080\u304b\u3069\u3046\u304b (\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u306fTrue)\n    fix_imports=True,       # Python2\u3068\u306e\u4e92\u63db\u6a5f\u80fd\u3001True\u306a\u3089py2\u88fd\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u8fbc\u3081\u308b\u3000\uff08\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u306fTrue\uff09\n    encoding=\"ASCII\",       # Python2\u3067\u4f5c\u3089\u308c\u305f\u6587\u5b57\u5217\u3092\u8aad\u307f\u8fbc\u3080\u3068\u304d\u306e\u6587\u5b57\u30b3\u30fc\u30c9\u6307\u5b9a\u3000\uff08\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u306fASCII\uff09\n)\n<\/pre>\n<p>\u57fa\u672c\u7684\u306b\u306f\u3001<span style=\"color: #0000ff;\"><strong>\u30d5\u30a1\u30a4\u30eb\u540d\u3092\u6307\u5b9a\u3059\u308b\u3060\u3051<\/strong><\/span>\u3067OK\u3067\u3059\u3002<\/p>\n<p>python2\u3068\u306e\u9023\u643a\u3092\u8003\u3048\u308b\u3068\u304d\u306f\u5c11\u3057\u9762\u5012\u3067\u3059\u304c\u3001\u6700\u8fd1\u3067\u306fPython3\u304c\u304b\u306a\u308a\u666e\u53ca\u3057\u305f\u306e\u3067\u3042\u307e\u308a\u4f7f\u3046\u3053\u3068\u306f\u7121\u3044\u3067\u3057\u3087\u3046\u3002<\/p>\n<h2>np.save\/np.load\u306e\u5b9f\u969b\u306e\u4f7f\u3044\u65b9<\/h2>\n<p>\u4f8b\u3048\u3070\u3001NumPy\u3067\u5b9f\u88c5\u3057\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\uff08\u7d50\u5408\u91cd\u307f\u3001\u30d0\u30a4\u30a2\u30b9\uff09\u3092\u30d5\u30a1\u30a4\u30eb\u306b\u4fdd\u5b58\u3057\u305f\u3044\u3068\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<p>\u3053\u3053\u3067\u306f\u3001\u5b9f\u969b\u306b\u7c21\u5358\u306a\u753b\u50cf\u5206\u985e\u554f\u984c\u3092\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3067\u5b66\u7fd2\u3057\u305f\u5f8c\u3001\u3053\u308c\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u4fdd\u5b58\u3068\u3001\u4fdd\u5b58\u3057\u305f\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\u307e\u3067\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n<h3>\u524d\u6e96\u5099<\/h3>\n<p>\u307e\u305a\u306f<span style=\"color: #0000ff;\"><strong>\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<\/strong><\/span>\u304b\u3089\u3001<span style=\"color: #0000ff;\"><strong>\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f<\/strong><\/span>\u307e\u3067\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n<pre class=\"lang:python decode:true \">from sklearn import datasets\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\n\ndataset = datasets.load_digits()\ntrain_data, test_data, train_label, test_label = train_test_split(dataset.data\/255, dataset.target)\n\nshow_shape = lambda X: [print(x.shape) for x in X]\n\nshow_shape([train_data, test_data, train_label, test_label])<\/pre>\n<p>[Output]<\/p>\n<pre class=\"lang:default decode:true\">(1347, 64)\n(450, 64)\n(1347,)\n(450,)<\/pre>\n<p>\u3053\u306e\u30c7\u30fc\u30bf(digits)\u306f0~9\u306e10\u30af\u30e9\u30b9\u3092\u6301\u3061\u307e\u3059\u3002<\/p>\n<p>training data\u3068test data\u306b\u5206\u5272\u3082\u7d42\u308f\u3063\u305f\u306e\u3067\u3001\u3053\u308c\u3092\u4f7f\u3063\u3066\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u5b66\u7fd2\u3057\u307e\u3059\u3002<\/p>\n<h3>\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u5b66\u7fd2\u3068\u8a55\u4fa1<\/h3>\n<p><span style=\"color: #008000;\"><strong>scikit-learn\u306eMLP\uff08\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff09\u30af\u30e9\u30b9<\/strong><\/span>\u3092\u4f7f\u3063\u3066\u5b66\u7fd2\u3068\u8a55\u4fa1\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n<p>\u307e\u305a\u306f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u8a2d\u5b9a\u3057\u3001\u305d\u306e\u5f8c\u5b66\u7fd2\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n<pre class=\"lang:python decode:true\">model = MLPClassifier(\n    hidden_layer_sizes=(100),  # \u96a0\u308c\u5c64\u306e\u30e6\u30cb\u30c3\u30c8\u6570\n    activation=\"relu\",         # \u96a0\u308c\u5c64\u306e\u6d3b\u6027\u5316\u95a2\u6570\n    solver=\"adam\",             # \u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\n    random_state=1234,         # \u4e71\u6570\u751f\u6210\u5668\u306e\u8a2d\u5b9a\n    max_iter=1000,             # \u6700\u5927\u5b66\u7fd2\u56de\u6570\n)\n\nmodel.fit(train_data, train_label) # \u30c7\u30fc\u30bf\u3068\u30e9\u30d9\u30eb\u3092\u6e21\u3057\u3066\u5b66\u7fd2<\/pre>\n<p>[Output]<\/p>\n<pre class=\"lang:default decode:true\">MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n       hidden_layer_sizes=100, learning_rate='constant',\n       learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n       nesterovs_momentum=True, power_t=0.5, random_state=1234,\n       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n       verbose=False, warm_start=False)<\/pre>\n<p>sklearn\u3067\u306f<span style=\"color: #0000ff;\"><strong>fit\u30e1\u30bd\u30c3\u30c9\u3067\u5b66\u7fd2<\/strong><\/span>\u304c\u884c\u306a\u3048\u307e\u3059\u3002<\/p>\n<p>\u3067\u306f\u3001\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u8a55\u4fa1\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<pre class=\"lang:python decode:true \">pred_label = model.predict(test_data)\n\naccuracy_score(test_label, pred_label)<\/pre>\n<p>[Output]<\/p>\n<pre class=\"lang:default decode:true \">0.9688888888888889<\/pre>\n<p>\u3055\u3066\u3001\u3053\u306e\u5024\u304c\u826f\u3044\u306e\u304b\u60aa\u3044\u306e\u304b\u306f\u3055\u3066\u304a\u304d\u3001\u5b66\u7fd2\u3057\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30e2\u30c7\u30eb\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u4fdd\u5b58\u3057\u3066\u304a\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n<h3>np.array\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\uff08\u30d1\u30e9\u30e1\u30fc\u30bf\uff09\u306e\u4fdd\u5b58<\/h3>\n<p>MLPClassifier\u30af\u30e9\u30b9\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\uff08model\u5909\u6570\uff09\u3067\u306f\u3001\u4ee5\u4e0b\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u5909\u6570\u3067\u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u307e\u3059\u3002<\/p>\n<ul>\n<li><span style=\"color: #008000;\"><strong>model.coefs_<\/strong><\/span>\n<ul>\n<li>\u7d50\u5408\u91cd\u307f\u306e\u30ea\u30b9\u30c8<\/li>\n<\/ul>\n<\/li>\n<li><span style=\"color: #008000;\"><strong>model.intercepts_<\/strong><\/span>\n<ul>\n<li>\u30d0\u30a4\u30a2\u30b9\u306e\u30ea\u30b9\u30c8<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>\u4f8b\u3048\u3070\u3001model.intercepts_\u306e\u4e2d\u8eab\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u5f62\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<pre class=\"lang:default decode:true\">[array([ 0.34682695, -0.18619194,  0.51089346, -0.0745376 , -0.06683674,\n        -0.10533423, -0.08774989,  0.3257384 ,  0.38348272, -0.09437606,\n         0.20785552,  0.1292196 , -0.19124118,  0.43848153, -0.16747779,\n        -0.12134393, -0.07412251, -0.07375998, -0.10972181, -0.04448673,\n         0.3020805 ,  0.15064405,  0.16899598, -0.11769587,  0.15454121,\n         0.27730995,  0.43474162,  0.24106031,  0.25595449,  0.2028516 ,\n         0.25106652,  0.45004029,  0.09671492,  0.13609258,  0.22260003,\n         0.35082012, -0.03981748,  0.41469217, -0.08377924,  0.07150102,\n         0.30826337,  0.32182736,  0.27367003,  0.16384482,  0.38533181,\n        -0.13447486, -0.10285442,  0.19841185,  0.34602021, -0.03515394,\n        -0.18140602, -0.04105884,  0.12466424,  0.17666507,  0.10474498,\n         0.35839253,  0.22411312,  0.17568221, -0.0971458 ,  0.34551279,\n        -0.151979  ,  0.21885159,  0.18677117,  0.11442799, -0.09858213,\n        -0.17738479, -0.17657904,  0.24919445,  0.20419114, -0.03517398,\n        -0.13896385, -0.16427148, -0.10505843, -0.14203025,  0.2926662 ,\n         0.30190493,  0.02037234,  0.18032657,  0.31760639,  0.2546752 ,\n         0.2467949 ,  0.18304099,  0.16728748, -0.15266362, -0.1059676 ,\n        -0.07523529,  0.40519387, -0.03681349, -0.12812912,  0.29106102,\n         0.14021797,  0.24270879,  0.1255138 ,  0.49001767,  0.33297187,\n         0.20794209,  0.26631199,  0.39582434, -0.02316582, -0.08422603]),\n array([ 0.09008411, -0.34033946, -0.14145383,  0.2416411 ,  0.17395041,\n         0.0324957 ,  0.21700254,  0.28717775,  0.10416904,  0.05591022])]<\/pre>\n<p>\u3053\u308c\u3089\u3092<span style=\"color: #0000ff;\"><strong>np.save\u3067\u307e\u3068\u3081\u3066\u4fdd\u5b58<\/strong><\/span>\u3057\u307e\u3059\u3002<\/p>\n<pre class=\"lang:python decode:true\">params = model.coefs_ + model.intercepts_ # \u3053\u306e\u6642\u70b9\u3067\u914d\u5217\u304c4\u3064\u5165\u3063\u305f\u30ea\u30b9\u30c8\u304c\u3067\u304d\u308b\n\nnp.save(\n    \"MLP_params\",  # \u30d5\u30a1\u30a4\u30eb\u540d\n    params # \u4fdd\u5b58\u3057\u305f\u3044\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\n)<\/pre>\n<p>\u306f\u3044\u3002\u3053\u308c\u3060\u3051\u3067OK\u3067\u3059\u3002<\/p>\n<p>\u305d\u3046\u3059\u308b\u3068\u3001<span style=\"color: #0000ff;\"><strong>Python\u3092\u5b9f\u884c\u3057\u3066\u3044\u308b\u30c7\u30a3\u30ec\u30af\u30c8\u30ea<\/strong><\/span>\u306b<span style=\"color: #0000ff;\"><strong>MLP_params.py<\/strong><\/span>\u3068\u3044\u3046\u30d5\u30a1\u30a4\u30eb\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>\u5f8c\u306f\u5225\u306ePython\u30b9\u30af\u30ea\u30d7\u30c8\u3084Jupyter Notebook\u304b\u3089\u3053\u306e\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u8fbc\u3081\u3070\u3001\u914d\u5217\u304c\u5fa9\u5143\u3067\u304d\u307e\u3059\u3088\uff01<\/p>\n<h3>npy\u30d5\u30a1\u30a4\u30eb\u306e\u8aad\u307f\u8fbc\u307f<\/h3>\n<p>\u3067\u306f\u5225\u306ePython\u30d5\u30a1\u30a4\u30eb\u306a\u3069\u304b\u3089\u3001<span style=\"color: #0000ff;\"><strong>npy\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u8fbc\u3093\u3067\u30c7\u30fc\u30bf\u3092\u5fa9\u5143<\/strong><\/span>\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n<pre class=\"lang:default decode:true \">data = \nnp.load(\n    file=\"MLP_params.npy\",  # npy\u304bnpz\u62e1\u5f35\u5b50\u306e\u30d5\u30a1\u30a4\u30eb\u3092\u6307\u5b9a\n)<\/pre>\n<p>\u3053\u308c\u3067data\u5909\u6570\u306b\u5148\u7a0b\u4fdd\u5b58\u3057\u305f\u30c7\u30fc\u30bf\u304c\u5165\u3063\u3066\u3044\u308b\u306f\u305a\u3067\u3059\u3002<\/p>\n<p>[data\u5909\u6570\u306e\u4e2d\u8eab]<\/p>\n<pre class=\"lang:default decode:true\">array([array([[ 2.04164655e-79,  4.82010231e-93, -7.43699714e-97, ...,\n         1.58132888e-78,  1.16132784e-68, -1.38242520e-90],\n       [ 1.77787549e+00,  1.38148454e-87,  2.00121196e-01, ...,\n         8.11974765e-01, -1.94455920e-80, -2.48317060e-68],\n       [ 1.23475102e+00, -1.11819465e-73,  2.00196626e-01, ...,\n        -1.56769746e-01, -3.71560789e-93,  1.55994215e-73],\n       ...,\n       [-6.41327607e-01, -1.06191740e-88, -1.27631161e+00, ...,\n         9.47833397e-01, -1.92493053e-88,  1.75168624e-98],\n       [-7.31629838e-02, -2.31818709e-69, -1.45759431e+00, ...,\n         1.53434268e+00,  3.61061455e-95, -1.51401637e-96],\n       [-8.12946446e-01,  2.30463983e-87, -1.31044154e+00, ...,\n         1.31016023e+00, -2.41307845e-90, -4.50949718e-83]]),\n       array([[ 8.51193237e-001, -1.68374839e+000,  1.61472743e+000,\n       ---\u4ee5\u4e0b\u7701\u7565---<\/pre>\n<p>\u3053\u306e\u3088\u3046\u306b\u3001\u7c21\u5358\u306b\u30c7\u30fc\u30bf\u306e\u4fdd\u5b58\u3068\u30d5\u30a1\u30a4\u30eb\u304b\u3089\u306e\u914d\u5217\u306e\u8aad\u307f\u8fbc\u307f\u304c\u3067\u304d\u307e\u3057\u305f\uff01<\/p>\n<h2>\u307e\u3068\u3081<\/h2>\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001<span style=\"color: #0000ff;\"><strong>np.array\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u30d5\u30a1\u30a4\u30eb\u306b\u4fdd\u5b58\u3059\u308bnp.save\u95a2\u6570<\/strong><\/span>\u3068\u3001<span style=\"color: #0000ff;\"><strong>\u4fdd\u5b58\u3057\u305f\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u8fbc\u3080np.load\u95a2\u6570<\/strong><\/span>\u3092\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002<\/p>\n<p>\u3053\u306e\u4ed6\u306b\u3082\u30d5\u30a1\u30a4\u30eb\u306e\u8aad\u307f\u8fbc\u307f\u30fb\u66f8\u304d\u8fbc\u307f\u3092\u884c\u3046\u95a2\u6570\u306f\u3042\u308a\u307e\u3059\u304c\u3001np.save\u3092\u4f7f\u3063\u305f\u4fdd\u5b58\u306f\u975e\u5e38\u306b\u624b\u8efd\u306a\u306e\u304c\u826f\u3044\u3067\u3059\u306d\u3002<\/p>\n<p>\u624b\u8efd\u306b\u4f7f\u3048\u308b\u306e\u3067\u3001\u662f\u975e\u3068\u3082\u666e\u6bb5\u306e\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u3067\u4f7f\u3063\u3066\u307f\u3066\u304f\u3060\u3055\u3044\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u3053\u3093\u306b\u3061\u306f\u3001\u30a4\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\u30fc\u306e\u30d5\u30af\u30ed\u30a6\u3067\u3059\uff01 \u7686\u3055\u3093\u306f\u6a5f\u68b0\u5b66\u7fd2\uff08\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306a\u3069\uff09\u306e\u5b66\u7fd2\u304c\u7d42\u308f\u3063\u305f\u5f8c\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u4fdd\u5b58\u3001\u3069\u3046\u3057\u3066\u3044\u307e\u3059\u304b\uff1f \u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306a\u3069\u3092\u5909\u3048\u3066\u4f55\u5ea6\u3082\u7e70\u308a\u8fd4\u3057\u3066\u5b66\u7fd2\u3092\u3059\u308b\u969b\u3084\u3001\u826f\u3044\u30d1\u30e9 [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":73069,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"swell_btn_cv_data":"","footnotes":""},"categories":[2350],"tags":[1888],"class_list":["post-73068","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python-study","tag-ai-python-numpy"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>\u3010NumPy\u5165\u9580 np.save\u3011\u914d\u5217\u3092\u30d5\u30a1\u30a4\u30eb\u306b\u4fdd\u5b58\u3059\u308bnp.save\u3092\u4f7f\u3044\u3053\u306a\u3059 | \u4f8d\u30a8\u30f3\u30b8\u30cb\u30a2\u30d6\u30ed\u30b0<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u8a18\u4e8b\u3067\u306f\u300c \u3010NumPy\u5165\u9580 np.save\u3011\u914d\u5217\u3092\u30d5\u30a1\u30a4\u30eb\u306b\u4fdd\u5b58\u3059\u308bnp.save\u3092\u4f7f\u3044\u3053\u306a\u3059 \u300d\u306b\u3064\u3044\u3066\u3001\u8ab0\u3067\u3082\u7406\u89e3\u3067\u304d\u308b\u3088\u3046\u306b\u89e3\u8aac\u3057\u307e\u3059\u3002\u3053\u306e\u8a18\u4e8b\u3092\u8aad\u3081\u3070\u3001\u3042\u306a\u305f\u306e\u60a9\u307f\u304c\u89e3\u6c7a\u3059\u308b\u3060\u3051\u3058\u3083\u306a\u304f\u3001\u65b0\u305f\u306a\u6c17\u4ed8\u304d\u3082\u767a\u898b\u3067\u304d\u308b\u3053\u3068\u3067\u3057\u3087\u3046\u3002\u304a\u60a9\u307f\u306e\u65b9\u306f\u305c\u3072\u3054\u4e00\u8aad\u304f\u3060\u3055\u3044\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.sejuku.net\/blog\/73068\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" 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np.save\u3011\u914d\u5217\u3092\u30d5\u30a1\u30a4\u30eb\u306b\u4fdd\u5b58\u3059\u308bnp.save\u3092\u4f7f\u3044\u3053\u306a\u3059\",\"datePublished\":\"2018-09-18T14:24:41+00:00\",\"dateModified\":\"2024-05-06T02:51:56+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.sejuku.net\\\/blog\\\/73068\"},\"wordCount\":87,\"publisher\":{\"@id\":\"https:\\\/\\\/www.sejuku.net\\\/blog\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.sejuku.net\\\/blog\\\/73068#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.sejuku.net\\\/blog\\\/wp-content\\\/uploads\\\/2018\\\/09\\\/npsave.png\",\"keywords\":[\"AI. python. 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