{"id":96949,"date":"2026-08-28T20:08:54","date_gmt":"2026-08-28T12:08:54","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/96949.html"},"modified":"2026-08-28T20:08:54","modified_gmt":"2026-08-28T12:08:54","slug":"%e7%ac%ac16%e8%af%be%ef%bc%9atensorflow%ef%bd%9c%e6%95%b0%e6%8d%ae%e9%9b%86%e5%8a%a0%e8%bd%bd%e4%b8%8e%e9%a2%84%e5%a4%84%e7%90%86%e3%80%90tf%e5%86%85%e7%bd%ae%e6%95%b0%e6%8d%ae%e9%9b%86%e3%80%81","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/96949.html","title":{"rendered":"\u7b2c16\u8bfe\uff1aTensorFlow\uff5c\u6570\u636e\u96c6\u52a0\u8f7d\u4e0e\u9884\u5904\u7406\u3010TF\u5185\u7f6e\u6570\u636e\u96c6\u3001\u81ea\u5b9a\u4e49\u6570\u636e\u96c6\u5236\u4f5c\u3001\u5f52\u4e00\u5316\u5904\u7406\u3011"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/08\/20260828120852-6a917a54487ff.jpg\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<\/p>\n<h4>\u6587\u7ae0\u76ee\u5f55<\/h4>\n<ul>\n<li>\n<ul>\n<li>1. \u8bfe\u524d\u5bfc\u8bfb<\/li>\n<li>\n<ul>\n<li>1.1 \u672c\u8282\u8bfe\u5b66\u4e60\u76ee\u6807<\/li>\n<li>1.2 \u77e5\u8bc6\u91cd\u96be\u70b9<\/li>\n<li>1.3 \u5b66\u4e60\u524d\u7f6e\u6761\u4ef6<\/li>\n<li>1.4 \u5b66\u5b8c\u53ef\u638c\u63e1\u80fd\u529b<\/li>\n<li>1.5 \u884c\u4e1a\u5e94\u7528\u573a\u666f<\/li>\n<\/ul>\n<\/li>\n<li>2. \u6838\u5fc3\u7406\u8bba\u7cbe\u8bb2<\/li>\n<li>\n<ul>\n<li>2.1 TensorFlow \u5185\u7f6e\u6570\u636e\u96c6<\/li>\n<li>2.2 &#096;tf.data.Dataset&#096; \u6838\u5fc3\u6982\u5ff5<\/li>\n<li>2.3 \u6570\u636e\u5f52\u4e00\u5316<\/li>\n<li>2.4 \u56fe\u50cf\u6570\u636e\u589e\u5f3a<\/li>\n<li>2.5 \u6570\u636e\u6d41\u6c34\u7ebf\u6027\u80fd\u4f18\u5316<\/li>\n<\/ul>\n<\/li>\n<li>3. \u73af\u5883\u642d\u5efa\u4e0e\u5de5\u5177\u914d\u7f6e<\/li>\n<li>4. \u4ee3\u7801\u5b9e\u6218\u6559\u5b66<\/li>\n<li>\n<ul>\n<li>4.1 \u52a0\u8f7d\u5185\u7f6e\u6570\u636e\u96c6<\/li>\n<li>4.2 \u4eceNumPy\u6570\u7ec4\u521b\u5efa\u6570\u636e\u96c6<\/li>\n<li>4.3 \u4eceCSV\u6587\u4ef6\u52a0\u8f7d\u6570\u636e&#xff08;\u4e0d\u4f9d\u8d56Pandas&#xff09;<\/li>\n<li>4.4 \u4ece\u56fe\u50cf\u6587\u4ef6\u5939\u52a0\u8f7d\u6570\u636e&#xff08;\u4f7f\u7528 image_dataset_from_directory&#xff09;<\/li>\n<li>4.5 \u624b\u52a8\u6784\u5efa\u56fe\u50cf\u6570\u636e\u96c6&#xff08;\u4f7f\u7528 &#096;tf.data.Dataset.list_files&#096;&#xff09;<\/li>\n<li>4.6 \u6570\u636e\u5f52\u4e00\u5316\u4e0e\u589e\u5f3a<\/li>\n<li>4.7 \u6027\u80fd\u4f18\u5316\u5bf9\u6bd4<\/li>\n<\/ul>\n<\/li>\n<li>5. \u6848\u4f8b\u5b9e\u64cd\u6f14\u7ec3<\/li>\n<li>\n<ul>\n<li>5.1 \u51c6\u5907\u6570\u636e&#xff08;\u4f7f\u7528\u5185\u7f6e\u6570\u636e\u96c6\u6a21\u62df&#xff09;<\/li>\n<li>5.2 \u6784\u5efa\u6d41\u6c34\u7ebf<\/li>\n<li>5.3 \u6a21\u578b\u8bad\u7ec3\u6f14\u793a&#xff08;\u7b80\u5355CNN&#xff09;<\/li>\n<\/ul>\n<\/li>\n<li>6. \u5e38\u89c1\u5751\u70b9\u4e0e\u6392\u9519\u603b\u7ed3<\/li>\n<li>\n<ul>\n<li>6.1 \u6570\u636e\u52a0\u8f7d\u5751\u70b9<\/li>\n<li>6.2 \u6570\u636e\u9884\u5904\u7406\u7684\u5e38\u89c1\u9519\u8bef<\/li>\n<li>6.3 \u6570\u636e\u589e\u5f3a\u5751\u70b9<\/li>\n<li>6.4 \u6027\u80fd\u4f18\u5316\u5751\u70b9<\/li>\n<\/ul>\n<\/li>\n<li>7. \u77e5\u8bc6\u70b9\u603b\u7ed3 &#043; \u8bfe\u540e\u4f5c\u4e1a<\/li>\n<li>\n<ul>\n<li>7.1 \u6838\u5fc3\u77e5\u8bc6\u70b9\u68b3\u7406<\/li>\n<li>7.2 \u57fa\u7840\u4f5c\u4e1a<\/li>\n<li>7.3 \u8fdb\u9636\u5b9e\u64cd\u4f5c\u4e1a<\/li>\n<li>7.4 \u601d\u8003\u62d3\u5c55\u9898<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li>&#x1f517;\u300aTensorFlow2.x: \u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u5230\u9ad8\u9636\u5b9e\u6218\u6559\u7a0b\u300b\u7cfb\u5217\u8bfe\u7a0b\u5bfc\u822a<\/li>\n<\/ul>\n<hr \/>\n<h3>1. \u8bfe\u524d\u5bfc\u8bfb<\/h3>\n<h4>1.1 \u672c\u8282\u8bfe\u5b66\u4e60\u76ee\u6807<\/h4>\n<ul>\n<li>\u638c\u63e1\u4f7f\u7528tf.keras.datasets\u52a0\u8f7d\u5e38\u7528\u5185\u7f6e\u6570\u636e\u96c6&#xff08;MNIST\u3001Fashion-MNIST\u3001CIFAR-10\/100\u3001IMDB\u3001\u6ce2\u58eb\u987f\u623f\u4ef7\u7b49&#xff09;\u3002<\/li>\n<li>\u7406\u89e3tf.data.Dataset\u7684\u6838\u5fc3\u6982\u5ff5&#xff0c;\u80fd\u591f\u4ece\u4e0d\u540c\u6570\u636e\u6e90&#xff08;NumPy\u6570\u7ec4\u3001Pandas DataFrame\u3001CSV\u6587\u4ef6\u3001\u56fe\u50cf\u76ee\u5f55&#xff09;\u521b\u5efa\u6570\u636e\u96c6\u3002<\/li>\n<li>\u719f\u7ec3\u5e94\u7528\u6570\u636e\u9884\u5904\u7406\u64cd\u4f5c&#xff1a;\u5f52\u4e00\u5316&#xff08;Min-Max\u3001Z-score&#xff09;\u3001\u72ec\u70ed\u7f16\u7801\u3001\u56fe\u50cf\u7f29\u653e\u4e0e\u6570\u636e\u589e\u5f3a\u3002<\/li>\n<li>\u638c\u63e1tf.data\u6d41\u6c34\u7ebf\u7684\u6027\u80fd\u4f18\u5316\u6280\u5de7&#xff1a;\u7f13\u5b58&#xff08;cache&#xff09;\u3001\u6df7\u6d17&#xff08;shuffle&#xff09;\u3001\u6279\u5904\u7406&#xff08;batch&#xff09;\u3001\u9884\u53d6&#xff08;prefetch&#xff09;\u3002<\/li>\n<li>\u80fd\u591f\u6784\u5efa\u5b8c\u6574\u7684\u56fe\u50cf\u5206\u7c7b\u6570\u636e\u52a0\u8f7d\u6d41\u6c34\u7ebf&#xff0c;\u652f\u6301\u8bad\u7ec3\/\u9a8c\u8bc1\/\u6d4b\u8bd5\u96c6\u5212\u5206\u3002<\/li>\n<\/ul>\n<h4>1.2 \u77e5\u8bc6\u91cd\u96be\u70b9<\/h4>\n<table>\n<tr>\u7c7b\u522b\u5185\u5bb9<\/tr>\n<tbody>\n<tr>\n<td>\u91cd\u70b9<\/td>\n<td>tf.data.Dataset\u7684\u521b\u5efa\u4e0e\u8f6c\u6362&#xff1b;\u5f52\u4e00\u5316\u7684\u5fc5\u8981\u6027\u53ca\u5b9e\u73b0\u65b9\u5f0f&#xff1b;\u56fe\u50cf\u6570\u636e\u589e\u5f3a&#xff08;tf.image&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u96be\u70b9<\/td>\n<td>\u81ea\u5b9a\u4e49\u6570\u636e\u96c6\u4e2d\u7684\u6807\u7b7e\u751f\u6210&#xff08;\u4ece\u76ee\u5f55\u540d\u6216\u6587\u4ef6\u540d\u89e3\u6790&#xff09;&#xff1b;map\u51fd\u6570\u7684\u5e76\u884c\u5316\u53ca\u6027\u80fd\u5f71\u54cd&#xff1b;\u5927\u6570\u636e\u96c6\u7684\u7f13\u5b58\u7b56\u7565&#xff08;\u5185\u5b58\/\u6587\u4ef6&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u6613\u6df7\u6dc6\u70b9<\/td>\n<td>shuffle\u7684buffer_size\u542b\u4e49&#xff1b;batch\u4e0eprefetch\u7684\u987a\u5e8f&#xff1b;\u8bad\u7ec3\u96c6\u4e0e\u9a8c\u8bc1\u96c6\u662f\u5426\u5e94\u4f7f\u7528\u76f8\u540c\u7684\u6570\u636e\u589e\u5f3a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>1.3 \u5b66\u4e60\u524d\u7f6e\u6761\u4ef6<\/h4>\n<ul>\n<li>\u5df2\u638c\u63e1\u7b2c2\u8bfe\u7684NumPy\u57fa\u7840\u548c\u7b2c5\u8bfe\u7684\u5f20\u91cf\u64cd\u4f5c\u3002<\/li>\n<li>\u80fd\u591f\u642d\u5efa\u7b80\u5355\u7684\u5168\u8fde\u63a5\u7f51\u7edc&#xff08;\u7b2c12\u300115\u8bfe&#xff09;\u3002<\/li>\n<li>\u719f\u6089\u6587\u4ef6\u7cfb\u7edf\u57fa\u672c\u64cd\u4f5c&#xff08;\u8def\u5f84\u3001\u8bfb\u53d6\u6587\u4ef6&#xff09;\u3002<\/li>\n<\/ul>\n<h4>1.4 \u5b66\u5b8c\u53ef\u638c\u63e1\u80fd\u529b<\/h4>\n<ul>\n<li>\u4e3a\u4efb\u4f55\u81ea\u5b9a\u4e49\u6570\u636e\u96c6&#xff08;\u5982\u56fe\u50cf\u5206\u7c7b\u3001\u6587\u672c\u5206\u7c7b&#xff09;\u6784\u5efa\u9ad8\u6548\u7684\u6570\u636e\u52a0\u8f7d\u7ba1\u9053\u3002<\/li>\n<li>\u72ec\u7acb\u5b8c\u6210\u6570\u636e\u6807\u51c6\u5316\u3001\u589e\u5f3a\u7b49\u9884\u5904\u7406\u6b65\u9aa4&#xff0c;\u63d0\u5347\u6a21\u578b\u6cdb\u5316\u80fd\u529b\u3002<\/li>\n<li>\u8bca\u65ad\u6570\u636e\u52a0\u8f7d\u74f6\u9888&#xff08;I\/O&#xff09;\u5e76\u4f7f\u7528prefetch\u3001cache\u7b49\u65b9\u6cd5\u4f18\u5316\u3002<\/li>\n<li>\u5c06\u6570\u636e\u96c6\u6309\u9700\u5212\u5206\u4e3a\u8bad\u7ec3\u3001\u9a8c\u8bc1\u3001\u6d4b\u8bd5\u96c6\u3002<\/li>\n<\/ul>\n<h4>1.5 \u884c\u4e1a\u5e94\u7528\u573a\u666f<\/h4>\n<ul>\n<li>\u8ba1\u7b97\u673a\u89c6\u89c9&#xff1a;\u4ece\u6587\u4ef6\u5939\u52a0\u8f7d\u6570\u767e\u4e07\u5f20\u56fe\u50cf&#xff0c;\u5b9e\u65bd\u5728\u7ebf\u6570\u636e\u589e\u5f3a&#xff08;\u968f\u673a\u7ffb\u8f6c\u3001\u989c\u8272\u6296\u52a8&#xff09;\u3002<\/li>\n<li>\u81ea\u7136\u8bed\u8a00\u5904\u7406&#xff1a;\u4eceCSV\u52a0\u8f7d\u8bc4\u8bba\u53ca\u6807\u7b7e&#xff0c;\u8fdb\u884c\u5206\u8bcd\u548c\u5e8f\u5217\u5316\u3002<\/li>\n<li>\u63a8\u8350\u7cfb\u7edf&#xff1a;\u4ece\u7528\u6237\u4ea4\u4e92\u65e5\u5fd7\u6784\u5efatf.data\u6d41\u6c34\u7ebf&#xff0c;\u5904\u7406\u7a00\u758f\u7279\u5f81\u3002<\/li>\n<li>\u751f\u4ea7\u90e8\u7f72&#xff1a;\u4f7f\u7528tf.data\u670d\u52a1\u7aef\u8fdb\u884c\u9ad8\u6548\u7684\u6570\u636e\u9884\u5904\u7406\u3002<\/li>\n<\/ul>\n<h3>2. \u6838\u5fc3\u7406\u8bba\u7cbe\u8bb2<\/h3>\n<h4>2.1 TensorFlow \u5185\u7f6e\u6570\u636e\u96c6<\/h4>\n<p>tf.keras.datasets\u63d0\u4f9b\u4e86\u591a\u4e2a\u7ecf\u5178\u6570\u636e\u96c6&#xff0c;\u9002\u5408\u5feb\u901f\u6d4b\u8bd5\u6a21\u578b\u548c\u7b97\u6cd5\u3002\u5e38\u7528\u6570\u636e\u96c6&#xff1a;<\/p>\n<ul>\n<li>MNIST&#xff1a;\u624b\u5199\u6570\u5b57&#xff08;28\u00d728\u7070\u5ea6&#xff0c;10\u7c7b&#xff0c;60000\u8bad\u7ec3&#043;10000\u6d4b\u8bd5&#xff09;<\/li>\n<li>Fashion-MNIST&#xff1a;\u670d\u88c5\u56fe\u50cf&#xff08;\u540cMNIST\u89c4\u683c&#xff09;<\/li>\n<li>CIFAR-10\/100&#xff1a;\u5f69\u8272\u56fe\u50cf&#xff08;32\u00d732\u00d73&#xff0c;10\/100\u7c7b&#xff0c;50000\u8bad\u7ec3&#043;10000\u6d4b\u8bd5&#xff09;<\/li>\n<li>IMDB&#xff1a;\u7535\u5f71\u8bc4\u8bba&#xff08;\u5df2\u9884\u5904\u7406\u7684\u8bcd\u7d22\u5f15\u5e8f\u5217&#xff0c;\u4e8c\u5206\u7c7b&#xff09;<\/li>\n<li>Boston Housing&#xff1a;\u623f\u4ef7\u56de\u5f52&#xff08;506\u6837\u672c&#xff0c;13\u7279\u5f81&#xff09;<\/li>\n<\/ul>\n<p>\u52a0\u8f7d\u65b9\u5f0f\u7edf\u4e00\u4e3a&#xff1a;<\/p>\n<p><span class=\"token punctuation\">(<\/span>x_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">(<\/span>x_test<span class=\"token punctuation\">,<\/span> y_test<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>datasets<span class=\"token punctuation\">.<\/span>mnist<span class=\"token punctuation\">.<\/span>load_data<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u8fd4\u56de\u7684\u5df2\u662fNumPy\u6570\u7ec4&#xff0c;\u53ef\u76f4\u63a5\u7528\u4e8e\u8bad\u7ec3\u3002\u4f46\u5185\u7f6e\u6570\u636e\u96c6\u89c4\u6a21\u5c0f&#xff0c;\u4ec5\u9002\u5408\u6559\u5b66\u548c\u5feb\u901f\u539f\u578b\u3002<\/p>\n<h4>2.2 tf.data.Dataset \u6838\u5fc3\u6982\u5ff5<\/h4>\n<p>tf.data.Dataset\u662fTensorFlow\u4e2d\u5904\u7406\u6570\u636e\u6d41\u7684\u4e3b\u529bAPI&#xff0c;\u652f\u6301&#xff1a;<\/p>\n<ul>\n<li>\u60f0\u6027\u6c42\u503c&#xff1a;\u5143\u7d20\u5728\u9700\u8981\u65f6\u624d\u751f\u6210&#xff0c;\u8282\u7701\u5185\u5b58\u3002<\/li>\n<li>\u51fd\u6570\u5f0f\u53d8\u6362&#xff1a;map\u3001filter\u3001batch\u3001shuffle\u7b49\u3002<\/li>\n<li>\u9ad8\u6027\u80fd&#xff1a;\u4e0eTF\u56fe\u6267\u884c\u7ed3\u5408&#xff0c;\u81ea\u52a8\u5e76\u884c\u5316\u3002<\/li>\n<\/ul>\n<p>\u521b\u5efa\u6570\u636e\u96c6\u7684\u4e3b\u8981\u65b9\u5f0f&#xff1a;<\/p>\n<li>\u4ece\u5185\u5b58\u5f20\u91cf&#xff1a;tf.data.Dataset.from_tensor_slices()<\/li>\n<li>\u4ece\u751f\u6210\u5668&#xff1a;tf.data.Dataset.from_generator()<\/li>\n<li>\u4ece\u6587\u4ef6&#xff08;TFRecord\u3001\u6587\u672c\u3001CSV&#xff09;&#xff1a;tf.data.TextLineDataset\u3001tf.data.TFRecordDataset<\/li>\n<h4>2.3 \u6570\u636e\u5f52\u4e00\u5316<\/h4>\n<p>\u5f52\u4e00\u5316\u5c06\u7279\u5f81\u7f29\u653e\u5230\u76f8\u4f3c\u8303\u56f4&#xff08;\u901a\u5e38[0,1]\u6216\u5747\u503c\u4e3a0\u65b9\u5dee1&#xff09;&#xff0c;\u539f\u56e0&#xff1a;<\/p>\n<ul>\n<li>\u68af\u5ea6\u4e0b\u964d\u6548\u7387&#xff1a;\u4e0d\u540c\u5c3a\u5ea6\u7684\u7279\u5f81\u4f1a\u5bfc\u81f4\u635f\u5931\u51fd\u6570\u7b49\u9ad8\u7ebf\u5448\u692d\u5706&#xff0c;\u68af\u5ea6\u4e0b\u964d\u7f13\u6162\u4e14\u9707\u8361\u3002<\/li>\n<li>\u9632\u6b62\u6570\u503c\u4e0d\u7a33\u5b9a&#xff1a;\u5927\u6570\u503c\u7279\u5f81\u53ef\u80fd\u9020\u6210\u68af\u5ea6\u7206\u70b8\u3002<\/li>\n<li>\u63d0\u9ad8\u6a21\u578b\u6cdb\u5316&#xff1a;\u90e8\u5206\u6b63\u5219\u5316\u65b9\u6cd5\u5bf9\u5c3a\u5ea6\u654f\u611f\u3002<\/li>\n<\/ul>\n<p>Min-Max\u5f52\u4e00\u5316&#xff1a; [ x_{\\\\text{norm}} &#061; \\\\frac{x &#8211; x_{\\\\min}}{x_{\\\\max} &#8211; x_{\\\\min}} ] \u5c06\u6570\u503c\u6620\u5c04\u5230[0,1]&#xff0c;\u9002\u7528\u4e8e\u5df2\u77e5\u8fb9\u754c\u7684\u7279\u5f81&#xff08;\u5982\u56fe\u50cf\u50cf\u7d200-255&#xff09;\u3002<\/p>\n<p>Z-score\u6807\u51c6\u5316&#xff1a; [ x_{\\\\text{std}} &#061; \\\\frac{x &#8211; \\\\mu}{\\\\sigma} ] \u4f7f\u6570\u636e\u5747\u503c\u4e3a0&#xff0c;\u6807\u51c6\u5dee\u4e3a1\u3002\u9002\u7528\u4e8e\u7279\u5f81\u5206\u5e03\u672a\u77e5\u6216\u5b58\u5728\u5f02\u5e38\u503c\u3002<\/p>\n<p>\u5728\u56fe\u50cf\u4efb\u52a1\u4e2d&#xff0c;\u901a\u5e38\u7b80\u5355\u5730\u5c06\u50cf\u7d20\u503c\u9664\u4ee5255.0&#xff08;Min-Max&#xff09;\u3002\u66f4\u9ad8\u7ea7\u7684\u6807\u51c6\u5316\u662f\u4f7f\u7528ImageNet\u7684\u5747\u503c\u548c\u6807\u51c6\u5dee&#xff08;[0.485, 0.456, 0.406] \/ [0.229, 0.224, 0.225]&#xff09;\u3002<\/p>\n<h4>2.4 \u56fe\u50cf\u6570\u636e\u589e\u5f3a<\/h4>\n<p>\u6570\u636e\u589e\u5f3a\u901a\u8fc7\u5bf9\u8bad\u7ec3\u56fe\u50cf\u65bd\u52a0\u968f\u673a\u53d8\u6362&#xff08;\u800c\u4e0d\u6539\u53d8\u6807\u7b7e&#xff09;&#xff0c;\u589e\u52a0\u8bad\u7ec3\u6837\u672c\u7684\u591a\u6837\u6027&#xff0c;\u7f13\u89e3\u8fc7\u62df\u5408\u3002\u5e38\u7528\u64cd\u4f5c&#xff1a;<\/p>\n<ul>\n<li>\u51e0\u4f55\u53d8\u6362&#xff1a;\u968f\u673a\u7ffb\u8f6c&#xff08;\u6c34\u5e73\/\u5782\u76f4&#xff09;\u3001\u65cb\u8f6c\u3001\u7f29\u653e\u3001\u88c1\u526a\u3001\u5e73\u79fb\u3002<\/li>\n<li>\u989c\u8272\u53d8\u6362&#xff1a;\u4eae\u5ea6\u3001\u5bf9\u6bd4\u5ea6\u3001\u9971\u548c\u5ea6\u3001\u8272\u8c03\u7684\u968f\u673a\u8c03\u6574\u3002<\/li>\n<li>\u566a\u58f0\u6ce8\u5165&#xff1a;\u9ad8\u65af\u566a\u58f0\u3001\u9ad8\u65af\u6a21\u7cca\u3002<\/li>\n<\/ul>\n<p>TensorFlow\u63d0\u4f9b\u4e86tf.image\u6a21\u5757\u4e2d\u7684\u591a\u79cd\u589e\u5f3a\u51fd\u6570\u3002\u5b9e\u8df5\u4e2d\u53ef\u7ec4\u5408\u4f7f\u7528\u3002<\/p>\n<p>\u6ce8\u610f\u4e8b\u9879&#xff1a;<\/p>\n<ul>\n<li>\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\u4e0d\u5e94\u4f7f\u7528\u6570\u636e\u589e\u5f3a&#xff08;\u4ec5\u505a\u5f52\u4e00\u5316\/\u4e2d\u5fc3\u88c1\u526a&#xff09;\u3002<\/li>\n<li>\u6570\u636e\u589e\u5f3a\u5e94\u5728map\u4e2d\u52a8\u6001\u5e94\u7528&#xff0c;\u800c\u975e\u9884\u5148\u5b58\u50a8&#xff0c;\u4ee5\u8282\u7701\u78c1\u76d8\u7a7a\u95f4\u3002<\/li>\n<\/ul>\n<h4>2.5 \u6570\u636e\u6d41\u6c34\u7ebf\u6027\u80fd\u4f18\u5316<\/h4>\n<ul>\n<li>shuffle(buffer_size)&#xff1a;\u968f\u673a\u6253\u4e71\u6570\u636e\u96c6\u3002buffer_size\u5e94\u5927\u4e8e\u6570\u636e\u96c6\u5927\u5c0f&#xff08;\u6216\u81f3\u5c11\u7b49\u4e8e\u4e00\u4e2aepoch\u7684\u6837\u672c\u6570&#xff09;&#xff0c;\u4ee5\u786e\u4fdd\u5145\u5206\u6df7\u6d17\u3002\u4f46\u8fc7\u5927\u4f1a\u589e\u52a0\u5185\u5b58\u3002<\/li>\n<li>batch(batch_size)&#xff1a;\u5c06\u8fde\u7eed\u5143\u7d20\u7ec4\u5408\u6210\u6279\u6b21\u3002<\/li>\n<li>map(map_func, num_parallel_calls)&#xff1a;\u5bf9\u6bcf\u4e2a\u5143\u7d20\u5e94\u7528\u9884\u5904\u7406\u3002\u8bbe\u7f6enum_parallel_calls&#061;tf.data.AUTOTUNE\u53ef\u81ea\u52a8\u5e76\u884c\u5316\u3002<\/li>\n<li>cache(filename&#061;&#039;&#039;)&#xff1a;\u5c06\u6570\u636e\u96c6\u7f13\u5b58\u5230\u5185\u5b58\u6216\u6587\u4ef6\u3002\u82e5\u5728\u7b2c\u4e00\u6b21epoch\u540e\u6570\u636e\u4e0d\u518d\u6539\u53d8&#xff0c;\u53ef\u663e\u8457\u52a0\u901f\u540e\u7eedepoch\u3002<\/li>\n<li>prefetch(buffer_size)&#xff1a;\u5728GPU\u8bad\u7ec3\u7684\u540c\u65f6\u9884\u53d6\u4e0b\u4e00\u6279\u6570\u636e&#xff0c;\u91cd\u53e0\u6570\u636e\u52a0\u8f7d\u4e0e\u8ba1\u7b97\u3002\u63a8\u8350prefetch(tf.data.AUTOTUNE)\u3002<\/li>\n<\/ul>\n<p>\u5178\u578b\u6d41\u6c34\u7ebf\u6a21\u5f0f&#xff1a;<\/p>\n<p>dataset <span class=\"token operator\">&#061;<\/span> dataset<span class=\"token punctuation\">.<\/span>shuffle<span class=\"token punctuation\">(<\/span><span class=\"token number\">10000<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">map<\/span><span class=\"token punctuation\">(<\/span>preprocess<span class=\"token punctuation\">,<\/span> num_parallel_calls<span class=\"token operator\">&#061;<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>batch<span class=\"token punctuation\">(<\/span><span class=\"token number\">128<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>prefetch<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><\/p>\n<h3>3. \u73af\u5883\u642d\u5efa\u4e0e\u5de5\u5177\u914d\u7f6e<\/h3>\n<p>\u6cbf\u7528\u7b2c15\u8bfe\u7684\u73af\u5883&#xff0c;\u9700\u989d\u5916\u5b89\u88c5Pillow\u5e93\u7528\u4e8e\u56fe\u50cf\u5904\u7406&#xff08;\u82e5\u9700\u52a0\u8f7d\u81ea\u5b9a\u4e49\u56fe\u50cf&#xff09;\u3002<\/p>\n<p>conda activate tf213<br \/>\npip <span class=\"token function\">install<\/span> pillow<\/p>\n<p>\u521b\u5efa\u9879\u76ee\u76ee\u5f55\u7ed3\u6784&#xff1a;<\/p>\n<p>data\/<br \/>\n    train\/<br \/>\n        cat\/<br \/>\n            cat1.jpg<br \/>\n            &#8230;<br \/>\n        dog\/<br \/>\n            &#8230;<br \/>\n    val\/<br \/>\n        cat\/<br \/>\n        dog\/<\/p>\n<p>\u5bfc\u5165\u6240\u9700\u6a21\u5757&#xff1a;<\/p>\n<p><span class=\"token keyword\">import<\/span> tensorflow <span class=\"token keyword\">as<\/span> tf<br \/>\n<span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np<br \/>\n<span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt<br \/>\n<span class=\"token keyword\">import<\/span> pathlib<br \/>\n<span class=\"token keyword\">import<\/span> PIL<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>model_selection <span class=\"token keyword\">import<\/span> train_test_split<\/p>\n<h3>4. \u4ee3\u7801\u5b9e\u6218\u6559\u5b66<\/h3>\n<h4>4.1 \u52a0\u8f7d\u5185\u7f6e\u6570\u636e\u96c6<\/h4>\n<p><span class=\"token comment\"># MNIST \u793a\u4f8b<\/span><br \/>\n<span class=\"token punctuation\">(<\/span>x_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">(<\/span>x_test<span class=\"token punctuation\">,<\/span> y_test<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>datasets<span class=\"token punctuation\">.<\/span>mnist<span class=\"token punctuation\">.<\/span>load_data<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;MNIST train shape: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>x_train<span class=\"token punctuation\">.<\/span>shape<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">, test shape: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>x_test<span class=\"token punctuation\">.<\/span>shape<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token comment\"># \u5f52\u4e00\u5316<\/span><br \/>\nx_train <span class=\"token operator\">&#061;<\/span> x_train<span class=\"token punctuation\">.<\/span>astype<span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">\/<\/span> <span class=\"token number\">255.0<\/span><br \/>\nx_test <span class=\"token operator\">&#061;<\/span> x_test<span class=\"token punctuation\">.<\/span>astype<span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">\/<\/span> <span class=\"token number\">255.0<\/span><\/p>\n<p><span class=\"token comment\"># \u8f6c\u6362\u4e3a Dataset<\/span><br \/>\ntrain_ds <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>Dataset<span class=\"token punctuation\">.<\/span>from_tensor_slices<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">(<\/span>x_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>batch<span class=\"token punctuation\">(<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>shuffle<span class=\"token punctuation\">(<\/span><span class=\"token number\">10000<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>4.2 \u4eceNumPy\u6570\u7ec4\u521b\u5efa\u6570\u636e\u96c6<\/h4>\n<p><span class=\"token comment\"># \u6a21\u62df\u6570\u636e<\/span><br \/>\nX <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>randn<span class=\"token punctuation\">(<\/span><span class=\"token number\">1000<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">20<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>astype<span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><br \/>\ny <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>randint<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> size<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">1000<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>astype<span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u521b\u5efa Dataset<\/span><br \/>\ndataset <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>Dataset<span class=\"token punctuation\">.<\/span>from_tensor_slices<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\ndataset <span class=\"token operator\">&#061;<\/span> dataset<span class=\"token punctuation\">.<\/span>batch<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>prefetch<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><\/p>\n<h4>4.3 \u4eceCSV\u6587\u4ef6\u52a0\u8f7d\u6570\u636e&#xff08;\u4e0d\u4f9d\u8d56Pandas&#xff09;<\/h4>\n<p><span class=\"token comment\"># \u521b\u5efa\u793a\u4f8bCSV<\/span><br \/>\n<span class=\"token keyword\">import<\/span> tempfile<br \/>\n<span class=\"token keyword\">import<\/span> csv<\/p>\n<p><span class=\"token keyword\">with<\/span> tempfile<span class=\"token punctuation\">.<\/span>NamedTemporaryFile<span class=\"token punctuation\">(<\/span>mode<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;w&#039;<\/span><span class=\"token punctuation\">,<\/span> suffix<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;.csv&#039;<\/span><span class=\"token punctuation\">,<\/span> delete<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">False<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">as<\/span> f<span class=\"token punctuation\">:<\/span><br \/>\n    writer <span class=\"token operator\">&#061;<\/span> csv<span class=\"token punctuation\">.<\/span>writer<span class=\"token punctuation\">(<\/span>f<span class=\"token punctuation\">)<\/span><br \/>\n    writer<span class=\"token punctuation\">.<\/span>writerow<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;feature1&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;feature2&#039;<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;label&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> _ <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">100<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        writer<span class=\"token punctuation\">.<\/span>writerow<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span>np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>rand<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>rand<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>randint<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    csv_path <span class=\"token operator\">&#061;<\/span> f<span class=\"token punctuation\">.<\/span>name<\/p>\n<p><span class=\"token comment\"># \u4f7f\u7528 TextLineDataset \u8bfb\u53d6<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">parse_csv_line<\/span><span class=\"token punctuation\">(<\/span>line<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token comment\"># \u8df3\u8fc7\u8868\u5934<\/span><br \/>\n    defaults <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">0.0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">0.0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span>  <span class=\"token comment\"># \u9ed8\u8ba4\u503c<\/span><br \/>\n    parsed <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>io<span class=\"token punctuation\">.<\/span>decode_csv<span class=\"token punctuation\">(<\/span>line<span class=\"token punctuation\">,<\/span> record_defaults<span class=\"token operator\">&#061;<\/span>defaults<span class=\"token punctuation\">)<\/span><br \/>\n    features <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>stack<span class=\"token punctuation\">(<\/span>parsed<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    label <span class=\"token operator\">&#061;<\/span> parsed<span class=\"token punctuation\">[<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> features<span class=\"token punctuation\">,<\/span> label<\/p>\n<p>dataset <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>TextLineDataset<span class=\"token punctuation\">(<\/span>csv_path<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>skip<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u8df3\u8fc7\u8868\u5934<\/span><br \/>\ndataset <span class=\"token operator\">&#061;<\/span> dataset<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">map<\/span><span class=\"token punctuation\">(<\/span>parse_csv_line<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>batch<span class=\"token punctuation\">(<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">for<\/span> feat<span class=\"token punctuation\">,<\/span> lab <span class=\"token keyword\">in<\/span> dataset<span class=\"token punctuation\">.<\/span>take<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;Features shape: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>feat<span class=\"token punctuation\">.<\/span>shape<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">, label: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>lab<span class=\"token punctuation\">.<\/span>shape<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>4.4 \u4ece\u56fe\u50cf\u6587\u4ef6\u5939\u52a0\u8f7d\u6570\u636e&#xff08;\u4f7f\u7528 image_dataset_from_directory&#xff09;<\/h4>\n<p>TensorFlow\u63d0\u4f9b\u9ad8\u7ea7API image_dataset_from_directory&#xff0c;\u81ea\u52a8\u6839\u636e\u5b50\u6587\u4ef6\u5939\u540d\u79f0\u751f\u6210\u6807\u7b7e\u3002<\/p>\n<p><span class=\"token comment\"># \u5047\u8bbe data\/train \u4e0b\u6709 cat \u548c dog \u5b50\u6587\u4ef6\u5939<\/span><br \/>\ntrain_ds <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>preprocessing<span class=\"token punctuation\">.<\/span>image_dataset_from_directory<span class=\"token punctuation\">(<\/span><br \/>\n    <span class=\"token string\">&#039;data\/train&#039;<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    validation_split<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    subset<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;training&#039;<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    seed<span class=\"token operator\">&#061;<\/span><span class=\"token number\">123<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    image_size<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">150<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">32<\/span><br \/>\n<span class=\"token punctuation\">)<\/span><\/p>\n<p>val_ds <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>preprocessing<span class=\"token punctuation\">.<\/span>image_dataset_from_directory<span class=\"token punctuation\">(<\/span><br \/>\n    <span class=\"token string\">&#039;data\/train&#039;<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    validation_split<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    subset<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;validation&#039;<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    seed<span class=\"token operator\">&#061;<\/span><span class=\"token number\">123<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    image_size<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">150<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">32<\/span><br \/>\n<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u67e5\u770b\u7c7b\u522b\u540d\u79f0<\/span><br \/>\nclass_names <span class=\"token operator\">&#061;<\/span> train_ds<span class=\"token punctuation\">.<\/span>class_names<br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;Classes:&#034;<\/span><span class=\"token punctuation\">,<\/span> class_names<span class=\"token punctuation\">)<\/span><\/p>\n<h4>4.5 \u624b\u52a8\u6784\u5efa\u56fe\u50cf\u6570\u636e\u96c6&#xff08;\u4f7f\u7528 tf.data.Dataset.list_files&#xff09;<\/h4>\n<p>\u5f53\u9700\u8981\u66f4\u7cbe\u7ec6\u7684\u63a7\u5236\u65f6&#xff0c;\u53ef\u624b\u52a8\u904d\u5386\u6587\u4ef6\u3002<\/p>\n<p>data_dir <span class=\"token operator\">&#061;<\/span> pathlib<span class=\"token punctuation\">.<\/span>Path<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;data\/train&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nimage_paths <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">list<\/span><span class=\"token punctuation\">(<\/span>data_dir<span class=\"token punctuation\">.<\/span>glob<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;*\/*.jpg&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\nimage_paths <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token builtin\">str<\/span><span class=\"token punctuation\">(<\/span>p<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">for<\/span> p <span class=\"token keyword\">in<\/span> image_paths<span class=\"token punctuation\">]<\/span><br \/>\nlabels <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span> <span class=\"token keyword\">if<\/span> <span class=\"token string\">&#039;dog&#039;<\/span> <span class=\"token keyword\">in<\/span> p<span class=\"token punctuation\">.<\/span>parent<span class=\"token punctuation\">.<\/span>name <span class=\"token keyword\">else<\/span> <span class=\"token number\">0<\/span> <span class=\"token keyword\">for<\/span> p <span class=\"token keyword\">in<\/span> image_paths<span class=\"token punctuation\">]<\/span>  <span class=\"token comment\"># \u793a\u4f8b<\/span><\/p>\n<p><span class=\"token comment\"># \u521b\u5efa Dataset<\/span><br \/>\npath_ds <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>Dataset<span class=\"token punctuation\">.<\/span>from_tensor_slices<span class=\"token punctuation\">(<\/span>image_paths<span class=\"token punctuation\">)<\/span><br \/>\nlabel_ds <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>Dataset<span class=\"token punctuation\">.<\/span>from_tensor_slices<span class=\"token punctuation\">(<\/span>labels<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">load_and_preprocess<\/span><span class=\"token punctuation\">(<\/span>path<span class=\"token punctuation\">,<\/span> label<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    image <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>io<span class=\"token punctuation\">.<\/span>read_file<span class=\"token punctuation\">(<\/span>path<span class=\"token punctuation\">)<\/span><br \/>\n    image <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>decode_jpeg<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> channels<span class=\"token operator\">&#061;<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    image <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>resize<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">150<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    image <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>cast<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">\/<\/span> <span class=\"token number\">255.0<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> image<span class=\"token punctuation\">,<\/span> label<\/p>\n<p>dataset <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>Dataset<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">zip<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">(<\/span>path_ds<span class=\"token punctuation\">,<\/span> label_ds<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\ndataset <span class=\"token operator\">&#061;<\/span> dataset<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">map<\/span><span class=\"token punctuation\">(<\/span>load_and_preprocess<span class=\"token punctuation\">,<\/span> num_parallel_calls<span class=\"token operator\">&#061;<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><br \/>\ndataset <span class=\"token operator\">&#061;<\/span> dataset<span class=\"token punctuation\">.<\/span>shuffle<span class=\"token punctuation\">(<\/span><span class=\"token number\">1000<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>batch<span class=\"token punctuation\">(<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>prefetch<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><\/p>\n<h4>4.6 \u6570\u636e\u5f52\u4e00\u5316\u4e0e\u589e\u5f3a<\/h4>\n<p><span class=\"token comment\"># \u5f52\u4e00\u5316\u51fd\u6570<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">normalize<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> label<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    image <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>cast<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">\/<\/span> <span class=\"token number\">255.0<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> image<span class=\"token punctuation\">,<\/span> label<\/p>\n<p><span class=\"token comment\"># \u6570\u636e\u589e\u5f3a\u51fd\u6570&#xff08;\u4ec5\u8bad\u7ec3\u96c6&#xff09;<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">augment<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> label<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    image <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>random_flip_left_right<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span><br \/>\n    image <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>random_brightness<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> max_delta<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    image <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>random_contrast<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> lower<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.8<\/span><span class=\"token punctuation\">,<\/span> upper<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1.2<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> image<span class=\"token punctuation\">,<\/span> label<\/p>\n<p><span class=\"token comment\"># \u6784\u5efa\u6d41\u6c34\u7ebf<\/span><br \/>\ntrain_ds <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>preprocessing<span class=\"token punctuation\">.<\/span>image_dataset_from_directory<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;data\/train&#039;<\/span><span class=\"token punctuation\">,<\/span> image_size<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntrain_ds <span class=\"token operator\">&#061;<\/span> train_ds<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">map<\/span><span class=\"token punctuation\">(<\/span>normalize<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">map<\/span><span class=\"token punctuation\">(<\/span>augment<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>shuffle<span class=\"token punctuation\">(<\/span><span class=\"token number\">1000<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>prefetch<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><\/p>\n<p>val_ds <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>preprocessing<span class=\"token punctuation\">.<\/span>image_dataset_from_directory<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;data\/val&#039;<\/span><span class=\"token punctuation\">,<\/span> image_size<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">)<\/span><br \/>\nval_ds <span class=\"token operator\">&#061;<\/span> val_ds<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">map<\/span><span class=\"token punctuation\">(<\/span>normalize<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>prefetch<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><\/p>\n<h4>4.7 \u6027\u80fd\u4f18\u5316\u5bf9\u6bd4<\/h4>\n<p><span class=\"token keyword\">import<\/span> time<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">create_pipeline_without_prefetch<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    ds <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>Dataset<span class=\"token punctuation\">.<\/span>from_tensor_slices<span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>rand<span class=\"token punctuation\">(<\/span><span class=\"token number\">10000<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>astype<span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    ds <span class=\"token operator\">&#061;<\/span> ds<span class=\"token punctuation\">.<\/span>batch<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">map<\/span><span class=\"token punctuation\">(<\/span><span class=\"token keyword\">lambda<\/span> x<span class=\"token punctuation\">:<\/span> x <span class=\"token operator\">*<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u6a21\u62df\u8ba1\u7b97<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> ds<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">create_pipeline_with_prefetch<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    ds <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>Dataset<span class=\"token punctuation\">.<\/span>from_tensor_slices<span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>rand<span class=\"token punctuation\">(<\/span><span class=\"token number\">10000<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>astype<span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    ds <span class=\"token operator\">&#061;<\/span> ds<span class=\"token punctuation\">.<\/span>batch<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">map<\/span><span class=\"token punctuation\">(<\/span><span class=\"token keyword\">lambda<\/span> x<span class=\"token punctuation\">:<\/span> x <span class=\"token operator\">*<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> num_parallel_calls<span class=\"token operator\">&#061;<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>prefetch<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> ds<\/p>\n<p><span class=\"token comment\"># \u6d4b\u91cf\u65f6\u95f4<\/span><br \/>\nstart <span class=\"token operator\">&#061;<\/span> time<span class=\"token punctuation\">.<\/span>time<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">for<\/span> _ <span class=\"token keyword\">in<\/span> create_pipeline_without_prefetch<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">pass<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;Without prefetch: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>time<span class=\"token punctuation\">.<\/span>time<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">&#8211;<\/span>start<span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.2f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">s&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>start <span class=\"token operator\">&#061;<\/span> time<span class=\"token punctuation\">.<\/span>time<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">for<\/span> _ <span class=\"token keyword\">in<\/span> create_pipeline_with_prefetch<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">pass<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;With prefetch: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>time<span class=\"token punctuation\">.<\/span>time<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">&#8211;<\/span>start<span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.2f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">s&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h3>5. \u6848\u4f8b\u5b9e\u64cd\u6f14\u7ec3<\/h3>\n<p>\u6848\u4f8b&#xff1a;\u732b\u72d7\u56fe\u50cf\u5206\u7c7b\u5b8c\u6574\u6570\u636e\u6d41\u6c34\u7ebf<\/p>\n<p>\u76ee\u6807&#xff1a;\u4ece\u5305\u542b\u732b\u548c\u72d7\u56fe\u50cf\u7684\u6587\u4ef6\u5939\u4e2d\u6784\u5efa\u8bad\u7ec3\/\u9a8c\u8bc1\u6570\u636e\u96c6&#xff0c;\u5e94\u7528\u5f52\u4e00\u5316\u548c\u6570\u636e\u589e\u5f3a&#xff0c;\u5e76\u53ef\u89c6\u5316\u4e00\u6279\u5904\u7406\u540e\u7684\u56fe\u50cf\u3002<\/p>\n<h4>5.1 \u51c6\u5907\u6570\u636e&#xff08;\u4f7f\u7528\u5185\u7f6e\u6570\u636e\u96c6\u6a21\u62df&#xff09;<\/h4>\n<p>\u5b9e\u9645\u4e2d\u53ef\u4e0b\u8f7d\u732b\u72d7\u6570\u636e\u96c6&#xff0c;\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528\u968f\u673a\u751f\u6210\u56fe\u50cf\u6a21\u62df\u3002<\/p>\n<p><span class=\"token keyword\">import<\/span> random<br \/>\n<span class=\"token keyword\">import<\/span> os<\/p>\n<p><span class=\"token comment\"># \u521b\u5efa\u6a21\u62df\u76ee\u5f55\u7ed3\u6784<\/span><br \/>\nbase_dir <span class=\"token operator\">&#061;<\/span> <span class=\"token string\">&#039;.\/pet_images&#039;<\/span><br \/>\nos<span class=\"token punctuation\">.<\/span>makedirs<span class=\"token punctuation\">(<\/span>os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span>base_dir<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;train\/cat&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> exist_ok<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><br \/>\nos<span class=\"token punctuation\">.<\/span>makedirs<span class=\"token punctuation\">(<\/span>os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span>base_dir<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;train\/dog&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> exist_ok<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><br \/>\nos<span class=\"token punctuation\">.<\/span>makedirs<span class=\"token punctuation\">(<\/span>os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span>base_dir<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;val\/cat&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> exist_ok<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><br \/>\nos<span class=\"token punctuation\">.<\/span>makedirs<span class=\"token punctuation\">(<\/span>os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span>base_dir<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;val\/dog&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> exist_ok<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u751f\u6210\u968f\u673a\u56fe\u50cf&#xff08;\u751f\u4ea7\u73af\u5883\u5e94\u4f7f\u7528\u771f\u5b9e\u56fe\u50cf&#xff09;<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">create_dummy_image<\/span><span class=\"token punctuation\">(<\/span>path<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    img <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>randint<span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">255<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">150<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">&#061;<\/span>np<span class=\"token punctuation\">.<\/span>uint8<span class=\"token punctuation\">)<\/span><br \/>\n    PIL<span class=\"token punctuation\">.<\/span>Image<span class=\"token punctuation\">.<\/span>fromarray<span class=\"token punctuation\">(<\/span>img<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>save<span class=\"token punctuation\">(<\/span>path<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">for<\/span> i <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">100<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    create_dummy_image<span class=\"token punctuation\">(<\/span>os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span>base_dir<span class=\"token punctuation\">,<\/span> <span class=\"token string-interpolation\"><span class=\"token string\">f&#039;train\/cat\/cat_<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>i<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">.jpg&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    create_dummy_image<span class=\"token punctuation\">(<\/span>os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span>base_dir<span class=\"token punctuation\">,<\/span> <span class=\"token string-interpolation\"><span class=\"token string\">f&#039;train\/dog\/dog_<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>i<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">.jpg&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">for<\/span> i <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">50<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    create_dummy_image<span class=\"token punctuation\">(<\/span>os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span>base_dir<span class=\"token punctuation\">,<\/span> <span class=\"token string-interpolation\"><span class=\"token string\">f&#039;val\/cat\/cat_<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>i<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">.jpg&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    create_dummy_image<span class=\"token punctuation\">(<\/span>os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span>base_dir<span class=\"token punctuation\">,<\/span> <span class=\"token string-interpolation\"><span class=\"token string\">f&#039;val\/dog\/dog_<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>i<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">.jpg&#039;<\/span><\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>5.2 \u6784\u5efa\u6d41\u6c34\u7ebf<\/h4>\n<p>IMG_SIZE <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">150<\/span><span class=\"token punctuation\">)<\/span><br \/>\nBATCH_SIZE <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">32<\/span><\/p>\n<p>train_ds <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>preprocessing<span class=\"token punctuation\">.<\/span>image_dataset_from_directory<span class=\"token punctuation\">(<\/span><br \/>\n    os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span>base_dir<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;train&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    image_size<span class=\"token operator\">&#061;<\/span>IMG_SIZE<span class=\"token punctuation\">,<\/span><br \/>\n    batch_size<span class=\"token operator\">&#061;<\/span>BATCH_SIZE<span class=\"token punctuation\">,<\/span><br \/>\n    shuffle<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    validation_split<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    subset<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;training&#039;<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    seed<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><br \/>\n<span class=\"token punctuation\">)<\/span><\/p>\n<p>val_ds <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>preprocessing<span class=\"token punctuation\">.<\/span>image_dataset_from_directory<span class=\"token punctuation\">(<\/span><br \/>\n    os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span>join<span class=\"token punctuation\">(<\/span>base_dir<span class=\"token punctuation\">,<\/span> <span class=\"token string\">&#039;train&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    image_size<span class=\"token operator\">&#061;<\/span>IMG_SIZE<span class=\"token punctuation\">,<\/span><br \/>\n    batch_size<span class=\"token operator\">&#061;<\/span>BATCH_SIZE<span class=\"token punctuation\">,<\/span><br \/>\n    shuffle<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">False<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    validation_split<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.2<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    subset<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;validation&#039;<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    seed<span class=\"token operator\">&#061;<\/span><span class=\"token number\">42<\/span><br \/>\n<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u5f52\u4e00\u5316\u51fd\u6570<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">normalize<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> label<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> tf<span class=\"token punctuation\">.<\/span>cast<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> tf<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">\/<\/span> <span class=\"token number\">255.0<\/span><span class=\"token punctuation\">,<\/span> label<\/p>\n<p><span class=\"token comment\"># \u6570\u636e\u589e\u5f3a\u51fd\u6570<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">augment<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> label<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    image <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>random_flip_left_right<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span><br \/>\n    image <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>random_brightness<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    image <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>image<span class=\"token punctuation\">.<\/span>random_contrast<span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.8<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1.2<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> image<span class=\"token punctuation\">,<\/span> label<\/p>\n<p>train_ds <span class=\"token operator\">&#061;<\/span> train_ds<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">map<\/span><span class=\"token punctuation\">(<\/span>normalize<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">map<\/span><span class=\"token punctuation\">(<\/span>augment<span class=\"token punctuation\">,<\/span> num_parallel_calls<span class=\"token operator\">&#061;<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><br \/>\nval_ds <span class=\"token operator\">&#061;<\/span> val_ds<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">map<\/span><span class=\"token punctuation\">(<\/span>normalize<span class=\"token punctuation\">)<\/span><\/p>\n<p>train_ds <span class=\"token operator\">&#061;<\/span> train_ds<span class=\"token punctuation\">.<\/span>prefetch<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><br \/>\nval_ds <span class=\"token operator\">&#061;<\/span> val_ds<span class=\"token punctuation\">.<\/span>prefetch<span class=\"token punctuation\">(<\/span>tf<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>AUTOTUNE<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u53ef\u89c6\u5316\u4e00\u4e2abatch<\/span><br \/>\n<span class=\"token keyword\">for<\/span> images<span class=\"token punctuation\">,<\/span> labels <span class=\"token keyword\">in<\/span> train_ds<span class=\"token punctuation\">.<\/span>take<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>figure<span class=\"token punctuation\">(<\/span>figsize<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">12<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">12<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> i <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">9<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        plt<span class=\"token punctuation\">.<\/span>subplot<span class=\"token punctuation\">(<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> i<span class=\"token operator\">&#043;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        plt<span class=\"token punctuation\">.<\/span>imshow<span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">[<\/span>i<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>numpy<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        plt<span class=\"token punctuation\">.<\/span>title<span class=\"token punctuation\">(<\/span>train_ds<span class=\"token punctuation\">.<\/span>class_names<span class=\"token punctuation\">[<\/span>labels<span class=\"token punctuation\">[<\/span>i<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        plt<span class=\"token punctuation\">.<\/span>axis<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;off&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    plt<span class=\"token punctuation\">.<\/span>show<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>5.3 \u6a21\u578b\u8bad\u7ec3\u6f14\u793a&#xff08;\u7b80\u5355CNN&#xff09;<\/h4>\n<p>model <span class=\"token operator\">&#061;<\/span> tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>Sequential<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><br \/>\n    tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Conv2D<span class=\"token punctuation\">(<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> activation<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;relu&#039;<\/span><span class=\"token punctuation\">,<\/span> input_shape<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">150<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>MaxPooling2D<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Conv2D<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> activation<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;relu&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>MaxPooling2D<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Flatten<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Dense<span class=\"token punctuation\">(<\/span><span class=\"token number\">128<\/span><span class=\"token punctuation\">,<\/span> activation<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;relu&#039;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    tf<span class=\"token punctuation\">.<\/span>keras<span class=\"token punctuation\">.<\/span>layers<span class=\"token punctuation\">.<\/span>Dense<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> activation<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;sigmoid&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\nmodel<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">compile<\/span><span class=\"token punctuation\">(<\/span>optimizer<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;adam&#039;<\/span><span class=\"token punctuation\">,<\/span> loss<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;binary_crossentropy&#039;<\/span><span class=\"token punctuation\">,<\/span> metrics<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;accuracy&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\nhistory <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>train_ds<span class=\"token punctuation\">,<\/span> validation_data<span class=\"token operator\">&#061;<\/span>val_ds<span class=\"token punctuation\">,<\/span> epochs<span class=\"token operator\">&#061;<\/span><span class=\"token number\">5<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h3>6. \u5e38\u89c1\u5751\u70b9\u4e0e\u6392\u9519\u603b\u7ed3<\/h3>\n<h4>6.1 \u6570\u636e\u52a0\u8f7d\u5751\u70b9<\/h4>\n<ul>\n<li>\n<p>\u57511&#xff1a;image_dataset_from_directory \u9ed8\u8ba4\u5c06\u6240\u6709\u5b50\u6587\u4ef6\u5939\u540d\u4f5c\u4e3a\u7c7b\u522b&#xff0c;\u4e14\u6309\u5b57\u6bcd\u987a\u5e8f\u5206\u914d\u6807\u7b7e\u3002\u82e5\u6587\u4ef6\u5939\u540d\u4e0d\u662f\u6570\u5b57&#xff0c;\u9700\u8c28\u614e\u5904\u7406\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3&#xff1a;\u4f7f\u7528class_names\u53c2\u6570\u663e\u5f0f\u6307\u5b9a\u987a\u5e8f&#xff0c;\u6216\u4ece\u8fd4\u56de\u503c\u83b7\u53d6class_names\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u57512&#xff1a;\u56fe\u7247\u89e3\u7801\u5931\u8d25&#xff08;\u683c\u5f0f\u4e0d\u652f\u6301\u6216\u635f\u574f&#xff09;\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3&#xff1a;\u5728map\u51fd\u6570\u4e2d\u52a0\u5165tf.image.decode_image\u5e76\u8bbe\u7f6e\u9519\u8bef\u5904\u7406&#xff08;try&#8230;except&#xff09;&#xff0c;\u6216\u4f7f\u7528tf.io.decode_jpeg\u9650\u5236\u683c\u5f0f\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u57513&#xff1a;CSV\u6587\u4ef6\u4e2d\u5305\u542b\u7f3a\u5931\u503c\u6216\u7279\u6b8a\u5b57\u7b26&#xff0c;\u5bfc\u81f4decode_csv\u5931\u8d25\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3&#xff1a;\u9884\u5904\u7406CSV\u6587\u4ef6&#xff0c;\u6216\u4f7f\u7528skip_errors\u8fc7\u6ee4\u5668\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4>6.2 \u6570\u636e\u9884\u5904\u7406\u7684\u5e38\u89c1\u9519\u8bef<\/h4>\n<ul>\n<li>\n<p>\u57514&#xff1a;\u5fd8\u8bb0\u5c06\u6807\u7b7e\u8f6c\u6362\u4e3a\u72ec\u70ed\u7f16\u7801\u800c\u76f4\u63a5\u4f7f\u7528\u4ea4\u53c9\u71b5&#xff08;categorical_crossentropy&#xff09;\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3&#xff1a;\u82e5\u6807\u7b7e\u4e3a\u6574\u6570&#xff0c;\u5e94\u4f7f\u7528sparse_categorical_crossentropy&#xff0c;\u6216\u624b\u52a8\u8f6c\u6362tf.one_hot\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u57515&#xff1a;\u5f52\u4e00\u5316\u65f6\u4f7f\u7528tf.cast\u540e\u518d\u9664\u4ee5255&#xff0c;\u4f46\u5fd8\u8bb0\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b\u4e3a\u6d6e\u70b9&#xff0c;\u6574\u6570\u9664\u6cd5\u5bfc\u81f4\u7ed3\u679c\u4e3a0\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3&#xff1a;\u5148cast\u540e\u9664\u6cd5&#xff0c;\u6216\u76f4\u63a5\u7528image \/ 255.0&#xff08;TensorFlow\u4f1a\u81ea\u52a8\u63d0\u5347\u7c7b\u578b&#xff09;\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4>6.3 \u6570\u636e\u589e\u5f3a\u5751\u70b9<\/h4>\n<ul>\n<li>\n<p>\u57516&#xff1a;\u9a8c\u8bc1\u96c6\u4e5f\u5e94\u7528\u4e86\u968f\u673a\u589e\u5f3a&#xff0c;\u5bfc\u81f4\u9a8c\u8bc1\u6307\u6807\u4e0d\u7a33\u5b9a\u4e14\u4e0d\u53ef\u590d\u73b0\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3&#xff1a;\u4ec5\u5bf9\u8bad\u7ec3\u96c6\u5e94\u7528\u589e\u5f3a&#xff0c;\u9a8c\u8bc1\u96c6\u53ea\u505a\u5f52\u4e00\u5316\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u57517&#xff1a;map\u51fd\u6570\u4e2d\u4f7f\u7528Python\u5e93&#xff08;\u5982PIL&#xff09;\u5bfc\u81f4\u6027\u80fd\u6781\u5dee&#xff0c;\u65e0\u6cd5\u5728num_parallel_calls\u4e0b\u53d7\u76ca\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3&#xff1a;\u4f18\u5148\u4f7f\u7528tf.image\u4e2d\u7684\u64cd\u4f5c&#xff0c;\u5b83\u4eec\u5728\u56fe\u5185\u6267\u884c&#xff0c;\u6548\u7387\u9ad8\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4>6.4 \u6027\u80fd\u4f18\u5316\u5751\u70b9<\/h4>\n<ul>\n<li>\n<p>\u57518&#xff1a;shuffle\u7684buffer_size\u8bbe\u7f6e\u8fc7\u5c0f&#xff08;\u598232&#xff09;&#xff0c;\u5bfc\u81f4\u6253\u4e71\u4e0d\u5145\u5206&#xff0c;\u6a21\u578b\u6cdb\u5316\u5dee\u3002<\/p>\n<ul>\n<li>\u5efa\u8bae&#xff1a;\u81f3\u5c11\u4e0e\u4e00\u4e2aepoch\u7684\u6837\u672c\u6570\u76f8\u5f53&#xff0c;\u4f46\u82e5\u5185\u5b58\u4e0d\u8db3\u53ef\u9002\u5f53\u51cf\u5c0f\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u57519&#xff1a;\u91cd\u590d\u591a\u6b21\u4f7f\u7528cache()\u4e14\u672a\u6307\u5b9a\u6587\u4ef6\u540d&#xff0c;\u5bfc\u81f4\u5185\u5b58\u5360\u7528\u98d9\u5347\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3&#xff1a;\u5bf9\u4e8e\u8d85\u5927\u6570\u636e\u96c6&#xff0c;\u4f7f\u7528cache(filename)\u7f13\u5b58\u5230\u78c1\u76d8&#xff0c;\u6216\u4ec5\u5728\u5c0f\u6570\u636e\u96c6\u4e0a\u5185\u5b58\u7f13\u5b58\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u575110&#xff1a;\u5728map\u4e2d\u6267\u884cIO\u64cd\u4f5c&#xff08;\u5982\u8bfb\u53d6\u6587\u4ef6&#xff09;\u800c\u672a\u8bbe\u7f6enum_parallel_calls&#xff0c;\u9020\u6210\u8bad\u7ec3\u74f6\u9888\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3&#xff1a;\u8bbe\u7f6enum_parallel_calls&#061;tf.data.AUTOTUNE&#xff0c;\u5e76\u5728map\u540e\u7d27\u8ddfprefetch\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3>7. \u77e5\u8bc6\u70b9\u603b\u7ed3 &#043; \u8bfe\u540e\u4f5c\u4e1a<\/h3>\n<h4>7.1 \u6838\u5fc3\u77e5\u8bc6\u70b9\u68b3\u7406<\/h4>\n<ul>\n<li>\u5185\u7f6e\u6570\u636e\u96c6&#xff1a;tf.keras.datasets\u5feb\u901f\u52a0\u8f7d\u7ecf\u5178\u6570\u636e\u96c6\u3002<\/li>\n<li>tf.data.Dataset&#xff1a;\u4ece\u591a\u79cd\u6570\u636e\u6e90\u521b\u5efa\u9ad8\u6548\u6d41\u6c34\u7ebf&#xff0c;\u652f\u6301\u51fd\u6570\u5f0f\u53d8\u6362\u3002<\/li>\n<li>\u5f52\u4e00\u5316&#xff1a;Min-Max&#xff08;[0,1]&#xff09;\u548cZ-score&#xff0c;\u52a0\u901f\u6536\u655b\u3002<\/li>\n<li>\u56fe\u50cf\u6570\u636e\u589e\u5f3a&#xff1a;tf.image\u968f\u673a\u53d8\u6362&#xff0c;\u63d0\u5347\u6cdb\u5316\u3002<\/li>\n<li>\u6d41\u6c34\u7ebf\u4f18\u5316&#xff1a;cache\u3001shuffle\u3001batch\u3001prefetch\u3001num_parallel_calls\u3002<\/li>\n<\/ul>\n<h4>7.2 \u57fa\u7840\u4f5c\u4e1a<\/h4>\n<li>\u4f7f\u7528tf.keras.datasets.cifar10\u52a0\u8f7d\u6570\u636e&#xff0c;\u5c06\u5176\u5f52\u4e00\u5316\u5e76\u8f6c\u6362\u4e3atf.data.Dataset&#xff0c;\u8bbe\u7f6e\u6279\u5927\u5c0f\u4e3a64&#xff0c;\u8bad\u7ec3\u4e00\u4e2a\u7b80\u5355\u7684\u5168\u8fde\u63a5\u7f51\u7edc&#xff08;\u5c55\u5e73\u540e&#xff09;&#xff0c;\u8bb0\u5f55\u6d4b\u8bd5\u51c6\u786e\u7387\u3002<\/li>\n<li>\u7f16\u5199\u4e00\u4e2a\u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u7279\u5f81&#xff08;5\u4e2a\u6570\u503c\u7279\u5f81&#xff09;\u548c\u6807\u7b7e&#xff08;\u4e8c\u5206\u7c7b&#xff09;\u7684\u6570\u636e\u6d41\u6c34\u7ebf&#xff0c;\u5e76\u5b9e\u73b0Z-score\u6807\u51c6\u5316&#xff08;\u4f7f\u7528\u8bad\u7ec3\u96c6\u7684\u5747\u503c\u548c\u6807\u51c6\u5dee&#xff09;\u3002<\/li>\n<li>\u89e3\u91ca\u4e3a\u4ec0\u4e48prefetch(tf.data.AUTOTUNE)\u80fd\u63d0\u9ad8\u8bad\u7ec3\u901f\u5ea6\u3002<\/li>\n<h4>7.3 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