{"id":2747,"date":"2024-03-13T17:17:00","date_gmt":"2024-03-13T08:17:00","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/keras%e3%81%a7one-shot%e5%ad%a6%e7%bf%92%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e5%ae%9f%e8%a3%85%e3%81%99%e3%82%8b%e6%96%b9%e6%b3%95%e3%81%af%e4%bd%95%e3%81%a7%e3%81%99%e3%81%8b%ef%bc%9f\/"},"modified":"2025-08-02T09:08:48","modified_gmt":"2025-08-02T00:08:48","slug":"keras%e3%81%a7one-shot%e5%ad%a6%e7%bf%92%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e5%ae%9f%e8%a3%85%e3%81%99%e3%82%8b%e6%96%b9%e6%b3%95%e3%81%af%e4%bd%95%e3%81%a7%e3%81%99%e3%81%8b%ef%bc%9f","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/keras%e3%81%a7one-shot%e5%ad%a6%e7%bf%92%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e5%ae%9f%e8%a3%85%e3%81%99%e3%82%8b%e6%96%b9%e6%b3%95%e3%81%af%e4%bd%95%e3%81%a7%e3%81%99%e3%81%8b%ef%bc%9f\/","title":{"rendered":"Keras\u3067One-Shot\u5b66\u7fd2\u30bf\u30b9\u30af\u3092\u5b9f\u88c5\u3059\u308b\u65b9\u6cd5\u306f\u4f55\u3067\u3059\u304b\uff1f"},"content":{"rendered":"<p>Keras\u3067\u306e\u30ef\u30f3\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u30bf\u30b9\u30af\u306e\u5b9f\u88c5\u306f\u3001\u901a\u5e38\u3001\u30b7\u30e3\u30e0\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u306b\u95a2\u9023\u3057\u3066\u3044\u307e\u3059\u3002\u30b7\u30e3\u30e0\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u30012\u3064\u306e\u540c\u3058\u30b5\u30d6\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u304c\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5171\u6709\u3059\u308b\u53cc\u5854\u69cb\u9020\u306e\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3067\u3042\u308a\u30012\u3064\u306e\u5165\u529b\u306e\u985e\u4f3c\u6027\u3092\u6bd4\u8f03\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/p>\n<p>Keras\u3092\u4f7f\u7528\u3057\u3066\u30ef\u30f3\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u30bf\u30b9\u30af\u3092\u5b9f\u88c5\u3059\u308b\u4e00\u822c\u7684\u306a\u624b\u9806\u306f\u6b21\u306e\u901a\u308a\u3067\u3059\uff1a<\/p>\n<ol>\n<li>: \u30b7\u30e3\u30e0\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u57fa\u672c\u69cb\u9020\u3092\u5b9a\u7fa9\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n<pre class=\"post-pre\"><code><span class=\"hljs-keyword\">from<\/span> keras.models <span class=\"hljs-keyword\">import<\/span> Model\r\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> Input, Conv2D, Flatten, Dense\r\n\r\n<span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">create_siamese_network<\/span>(<span class=\"hljs-params\">input_shape<\/span>):\r\n    input_layer = Input(shape=input_shape)\r\n    \r\n    conv1 = Conv2D(<span class=\"hljs-number\">32<\/span>, (<span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">3<\/span>), activation=<span class=\"hljs-string\">'relu'<\/span>)(input_layer)\r\n    <span class=\"hljs-comment\"># Add more convolutional layers if needed<\/span>\r\n    \r\n    flattened = Flatten()(conv1)\r\n    \r\n    dense1 = Dense(<span class=\"hljs-number\">128<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>)(flattened)\r\n    \r\n    model = Model(inputs=input_layer, outputs=dense1)\r\n    \r\n    <span class=\"hljs-keyword\">return<\/span> model\r\n<\/code><\/pre>\n<ol>\n<li>Siamese\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4f5c\u6210\u3057\u3001\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5171\u6709\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n<pre class=\"post-pre\"><code>input_shape = (<span class=\"hljs-number\">28<\/span>, <span class=\"hljs-number\">28<\/span>, <span class=\"hljs-number\">1<\/span>)\r\nsiamese_network = create_siamese_network(input_shape)\r\n\r\ninput_a = Input(shape=input_shape)\r\ninput_b = Input(shape=input_shape)\r\n\r\noutput_a = siamese_network(input_a)\r\noutput_b = siamese_network(input_b)\r\n<\/code><\/pre>\n<ol>\n<li>2\u3064\u306e\u5165\u529b\u306e\u985e\u4f3c\u6027\u3092\u8a08\u7b97\u3059\u308b\u640d\u5931\u95a2\u6570\u3092\u8a18\u8ff0\u3059\u308b\u3002<\/li>\n<\/ol>\n<pre class=\"post-pre\"><code><span class=\"hljs-keyword\">from<\/span> keras <span class=\"hljs-keyword\">import<\/span> backend <span class=\"hljs-keyword\">as<\/span> K\r\n\r\n<span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">euclidean_distance<\/span>(<span class=\"hljs-params\">vects<\/span>):\r\n    x, y = vects\r\n    sum_square = K.<span class=\"hljs-built_in\">sum<\/span>(K.square(x - y), axis=<span class=\"hljs-number\">1<\/span>, keepdims=<span class=\"hljs-literal\">True<\/span>)\r\n    <span class=\"hljs-keyword\">return<\/span> K.sqrt(K.maximum(sum_square, K.epsilon()))\r\n\r\n<span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">eucl_dist_output_shape<\/span>(<span class=\"hljs-params\">shapes<\/span>):\r\n    shape1, shape2 = shapes\r\n    <span class=\"hljs-keyword\">return<\/span> (shape1[<span class=\"hljs-number\">0<\/span>], <span class=\"hljs-number\">1<\/span>)\r\n\r\ndistance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([output_a, output_b])\r\n<\/code><\/pre>\n<ol>\n<li>\u30e2\u30c7\u30eb\u3092\u30b3\u30f3\u30d1\u30a4\u30eb\u3057\u3066\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3046<\/li>\n<\/ol>\n<pre class=\"post-pre\"><code><span class=\"hljs-keyword\">from<\/span> keras.models <span class=\"hljs-keyword\">import<\/span> Model\r\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> Lambda\r\n<span class=\"hljs-keyword\">from<\/span> keras.optimizers <span class=\"hljs-keyword\">import<\/span> Adam\r\n\r\nsiamese_model = Model(inputs=[input_a, input_b], outputs=distance)\r\n\r\nsiamese_model.<span class=\"hljs-built_in\">compile<\/span>(loss=<span class=\"hljs-string\">'binary_crossentropy'<\/span>, optimizer=Adam(), metrics=[<span class=\"hljs-string\">'accuracy'<\/span>])\r\n\r\nsiamese_model.fit([X_train_pairs[:, <span class=\"hljs-number\">0<\/span>], X_train_pairs[:, <span class=\"hljs-number\">1<\/span>]], y_train, batch_size=<span class=\"hljs-number\">128<\/span>, epochs=<span class=\"hljs-number\">10<\/span>)\r\n<\/code><\/pre>\n<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306e\u904e\u7a0b\u3067\u3001\u6b63\u4f8b\u3068\u8ca0\u4f8b\u306e\u30da\u30a2\u3092\u542b\u3080\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u3092\u6e96\u5099\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u6b63\u4f8b\u30da\u30a2\u306f\u540c\u3058\u30ab\u30c6\u30b4\u30ea\u306e2\u3064\u306e\u30b5\u30f3\u30d7\u30eb\u3092\u8868\u3057\u3001\u8ca0\u4f8b\u30da\u30a2\u306f\u7570\u306a\u308b\u30ab\u30c6\u30b4\u30ea\u306e2\u3064\u306e\u30b5\u30f3\u30d7\u30eb\u3092\u8868\u3057\u307e\u3059\u3002\u3053\u3053\u3067\u3001X_train_pairs\u306f\u5165\u529b\u306e\u30b5\u30f3\u30d7\u30eb\u30da\u30a2\u3067\u3042\u308a\u3001y_train\u306f\u305d\u308c\u306b\u5bfe\u5fdc\u3059\u308b\u30e9\u30d9\u30eb\u3067\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Keras\u3067\u306e\u30ef\u30f3\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u30bf\u30b9\u30af\u306e\u5b9f\u88c5\u306f\u3001\u901a\u5e38\u3001\u30b7\u30e3\u30e0\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u306b\u95a2\u9023\u3057\u3066\u3044\u307e\u3059\u3002\u30b7\u30e3\u30e0\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u30012\u3064\u306e\u540c\u3058\u30b5\u30d6\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u304c\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5171\u6709\u3059\u308b\u53cc\u5854\u69cb\u9020\u306e\u30cb\u30e5 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[26,61],"class_list":["post-2747","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-26","tag-61"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.5 (Yoast SEO v21.5) - 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Silicon Cloud\" \/>\n<meta property=\"article:published_time\" content=\"2024-03-13T08:17:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-08-02T00:08:48+00:00\" \/>\n<meta name=\"author\" content=\"\u967d, \u5411\u5b87\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u57f7\u7b46\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u967d, \u5411\u5b87\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u63a8\u5b9a\u8aad\u307f\u53d6\u308a\u6642\u9593\" \/>\n\t<meta name=\"twitter:data2\" content=\"5\u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.silicloud.com\/ja\/blog\/keras%e3%81%a7one-shot%e5%ad%a6%e7%bf%92%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e5%ae%9f%e8%a3%85%e3%81%99%e3%82%8b%e6%96%b9%e6%b3%95%e3%81%af%e4%bd%95%e3%81%a7%e3%81%99%e3%81%8b%ef%bc%9f\/\",\"url\":\"https:\/\/www.silicloud.com\/ja\/blog\/keras%e3%81%a7one-shot%e5%ad%a6%e7%bf%92%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e5%ae%9f%e8%a3%85%e3%81%99%e3%82%8b%e6%96%b9%e6%b3%95%e3%81%af%e4%bd%95%e3%81%a7%e3%81%99%e3%81%8b%ef%bc%9f\/\",\"name\":\"Keras\u3067One-Shot\u5b66\u7fd2\u30bf\u30b9\u30af\u3092\u5b9f\u88c5\u3059\u308b\u65b9\u6cd5\u306f\u4f55\u3067\u3059\u304b\uff1f - 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