{"id":1258,"date":"2017-12-06T21:18:07","date_gmt":"2017-12-06T21:18:07","guid":{"rendered":"http:\/\/demensdeum.com\/blog\/?p=1258"},"modified":"2024-12-16T22:32:44","modified_gmt":"2024-12-16T19:32:44","slug":"simple-example-tensorflow","status":"publish","type":"post","link":"https:\/\/demensdeum.com\/blog\/ja\/2017\/12\/06\/simple-example-tensorflow\/","title":{"rendered":"\u5358\u7d14\u306a TensorFlow \u306e\u4f8b"},"content":{"rendered":"

\u3053\u3053\u3067\u306f\u3001\u30c7\u30a3\u30fc\u30d7 \u30e9\u30fc\u30cb\u30f3\u30b0\u3092\u4f7f\u7528\u3059\u308b\u305f\u3081\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3092\u4f7f\u7528\u3059\u308b\u7c21\u5358\u306a\u4f8b\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\u30c6\u30f3\u30bd\u30eb\u30d5\u30ed\u30fc\u3002\u3053\u306e\u4f8b\u3067\u306f\u3001\u30cb\u30e5\u30fc\u30e9\u30eb \u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u6b63\u3001\u8ca0\u306e\u6570\u3001\u304a\u3088\u3073\u30bc\u30ed\u3092\u691c\u51fa\u3059\u308b\u3088\u3046\u6559\u3048\u307e\u3059\u3002 TensorFlow<\/a> \u3068 CUDA<\/a> \u6559\u3048\u3066\u304a\u304d\u307e\u3059\u304c\u3001\u3053\u306e\u30bf\u30b9\u30af\u306f\u5b9f\u969b\u306b\u306f\u7c21\u5358\u3067\u306f\u3042\u308a\u307e\u305b\u3093)<\/p>\n

\u5206\u985e\u554f\u984c\u3092\u89e3\u6c7a\u3059\u308b\u306b\u306f\u3001\u5206\u985e\u5b50<\/a>\u3002 TensorFlow<\/a> \u306b\u306f\u3001\u6700\u5c0f\u9650\u306e\u69cb\u6210\u3067\u52d5\u4f5c\u3059\u308b\u65e2\u88fd\u306e\u9ad8\u30ec\u30d9\u30eb\u5206\u985e\u5668\u304c\u3044\u304f\u3064\u304b\u3042\u308a\u307e\u3059\u3002\u307e\u305a\u3001DNNClassifier<\/a> \u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u3059\u3002\u6b63\u3001\u8ca0\u306e\u6570\u5024\u3001\u30bc\u30ed\u3092\u542b\u3080\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u6b63\u3057\u3044\u300c\u30e9\u30d9\u30eb\u300d\u3092\u4ed8\u3051\u3066\u304f\u3060\u3055\u3044\u3002\u4eba\u9593\u306e\u30ec\u30d9\u30eb\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u5206\u985e\u7d50\u679c (\u30e9\u30d9\u30eb) \u3092\u542b\u3080\u4e00\u9023\u306e\u6570\u5024\u3067\u3059\u3002<\/p>\n

10 –\u30dd\u30b8\u30c6\u30a3\u30d6<\/em><\/strong>
-22 –\u30cd\u30ac\u30c6\u30a3\u30d6<\/em><\/strong>
0 –\u30bc\u30ed<\/em><\/strong>
42 –\u30dd\u30b8\u30c6\u30a3\u30d6
\u306a…\u5206\u985e\u306e\u3042\u308b\u305d\u306e\u4ed6\u306e\u756a\u53f7
<\/em><\/strong>
\u6b21\u306b\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u304c\u958b\u59cb\u3055\u308c\u3001\u305d\u306e\u5f8c\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3082\u542b\u307e\u308c\u3066\u3044\u306a\u3044\u6570\u5024\u3092\u5165\u529b\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u30cb\u30e5\u30fc\u30e9\u30eb \u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u305d\u308c\u3089\u3092\u6b63\u3057\u304f\u8b58\u5225\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002
\u4ee5\u4e0b\u306f\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u304a\u3088\u3073\u5165\u529b\u30c7\u30fc\u30bf\u7528\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8 \u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u30fc\u3092\u5099\u3048\u305f\u5206\u985e\u5b50\u306e\u5b8c\u5168\u306a\u30b3\u30fc\u30c9\u3067\u3059\u3002
<\/p>\n

\n
\u30a4\u30f3\u30dd\u30fc\u30c8<\/span> \u30c6\u30f3\u30bd\u30eb\u30d5\u30ed\u30fc<\/span>\u30a4\u30f3\u30dd\u30fc\u30c8<\/span> itertools<\/span>\u30a4\u30f3\u30dd\u30fc\u30c8<\/span>\u30e9\u30f3\u30c0\u30e0<\/span>\u304b\u3089<\/span>\u6642\u523b<\/span> \u30a4\u30f3\u30dd\u30fc\u30c8<\/span>\u6642\u9593\u30af\u30e9\u30b9<\/span> \u5206\u985e\u756a\u53f7<\/span>:__number =<\/span> 0<\/span>__classifiedAs =<\/span> 3<\/span>def<\/span> __init__<\/span>(\u81ea\u5206<\/span>\u3001\u756a\u53f7):\u81ea\u5206<\/span>.<\/span>__number =<\/\u30b9\u30d1\u30f3>\u6570\u5024if<\/span> \u6570\u5024 ==<\/span> 0<\/span>:self<\/span>.<\/span>__classifiedAs =<\/\u30b9\u30d1\u30f3> <\u30b9\u30d1\u30f3 \u30b9\u30bf\u30a4\u30eb=\"\u30ab\u30e9\u30fc: #0000dd; font-weight: \u30dc\u30fc\u30eb\u30c9;\">0<\/span> <\u30b9\u30d1\u30f3 \u30b9\u30bf\u30a4\u30eb=\"\u30ab\u30e9\u30fc: #888888;\">#\u30bc\u30ed\u30a8\u30ea\u30d5<\/span> \u756a\u53f7 ><\/span> 0<\/span>:self<\/span>.<\/span>__classifiedAs =<\/\u30b9\u30d1\u30f3> <\u30b9\u30d1\u30f3 \u30b9\u30bf\u30a4\u30eb=\"\u30ab\u30e9\u30fc: #0000dd; font-weight: \u30dc\u30fc\u30eb\u30c9;\">1<\/span> <\u30b9\u30d1\u30f3 \u30b9\u30bf\u30a4\u30eb=\"\u30ab\u30e9\u30fc: #888888;\">#\u30dd\u30b8\u30c6\u30a3\u30d6<\/\u30b9\u30d1\u30f3>\u30a8\u30ea\u30d5<\/span> \u756a\u53f7 <<\/span> 0<\/span>:self<\/span>.<\/span>__classifiedAs =<\/\u30b9\u30d1\u30f3> <\u30b9\u30d1\u30f3 \u30b9\u30bf\u30a4\u30eb=\"\u30ab\u30e9\u30fc: #0000dd; font-weight: \u30dc\u30fc\u30eb\u30c9;\">2<\/span> <\u30b9\u30d1\u30f3 \u30b9\u30bf\u30a4\u30eb=\"\u30ab\u30e9\u30fc: #888888;\">#\u5426\u5b9a\u7684<\/span>def<\/span> \u6570\u5024<\/span>(\u81ea\u5206<\/span>):\u623b\u308b<\/span> \u81ea\u5206\u81ea\u8eab<\/span>.<\/span>__\u756a\u53f7def<\/span> classifiedAs<\/span>(\u81ea\u5206<\/span>):\u623b\u308b<\/span> \u81ea\u5206\u81ea\u8eab<\/span>.<\/span>__classifiedAsdef<\/span> classifiedAsString<\/span>(classifiedAs):if<\/span> \u3068\u3057\u3066\u5206\u985e==<\/span> 0<\/span>:return<\/span> \u300c\u30bc\u30ed\u300d<\/span>elif<\/span> \u304c ==<\/span> \u3068\u3057\u3066\u5206\u985e\u3055\u308c\u307e\u3057\u305f 1<\/span>:return<\/span> \u300c\u30dd\u30b8\u30c6\u30a3\u30d6\u300d<\/span>elif<\/span> \u304c ==<\/span> \u3068\u3057\u3066\u5206\u985e\u3055\u308c\u307e\u3057\u305f 2<\/span>:return<\/span> \"Negative\"<\/span>def<\/span> trainDatasetFunction<\/span>():trainNumbers =<\/span> []trainNumberLabels =<\/span> []\u79c1\u306f<\/span>\u4e2d<\/span> \u7bc4\u56f2<\/span>(-<\/span>1000<\/span>\u30011001<\/span>):\u6570\u5024 =<\/span> ClassifiedNumber(i)trainNumbers.<\/span>append(number.<\/span>number())trainNumberLabels.<\/span>append(number.<\/span>classifiedAs())return<\/span> ( {\"number\"<\/span> : trainNumbers } , trainNumberLabels)def<\/span> inputDatasetFunction<\/span>():\u30b0\u30ed\u30fc\u30d0\u30eb<\/span> \u30e9\u30f3\u30c0\u30e0\u30b7\u30fc\u30c9random.<\/span>seed(randomSeed) # \u540c\u3058\u7d50\u679c\u3092\u5f97\u308b<\/span>\u6570\u5b57=<\/span> []\u79c1\u306f<\/span>\u4e2d<\/span> \u7bc4\u56f2<\/span>(0<\/span>, 4<\/span>):\u6570\u5024.<\/span>append(random.<\/span>randint(-<\/span>9999999<\/span>\u30019999999<\/span>))return<\/span> {\"number\"<\/span> : \u6570\u5024 }def<\/span> \u30e1\u30a4\u30f3<\/span>():print<\/span>(\"TensorFlow \u6b63\u8ca0\u30bc\u30ed\u6570\u5024\u5206\u985e\u5b50\u30c6\u30b9\u30c8 (demensdeum 2017 \u306b\u3088\u308b) (demensdeum@gmail. com)\"<\/span>)maximalClassesCount =<\/span> len<\/span>(set< \/span>(trainDatasetFunction()[1<\/span>])) +<\/span> 1<\/span>numberFeature =<\/span> tensorflow.<\/span>feature_column\u3002 <\/span>numeric_column(\"\u6570\u5024\"<\/span>)\u5206\u985e\u5b50=<\/span> tensorflow.<\/span>\u63a8\u5b9a\u5b50\u3002 <\/span>DNNClassifier(feature_columns =<\/span> [numberFeature], hidden_\u200b\u200bunits =<\/span> [10<\/span>\u300120<\/span>\u300110<\/span>]\u3001n_classes =<\/span> maximalClassesCount)\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u30fc=<\/span> \u5206\u985e\u5b50.<\/span>train(input_fn =<\/span> trainDatasetFunction\u3001\u30b9\u30c6\u30c3\u30d7 =<\/span> 1000<\/span>).<\/span>predict(input_fn = <\/span> inputDatasetFunction)inputDataset =<\/span> inputDatasetFunction()\u7d50\u679c =<\/span> \u30ea\u30b9\u30c8<\/span>(itertools)\u3002 <\/span>islice(generator, len<\/span>(inputDatasetFunction()[\"\u6570\u5024\"<\/span>])))i =<\/span> 0<\/span>\u7d50\u679c\u306e\u7d50\u679c<\/span>:print<\/span>(\"\u756a\u53f7: %d \u306f %s \u3068\u3057\u3066\u5206\u985e\u3055\u308c\u307e\u3057\u305f\"<\/span> %<\/span> (inputDataset[\"number\"<\/span>][i], generatedAsString(result[\"class_ids\"<\/span>][0<\/span> ])))i +=<\/span> 1<\/span>\u30e9\u30f3\u30c0\u30e0\u30b7\u30fc\u30c9 =<\/span> time()\u4e3b\u8981\uff08\uff09<\/pre>\n<\/div>\n
\u3059\u3079\u3066\u306f main() \u30e1\u30bd\u30c3\u30c9\u3067\u59cb\u307e\u308a\u3001\u5206\u985e\u5b50\u304c\u6a5f\u80fd\u3059\u308b\u6570\u5024\u5217\u3092\u8a2d\u5b9a\u3057\u307e\u3059 – tensorflow.feature_column.numeric_column(“number”)<\/strong> \u6b21\u306b\u3001\u5206\u985e\u5b50\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002 API \u306f\u6bce\u65e5\u5909\u66f4\u3055\u308c\u308b\u305f\u3081\u3001\u73fe\u5728\u306e\u521d\u671f\u5316\u5f15\u6570\u3092\u8aac\u660e\u3059\u308b\u3053\u3068\u306f\u5f79\u306b\u7acb\u3061\u307e\u305b\u3093\u3002\u53e4\u3044\u30de\u30cb\u30e5\u30a2\u30eb\u306b\u4f9d\u5b58\u305b\u305a\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u30d0\u30fc\u30b8\u30e7\u30f3\u306e TensorFlow \u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3092\u5fc5\u305a\u53c2\u7167\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n
\u6b21\u306b\u3001\u6b63\u3001\u8ca0\u3001\u307e\u305f\u306f\u30bc\u30ed\u306b\u57fa\u3065\u3044\u3066\u3053\u308c\u3089\u306e\u6570\u5024\u3092\u6b63\u3057\u304f\u5206\u985e\u3057\u3066\u3001-1000 \u304b\u3089 1000 \u307e\u3067\u306e\u6570\u5024\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8fd4\u3059\u95a2\u6570 (trainDatasetFunction<\/strong>) \u3092\u793a\u3059\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u304c\u958b\u59cb\u3055\u308c\u307e\u3059\u3002\u6b21\u306b\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u542b\u307e\u308c\u3066\u3044\u306a\u3044\u6570\u5024\u3092\u5165\u529b\u3068\u3057\u3066\u9001\u4fe1\u3057\u307e\u3059\u3002 -9999999 \u304b\u200b\u200b\u3089 9999999 \u307e\u3067\u306e\u30e9\u30f3\u30c0\u30e0 (inputDatasetFunction<\/strong>) \u3092\u4f7f\u7528\u3057\u3066\u5206\u985e\u3057\u307e\u3059\u3002<\/p>\n
\u6700\u5f8c\u306b\u3001\u5165\u529b\u30c7\u30fc\u30bf (itertools.islice<\/strong>) \u306e\u6570\u306b\u57fa\u3065\u3044\u3066\u53cd\u5fa9\u3092\u958b\u59cb\u3057\u3001\u7d50\u679c\u3092\u51fa\u529b\u3057\u3066\u5b9f\u884c\u3059\u308b\u3068\u3001\u9a5a\u304f\u3079\u304d\u7d50\u679c\u304c\u5f97\u3089\u308c\u307e\u3059\u3002<\/p>\n
<\/p>\n
\n
\u756a\u53f7: 4063470 \u306f\u967d\u6027\u3068\u3057\u3066\u5206\u985e\u3055\u308c\u307e\u3057\u305f\u756a\u53f7: 6006715 \u967d\u6027\u3068\u3057\u3066\u5206\u985e\u756a\u53f7: -5367127 \u306f\u9670\u6027\u3068\u3057\u3066\u5206\u985e\u3055\u308c\u307e\u3057\u305f\u756a\u53f7: -7834276 \u9670\u6027\u3068\u3057\u3066\u5206\u985e<\/pre>\n<\/div>\n
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iT’S ALIVE<\/a <\/p>\n<\/blockquote>\n