{"id":6291,"date":"2024-03-14T17:34:05","date_gmt":"2024-03-14T08:34:05","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/%e7%94%bb%e5%83%8f%e5%88%86%e9%a1%9e%e3%82%92tensorflow%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6%e8%a1%8c%e3%81%86\/"},"modified":"2025-08-03T08:17:25","modified_gmt":"2025-08-02T23:17:25","slug":"%e7%94%bb%e5%83%8f%e5%88%86%e9%a1%9e%e3%82%92tensorflow%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6%e8%a1%8c%e3%81%86","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/%e7%94%bb%e5%83%8f%e5%88%86%e9%a1%9e%e3%82%92tensorflow%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6%e8%a1%8c%e3%81%86\/","title":{"rendered":"TensorFlow\u753b\u50cf\u5206\u985e\uff1aPython\u5b9f\u88c5\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb"},"content":{"rendered":"

\u753b\u50cf\u5206\u985e\u3092\u3059\u308b\u306b\u306f\u3001\u6700\u521d\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u6e96\u5099\u3057\u3066\u3001\u30e9\u30d9\u30eb\u4ed8\u304d\u306e\u753b\u50cf\uff08\u4f8b\u3048\u3070\u72ac\u3001\u732b\u3001\u8eca\u306a\u3069\uff09\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

TensorFlow\u3092\u4f7f\u7528\u3057\u3066\u753b\u50cf\u5206\u985e\u3092\u884c\u3046\u7c21\u5358\u306a\u30b9\u30c6\u30c3\u30d7\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n

    \n
  1. \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/li>\n<\/ol>\n
    import<\/span> tensorflow as<\/span> tf\r\nfrom<\/span> tensorflow.keras import<\/span> datasets, layers, models\r\nimport<\/span> matplotlib.pyplot as<\/span> plt\r\n<\/code><\/pre>\n
      \n
    1. \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u3080:<\/li>\n<\/ol>\n
      (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\r\n<\/code><\/pre>\n
        \n
      1. \u30c7\u30fc\u30bf\u306e\u524d\u51e6\u7406\u3092\u884c\u3046:<\/li>\n<\/ol>\n
        train_images, test_images = train_images \/ 255.0<\/span>, test_images \/ 255.0<\/span>\r\n<\/code><\/pre>\n
          \n
        1. \u30e2\u30c7\u30eb\u306e\u69cb\u7bc9:<\/li>\n<\/ol>\n
          model = models.Sequential([\r\n    layers.Conv2D(32<\/span>, (3<\/span>, 3<\/span>), activation='relu'<\/span>, input_shape=(32<\/span>, 32<\/span>, 3<\/span>)),\r\n    layers.MaxPooling2D((2<\/span>, 2<\/span>)),\r\n    layers.Conv2D(64<\/span>, (3<\/span>, 3<\/span>), activation='relu'<\/span>),\r\n    layers.MaxPooling2D((2<\/span>, 2<\/span>)),\r\n    layers.Conv2D(64<\/span>, (3<\/span>, 3<\/span>), activation='relu'<\/span>),\r\n    layers.Flatten(),\r\n    layers.Dense(64<\/span>, activation='relu'<\/span>),\r\n    layers.Dense(10<\/span>)\r\n])\r\n<\/code><\/pre>\n
            \n
          1. \u30e2\u30c7\u30eb\u3092\u30b3\u30f3\u30d1\u30a4\u30eb\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n
            model.compile<\/span>(optimizer='adam'<\/span>,\r\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True<\/span>),\r\n              metrics=['accuracy'<\/span>])\r\n<\/code><\/pre>\n
              \n
            1. \u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0:<\/li>\n<\/ol>\n
              history = model.fit(train_images, train_labels, epochs=10<\/span>, \r\n                    validation_data=(test_images, test_labels))\r\n<\/code><\/pre>\n
                \n
              1. \u30e2\u30c7\u30eb\u306e\u8a55\u4fa1:<\/li>\n<\/ol>\n
                test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2<\/span>)\r\nprint<\/span>('\\nTest accuracy:'<\/span>, test_acc)\r\n<\/code><\/pre>\n
                \u4e0a\u8a18\u306e\u624b\u9806\u306b\u5f93\u3063\u3066\u3001TensorFlow\u3092\u4f7f\u7528\u3057\u3066\u7c21\u5358\u306a\u753b\u50cf\u5206\u985e\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u3001\u8a13\u7df4\u3057\u3001\u305d\u306e\u7cbe\u5ea6\u3092\u8a55\u4fa1\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001\u30e2\u30c7\u30eb\u306e\u69cb\u9020\u3084\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306a\u3069\u3092\u8abf\u6574\u3057\u3066\u30e2\u30c7\u30eb\u306e\u6027\u80fd\u3092\u5411\u4e0a\u3055\u305b\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
                \u753b\u50cf\u5206\u985e\u3092\u3059\u308b\u306b\u306f\u3001\u6700\u521d\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u6e96\u5099\u3057\u3066\u3001\u30e9\u30d9\u30eb\u4ed8\u304d\u306e\u753b\u50cf\uff08\u4f8b\u3048\u3070\u72ac\u3001\u732b\u3001\u8eca\u306a\u3069\uff09\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002 TensorFlow\u3092\u4f7f\u7528\u3057\u3066\u753b\u50cf\u5206\u985e\u3092\u884c\u3046\u7c21\u5358\u306a\u30b9\u30c6\u30c3\u30d7\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002 \u5fc5\u8981\u306a […]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[31,1638,1640,30,1801],"class_list":["post-6291","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-python","tag-tensorflow","tag-1640","tag-30","tag-1801"],"yoast_head":"\nTensorFlow\u753b\u50cf\u5206\u985e\uff1aPython\u5b9f\u88c5\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb - 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