{"id":5700,"date":"2025-07-23T08:11:22","date_gmt":"2025-07-22T23:11:22","guid":{"rendered":"https:\/\/www.wetch.co.jp\/note\/?p=5700"},"modified":"2025-07-23T08:11:24","modified_gmt":"2025-07-22T23:11:24","slug":"%e3%83%95%e3%82%a1%e3%82%a4%e3%83%b3%e3%83%81%e3%83%a5%e3%83%bc%e3%83%8b%e3%83%b3%e3%82%b0%ef%bc%88%ef%bc%91%ef%bc%89","status":"publish","type":"post","link":"https:\/\/www.wetch.co.jp\/note\/%e3%83%95%e3%82%a1%e3%82%a4%e3%83%b3%e3%83%81%e3%83%a5%e3%83%bc%e3%83%8b%e3%83%b3%e3%82%b0%ef%bc%88%ef%bc%91%ef%bc%89\/","title":{"rendered":"\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\uff08\uff11\uff09"},"content":{"rendered":"\n

\u524d\u56de\u306e\u6295\u7a3f\uff08\u30e9\u30d9\u30eb\u753b\u50cf\u306e\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0<\/a>\uff09\u306b\u304a\u3044\u3066\u3001\u300c\u3084\u306f\u308a\u30e9\u30d9\u30eb\u306e\u8b58\u5225\u306b\u7279\u5316\u3057\u305f\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0\u65b9\u6cd5\u304c\u5fc5\u8981\u306a\u6c17\u304c\u3057\u307e\u3059\u300d\u3068\u7d50\u8ad6\u3065\u3051\u305f\u306e\u3067\u3001\u305d\u306e\u8fba\u3092Gemini\u5148\u751f\u306b\u76f8\u8ac7\u3057\u305f\u3068\u3053\u308d\u3001\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3092\u8a66\u3057\u3066\u307f\u308b\u3088\u3046\u30a2\u30c9\u30d0\u30a4\u30b9\u3092\u53d7\u3051\u307e\u3057\u305f\u3002<\/p>\n\n\n\n

\u3068\u8a00\u3046\u3053\u3068\u3067\u3001\u4eca\u56de\u306fResNet50\u3092\u30e9\u30d9\u30eb\u753b\u50cf\u8b58\u5225\u7528\u306b\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3057\u3066\u307f\u307e\u3059\u3002
\u5f53\u7136\u306a\u304c\u3089\u3001\u5b9f\u88c5\u306fGemini\u5148\u751f\u4f9d\u5b58\u3067\u3059\u3002<\/p>\n\n\n\n

\u95a2\u9023\u30d1\u30c3\u30b1\u30fc\u30b8\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/h2>\n\n\n\n

\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u306b\u95a2\u3057\u3066\u3082\u3001\u65b0\u305f\u306aPython\u306evenv\u74b0\u5883\u3092\u69cb\u7bc9\u3057\u3066\u304a\u304d\u307e\u3059\u3002
\u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u3067\u3059\u3002<\/p>\n\n\n\n

# pip install opencv-python tensorflow tensorflow-metal scikit-learn<\/code><\/pre>\n\n\n\n
\u76ee\u65b0\u3057\u3044\u3082\u306e\u3068\u8a00\u3048\u3070\u300cscikit-learn\u300d\u3067\u3059\u304c\u3001\u4eca\u56de\u306f\u540c\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u300ctrain_test_split\u300d\u306a\u308b\u95a2\u6570\u306e\u307f\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059\u3002
\u3053\u308c\u306f\u3001NumPy\u914d\u5217\u3084\u30ea\u30b9\u30c8\u3067\u7ba1\u7406\u3055\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\u3092\u5b66\u7fd2\u7528\u3068\u8a55\u4fa1\u7528\u306b\u7c21\u5358\u306b\u5206\u3051\u3089\u308c\u308b\u6a5f\u80fd\u3067\u3059\u3002<\/p>\n\n\n\n
\u305f\u3060\u3001\u540c\u69d8\u306e\u3053\u3068\u306f\u30bb\u30de\u30f3\u30c6\u30a3\u30c3\u30af\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306b\u95a2\u3059\u308b\u5b66\u7fd2\u6642\u306b\u3082\u5b9f\u65bd\u3057\u3066\u304a\u308a\u3001\u305d\u306e\u6642\u306f\u4e0a\u8a18\u95a2\u6570\u306f\u4f7f\u7528\u305b\u305a\u3001\u72ec\u81ea\u306b\u5206\u5225\u3092\u884c\u3063\u3066\u3044\u307e\u3057\u305f\u3002
Gemini\u5148\u751f\u3082\u5927\u5206\u6c17\u307e\u3050\u308c\u306a\u3088\u3046\u3067\u3059\u3002<\/p>\n\n\n\n
\u30c7\u30fc\u30bf\u306e\u6e96\u5099<\/h2>\n\n\n\n
\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u306b\u4f7f\u7528\u3059\u308b\u30ef\u30a4\u30f3\u30e9\u30d9\u30eb\u306e\u753b\u50cf\u30c7\u30fc\u30bf\u306b\u95a2\u3057\u3066\u306f\u3001\u300c\u30e9\u30d9\u30eb\u753b\u50cf\u306e\u30c8\u30ea\u30df\u30f3\u30b0\uff08\uff14\uff09<\/a>\u300d\u3067\u7528\u610f\u3057\u305f200\u70b9\u306e\u753b\u50cf\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002
\u306a\u304a\u3001\u672c\u5b66\u7fd2\u306b\u304a\u3044\u3066\u3082\u80cc\u666f\u7b49\u306e\u4f59\u8a08\u306a\u60c5\u5831\u306f\u7121\u3044\u65b9\u304c\u826f\u3044\u3088\u3046\u306a\u306e\u3067\u3001\u4e0a\u8a18\u6295\u7a3f\u6642\u306b\u4f75\u305b\u3066\u751f\u6210\u3057\u305f\u30de\u30b9\u30af\u753b\u50cf\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e9\u30d9\u30eb\u90e8\u5206\u4ee5\u5916\u3092\u9ed2\u304f\u5857\u308a\u3064\u3076\u3057\u3066\u304a\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u306a\u304a\u3001\u30c7\u30fc\u30bf\u6570\u3068\u3057\u3066\u306f\uff08\u3044\u3064\u3082\u306e\u4e8b\u306a\u3089\u304c\uff09\u5fc3\u8a31\u306a\u3044\u3088\u3046\u3067\u3059\u304c\u3001\u305d\u306e\u8fba\u306f\u4f8b\u306b\u3088\u3063\u3066\u30c7\u30fc\u30bf\u62e1\u5f35\u3067\u6c34\u5897\u3057\u4e88\u5b9a\u3067\u3059\u3002<\/p>\n\n\n\n
\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u306e\u5b9f\u88c5<\/h2>\n\n\n\n
\u4ee5\u4e0b\u306b\u5b9f\u88c5\u5185\u5bb9\u3092\u7d39\u4ecb\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u5168\u4f53\u3068\u3057\u3066\u306f\u76f8\u5fdc\u306b\u91cf\u304c\u3042\u308b\u306e\u3067\u3001\u8aac\u660e\u3082\u304b\u306a\u308a\u7aef\u6298\u308a\u307e\u3059\u3002
\u5185\u5bb9\u306b\u95a2\u3057\u3066\u6c17\u306b\u306a\u308b\u65b9\u306f\u3001Gemini\u5148\u751f\u3092\u306f\u3058\u3081\u3068\u3059\u308bLLM\u8af8\u5148\u751f\u65b9\u306b\u8a72\u5f53\u7b87\u6240\u3092\u30b3\u30d4\u30da\u3057\u3066\u3001\u805e\u3044\u3066\u304f\u3060\u3055\u3044\u3002
\u61c7\u5207\u4e01\u5be7\u306a\u89e3\u8aac\u304c\u5f97\u3089\u308c\u308b\u3068\u601d\u3044\u307e\u3059\uff08\u5b9f\u88c5\u3082\u89e3\u8aac\u3082\u4ed6\u4eba\u4efb\u305b\uff09\u3002<\/p>\n\n\n\n
\u753b\u50cf\u306e\u8aad\u307f\u8fbc\u307f\u3068\u30c7\u30fc\u30bf\u62e1\u5f35<\/h3>\n\n\n\n
import tensorflow as tf\n\ndef augment_image(image, augmentation_layers):    \n    image = tf.image.random_brightness(image, max_delta=0.2)\n    image = tf.image.random_contrast(image, lower=0.8, upper=1.2)\n    image = tf.image.random_saturation(image, lower=0.8, upper=1.2)\n\n    for layer in augmentation_layers:\n        image = layer(image)\n\n    noise = tf.random.normal(shape=tf.shape(image), mean=0.0, stddev=10.0, dtype=tf.float32)\n    image = image + noise\n    image = tf.clip_by_value(image, 0.0, 255.0)\n\n    return image\n\ndef preprocess_image(image_path, image_size):\n    image_bytes = tf.io.read_file(image_path)\n    image = tf.image.decode_jpeg(image_bytes, channels=3)\n    image = tf.image.convert_image_dtype(image, tf.float32) * 255.0\n    image = tf.image.resize(image, image_size)\n    image = tf.keras.applications.resnet50.preprocess_input(image)\n    \n    return image\n\ndef train_preprocess(image_path, image_size, augmentation_layers):\n    image = preprocess_image(image_path, image_size)\n    image = augment_image(image, augmentation_layers)\n    return image\n\ndef val_preprocess(image_path, image_size):\n    image = preprocess_image(image_path, image_size)\n    return image<\/code><\/pre>\n\n\n\n
\u5916\u90e8\u304b\u3089\u4f7f\u7528\u3055\u308c\u308b\u95a2\u6570\u306ftrain_preprocess\uff08\u5b66\u7fd2\u7528\u30c7\u30fc\u30bf\u306e\u4e8b\u524d\u51e6\u7406\uff09\u3068val_preprocess\uff08\u8a55\u4fa1\u7528\u30c7\u30fc\u30bf\u306e\u4e8b\u524d\u51e6\u7406\uff09\u306e2\u3064\u3067\u3059\u3002
\u4e21\u8005\u306e\u9055\u3044\u306f\u30c7\u30fc\u30bf\u62e1\u5f35\uff08augment_image\uff09\u3092\u5b9f\u884c\u3059\u308b\u304b\u3069\u3046\u304b\u306e\u307f\u3067\u3059\u3002<\/p>\n\n\n\n
augment_image\u3067\u306f\u5404\u7a2e\u5909\u63db\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u304c\u3001\u4e00\u90e8\u306b\u95a2\u3057\u3066\u306f\u3001\u5916\u90e8\u3067\u7528\u610f\u3057\u305f\u5909\u63db\u51e6\u7406\uff08augmentation_layers\uff09\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u30c8\u30ea\u30d7\u30ec\u30c3\u30c8\u751f\u6210<\/h3>\n\n\n\n
\u3053\u3053\u3067\u8a00\u3046\u300c\u30c8\u30ea\u30d7\u30ec\u30c3\u30c8\uff083\u500b1\u30bb\u30c3\u30c8\uff09\u300d\u3068\u306f\u30a2\u30f3\u30ab\u30fc\u3001\u30dd\u30b8\u30c6\u30a3\u30d6\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\u306e3\u3064\u306e\u753b\u50cf\u30d1\u30b9\u306e\u30bb\u30c3\u30c8\u3092\u610f\u5473\u3057\u307e\u3059\u3002
\u30a2\u30f3\u30ab\u30fc\u3068\u306f\u6bd4\u8f03\u306e\u57fa\u6e96\u3068\u306a\u308b\u753b\u50cf\u3001\u30dd\u30b8\u30c6\u30a3\u30d6\u306f\u30a2\u30f3\u30ab\u30fc\u306e\u4ef2\u9593\u3068\u307f\u306a\u3057\u305f\u3044\u753b\u50cf\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\u306f\u30a2\u30f3\u30ab\u30fc\u306e\u4ef2\u9593\u306b\u542b\u3081\u305f\u304f\u306a\u3044\u753b\u50cf\u3067\u3042\u308a\u3001\u4eca\u56de\u306b\u95a2\u3057\u3066\u8a00\u3048\u3070\u3001\u30dd\u30b8\u30c6\u30a3\u30d6\u306f\u540c\u3058\u30ef\u30a4\u30f3\u30e9\u30d9\u30eb\u3092\u5199\u3057\u305f\u5225\u753b\u50cf\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\u306f\u5225\u306e\u30ef\u30a4\u30f3\u30e9\u30d9\u30eb\u3092\u5199\u3057\u305f\u753b\u50cf\u3001\u3068\u8a00\u3046\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
import random\n\ndef get_triplet(label_to_paths, available_labels):\n    anchor_label = random.choice(available_labels)\n    anchor_path = random.choice(label_to_paths[anchor_label])\n    \n    positive_path = anchor_path\n\n    negative_label = random.choice([l for l in available_labels if l != anchor_label])\n    negative_path = random.choice(label_to_paths[negative_label])\n    \n    return anchor_path, positive_path, negative_path\n\ndef triplet_generator(label_to_paths_subset, subset_labels):\n    while True:\n        yield get_triplet(label_to_paths_subset, subset_labels)<\/code><\/pre>\n\n\n\n
\u3053\u3053\u3067\u3082\u3001\u5916\u90e8\u304b\u3089\u547c\u3070\u308c\u308b\u306e\u306ftriplet_generator\u306e\u307f\u3067\u3059\u3002<\/p>\n\n\n\n
get_triplet\u3067\u306f\u30a2\u30f3\u30ab\u30fc\u3001\u30dd\u30b8\u30c6\u30a3\u30d6\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\u306e3\u3064\u306e\u753b\u50cf\u306e\u30d1\u30b9\u3092\u8fd4\u3057\u307e\u3059\u304c\u3001\u5143\u60c5\u5831\u3068\u3057\u3066\u3001label_to_paths\uff08\u30d5\u30a1\u30a4\u30eb\u540d\u3092\u30ad\u30fc\u3068\u3057\u3001\u30d1\u30b9\u3092\u5024\u3068\u3059\u308b\u8f9e\u66f8\uff09\u3068available_labels\uff08\u30d5\u30a1\u30a4\u30eb\u540d\u306e\u30ea\u30b9\u30c8\uff09\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
available_labels\u304b\u3089\u30e9\u30f3\u30c0\u30e0\u306b1\u3064\u30d5\u30a1\u30a4\u30eb\u540d\u3092\u53d6\u5f97\u3057\u3001\u5bfe\u5fdc\u3059\u308b\u30d1\u30b9\u3092\u30a2\u30f3\u30ab\u30fc\u3068\u30dd\u30b8\u30c6\u30a3\u30d6\u306e\u4e21\u65b9\u306e\u30d1\u30b9\u3068\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u4e00\u65b9\u3067\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\u306b\u95a2\u3057\u3066\u306f\u4e0a\u8a18\u3068\u306f\u5225\u306e\u30d5\u30a1\u30a4\u30eb\u540d\u306b\u5bfe\u5fdc\u3059\u308b\u30d1\u30b9\u3092\u63a1\u7528\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u30e2\u30c7\u30eb\u306e\u751f\u6210<\/h3>\n\n\n\n
\u4eca\u56de\u306e\u4e3b\u76ee\u7684\u3067\u3042\u308b\u3001\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u306e\u5bfe\u8c61\u3068\u306a\u308b\u30e2\u30c7\u30eb\u3092\u751f\u6210\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
import tensorflow as tf\nfrom tensorflow import keras\n\ndef build_encoder(image_size):\n    input_shape=(image_size[0], image_size[1], 3)\n\n    base_model = tf.keras.applications.ResNet50(\n        input_shape=input_shape,\n        include_top=False,\n        weights='imagenet',\n        pooling='avg'\n    )\n\n    for layer in base_model.layers:\n        layer.trainable = False\n\n    set_trainable = False\n    for layer in base_model.layers:\n        if layer.name == 'conv5_block1_out':\n            set_trainable = True\n        \n        if set_trainable:\n            layer.trainable = True\n        \n    inputs = tf.keras.Input(shape=input_shape)\n    \n    outputs = base_model(inputs)\n    outputs = keras.layers.UnitNormalization(axis=1)(outputs)\n    \n    encoder = tf.keras.Model(inputs, outputs, name=\"encoder\")\n    return encoder<\/code><\/pre>\n\n\n\n
ResNet50\u306e\u30e2\u30c7\u30eb\u3092\u53d6\u5f97\u3057\u305f\u5f8c\u306b\u3001\u5168\u4f53\u306e\u5c64\u3092\u4e00\u65e6\u8a13\u7df4\u4e0d\u53ef\u3068\u3057\u3001\u305d\u306e\u5f8c\u306b\u300cconv5_block1_out\u300d\u4ee5\u964d\u306e\u5c64\u306e\u307f\u8a13\u7df4\u53ef\u80fd\u306b\u5909\u66f4\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u3053\u3053\u304c\u4eca\u56de\u306e\u5b66\u7fd2\u306e\u30df\u30bd\u3067\u3001Gemini\u5148\u751f\u66f0\u304f\u3001<\/p>\n\n\n\n
ResNet\u306e\u3088\u3046\u306a\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f\u30e2\u30c7\u30eb\u3067\u306f\u3001\u521d\u671f\u306e\u5c64\uff08Stage 1-4\uff09\u306f\u4e00\u822c\u7684\u306a\u753b\u50cf\u7279\u5fb4\uff08\u30a8\u30c3\u30b8\u3001\u30c6\u30af\u30b9\u30c1\u30e3\u3001\u5358\u7d14\u306a\u5f62\u72b6\u306a\u3069\uff09\u3092\u5b66\u7fd2\u3057\u3066\u304a\u308a\u3001\u3053\u308c\u3089\u306f\u591a\u304f\u306e\u753b\u50cf\u8a8d\u8b58\u30bf\u30b9\u30af\u3067\u518d\u5229\u7528\u53ef\u80fd\u3067\u3059\u3002\u6700\u5f8c\u306e\u30b9\u30c6\u30fc\u30b8\uff08Stage 5\uff09\u306f\u3088\u308a\u30bf\u30b9\u30af\u56fa\u6709\u306e\u7279\u5fb4\u3092\u5b66\u7fd2\u3059\u308b\u305f\u3081\u3001\u3053\u3053\u304b\u3089\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3092\u958b\u59cb\u3059\u308b\u3053\u3068\u3067\u3001ImageNet\u3067\u5b66\u7fd2\u3057\u305f\u6c4e\u7528\u7684\u306a\u77e5\u8b58\u3092\u4fdd\u6301\u3057\u3064\u3064\u3001\u30ef\u30a4\u30f3\u30e9\u30d9\u30eb\u3068\u3044\u3046\u7279\u5b9a\u306e\u30bf\u30b9\u30af\u306b\u30e2\u30c7\u30eb\u3092\u9069\u5fdc\u3055\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/code><\/pre>\n\n\n\n
\u3068\u306e\u3053\u3068\u3067\u3059\u3002<\/p>\n\n\n\n
\u3064\u307e\u308a\u3001\u4eca\u56de\u306e\u76ee\u7684\u3067\u3042\u308b\u300c\u30ef\u30a4\u30f3\u30e9\u30d9\u30eb\u306e\u8b58\u5225\u300d\u306b\u5bfe\u3057\u3066\u52b9\u7387\u306e\u826f\u3044\u5b66\u7fd2\u304c\u884c\u3048\u308b\u3088\u3046\u3001ResNet50\u306e\u65e2\u5b58\u80fd\u529b\u3092\u305d\u306e\u307e\u307e\u6d3b\u304b\u305b\u308b\u90e8\u5206\u306f\u89e6\u3089\u305a\u306b\u3001\u5fc5\u8981\u306a\u90e8\u5206\u3060\u3051\u6539\u5909\u3059\u308b\u3088\u3046\u306b\u8a2d\u5b9a\u3057\u3066\u3044\u308b\u8a33\u3067\u3059\u3002<\/p>\n\n\n\n
\u5b66\u7fd2\u7528\u30af\u30e9\u30b9\u306e\u5b9a\u7fa9<\/h3>\n\n\n\n
\u524d\u8ff0\u306e\u30e2\u30c7\u30eb\u306b\u5bfe\u3057\u3066\u5b66\u7fd2\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306e\u30af\u30e9\u30b9\u3067\u3059\u3002<\/p>\n\n\n\n
tf.keras.Model\u3092\u7d99\u627f\u3057\u3066\u3044\u308b\u3053\u3068\u3067\u3001Keras\u306e\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u30e1\u30bd\u30c3\u30c9\u3067\u3042\u308bcompile\u3084fit\u304c\u4f7f\u3048\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
import tensorflow as tf\n\nclass TripletModel(tf.keras.Model):\n    def __init__(self, encoder, margin=1.0):\n        super().__init__()\n        self.encoder = encoder\n        self.margin = margin\n        self.train_loss_tracker = tf.keras.metrics.Mean(name=\"loss\")\n        self.val_loss_tracker = tf.keras.metrics.Mean(name=\"val_loss\")\n\n    def call(self, inputs):\n        anchor, positive, negative = inputs\n        anchor_embedding = self.encoder(anchor)\n        positive_embedding = self.encoder(positive)\n        negative_embedding = self.encoder(negative)\n\n        return anchor_embedding, positive_embedding, negative_embedding\n\n    @property\n    def metrics(self):\n        return [self.train_loss_tracker, self.val_loss_tracker]\n\n    def train_step(self, data):\n        inputs, _ = data\n\n        with tf.GradientTape() as tape:\n            anchor_embedding, positive_embedding, negative_embedding = self(inputs, training=True)\n\n            pos_dist = tf.reduce_sum(tf.square(anchor_embedding - positive_embedding), axis=1)\n            neg_dist = tf.reduce_sum(tf.square(anchor_embedding - negative_embedding), axis=1)\n            triplet_loss_value = tf.maximum(0.0, pos_dist - neg_dist + self.margin)\n            total_loss = tf.reduce_mean(triplet_loss_value)\n\n        trainable_vars = self.trainable_variables\n        gradients = tape.gradient(total_loss, trainable_vars)\n        self.optimizer.apply_gradients(zip(gradients, trainable_vars))\n\n        self.train_loss_tracker.update_state(total_loss)\n        return {\"loss\": self.train_loss_tracker.result()}\n\n    def test_step(self, data):\n        inputs, _ = data\n\n        anchor_embedding, positive_embedding, negative_embedding = self(inputs, training=False)\n\n        pos_dist = tf.reduce_sum(tf.square(anchor_embedding - positive_embedding), axis=1)\n        neg_dist = tf.reduce_sum(tf.square(anchor_embedding - negative_embedding), axis=1)\n        triplet_loss_value = tf.maximum(0.0, pos_dist - neg_dist + self.margin)\n        total_loss = tf.reduce_mean(triplet_loss_value)\n\n        self.val_loss_tracker.update_state(total_loss)\n\n        return {\"loss\": self.val_loss_tracker.result()}<\/code><\/pre>\n\n\n\n
\u7279\u306b\u91cd\u8981\u306a\u30dd\u30a4\u30f3\u30c8\u306e\u307f\u89e6\u308c\u3066\u304a\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
        with tf.GradientTape() as tape:\n            anchor_embedding, positive_embedding, negative_embedding = self(inputs, training=True)\n\n            pos_dist = tf.reduce_sum(tf.square(anchor_embedding - positive_embedding), axis=1)\n            neg_dist = tf.reduce_sum(tf.square(anchor_embedding - negative_embedding), axis=1)\n            triplet_loss_value = tf.maximum(0.0, pos_dist - neg_dist + self.margin)\n            total_loss = tf.reduce_mean(triplet_loss_value)<\/code><\/pre>\n\n\n\n
\u307e\u305a\u3001train_step\u306f\u30d0\u30c3\u30c1\u5358\u4f4d\u3067\u5b9f\u884c\u3055\u308c\u308b\u3088\u3046\u3067\u3059\u3002
\u3088\u3063\u3066\u3001\u4e0a\u8a18\u30d6\u30ed\u30c3\u30af\u5185\u3067\u306f1\u3064\u306e\u30d0\u30c3\u30c1\u306b\u95a2\u3059\u308b\u640d\u5931\u5024\u3092\u8a08\u7b97\u3057\u3066\u3044\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
anchor_embedding, positive_embedding, negative_embedding \u306f\u3001\u305d\u308c\u305e\u308c\u30a2\u30f3\u30ab\u30fc\u3001\u30dd\u30b8\u30c6\u30a3\u30d6\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\u306e\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0\u7d50\u679c\u3092\u30d0\u30c3\u30c1\u5185\u3067\u51e6\u7406\u3057\u305f\u30c8\u30ea\u30d7\u30ec\u30c3\u30c8\u6570\u3060\u3051\u683c\u7d0d\u3057\u305ftf.Tensor\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3067\u3001\u4eca\u56de\u306f\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u304c32\u3001\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0\u7d50\u679c\u306f2048\u6b21\u5143\u306b\u306a\u308b\u306e\u3067\u3001\u5177\u4f53\u7684\u306a\u5f62\u72b6\uff08shape\uff09\u306f (32, 2048) \u3068\u306a\u308b\u3088\u3046\u3067\u3059\u3002<\/p>\n\n\n\n
\u4e0a\u8a18\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0\u7d50\u679c\u306b\u5bfe\u3057\u3066\u30e6\u30fc\u30af\u30ea\u30c3\u30c9\u8ddd\u96e2\u306e\u4e8c\u4e57\u3092\u8a08\u7b97\u3057\u305f\u7d50\u679c\u304c\u3001pos_dist\uff08\u30a2\u30f3\u30ab\u30fc\u30fb\u30dd\u30b8\u30c6\u30a3\u30d6\u9593\uff09\u3001 neg_dist\uff08\u30a2\u30f3\u30ab\u30fc\u30cd\u30ac\u30c6\u30a3\u30d6\u9593\uff09\u3067\u3042\u308a\u3001\u5f62\u72b6\u306f (32, ) \u3068\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u4e0a\u8a18\u306b\u5bfe\u3057\u3066 “pos_dist – neg_dist + self.margin” \u3092\u8a08\u7b97\u3057\u3066\u3044\u307e\u3059\u304c\u3001\u3053\u308c\u306f\u300cneg_dist\u304cpos_dist\u3088\u308a\u3082margin\u4ee5\u4e0a\u5927\u304d\u3044\uff08\u9060\u3044\uff09\u304b\u3069\u3046\u304b\u300d\u3092\u5224\u5b9a\u3057\u3066\u304a\u308a\u3001\u9060\u3051\u308c\u3070\u30de\u30a4\u30ca\u30b9\u3001\u9060\u304f\u306a\u3051\u308c\u3070\u30d7\u30e9\u30b9\u306e\u7d50\u679c\u306b\u306a\u308a\u307e\u3059\u3002
\u305d\u308c\u30680.0\u306e\u5927\u304d\u306a\u65b9\u3092 triplet_loss_value\uff08\u30d0\u30c3\u30c1\u5185\u306e\u5404\u30c8\u30ea\u30d7\u30ec\u30c3\u30c8\u3054\u3068\u306e\u640d\u5931\u5024\uff09 \u3068\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u6700\u5f8c\u306b\u3001\u305d\u308c\u3089\u306e\u5e73\u5747\u5024\u3092\u7b97\u51fa\u3057\u3001\u3053\u308c\u3092 total_loss\uff08\u5f53\u8a72\u30d0\u30c3\u30c1\u306e\u640d\u5931\u5024\uff09\u3068\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u91cd\u8981\u306a\u306e\u306f\u3001\u4e0a\u8a18\u51e6\u7406\u3092 “with tf.GradientTape() as tape:” \u30d6\u30ed\u30c3\u30af\u5185\u3067\u884c\u306a\u3063\u3066\u3044\u308b\u70b9\u3067\u3059\u3002
\u3053\u306e\u3088\u3046\u306b\u3059\u308b\u3053\u3068\u3067\u3001\u5f8c\u8ff0\u3059\u308b\u52fe\u914d\u8a08\u7b97\u304c\u7c21\u5358\u306b\u884c\u306a\u3048\u308b\u3053\u3068\u306b\u306a\u308b\u3088\u3046\u3067\u3059\u3002<\/p>\n\n\n\n
        trainable_vars = self.trainable_variables\n        gradients = tape.gradient(total_loss, trainable_vars)\n        self.optimizer.apply_gradients(zip(gradients, trainable_vars))<\/code><\/pre>\n\n\n\n
trainable_vars \u306f\u3001\u5f53\u8a72\u30e2\u30c7\u30eb\u306e\u8a13\u7df4\u53ef\u80fd\u306a\u5168\u3066\u306e\u300c\u91cd\u307f\u300d\u3067\u3042\u308a\u3001\u5148\u306b\u89e6\u308c\u305f\u3088\u3046\u306b\u4eca\u56de\u306f\u300cconv5_block1_out\u300d\u5c64\u4ee5\u964d\u306e\u307f\u8a13\u7df4\u53ef\u80fd\u3068\u3057\u3066\u3044\u308b\u306e\u3067\u3001\u305d\u308c\u3089\u5c64\u306b\u5c5e\u3059\u308b\u300c\u91cd\u307f\u300d\u3068\u8a00\u3046\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
gradients \u306f total_loss \u3092\u5143\u306b trainable_vars \u306e\u5404\u300c\u91cd\u307f\u300d\u306e\u5909\u66f4\u91cf\u3068\u65b9\u5411\uff08\u3064\u307e\u308a\u306f\u300c\u52fe\u914d\u300d\uff09\u3092\u7b97\u51fa\u3057\u305f\u3082\u306e\u3067\u3059\u3002
\u672c\u6765\u3067\u3042\u308c\u3070\u3001\u3053\u306e\u90e8\u5206\u306f\u76f8\u5f53\u306b\u9762\u5012\u306a\u8a08\u7b97\uff08\u9006\u4f1d\u64ad\uff09\u3092\u884c\u306a\u3046\u3053\u3068\u306b\u306a\u308b\u306f\u305a\u306a\u306e\u3067\u3059\u304c\u3001\u524d\u8ff0\u306e\u300ctf.GradientTape\u300d\u3092\u4f7f\u7528\u3057\u3066\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0\uff08\u9806\u4f1d\u64ad\uff09\u304a\u3088\u3073\u640d\u5931\u8a08\u7b97\u306b\u95a2\u3059\u308b\u4e00\u9023\u306e\u64cd\u4f5c\u3092\u8a18\u9332\u3057\u3066\u304a\u304f\u3053\u3068\u3067\u3001\u4e0a\u8a18\u306b\u3042\u308b\u3088\u3046\u306b1\u884c\u3067\u52fe\u914d\u8a08\u7b97\u304c\u3067\u304d\u3066\u3057\u307e\u3046\u3088\u3046\u3067\u3059\u3002
Python\uff08Tensorflow\uff09\u6050\u308b\u3079\u3057\u3002<\/p>\n\n\n\n
\u3067\u3001\u6700\u5f8c\u306b trainable_vars \u306e\u5404\u300c\u91cd\u307f\u300d\u3092 gradients\uff08\u52fe\u914d\uff09\u306b\u3088\u3063\u3066\u66f4\u65b0\u3057\u3066\u3044\u307e\u3059\u3002
\u3053\u306e\u8fba\u306f\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\uff08self.optimizer\uff09\u304c\u884c\u306a\u3046\u3088\u3046\u3067\u3059\u3002
\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u306e\u6307\u5b9a\u306b\u95a2\u3057\u3066\u306f\u5f8c\u8ff0\u3057\u307e\u3059\u304c\u3001\u4eca\u56de\u3082 Adam \u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u5168\u4f53\u306e\u5b9f\u884c<\/h3>\n\n\n\n
\u4eca\u307e\u3067\u793a\u3057\u305f\u5404\u7a2e\u51e6\u7406\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u3067\u3001\u76ee\u7684\u3067\u3042\u308b\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
import tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\nimport os\nimport random\nfrom glob import glob\nfrom sklearn.model_selection import train_test_split\n\nfrom lib.img_preprocess import train_preprocess, val_preprocess\nfrom lib.triplet_generator import triplet_generator\nfrom lib.build_encoder import build_encoder\nfrom lib.triplet_model import TripletModel\n\n# --- \u5b9a\u6570\u306e\u5b9a\u7fa9 ---\nDATA_DIR = 'wine_labels'\nIMAGE_SIZE = (224, 224)\n\nBATCH_SIZE = 32\nEPOCHS = 100\nSTEPS_PER_EPOCH_TRAIN = 100\nSTEPS_PER_EPOCH_VAL = STEPS_PER_EPOCH_TRAIN \/\/ 5\n\nMARGIN = 1.0\nLEARNING_RATE = 0.0000128\nES_PATIENCE = 40\nLR_PATIENCE = 20\nMIN_LR = 1e-6\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # FATAL messages only\n\nimage_paths = glob(os.path.join(DATA_DIR, '*.jpeg'))\nlabels = [os.path.basename(path).split('.')[0] for path in image_paths]\nlabel_to_paths = {label: [path] for label, path in zip(labels, image_paths)}\n\ntrain_labels, val_labels = train_test_split(labels, test_size=0.2, random_state=42)\n\ntrain_augmentation_layers = [\n    keras.layers.RandomRotation(factor=0.02),\n    keras.layers.RandomZoom(height_factor=(-0.05, 0.05), width_factor=(-0.05, 0.05)),\n    keras.layers.RandomTranslation(\n        height_factor=0.1,\n        width_factor=0.1,\n        fill_mode='constant',\n        fill_value=0.0\n    )\n]\n\ntrain_dataset = tf.data.Dataset.from_generator(\n    lambda: triplet_generator({l: label_to_paths[l] for l in train_labels}, train_labels),\n    output_signature=(\n        tf.TensorSpec(shape=(), dtype=tf.string),\n        tf.TensorSpec(shape=(), dtype=tf.string),\n        tf.TensorSpec(shape=(), dtype=tf.string)\n    )\n)\ntrain_dataset = train_dataset.map(lambda a, p, n: (\n    (val_preprocess(a, IMAGE_SIZE),\n     train_preprocess(p, IMAGE_SIZE, train_augmentation_layers),\n     train_preprocess(n, IMAGE_SIZE, train_augmentation_layers)),\n    tf.constant(0.0)\n), num_parallel_calls=tf.data.AUTOTUNE)\ntrain_dataset = train_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)\ntrain_dataset = train_dataset.take(STEPS_PER_EPOCH_TRAIN)\n\nval_dataset = tf.data.Dataset.from_generator(\n    lambda: triplet_generator({l: label_to_paths[l] for l in val_labels}, val_labels),\n    output_signature=(\n        tf.TensorSpec(shape=(), dtype=tf.string),\n        tf.TensorSpec(shape=(), dtype=tf.string),\n        tf.TensorSpec(shape=(), dtype=tf.string)\n    )\n)\nval_dataset = val_dataset.map(lambda a, p, n: (\n    (val_preprocess(a, IMAGE_SIZE),\n     val_preprocess(p, IMAGE_SIZE),\n     val_preprocess(n, IMAGE_SIZE)),\n    tf.constant(0.0)\n), num_parallel_calls=tf.data.AUTOTUNE)\nval_dataset = val_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)\nval_dataset = val_dataset.take(STEPS_PER_EPOCH_VAL)\n\nencoder_model = build_encoder(IMAGE_SIZE)\ntriplet_model = TripletModel(encoder=encoder_model, margin=MARGIN)\ntriplet_model.compile(\n    optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)\n)\ntriplet_model.fit(\n    train_dataset,\n    epochs=EPOCHS,\n    validation_data=val_dataset,\n    callbacks=[\n        tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n                                         patience=ES_PATIENCE,\n                                         restore_best_weights=True),\n        tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n                                             factor=0.5,\n                                             patience=LR_PATIENCE,\n                                             min_lr=MIN_LR)\n    ]\n)\nencoder_model.save('trained_wine_label_encoder.keras')<\/code><\/pre>\n\n\n\n
\u3053\u3053\u3082\u3001\u30dd\u30a4\u30f3\u30c8\u306b\u306a\u308a\u305d\u3046\u306a\u90e8\u5206\u306e\u307f\u89e6\u308c\u3066\u304a\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
train_augmentation_layers = [\n    keras.layers.RandomRotation(factor=0.02),\n    keras.layers.RandomZoom(height_factor=(-0.05, 0.05), width_factor=(-0.05, 0.05)),\n    keras.layers.RandomTranslation(\n        height_factor=0.1,\n        width_factor=0.1,\n        fill_mode='constant',\n        fill_value=0.0\n    )\n]<\/code><\/pre>\n\n\n\n
\u5148\u306b\u30c7\u30fc\u30bf\u62e1\u5f35\u51e6\u7406\uff08augment_image\uff09\u306b\u95a2\u3057\u3066\u7d39\u4ecb\u3057\u307e\u3057\u305f\u304c\u3001\u305d\u306e\u969b\u306b\u3001\u4e00\u90e8\u306b\u95a2\u3057\u3066\u306f\u5916\u90e8\u3067\u7528\u610f\u3057\u305f\u5909\u63db\u51e6\u7406\u3092\u4f7f\u7528\u3059\u308b\u70b9\u306b\u89e6\u308c\u3066\u304a\u304d\u307e\u3057\u305f\u3002
\u4e0a\u8a18\u304c\u305d\u306e\u5909\u63db\u51e6\u7406\u3067\u3001\u300cTensorFlow 2.x \u304a\u3088\u3073 Keras \u3067\u63d0\u4f9b\u3055\u308c\u308b\u524d\u51e6\u7406\u30ec\u30a4\u30e4\u30fc\uff08Preprocessing Layer\uff09\u306e\u6a5f\u80fd\u300d\u3089\u3057\u3044\u3067\u3059\u3002
\u5b9f\u306f\u672c\u51e6\u7406\u3092 augment_image \u5185\u306b\u66f8\u304f\u3068\u30a8\u30e9\u30fc\u306b\u306a\u308b\u305f\u3081\u4e0a\u8a18\u306e\u3088\u3046\u306b\u3057\u3066\u3044\u308b\u306e\u3067\u3059\u304c\u3001\u30a8\u30e9\u30fc\u306e\u539f\u56e0\u306b\u95a2\u3057\u3066\u306f\u6df1\u304f\u8ffd\u6c42\u3057\u3066\u3044\u307e\u305b\u3093\uff08\u300ctf.data.Dataset.map\u300d\u5185\u306b\u6e21\u3055\u308c\u308b\u95a2\u6570\u306f\u3001\u5185\u90e8\u3067TensorFlow\u306e\u30b0\u30e9\u30d5\u30e2\u30fc\u30c9\u306b\u5909\u63db\u3055\u308c\u4e91\u3005\u2026\u3068\u8a00\u3046\u3088\u3046\u306a\u8aac\u660e\u3092\u3055\u308c\u305f\u306e\u3067\u3059\u304c\u3001\u9014\u4e2d\u3067\u8ff7\u5b50\u306b\u306a\u308a\u307e\u3057\u305f\uff09\u3002<\/p>\n\n\n\n
train_dataset = tf.data.Dataset.from_generator(\n    lambda: triplet_generator({l: label_to_paths[l] for l in train_labels}, train_labels),\n    output_signature=(\n        tf.TensorSpec(shape=(), dtype=tf.string),\n        tf.TensorSpec(shape=(), dtype=tf.string),\n        tf.TensorSpec(shape=(), dtype=tf.string)\n    )\n)<\/code><\/pre>\n\n\n\n
\u307e\u305a\u3001tf.data.Dataset.from_generator \u306f\u300cPython\u306e\u901a\u5e38\u306e\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u95a2\u6570\uff08yield\u3092\u4f7f\u7528\u3059\u308b\u95a2\u6570\uff09\u3092\u3001TensorFlow\u306e tf.data.Dataset \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306b\u5909\u63db\u3059\u308b\u305f\u3081\u306e\u30d5\u30a1\u30af\u30c8\u30ea\u30e1\u30bd\u30c3\u30c9\u300d\u3068\u306e\u3053\u3068\u3067\u3001\u5927\u96d1\u628a\u306b\u8a00\u3048\u3070\u3001triplet_generator\u95a2\u6570\u306b\u3088\u3063\u3066tf.data.Dataset\u5185\u306e\u5024\u3092\u52d5\u7684\u306b\u751f\u6210\u3059\u308b\u3088\u3046\u306b\u8a2d\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
output_signature\u306f\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\uff08triplet_generator\uff09\u304c\u8fd4\u3059\u5024\u306e\u69cb\u9020\uff08\u5f62\u72b6\u3068\u30c7\u30fc\u30bf\u578b\uff09\u3092\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002
\u4eca\u56de\u306e\u30b1\u30fc\u30b9\u3067\u306f\u3001triplet_generator \u304c\u8fd4\u3059\u306e\u306f\u30a2\u30f3\u30ab\u30fc\u3001\u30dd\u30b8\u30c6\u30a3\u30d6\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\u306e\u30d1\u30b9\uff08\u6587\u5b57\u5217\uff09\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
train_dataset = train_dataset.map(lambda a, p, n: (\n    (val_preprocess(a, IMAGE_SIZE),\n     train_preprocess(p, IMAGE_SIZE, train_augmentation_layers),\n     train_preprocess(n, IMAGE_SIZE, train_augmentation_layers)),\n    tf.constant(0.0)\n), num_parallel_calls=tf.data.AUTOTUNE)<\/code><\/pre>\n\n\n\n
tf.data.Dataset.map \u30e1\u30bd\u30c3\u30c9\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5404\u8981\u7d20\u306b\u5bfe\u3057\u3066\u3001\u6307\u5b9a\u3055\u308c\u305f\u95a2\u6570\u3092\u9069\u7528\u3057\u3001\u5909\u63db\u3055\u308c\u305f\u65b0\u3057\u3044\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8fd4\u3059\u3082\u306e\u3067\u3059\u3002<\/p>\n\n\n\n
\u5148\u306e triplet_generator \u306e\u51fa\u529b\u306f\u3001\u3042\u304f\u307e\u3067\u5404\u7a2e\u753b\u50cf\u306e\u30d1\u30b9\u3060\u3063\u305f\u306e\u3067\u3001val_preprocess, train_preprocess \u306b\u3088\u3063\u3066\u753b\u50cf\u306e\u8aad\u307f\u8fbc\u307f\u3084\u30c7\u30fc\u30bf\u62e1\u5f35\uff08train_preprocess\u306e\u307f\uff09\u3092\u884c\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u306a\u304a\u3001\u5b66\u7fd2\u7528\u30c7\u30fc\u30bf\u306f “(\u5b66\u7fd2\u5bfe\u8c61\u3068\u306a\u308b\u30c7\u30fc\u30bf, \u6b63\u89e3\u30c7\u30fc\u30bf)” \u306e\u3088\u3046\u306a\u69cb\u9020\u3092\u6301\u3064\u3088\u3046\u3067\u3059\u304c\u3001\u4eca\u56de\u306e\u30c8\u30ea\u30d7\u30ec\u30c3\u30c8\u640d\u5931\u5b66\u7fd2\u3067\u306f\u3001\u640d\u5931\u306f3\u3064\u306e\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0\u9593\u306e\u8ddd\u96e2\u306b\u57fa\u3065\u3044\u3066\u8a08\u7b97\u3055\u308c\u308b\u305f\u3081\u3001\u6b63\u89e3\u30c7\u30fc\u30bf\u306e\u90e8\u5206\u306f\u5fc5\u8981\u3042\u308a\u307e\u305b\u3093\u3002
\u3088\u3063\u3066\u3001\u30a4\u30f3\u30bf\u30d5\u30a7\u30fc\u30b9\u306e\u6574\u5408\u6027\u3092\u4fdd\u3064\u305f\u3081\u306e\u30c0\u30df\u30fc\u30c7\u30fc\u30bf\u3068\u3057\u3066\u3001\u6b63\u89e3\u30c7\u30fc\u30bf\u90e8\u5206\u306b\u306f\u30b9\u30ab\u30e9\u30fc\u50240.0\u3092\u6301\u3064\u5b9a\u6570\u30c6\u30f3\u30bd\u30eb\u3067\u3042\u308b\u300ctf.constant(0.0)\u300d\u3092\u6307\u5b9a\u3057\u3066\u3044\u308b\u3088\u3046\u3067\u3059\uff08\u5358\u306b0.0\u3067\u306f\u306a\u304f tf.constant(0.0) \u306a\u3069\u3068\u5468\u308a\u304f\u3069\u3044\u66f8\u304d\u65b9\u3092\u3057\u3066\u3044\u308b\u306e\u306f\u30c7\u30fc\u30bf\u578b\u306e\u4e00\u8cab\u6027\u3092\u8003\u616e\u3057\u3066\u306e\u3053\u3068\u3089\u3057\u3044\u3067\u3059\uff09\u3002<\/p>\n\n\n\n
\u307e\u305f\u3001num_parallel_calls\u306f\u3001\u524d\u8ff0\u306e\u5909\u63db\u3092\u4e26\u5217\u3067\u5b9f\u884c\u3059\u308b\u969b\u306e\u4e26\u5217\u5ea6\uff08\u540c\u6642\u306b\u5b9f\u884c\u3059\u308b\u95a2\u6570\u306e\u547c\u3073\u51fa\u3057\u6570\uff09\u3092\u5236\u5fa1\u3059\u308b\u305f\u3081\u306e\u8a2d\u5b9a\u3068\u306e\u3053\u3068\u3067\u3059\u3002
\u300ctf.data.AUTOTUNE\u300d\u306f\u300c\u6700\u9069\u306a\u4e26\u5217\u5ea6\u3092\u81ea\u52d5\u7684\u306b\u6c7a\u5b9a\u3059\u308b\u300d\u3068\u3044\u3046\u3053\u3068\u306e\u3088\u3046\u3067\u3001\u8981\u306f\u300c\u3088\u3057\u306a\u306b\u300d\u3068\u8a00\u3046\u3053\u3068\u3067\u3059\u306d\u3002<\/p>\n\n\n\n
train_dataset = train_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)\ntrain_dataset = train_dataset.take(STEPS_PER_EPOCH_TRAIN)<\/code><\/pre>\n\n\n\n
1\u884c\u76ee\u3067\u306f\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u3092\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u4ee5\u524d\u300c\u30d0\u30c3\u30c1\u3063\u3066\u826f\u304f\u5206\u304b\u3089\u3093\u300d\u3068\u8a00\u3046\u3053\u3068\u3067\u7406\u89e3\u3092\u653e\u68c4\u3057\u3066\u3044\u307e\u3057\u305f\u306e\u3067\u3001\u4eca\u66f4\u306a\u304c\u3089\u89e6\u308c\u3066\u304a\u304f\u3068\u3001\u300c\u91cd\u307f\u66f4\u65b0\u306e\u6700\u5c0f\u5358\u4f4d\u300d\u3068\u8a00\u3063\u305f\u7406\u89e3\u3067\u826f\u3055\u305d\u3046\u3067\u3059\u3002
\u7528\u610f\u3055\u308c\u305f\u5b66\u7fd2\u7528\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5168\u3066\u306b\u95a2\u3057\u3066\u5b66\u7fd2\u30921\u56de\u5b9f\u65bd\u3059\u308b\u3053\u3068\u3092\u610f\u5473\u3059\u308b\u5358\u4f4d\u304cepoch\u3067\u3059\u304c\u3001\u5168\u3066\u306e\u30c7\u30fc\u30bf\u306b\u95a2\u3057\u3066\u307e\u3068\u3081\u3066\u5b66\u7fd2\u3092\u5b9f\u65bd\u3059\u308b\u8a33\u3067\u306f\u306a\u304f\u3001\u5c0f\u3055\u306a\u30b0\u30eb\u30fc\u30d7\u306b\u5206\u3051\u3001\u305d\u308c\u305e\u308c\u306b\u5bfe\u3057\u3066\u5b66\u7fd2\u3092\u5b9f\u65bd\u3059\u308b\uff08\u91cd\u307f\u306e\u66f4\u65b0\u3092\u884c\u306a\u3046\uff09\u3088\u3046\u306b\u3057\u3066\u3044\u3066\u3001\u3053\u306e\u30b0\u30eb\u30fc\u30d7\u304c\u300c\u30d0\u30c3\u30c1\u300d\u3067\u3059\u3002<\/p>\n\n\n\n
\u4f8b\u3048\u3070\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c100\u70b9\u3042\u3063\u3066\u3001\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u304c32\u3060\u3063\u305f\u5834\u5408\u306f\u30011epoch\u30674\u56de\uff08\u5b66\u7fd2\u7528\u30c7\u30fc\u30bf\u6570\u306f32,32,32,4\u500b\uff09\u306e\u30d0\u30c3\u30c1\u51e6\u7406\uff08\u91cd\u307f\u66f4\u65b0\uff09\u304c\u884c\u308f\u308c\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u4e0a\u8a18\u3092\u8e0f\u307e\u3048\u30662\u884c\u76ee\u3067\u3059\u304c\u30011epoch\u3067\u5b9f\u884c\u3059\u308b\u30d0\u30c3\u30c1\u6570\u3092\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u5148\u306b\u89e6\u308c\u305f\u3088\u3046\u306b\u3001\u901a\u5e38\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u6570\u304c\u6c7a\u307e\u3063\u3066\u3044\u308b\u306e\u3067\u3001\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u304c\u6c7a\u307e\u308c\u3070\u30011epoch\u3067\u4f55\u56de\u306e\u30d0\u30c3\u30c1\u51e6\u7406\u304c\u884c\u308f\u308c\u308b\u304b\u304c\u78ba\u5b9a\u3057\u307e\u3059\u3002
\u3057\u304b\u3057\u3001\u4eca\u56de\u5b9f\u88c5\u3057\u305f triplet_generator \u306f\u7121\u9650\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u63d0\u4f9b\u3057\u7d9a\u3051\u308b\u306e\u3067\u3001\u9006\u306b1epoch\u3067\u4f55\u56de\u306e\u30d0\u30c3\u30c1\u51e6\u7406\u3092\u884c\u306a\u3046\u304b\u3092\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u4eca\u56de\u306fSTEPS_PER_EPOCH_TRAIN\u3092100\u3068\u3057\u3066\u3044\u308b\u306e\u3067\u3001100\u30d0\u30c3\u30c1\u51e6\u7406\u3057\u7d42\u308f\u3063\u305f\u30891epoch\u7d42\u4e86\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
val_dataset = val_dataset.map(lambda a, p, n: (\n    (val_preprocess(a, IMAGE_SIZE),\n     val_preprocess(p, IMAGE_SIZE),\n     val_preprocess(n, IMAGE_SIZE)),\n    tf.constant(0.0)\n), num_parallel_calls=tf.data.AUTOTUNE)<\/code><\/pre>\n\n\n\n
\u5b66\u7fd2\u7528\u30c7\u30fc\u30bf\u3068\u540c\u69d8\u306b\u8a55\u4fa1\u7528\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\u3092\u884c\u306a\u3063\u3066\u3044\u307e\u3059\u3002
\u6ce8\u610f\u304c\u5fc5\u8981\u306a\u306e\u306f\u3001\u30a2\u30f3\u30ab\u30fc\u3068\u30dd\u30b8\u30c6\u30a3\u30d6\u306e\u4e21\u65b9\u306b val_preprocess \u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u70b9\u3067\u3059\u3002
val_preprocess \u306f\u30c7\u30fc\u30bf\u62e1\u5f35\u3092\u884c\u308f\u306a\u3044\u306e\u3067\u3001\u30a2\u30f3\u30ab\u30fc\u3068\u30dd\u30b8\u30c6\u30a3\u30d6\u306e\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0\u7d50\u679c\u306f\u5168\u304f\u540c\u3058\u306b\u306a\u308a\u3001\u5f53\u7136\u306a\u304c\u3089\u4e21\u8005\u306e\u8ddd\u96e2\u306f0\u306b\u306a\u308a\u307e\u3059\u3002
\u3064\u307e\u308a\u3001\u8a55\u4fa1\u6642\u306b\u7b97\u51fa\u3055\u308c\u308bval_loss\u306f\u3001\u30a2\u30f3\u30ab\u30fc\u3068\u30cd\u30ac\u30c6\u30a3\u30d6\u306e\u8ddd\u96e2\uff08\u6b63\u78ba\u306b\u306f\u30e6\u30fc\u30af\u30ea\u30c3\u30c9\u8ddd\u96e2\u306e\u4e8c\u4e57\uff09\u3068 margin \u3068\u306e\u6bd4\u8f03\u306e\u307f\u3068\u306a\u308a\u3001\u30a2\u30f3\u30ab\u30fc\u3068\u30dd\u30b8\u30c6\u30a3\u30d6\u306e\u8ddd\u96e2\u306f\u8003\u616e\u3055\u308c\u307e\u305b\u3093\u3002<\/p>\n\n\n\n
\u500b\u4eba\u7684\u306b\u306f\u8a55\u4fa1\u7528\u306e\u30dd\u30b8\u30c6\u30a3\u30d6\u306b\u3082 train_preprocess \u3092\u9069\u7528\u3057\u3066\u826f\u3044\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u601d\u3063\u305f\u306e\u3067\u3059\u304c\u3001\u300c\u30c7\u30fc\u30bf\u62e1\u5f35\u306f\u30e2\u30c7\u30eb\u306e\u6c4e\u5316\u80fd\u529b\u3092\u5411\u4e0a\u3055\u305b\u308b\u76ee\u7684\u3067\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6642\u306e\u307f\u4f7f\u7528\u3059\u3079\u304d\u30c6\u30af\u30cb\u30c3\u30af\u3067\u3042\u308a\u3001\u4e00\u8cab\u3057\u305f\u8a55\u4fa1\u57fa\u6e96\u3067\u3042\u308b\u3079\u304dval_loss\u306e\u8a08\u7b97\u306b\u7528\u3044\u308b\u30c7\u30fc\u30bf\u306b\u9069\u7528\u3059\u3079\u304d\u3067\u306f\u306a\u3044\u300d\u3068\u306eGemini\u5148\u751f\u306e\u3054\u6307\u5c0e\u304c\u3042\u308a\u307e\u3057\u305f\u306e\u3067\u3001\u73fe\u72b6\u306f\u4e0a\u8a18\u306e\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u672c\u6765\u3067\u3042\u308c\u3070\u3001\u30a2\u30f3\u30ab\u30fc\u3068\u30dd\u30b8\u30c6\u30a3\u30d6\u306f\uff08\u540c\u3058\u30e9\u30d9\u30eb\u3092\u5199\u3057\u305f\uff09\u5225\u753b\u50cf\u3092\u4f7f\u7528\u3059\u3079\u304d\u3067\u3042\u308a\u3001\u30a2\u30f3\u30ab\u30fc\u3068\u540c\u3058\u753b\u50cf\u306b\u30c7\u30fc\u30bf\u62e1\u5f35\u3092\u884c\u306a\u3063\u3066\u30dd\u30b8\u30c6\u30a3\u30d6\u3092\u751f\u6210\u3059\u308b\u306e\u306f\u82e6\u8089\u306e\u7b56\u3067\u3042\u308b\u305f\u3081\u3001val_loss\u306e\u8a08\u7b97\u306b\u3082\u524d\u8ff0\u306e\u3088\u3046\u306a\u4e0d\u5b8c\u5168\u3055\u304c\u751f\u3058\u308b\u306e\u306f\u4ed5\u65b9\u304c\u306a\u3044\u3053\u3068\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002<\/p>\n\n\n\n
encoder_model = build_encoder(IMAGE_SIZE)\ntriplet_model = TripletModel(encoder=encoder_model, margin=MARGIN)\ntriplet_model.compile(\n    optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)\n)<\/code><\/pre>\n\n\n\n
\u5148\u306b\u793a\u3057\u305f\u3001build_encoder, TripletModel \u3092\u4f7f\u7528\u3057\u3066\u3001\u5b66\u7fd2\u7528\u30e2\u30c7\u30eb\u3092\u751f\u6210\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
margin \u306f train_step \u306e\u8aac\u660e\u3067\u89e6\u308c\u305f\u3088\u3046\u306b\u3001\u30dd\u30b8\u30c6\u30a3\u30d6\u3068\u30cd\u30ac\u30c6\u30a3\u30d6\u306e\u8ddd\u96e2\u306e\u5dee\u3092\u3069\u306e\u7a0b\u5ea6\u306b\u671f\u5f85\u3059\u308b\u304b\u3092\u793a\u3059\u91cd\u8981\u306a\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3067\u3059\u3002
margin \u304c\u5c0f\u3055\u3044\u3068\u3001\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0\u7d50\u679c\u3067\u30dd\u30b8\u30c6\u30a3\u30d6\uff08\u540c\u3058\u3082\u306e\uff09\u3068\u30cd\u30ac\u30c6\u30a3\u30d6\uff08\u9055\u3046\u3082\u306e\uff09\u306e\u5dee\u304c\u5c0f\u3055\u304f\u306a\u308a\u3001\u672c\u6765\u306e\u76ee\u7684\u3067\u3042\u308b\u4e21\u8005\u306e\u533a\u5225\u304c\u96e3\u3057\u304f\u306a\u308a\u307e\u3059\u3002
margin \u304c\u5927\u304d\u3044\u3068\u3001\u5b66\u7fd2\u81ea\u4f53\u304c\u96e3\u3057\u304f\u306a\u308b\u3088\u3046\u3067\u3059\u3002<\/p>\n\n\n\n
\u3068\u8a00\u3046\u3053\u3068\u3067\u3001\u4e00\u822c\u7684\u306b\u306f margin \u306f0.1\u301c1.0\u304f\u3089\u3044\u306b\u3059\u308b\u3088\u3046\u3067\u3059\u306e\u3067\u3001\u4eca\u56de\u306f\u6700\u5927\u9650\u306e\u52b9\u679c\u3092\u671f\u5f85\u3057\u30661.0\u306b\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u306b\u306fAdam\u3092\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u304c\u3001\u30bb\u30de\u30f3\u30c6\u30a3\u30c3\u30af\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u969b\u306b\u3082\u91cd\u8981\u3060\u3063\u305f\u300c\u5b66\u7fd2\u7387\u300d\u306b\u95a2\u3057\u3066\u306f\u64cd\u4f5c\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
triplet_model.fit(\n    train_dataset,\n    epochs=EPOCHS,\n    validation_data=val_dataset,\n    callbacks=[\n        tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n                                         patience=ES_PATIENCE,\n                                         restore_best_weights=True),\n        tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n                                             factor=0.5,\n                                             patience=LR_PATIENCE,\n                                             min_lr=MIN_LR)\n    ]\n)<\/code><\/pre>\n\n\n\n
\u5b66\u7fd2\u306e\u5b9f\u884c\u306b\u95a2\u3057\u3066\u3082\u30bb\u30de\u30f3\u30c6\u30a3\u30c3\u30af\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u6642\u3068\u540c\u69d8\u3067\u3059\u304c\u3001ReduceLROnPlateau \u306b\u52a0\u3048\u3066 EarlyStopping \u3092\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002
\u3053\u308c\u306f\u6307\u5b9a\u3057\u305f\u6307\u6a19\u304c\u4e00\u5b9a\u671f\u9593\u6539\u5584\u3057\u306a\u304b\u3063\u305f\u3089\u5b66\u7fd2\u3092\u4e2d\u65ad\u3059\u308b\u3068\u3044\u3046\u3082\u306e\u3067\u3059\u3002<\/p>\n\n\n\n
monitor \u306f\u57fa\u6e96\u3068\u3059\u308b\u6307\u6a19\u3092\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002
patience \u306f\u6539\u5584\u304c\u306a\u3044\u671f\u9593\uff08epoch\uff09\u3092\u6307\u5b9a\u3059\u308b\u3082\u306e\u3067\u3001\u3053\u3053\u3067\u6307\u5b9a\u3057\u305fepoch\u306e\u9593\u306bmonitor\u3067\u6307\u5b9a\u3057\u305f\u6307\u6a19\u304c\u6539\u5584\u3057\u306a\u3051\u308c\u3070\u5b66\u7fd2\u3092\u4e2d\u65ad\u3057\u307e\u3059\u3002
restore_best_weights \u306fmonitor\u3067\u6307\u5b9a\u3057\u305f\u6307\u6a19\u304c\u6700\u3082\u826f\u304b\u3063\u305fepoch\u306e\u91cd\u307f\u3092\u63a1\u7528\u3059\u308b\u304b\u3069\u3046\u304b\u3092\u6307\u5b9a\u3059\u308b\u3082\u306e\u3067\u3001True\u306b\u3059\u308b\u3053\u3068\u3067\u3001\u5f53\u8a72\u5b66\u7fd2\u5168\u822c\u3092\u901a\u3057\u3066\u6700\u3082monitor\uff08\u4eca\u56de\u306fval_loss\uff09\u304c\u826f\u304b\u3063\u305f\uff08\u5c0f\u3055\u304b\u3063\u305f\uff09\u5b66\u7fd2\u7d50\u679c\u304c\u6b8b\u3055\u308c\u307e\u3059\u3002<\/p>\n\n\n\n
\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u306e\u5b9f\u884c<\/h2>\n\n\n\n
\u5b9f\u969b\u306b\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3092\u5b9f\u884c\u3057\u3066\u307f\u307e\u3057\u305f\u3002
\u5b66\u7fd2\u72b6\u6cc1\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3002<\/p>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
loss\u306e\u65b9\u306f\u4f55\u3068\u306a\u304f\u6e1b\u8870\u50be\u5411\u306b\u3042\u308b\u3088\u3046\u3067\u3059\u304c\u3001val_loss\u306e\u65b9\u306f\u602a\u3057\u3044\u3067\u3059\u306d\u3002
val_loss\u304c\u6700\u5c0f\u3068\u306a\u3063\u305f\u72b6\u6cc1\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n\n\n\n
epoch<\/td>41<\/td><\/tr>
loss<\/td>0.0036<\/td><\/tr>
val_loss<\/td>0.0224<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
41epoch\u4ee5\u964dval_loss\u304c\u6539\u5584\u3055\u308c\u306a\u304b\u3063\u305f\u305f\u3081\u3001ES_PATIENCE\u3067\u6307\u5b9a\u3057\u305f40epoch\u7d4c\u904e\u3057\u305f81epoch\u3092\u4ee5\u3066\u5b66\u7fd2\u3092\u65e9\u671f\u7d42\u4e86\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u30b3\u30b5\u30a4\u30f3\u8ddd\u96e2\u306e\u78ba\u8a8d<\/h2>\n\n\n\n
\u300c\u30e9\u30d9\u30eb\u753b\u50cf\u306e\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0<\/a>\u300d\u3068\u540c\u69d8\u306b\u30b3\u30b5\u30a4\u30f3\u8ddd\u96e2\u306e\u78ba\u8a8d\u3092\u884c\u306a\u3063\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n\n
<\/td>11<\/td>12<\/td>13<\/td>14<\/td>15<\/td>21<\/td>22<\/td>23<\/td>24<\/td>25<\/td>31<\/td>32<\/td>33<\/td>34<\/td>35<\/td>41<\/td>42<\/td>43<\/td>44<\/td>45<\/td><\/tr>
11<\/td>\u2013<\/td>0<\/mark><\/td>0<\/mark><\/td>0<\/mark><\/td>0<\/mark><\/td>5<\/td>5<\/td>5<\/td>5<\/td>5<\/td>4<\/td>4<\/td>4<\/td>3<\/td>4<\/td>5<\/td>5<\/td>5<\/td>5<\/td>5<\/td><\/tr>
12<\/td><\/td>\u2013<\/td>0<\/mark><\/td>0<\/mark><\/td>0<\/mark><\/td>5<\/td>5<\/td>6<\/td>5<\/td>5<\/td>4<\/td>4<\/td>4<\/td>4<\/td>4<\/td>5<\/td>5<\/td>5<\/td>5<\/td>5<\/td><\/tr>
13<\/td><\/td><\/td>\u2013<\/td>0<\/mark><\/td>0<\/mark><\/td>5<\/td>5<\/td>5<\/td>5<\/td>5<\/td>4<\/td>4<\/td>4<\/td>3<\/td>3<\/td>5<\/td>5<\/td>5<\/td>5<\/td>5<\/td><\/tr>
14<\/td><\/td><\/td><\/td>\u2013<\/td>0<\/mark><\/td>5<\/td>6<\/td>6<\/td>5<\/td>5<\/td>4<\/td>4<\/td>4<\/td>3<\/td>4<\/td>5<\/td>5<\/td>5<\/td>5<\/td>5<\/td><\/tr>
15<\/td><\/td><\/td><\/td><\/td>\u2013<\/td>5<\/td>5<\/td>6<\/td>5<\/td>5<\/td>4<\/td>4<\/td>4<\/td>4<\/td>3<\/td>5<\/td>5<\/td>5<\/td>5<\/td>5<\/td><\/tr>
21<\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>0<\/mark><\/td>0<\/mark><\/td>0<\/mark><\/td>0<\/mark><\/td>4<\/td>4<\/td>4<\/td>4<\/td>5<\/td>4<\/td>4<\/td>4<\/td>4<\/td>4<\/td><\/tr>
22<\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>0<\/mark><\/td>0<\/mark><\/td>0<\/mark><\/td>4<\/td>4<\/td>5<\/td>4<\/td>5<\/td>4<\/td>4<\/td>4<\/td>4<\/td>4<\/td><\/tr>
23<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>1<\/mark><\/td>1<\/mark><\/td>4<\/td>4<\/td>4<\/td>4<\/td>5<\/td>4<\/td>4<\/td>4<\/td>4<\/td>4<\/td><\/tr>
24<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>0<\/mark><\/td>4<\/td>4<\/td>4<\/td>4<\/td>5<\/td>4<\/td>4<\/td>4<\/td>3<\/td>4<\/td><\/tr>
25<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>4<\/td>4<\/td>4<\/td>4<\/td>4<\/td>4<\/td>4<\/td>4<\/td>3<\/td>4<\/td><\/tr>
31<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>0<\/mark><\/td>1<\/mark><\/td>0<\/mark><\/td>1<\/mark><\/td>4<\/td>4<\/td>4<\/td>4<\/td>4<\/td><\/tr>
32<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>1<\/mark><\/td>0<\/mark><\/td>2<\/mark><\/td>4<\/td>4<\/td>4<\/td>4<\/td>4<\/td><\/tr>
33<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>1<\/mark><\/td>1<\/mark><\/td>4<\/td>4<\/td>4<\/td>4<\/td>3<\/td><\/tr>
34<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>1<\/mark><\/td>4<\/td>4<\/td>4<\/td>4<\/td>4<\/td><\/tr>
35<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>4<\/td>4<\/td>4<\/td>4<\/td>3<\/td><\/tr>
41<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>0<\/mark><\/td>0<\/mark><\/td>0<\/mark><\/td>0<\/mark><\/td><\/tr>
42<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>0<\/mark><\/td>0<\/mark><\/td>0<\/mark><\/td><\/tr>
43<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>0<\/mark><\/td>0<\/mark><\/td><\/tr>
44<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td>0<\/mark><\/td><\/tr>
45<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\u2013<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
ResNet50\u30aa\u30ea\u30b8\u30ca\u30eb\uff08\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u672a\u5b9f\u65bd\uff09\u306e\u7d50\u679c\u3068\u6bd4\u8f03\u3057\u3066\u3001\u30dd\u30b8\u30c6\u30a3\u30d6\uff08\u540c\u3058\u30e9\u30d9\u30eb\uff09\u3068\u306e\u8ddd\u96e2\u306b\u95a2\u3057\u3066\u306f1\u3064\u306e\u30b1\u30fc\u30b9\uff0832-35\uff09\u3092\u9664\u3044\u30660.2\u672a\u6e80\u306b\u7d0d\u307e\u3063\u3066\u3044\u307e\u3059\u3002
0.2\u3092\u8d85\u3048\u305f\u30b1\u30fc\u30b9\u306b\u95a2\u3057\u3066\u3082\u3001\u5f8c\u8ff0\u3059\u308b\u3088\u3046\u306b\u969b\u3069\u3044\u5024\uff080.2044\u2026\uff09\u3067\u3001\u3082\u3046\u5c11\u3057\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u7cbe\u5ea6\u304c\u826f\u304b\u3063\u305f\u308a\u3001\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u304c\u3046\u307e\u304f\u884c\u3051\u3070\u30010.2\u672a\u6e80\u306b\u7d0d\u307e\u3063\u305f\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002<\/p>\n\n\n\n
\u4e00\u65b9\u3067\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\uff08\u5225\u306e\u30e9\u30d9\u30eb\uff09\u3068\u306e\u8ddd\u96e2\u306b\u95a2\u3057\u3066\u306f0.3\u672a\u6e80\u306e\u30b1\u30fc\u30b9\u304c\u306a\u304f\u306a\u308a\u300190\uff05\u4ee5\u4e0a\u306f0.4\u4ee5\u4e0a\u3067\u3059\u3002<\/p>\n\n\n\n
\u3082\u3046\u5c11\u3057\u7d30\u304b\u304f\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\u30aa\u30ea\u30b8\u30ca\u30eb\u3001\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u5f8c\u305d\u308c\u305e\u308c\u306e\u30dd\u30b8\u30c6\u30a3\u30d6\uff08pos\uff09\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\uff08neg\uff09\u306b\u95a2\u3057\u3066\u30010.05\u5358\u4f4d\u3067\u533a\u5207\u3063\u305f\u5404\u7bc4\u56f2\u306b\u4f55\u4ef6\u306e\u30b1\u30fc\u30b9\u304c\u5b58\u5728\u3059\u308b\u304b\u3092\u6574\u7406\u3057\u3066\u307f\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
<\/td>\u30aa\u30ea\u30b8\u30ca\u30eb
pos<\/td>		\u30aa\u30ea\u30b8\u30ca\u30eb
neg<\/td>		\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u5f8c
pos<\/td>		\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u5f8c
neg<\/td><\/tr>
0.0 – 0.05<\/td>12<\/td>0<\/td>7<\/td>0<\/td><\/tr>
0.05 – 0.1<\/td>21<\/td>0<\/td>24<\/td>0<\/td><\/tr>
0.1 – 0.15<\/td>4<\/td>0<\/td>5<\/td>0<\/td><\/tr>
0.15 – 0.2<\/td>1<\/td>0<\/td>3<\/td>0<\/td><\/tr>
0.2 – 0.25<\/td>2<\/td>0<\/td>1<\/td>0<\/td><\/tr>
0.25 – 0.3<\/td>0<\/td>11<\/td>0<\/mark><\/td>0<\/mark><\/td><\/tr>
0.3 – 0.35<\/td>0<\/td>36<\/td>0<\/mark><\/td>0<\/mark><\/td><\/tr>
0.35 -0.4<\/td>0<\/td>42<\/td>0<\/td>9<\/td><\/tr>
0.4 – 0.45<\/td>0<\/td>10<\/td>0<\/td>43<\/td><\/tr>
0.45 – 0.5<\/td>0<\/td>39<\/td>0<\/td>43<\/td><\/tr>
0.5 – 0.55<\/td>0<\/td>12<\/td>0<\/td>24<\/td><\/tr>
0.55 – 0.6<\/td>0<\/td>0<\/td>0<\/td>27<\/td><\/tr>
0.6 – 0.65<\/td>0<\/td>0<\/td>0<\/td>4<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u76f4\u611f\u7684\u306b\u72b6\u6cc1\u3092\u628a\u63e1\u3067\u304d\u308b\u3088\u3046\u30b0\u30e9\u30d5\u5316\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n
\n
\"\"
ResNet50\u30aa\u30ea\u30b8\u30ca\u30eb<\/figcaption><\/figure>\n<\/div>\n\n
\n
\"\"
ResNet50\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u5f8c<\/figcaption><\/figure>\n<\/div>\n\n\n
\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u5f8c\u306e\u65b9\u304c\u3001\u3088\u308a\u4e8c\u6975\u5316\u304c\u9032\u3093\u3067\u3044\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002
\u30cd\u30ac\u30c6\u30a3\u30d6\u306e\u65b9\u304c\u5168\u4f53\u7684\u306b0.1\u7a0b\u5ea6\u5f8c\u308d\u306b\u305a\u308c\u305f\u3088\u3046\u306a\u5370\u8c61\u3067\u3059\u3002
0.25\u304b\u30890.35\u8fba\u308a\u306b\u5168\u304f\u30c7\u30fc\u30bf\u304c\u5b58\u5728\u3057\u306a\u3044\u533a\u9593\u304c\u3042\u308b\u306e\u3067\u3001\u3082\u3046\u5c11\u3057\u7d1b\u3089\u308f\u3057\u3044\uff08\u4f3c\u3066\u3044\u308b\uff09\u30e9\u30d9\u30eb\u76f8\u4e92\u306e\u533a\u5225\u306b\u969b\u3057\u3066\u3082\u3001\u6df7\u540c\u3059\u308b\u53ef\u80fd\u6027\u304c\u8efd\u6e1b\u3057\u3066\u3044\u308b\u3068\u306e\u671f\u5f85\u304c\u6301\u3066\u307e\u3059\u3002<\/p>\n\n\n\n
\u524d\u56de\u540c\u69d8\u3001\u30dd\u30a4\u30f3\u30c8\u306b\u306a\u308a\u305d\u3046\u306a\u8ddd\u96e2\u3092\u5177\u4f53\u7684\u306b\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
<\/td>\u30aa\u30ea\u30b8\u30ca\u30eb<\/td>\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u5f8c<\/td><\/tr>
\u30dd\u30b8\u30c6\u30a3\u30d6\u6700\u5c0f\u8ddd\u96e2<\/td>0.024057833544022023\uff0842-43\uff09<\/td>0.027953190186555288\uff0841-43\uff09<\/td><\/tr>
\u30dd\u30b8\u30c6\u30a3\u30d6\u6700\u5927\u8ddd\u96e2<\/td>0.2340515825328603\uff0834-35\uff09<\/td>0.20441832463412357\uff0832-35\uff09<\/td><\/tr>
\u30dd\u30b8\u30c6\u30a3\u30d6\u6700\u5927\u8ddd\u96e2\uff0835\u3092\u9664\u304f\uff09<\/td>0.12959717844480667\uff0832-33\uff09<\/td>0.16018944255546785\uff0832-33\uff09<\/td><\/tr>
\u30cd\u30ac\u30c6\u30a3\u30d6\u6700\u5c0f\u8ddd\u96e2<\/td>0.2601928593368079\uff0824-44\uff09<\/td>0.37765589293100565\uff0835-45\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u30dd\u30b8\u30c6\u30a3\u30d6\u306b\u95a2\u3059\u308b\u5404\u7a2e\u8ddd\u96e2\u304c\u5927\u304d\u304f\u5909\u308f\u3063\u3066\u3044\u306a\u3044\u306e\u306b\u5bfe\u3057\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\u306b\u95a2\u3059\u308b\u6700\u5c0f\u8ddd\u96e2\u304c0.1\u4ee5\u4e0a\u5927\u304d\u304f\u306a\u3063\u3066\u3044\u307e\u3059\u3002
\u3053\u306e\u70b9\u306f\u3001\u5148\u306b\u793a\u3057\u305f\u30b0\u30e9\u30d5\u306e\u5370\u8c61\u3068\u4e00\u81f4\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u306a\u304a\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\u6700\u5c0f\u8ddd\u96e2\u3068\u306a\u3063\u305f\u30b1\u30fc\u30b9\u306f\u4ee5\u4e0b\u3067\u3059\u3002<\/p>\n\n\n\n
35<\/td>45<\/td><\/tr>
\"\"<\/td>\"\"<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u767d\u5730\u306b\u9ed2\u6587\u5b57\u3084\u9ed2\u3044\u30a4\u30e9\u30b9\u30c8\u304c\u66f8\u304b\u308c\u3066\uff08\u63cf\u304b\u308c\u3066\uff09\u3044\u308b\u3068\u3044\u3046\u70b9\u3067\u898b\u308c\u3070\u3001\u30aa\u30ea\u30b8\u30ca\u30eb\u3067\u30cd\u30ac\u30c6\u30a3\u30d6\u6700\u5c0f\u8ddd\u96e2\u3068\u306a\u3063\u305f\u4ee5\u4e0b\u306e\u30b1\u30fc\u30b9\u3088\u308a\u306f\u300c\u4f3c\u3066\u3044\u308b\u300d\u3068\u8a00\u3063\u3066\u826f\u3044\u304b\u3082\u3067\u3059\u3002<\/p>\n\n\n\n
24<\/td>44<\/td><\/tr>
\"\"<\/td>\"\"<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u305f\u3060\u3001\u500b\u4eba\u7684\u306a\u611f\u60f3\u3068\u3057\u3066\u306f\u3001\u4e0a\u8a18\u304f\u3089\u3044\u306e\u9055\u3044\u304c\u3042\u308c\u3070\u3001\u3084\u306f\u308a0.5\u4ee5\u4e0a\u306e\u8ddd\u96e2\u306b\u306a\u308b\u3053\u3068\u3092\u671f\u5f85\u3057\u305f\u304f\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u307e\u3068\u3081<\/h2>\n\n\n\n
\u3068\u308a\u3042\u3048\u305a\u3001\u30aa\u30ea\u30b8\u30ca\u30eb\u306eResNet50\uff08+ImageNet\uff09\u306b\u5bfe\u3057\u3066\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3092\u5b9f\u65bd\u3057\u3001\u30ef\u30a4\u30f3\u30e9\u30d9\u30eb\u306e\u8b58\u5225\u306b\u3088\u308a\u9069\u5408\u3057\u305f\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0\u7528\u30e2\u30c7\u30eb\u306e\u751f\u6210\u304c\u3067\u304d\u308b\u3053\u3068\u306f\u78ba\u8a8d\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
\u305f\u3060\u3001\u5b66\u7fd2\u306e\u63a8\u79fb\uff08val_loss\u306e\u6e1b\u8870\u50be\u5411\uff09\u306f\u304b\u306a\u308a\u602a\u3057\u3044\u3067\u3059\u3057\u3001\u30a8\u30f3\u30d9\u30c7\u30a3\u30f3\u30b0\u306e\u7cbe\u5ea6\u306e\u5411\u4e0a\uff08\u30dd\u30b8\u30c6\u30a3\u30d6\u3068\u30cd\u30ac\u30c6\u30a3\u30d6\u306e\u5dee\u306e\u62e1\u5927\uff09\u306b\u95a2\u3057\u3066\u3082\u3001\u3082\u3046\u5c11\u3057\u9811\u5f35\u3063\u3066\u307f\u305f\u3044\u3068\u3053\u308d\u3067\u3059\u3002<\/p>\n\n\n\n
\u6700\u512a\u5148\u8ab2\u984c\u3068\u3057\u3066\u3001\u5b66\u7fd2\u7528\u30c7\u30fc\u30bf\u3092\u5145\u5b9f\u3055\u305b\u308b\u3053\u3068\uff08\u30a2\u30f3\u30ab\u30fc\u3068\u30dd\u30b8\u30c6\u30a3\u30d6\u3092\u5225\u753b\u50cf\u306b\u3059\u308b\u7b49\uff09\u3092\u518d\u4e09Gemini\u5148\u751f\u304b\u3089\u6307\u6458\u3055\u308c\u3066\u3044\u307e\u3059\u304c\u3001\u7c21\u5358\u306b\u624b\u3092\u51fa\u3057\u306b\u304f\u3044\u90e8\u5206\u3067\u306f\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u4e00\u65b9\u3067\u3001\u4eca\u56de\u306e\u65b9\u5f0f\u306b\u95a2\u3057\u3066\u3001\u65e2\u306b\u73fe\u6642\u70b9\u30672\u3064\u306e\u6539\u5584\u70b9\u304c\u5206\u304b\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
    \n
  • \u52fe\u914d\u8a08\u7b97\u306b\u30e6\u30fc\u30af\u30ea\u30c3\u30c9\u8ddd\u96e2\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u70b9
    \u30ef\u30a4\u30f3\u30e9\u30d9\u30eb\u306e\u8b58\u5225\u3068\u8a00\u3046\u30c6\u30fc\u30de\u306b\u5bfe\u3057\u3066\u306f\u30b3\u30b5\u30a4\u30f3\u8ddd\u96e2\u306e\u65b9\u304c\u9069\u3057\u3066\u304a\u308a\u3001\u6700\u7d42\u7684\u306a\u30d9\u30af\u30c8\u30eb\u691c\u7d22\u6642\u306b\u3082\u30b3\u30b5\u30a4\u30f3\u8ddd\u96e2\u3092\u5229\u7528\u3059\u308b\u4e88\u5b9a\u3060\u304c\u3001\u73fe\u72b6\u306e\u52fe\u914d\u8a08\u7b97\u306b\u306f\u30e6\u30fc\u30af\u30ea\u30c3\u30c9\u8ddd\u96e2\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u3002
    \u5b66\u7fd2\u6642\u3068\u904b\u7528\u6642\u3067\u306f\u540c\u3058\u8ddd\u96e2\u3092\u4f7f\u7528\u3059\u3079\u304d\u3002<\/li>\n\n\n\n
  • \u30cd\u30ac\u30c6\u30a3\u30d6\u3092\u30e9\u30f3\u30c0\u30e0\u306b\u62bd\u51fa\u3057\u3066\u3044\u308b\u70b9
    \u5b66\u7fd2\u52b9\u7387\u30fb\u6c4e\u5316\u6027\u80fd\u306e\u5411\u4e0a\u306e\u305f\u3081\u306b\u306f\u30cd\u30ac\u30c6\u30a3\u30d6\u306f\u30a2\u30f3\u30ab\u30fc\u3068\u4f3c\u305f\u3082\u306e\uff08\u30cf\u30fc\u30c9\u30cd\u30ac\u30c6\u30a3\u30d6\u3068\u547c\u3076\u3089\u3057\u3044\uff09\u3092\u4f7f\u7528\u3057\u305f\u65b9\u304c\u52b9\u679c\u7684\u3002
    \u3088\u3063\u3066\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\u3092\u30e9\u30f3\u30c0\u30e0\u306b\u9078\u3076\u306e\u3067\u306f\u306a\u304f\u3001\u30a2\u30f3\u30ab\u30fc\u3068\u8ddd\u96e2\u306e\u8fd1\u3044\u3082\u306e\u3092\u4e8b\u524d\u306b\u78ba\u8a8d\u3057\u3066\u304a\u304d\u3001\u3053\u308c\u3089\u3092\u4f7f\u7528\u3059\u3079\u304d\u3002<\/li>\n<\/ul>\n\n\n\n
    \u6b21\u56de\u306f\u4e0a\u8a18\u306b\u95a2\u3057\u3066\u30c1\u30e3\u30ec\u30f3\u30b8\u3057\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
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