Path: blob/master/translate_cache/unet/experiment.ja.json
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{1"<h1>Training <a href=\"index.html\">U-Net</a></h1>\n<p>This trains a <a href=\"index.html\">U-Net</a> model on <a href=\"carvana.html\">Carvana dataset</a>. You can find the download instructions <a href=\"https://www.kaggle.com/competitions/carvana-image-masking-challenge/data\">on Kaggle</a>.</p>\n<p>Save the training images inside <span translate=no>_^_0_^_</span> folder and the masks in <span translate=no>_^_1_^_</span> folder.</p>\n<p>For simplicity, we do not do a training and validation split.</p>\n": "<h1>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 <a href=\"index.html\">U-\u30cd\u30c3\u30c8</a></h1>\n<p>\u3053\u308c\u306b\u3088\u308a\u3001<a href=\"index.html\"><a href=\"carvana.html\">Carvana\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067U-Net\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u3059</a></a>\u3002\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u624b\u9806\u306f <a href=\"https://www.kaggle.com/competitions/carvana-image-masking-challenge/data\">Kaggle \u3067\u78ba\u8a8d\u3067\u304d\u307e\u3059</a></p>\u3002\n<p><span translate=no>_^_0_^_</span>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u753b\u50cf\u3092\u30d5\u30a9\u30eb\u30c0\u30fc\u5185\u306b\u4fdd\u5b58\u3057\u3001<span translate=no>_^_1_^_</span>\u30de\u30b9\u30af\u3092\u30d5\u30a9\u30eb\u30c0\u30fc\u306b\u4fdd\u5b58\u3057\u307e\u3059\u3002</p>\n<p>\u308f\u304b\u308a\u3084\u3059\u304f\u3059\u308b\u305f\u3081\u306b\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3068\u691c\u8a3c\u306e\u5206\u5272\u306f\u884c\u3063\u3066\u3044\u307e\u305b\u3093\u3002</p>\n",2"<h2>Configurations</h2>\n": "<h2>\u30b3\u30f3\u30d5\u30a3\u30ae\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3</h2>\n",3"<h3>Sample images</h3>\n": "<h3>\u30b5\u30f3\u30d7\u30eb\u753b\u50cf</h3>\n",4"<h3>Train for an epoch</h3>\n": "<h3>\u4e00\u6642\u4ee3\u3092\u62d3\u304f\u5217\u8eca</h3>\n",5"<h3>Training loop</h3>\n": "<h3>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30eb\u30fc\u30d7</h3>\n",6"<p> </p>\n": "<p></p>\n",7"<p><a href=\"index.html\">U-Net</a> model </p>\n": "<p><a href=\"index.html\">U-\u30cd\u30c3\u30c8\u30e2\u30c7\u30eb</a></p>\n",8"<p>Adam optimizer </p>\n": "<p>\u30a2\u30c0\u30e0\u30fb\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc</p>\n",9"<p>Batch size </p>\n": "<p>\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba</p>\n",10"<p>Calculate loss </p>\n": "<p>\u640d\u5931\u306e\u8a08\u7b97</p>\n",11"<p>Compute gradients </p>\n": "<p>\u52fe\u914d\u306e\u8a08\u7b97</p>\n",12"<p>Create configurations </p>\n": "<p>\u69cb\u6210\u306e\u4f5c\u6210</p>\n",13"<p>Create dataloader </p>\n": "<p>\u30c7\u30fc\u30bf\u30ed\u30fc\u30c0\u30fc\u306e\u4f5c\u6210</p>\n",14"<p>Create experiment </p>\n": "<p>\u5b9f\u9a13\u3092\u4f5c\u6210</p>\n",15"<p>Create optimizer </p>\n": "<p>\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc\u3092\u4f5c\u6210</p>\n",16"<p>Crop the image to the size of the mask </p>\n": "<p>\u753b\u50cf\u3092\u30de\u30b9\u30af\u306e\u30b5\u30a4\u30ba\u306b\u30c8\u30ea\u30df\u30f3\u30b0\u3057\u307e\u3059</p>\n",17"<p>Crop the target mask to the size of the logits. Size of the logits will be smaller if we don't use padding in convolutional layers in the U-Net. </p>\n": "<p>\u30bf\u30fc\u30b2\u30c3\u30c8\u30de\u30b9\u30af\u3092\u30ed\u30b8\u30c3\u30c8\u306e\u30b5\u30a4\u30ba\u306b\u30c8\u30ea\u30df\u30f3\u30b0\u3057\u307e\u3059\u3002U-Net\u306e\u7573\u307f\u8fbc\u307f\u5c64\u306b\u30d1\u30c7\u30a3\u30f3\u30b0\u3092\u4f7f\u308f\u306a\u3044\u3068\u3001\u30ed\u30b8\u30c3\u30c8\u306e\u30b5\u30a4\u30ba\u306f\u5c0f\u3055\u304f\u306a\u308a\u307e\u3059</p>\u3002\n",18"<p>Dataloader </p>\n": "<p>\u30c7\u30fc\u30bf\u30ed\u30fc\u30c0\u30fc</p>\n",19"<p>Dataset </p>\n": "<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8</p>\n",20"<p>Device to train the model on. <a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.device.DeviceConfigs\"><span translate=no>_^_0_^_</span></a> picks up an available CUDA device or defaults to CPU. </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u30c7\u30d0\u30a4\u30b9\u3002<a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.device.DeviceConfigs\"><span translate=no>_^_0_^_</span></a>\u4f7f\u7528\u53ef\u80fd\u306a CUDA \u30c7\u30d0\u30a4\u30b9\u3092\u9078\u629e\u3059\u308b\u304b\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u3067 CPU \u306b\u8a2d\u5b9a\u3057\u307e\u3059</p>\u3002\n",21"<p>Get a random sample </p>\n": "<p>\u30e9\u30f3\u30c0\u30e0\u30b5\u30f3\u30d7\u30eb\u3092\u5165\u624b</p>\n",22"<p>Get predicted mask </p>\n": "<p>\u4e88\u6e2c\u30de\u30b9\u30af\u3092\u53d6\u5f97</p>\n",23"<p>Get predicted mask logits </p>\n": "<p>\u4e88\u6e2c\u3055\u308c\u305f\u30de\u30b9\u30af\u30ed\u30b8\u30c3\u30c8\u306e\u53d6\u5f97</p>\n",24"<p>Image logging </p>\n": "<p>\u753b\u50cf\u30ed\u30ae\u30f3\u30b0</p>\n",25"<p>Increment global step </p>\n": "<p>\u30b0\u30ed\u30fc\u30d0\u30eb\u30b9\u30c6\u30c3\u30d7\u3092\u30a4\u30f3\u30af\u30ea\u30e1\u30f3\u30c8</p>\n",26"<p>Initialize </p>\n": "<p>[\u521d\u671f\u5316]</p>\n",27"<p>Initialize the <a href=\"carvana.html\">Carvana dataset</a> </p>\n": "<p><a href=\"carvana.html\">Carvana</a> \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u521d\u671f\u5316\u3057\u307e\u3059</p>\n",28"<p>Initialize the model </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u521d\u671f\u5316</p>\n",29"<p>Iterate through the dataset. Use <a href=\"https://docs.labml.ai/api/monit.html#labml.monit.mix\"><span translate=no>_^_0_^_</span></a> to sample <span translate=no>_^_1_^_</span> times per epoch. </p>\n": "<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u7e70\u308a\u8fd4\u3057\u51e6\u7406\u3057\u307e\u3059\u3002<a href=\"https://docs.labml.ai/api/monit.html#labml.monit.mix\"><span translate=no>_^_0_^_</span></a><span translate=no>_^_1_^_</span>\u30a8\u30dd\u30c3\u30af\u3042\u305f\u308a\u306e\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u6642\u9593\u306b\u4f7f\u7528\u3057\u307e\u3059</p>\u3002\n",30"<p>Learning rate </p>\n": "<p>\u5b66\u7fd2\u7387</p>\n",31"<p>Log samples </p>\n": "<p>\u30ed\u30b0\u30b5\u30f3\u30d7\u30eb</p>\n",32"<p>Loss function </p>\n": "<p>\u640d\u5931\u95a2\u6570</p>\n",33"<p>Make the gradients zero </p>\n": "<p>\u30b0\u30e9\u30c7\u30fc\u30b7\u30e7\u30f3\u3092\u30bc\u30ed\u306b\u3059\u308b</p>\n",34"<p>Move data to device </p>\n": "<p>\u30c7\u30fc\u30bf\u3092\u30c7\u30d0\u30a4\u30b9\u306b\u79fb\u52d5</p>\n",35"<p>New line in the console </p>\n": "<p>\u30b3\u30f3\u30bd\u30fc\u30eb\u306e\u65b0\u3057\u3044\u884c</p>\n",36"<p>Number of channels in the image. <span translate=no>_^_0_^_</span> for RGB. </p>\n": "<p>\u753b\u50cf\u5185\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u3002<span translate=no>_^_0_^_</span>RGB \u7528\u3067\u3059\u3002</p>\n",37"<p>Number of channels in the output mask. <span translate=no>_^_0_^_</span> for binary mask. </p>\n": "<p>\u51fa\u529b\u30de\u30b9\u30af\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u3002<span translate=no>_^_0_^_</span>\u30d0\u30a4\u30ca\u30ea\u30de\u30b9\u30af\u7528\u3002</p>\n",38"<p>Number of training epochs </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30a8\u30dd\u30c3\u30af\u306e\u6570</p>\n",39"<p>Save the model </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u4fdd\u5b58\u3059\u308b</p>\n",40"<p>Set configurations. You can override the defaults by passing the values in the dictionary. </p>\n": "<p>\u69cb\u6210\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002\u30c7\u30a3\u30af\u30b7\u30e7\u30ca\u30ea\u306b\u5024\u3092\u6e21\u3059\u3053\u3068\u3067\u30c7\u30d5\u30a9\u30eb\u30c8\u3092\u30aa\u30fc\u30d0\u30fc\u30e9\u30a4\u30c9\u3067\u304d\u307e\u3059\u3002</p>\n",41"<p>Set models for saving and loading </p>\n": "<p>\u4fdd\u5b58\u304a\u3088\u3073\u8aad\u307f\u8fbc\u307f\u7528\u306e\u30e2\u30c7\u30eb\u3092\u8a2d\u5b9a\u3059\u308b</p>\n",42"<p>Sigmoid function for binary classification </p>\n": "<p>\u30d0\u30a4\u30ca\u30ea\u5206\u985e\u7528\u306e\u30b7\u30b0\u30e2\u30a4\u30c9\u95a2\u6570</p>\n",43"<p>Start and run the training loop </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30eb\u30fc\u30d7\u3092\u958b\u59cb\u3057\u3066\u5b9f\u884c\u3059\u308b</p>\n",44"<p>Take an optimization step </p>\n": "<p>\u6700\u9069\u5316\u306e\u4e00\u6b69\u3092\u8e0f\u307f\u51fa\u3059</p>\n",45"<p>Track the loss </p>\n": "<p>\u640d\u5931\u3092\u30c8\u30e9\u30c3\u30ad\u30f3\u30b0</p>\n",46"<p>Train the model </p>\n": "<p>\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0</p>\n",47"Code for training a U-Net model on Carvana dataset.": "Carvana\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067U-Net\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u305f\u3081\u306e\u30b3\u30fc\u30c9\u3002",48"Training a U-Net on Carvana dataset": "Carvana\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u306eU-Net\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0"49}5051