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translate_cache/uncertainty/__init__.ja.json
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{
"<h1>Neural Networks with Uncertainty Estimation</h1>\n<p>These are neural network architectures that estimate the uncertainty of the predictions.</p>\n<ul><li><a href=\"evidence/index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a></li></ul>\n": "<h1>\u4e0d\u78ba\u5b9f\u6027\u63a8\u5b9a\u3092\u7528\u3044\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af</h1>\n<p>\u3053\u308c\u3089\u306f\u3001\u4e88\u6e2c\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u63a8\u5b9a\u3059\u308b\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3067\u3059\u3002</p>\n<ul><li><a href=\"evidence/index.html\">\u5206\u985e\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u5b9a\u91cf\u5316\u3059\u308b\u30a8\u30d3\u30c7\u30f3\u30b7\u30e3\u30eb\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0</a></li></ul>\n",
"A set of PyTorch implementations/tutorials related to uncertainty estimation": "\u4e0d\u78ba\u5b9f\u6027\u63a8\u5b9a\u306b\u95a2\u9023\u3059\u308bPyTorch\u306e\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306e\u30bb\u30c3\u30c8",
"Neural Networks with Uncertainty Estimation": "\u4e0d\u78ba\u5b9f\u6027\u63a8\u5b9a\u3092\u7528\u3044\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af"
}
translate_cache/uncertainty/__init__.si.json
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{
"<h1>Neural Networks with Uncertainty Estimation</h1>\n<p>These are neural network architectures that estimate the uncertainty of the predictions.</p>\n<ul><li><a href=\"evidence/index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a></li></ul>\n": "<h1>\u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf\u0d87\u0dc3\u0dca\u0dad\u0db8\u0dda\u0db1\u0dca\u0dad\u0dd4 \u0dc3\u0dc4\u0dd2\u0dad \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd</h1>\n<p>\u0db8\u0dda\u0dc0\u0dcf\u0db4\u0dd4\u0dbb\u0ddd\u0d9a\u0dae\u0db1\u0dc0\u0dbd \u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf\u0dc0 \u0dad\u0d9a\u0dca\u0dc3\u0dda\u0dbb\u0dd4 \u0d9a\u0dbb\u0db1 \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd \u0d9c\u0dd8\u0dc4 \u0db1\u0dd2\u0dbb\u0dca\u0db8\u0dcf\u0dab \u0dc0\u0dda. </p>\n<ul><li><a href=\"evidence/index.html\">\u0dc0\u0dbb\u0dca\u0d9c\u0dd3\u0d9a\u0dbb\u0dab \u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf\u0dc0 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a></li></ul>\n",
"A set of PyTorch implementations/tutorials related to uncertainty estimation": "\u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad \u0dad\u0d9a\u0dca\u0dc3\u0dda\u0dbb\u0dd4\u0d9a\u0dbb\u0dab\u0dba\u0da7 \u0d85\u0daf\u0dcf\u0dc5 PyTorch \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dca/\u0db1\u0dd2\u0db6\u0db1\u0dca\u0db0\u0db1 \u0dc3\u0db8\u0dd6\u0dc4\u0dba\u0d9a\u0dca",
"Neural Networks with Uncertainty Estimation": "\u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf \u0d87\u0dc3\u0dca\u0dad\u0db8\u0dda\u0db1\u0dca\u0dad\u0dd4 \u0dc3\u0dc4\u0dd2\u0dad \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd"
}
translate_cache/uncertainty/__init__.zh.json
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{
"<h1>Neural Networks with Uncertainty Estimation</h1>\n<p>These are neural network architectures that estimate the uncertainty of the predictions.</p>\n<ul><li><a href=\"evidence/index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a></li></ul>\n": "<h1>\u5177\u6709\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\u7684\u795e\u7ecf\u7f51\u7edc</h1>\n<p>\u8fd9\u4e9b\u662f\u4f30\u8ba1\u9884\u6d4b\u4e0d\u786e\u5b9a\u6027\u7684\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\u3002</p>\n<ul><li><a href=\"evidence/index.html\">\u7528\u4e8e\u91cf\u5316\u5206\u7c7b\u4e0d\u786e\u5b9a\u6027\u7684\u8bc1\u636e\u6027\u6df1\u5ea6\u5b66\u4e60</a></li></ul>\n",
"A set of PyTorch implementations/tutorials related to uncertainty estimation": "\u4e00\u7ec4\u4e0e\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\u76f8\u5173\u7684 PyTorch \u5b9e\u73b0/\u6559\u7a0b",
"Neural Networks with Uncertainty Estimation": "\u5177\u6709\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\u7684\u795e\u7ecf\u7f51\u7edc"
}
translate_cache/uncertainty/evidence/__init__.ja.json
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translate_cache/uncertainty/evidence/__init__.si.json
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translate_cache/uncertainty/evidence/__init__.zh.json
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translate_cache/uncertainty/evidence/experiment.ja.json
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{
"<h1><a href=\"index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a> Experiment</h1>\n<p>This trains a model based on <a href=\"index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a> on MNIST dataset.</p>\n": "<h1><a href=\"index.html\">\u5206\u985e\u4e0d\u78ba\u5b9a\u6027\u5b9f\u9a13\u3092\u5b9a\u91cf\u5316\u3059\u308b\u305f\u3081\u306e\u30a8\u30d3\u30c7\u30f3\u30b7\u30e3\u30eb\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0</a></h1>\n<p>\u3053\u308c\u306b\u3088\u308a\u3001<a href=\"index.html\">\u30a8\u30d3\u30c7\u30f3\u30b7\u30e3\u30eb\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306b\u57fa\u3065\u304f\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u3066\u3001MNIST\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5206\u985e\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u5b9a\u91cf\u5316\u3057\u307e\u3059</a>\u3002</p>\n",
"<h2>Configurations</h2>\n<p>We use <a href=\"../../experiments/mnist.html#MNISTConfigs\"><span translate=no>_^_0_^_</span></a> configurations.</p>\n": "<h2>\u30b3\u30f3\u30d5\u30a3\u30ae\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3</h2>\n<p><a href=\"../../experiments/mnist.html#MNISTConfigs\"><span translate=no>_^_0_^_</span></a>\u69cb\u6210\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002</p>\n",
"<h2>LeNet based model fro MNIST classification</h2>\n": "<h2>MINST \u5206\u985e\u7528\u306e Lenet \u30d9\u30fc\u30b9\u306e\u30e2\u30c7\u30eb</h2>\n",
"<h3>Create model</h3>\n": "<h3>\u30e2\u30c7\u30eb\u4f5c\u6210</h3>\n",
"<h3>Initialization</h3>\n": "<h3>\u521d\u671f\u5316</h3>\n",
"<h3>KL Divergence Loss Coefficient Schedule</h3>\n": "<h3>KL \u30c0\u30a4\u30d0\u30fc\u30b8\u30a7\u30f3\u30b9\u640d\u5931\u4fc2\u6570\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb</h3>\n",
"<h3>Training or validation step</h3>\n": "<h3>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u307e\u305f\u306f\u691c\u8a3c\u30b9\u30c6\u30c3\u30d7</h3>\n",
"<p> </p>\n": "<p></p>\n",
"<p>&#x27;loss_func&#x27;: &#x27;max_likelihood_loss&#x27;, &#x27;loss_func&#x27;: &#x27;cross_entropy_bayes_risk&#x27;, </p>\n": "<p>'loss_func': 'max_likelihood_loss', 'loss_func': 'cross_entropy_bayes_risk',</p>\n",
"<p><a href=\"index.html#CrossEntropyBayesRisk\">Cross Entropy Bayes Risk</a> </p>\n": "<p><a href=\"index.html#CrossEntropyBayesRisk\">\u30af\u30ed\u30b9\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u30d9\u30a4\u30ba\u30ea\u30b9\u30af</a></p>\n",
"<p><a href=\"index.html#KLDivergenceLoss\">KL Divergence regularization</a> </p>\n": "<p><a href=\"index.html#KLDivergenceLoss\">KL \u30c0\u30a4\u30d0\u30fc\u30b8\u30a7\u30f3\u30b9\u6b63\u5247\u5316</a></p>\n",
"<p><a href=\"index.html#MaximumLikelihoodLoss\">Maximum Likelihood Loss</a> </p>\n": "<p><a href=\"index.html#MaximumLikelihoodLoss\">\u6700\u5927\u78ba\u7387\u640d\u5931</a></p>\n",
"<p><a href=\"index.html#SquaredErrorBayesRisk\">Squared Error Bayes Risk</a> </p>\n": "<p><a href=\"index.html#SquaredErrorBayesRisk\">\u4e8c\u4e57\u8aa4\u5dee\u30d9\u30a4\u30ba\u30ea\u30b9\u30af</a></p>\n",
"<p><a href=\"index.html#TrackStatistics\">Stats module</a> for tracking </p>\n": "<p><a href=\"index.html#TrackStatistics\">\u8ffd\u8de1\u7528\u7d71\u8a08\u30e2\u30b8\u30e5\u30fc\u30eb</a></p>\n",
"<p><span translate=no>_^_0_^_</span> max-pooling </p>\n": "<p><span translate=no>_^_0_^_</span>\u30de\u30c3\u30af\u30b9\u30d7\u30fc\u30ea\u30f3\u30b0</p>\n",
"<p>Apply dropout </p>\n": "<p>\u30c9\u30ed\u30c3\u30d7\u30a2\u30a6\u30c8\u3092\u9069\u7528</p>\n",
"<p>Apply final layer and return </p>\n": "<p>\u6700\u7d42\u30ec\u30a4\u30e4\u30fc\u3092\u9069\u7528\u3057\u3066\u623b\u308b</p>\n",
"<p>Apply first convolution and max pooling. The result has shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6700\u521d\u306e\u30b3\u30f3\u30dc\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u3068\u30de\u30c3\u30af\u30b9\u30d7\u30fc\u30ea\u30f3\u30b0\u3092\u9069\u7528\u3057\u307e\u3059\u3002\u7d50\u679c\u306b\u306f\u5f62\u304c\u3042\u308a\u307e\u3059 <span translate=no>_^_0_^_</span></p>\n",
"<p>Apply hidden layer </p>\n": "<p>\u96a0\u3057\u30ec\u30a4\u30e4\u30fc\u3092\u9069\u7528</p>\n",
"<p>Apply second convolution and max pooling. The result has shape <span translate=no>_^_0_^_</span> </p>\n": "<p>2 \u56de\u76ee\u306e\u30b3\u30f3\u30dc\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u3068\u6700\u5927\u30d7\u30fc\u30ea\u30f3\u30b0\u3092\u9069\u7528\u3057\u307e\u3059\u3002\u7d50\u679c\u306b\u306f\u5f62\u304c\u3042\u308a\u307e\u3059 <span translate=no>_^_0_^_</span></p>\n",
"<p>Calculate KL Divergence regularization loss </p>\n": "<p>KL \u30c0\u30a4\u30d0\u30fc\u30b8\u30a7\u30f3\u30b9\u6b63\u5247\u5316\u640d\u5931\u306e\u8a08\u7b97</p>\n",
"<p>Calculate gradients </p>\n": "<p>\u52fe\u914d\u306e\u8a08\u7b97</p>\n",
"<p>Calculate loss </p>\n": "<p>\u640d\u5931\u306e\u8a08\u7b97</p>\n",
"<p>Clear the gradients </p>\n": "<p>\u30b0\u30e9\u30c7\u30fc\u30b7\u30e7\u30f3\u3092\u30af\u30ea\u30a2</p>\n",
"<p>Create a <a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.schedule.Piecewise\">relative piecewise schedule</a> </p>\n": "<p><a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.schedule.Piecewise\">\u76f8\u5bfe\u7684\u306a\u533a\u5206\u7684\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb\u306e\u4f5c\u6210</a></p>\n",
"<p>Create configurations </p>\n": "<p>\u69cb\u6210\u306e\u4f5c\u6210</p>\n",
"<p>Create experiment </p>\n": "<p>\u5b9f\u9a13\u3092\u4f5c\u6210</p>\n",
"<p>Dropout </p>\n": "<p>\u30c9\u30ed\u30c3\u30d7\u30a2\u30a6\u30c8</p>\n",
"<p>Dropout for the hidden layer </p>\n": "<p>\u96a0\u3057\u30ec\u30a4\u30e4\u30fc\u306e\u30c9\u30ed\u30c3\u30d7\u30a2\u30a6\u30c8</p>\n",
"<p>Final fully connected layer to output evidence for <span translate=no>_^_0_^_</span> classes. The ReLU or Softplus activation is applied to this outside the model to get the non-negative evidence </p>\n": "<p><span translate=no>_^_0_^_</span>\u30af\u30e9\u30b9\u306e\u30a8\u30d3\u30c7\u30f3\u30b9\u3092\u51fa\u529b\u3059\u308b\u305f\u3081\u306e\u6700\u5f8c\u306e\u5b8c\u5168\u63a5\u7d9a\u30ec\u30a4\u30e4\u30fc\u3002\u3053\u308c\u306bReLU\u307e\u305f\u306fSoftplus\u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30b7\u30e7\u30f3\u3092\u30e2\u30c7\u30eb\u5916\u3067\u9069\u7528\u3059\u308b\u3068\u3001\u975e\u9670\u6027\u30a8\u30d3\u30c7\u30f3\u30b9\u304c\u5f97\u3089\u308c\u307e\u3059</p>\u3002\n",
"<p>First <span translate=no>_^_0_^_</span> convolution layer </p>\n": "<p><span translate=no>_^_0_^_</span>\u6700\u521d\u306e\u7573\u307f\u8fbc\u307f\u5c64</p>\n",
"<p>First fully-connected layer that maps to <span translate=no>_^_0_^_</span> features </p>\n": "<p>\u30d5\u30a3\u30fc\u30c1\u30e3\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3055\u308c\u308b\u6700\u521d\u306e\u5b8c\u5168\u63a5\u7d9a\u30ec\u30a4\u30e4\u30fc <span translate=no>_^_0_^_</span></p>\n",
"<p>Flatten the tensor to shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30c6\u30f3\u30bd\u30eb\u3092\u5e73\u3089\u306b\u3057\u3066\u5f62\u3092\u6574\u3048\u308b <span translate=no>_^_0_^_</span></p>\n",
"<p>Get evidences <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8a3c\u62e0\u3092\u53d6\u5f97 <span translate=no>_^_0_^_</span></p>\n",
"<p>Get model outputs </p>\n": "<p>\u30e2\u30c7\u30eb\u51fa\u529b\u3092\u53d6\u5f97</p>\n",
"<p>KL Divergence loss coefficient <span translate=no>_^_0_^_</span> </p>\n": "<p>KL \u767a\u6563\u640d\u5931\u4fc2\u6570 <span translate=no>_^_0_^_</span></p>\n",
"<p>KL Divergence regularization coefficient schedule </p>\n": "<p>KL \u30c0\u30a4\u30d0\u30fc\u30b8\u30a7\u30f3\u30b9\u6b63\u5247\u5316\u4fc2\u6570\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb</p>\n",
"<p>Load configurations </p>\n": "<p>\u69cb\u6210\u3092\u30ed\u30fc\u30c9</p>\n",
"<p>Module to convert the model output to non-zero evidences </p>\n": "<p>\u30e2\u30c7\u30eb\u51fa\u529b\u3092\u30bc\u30ed\u4ee5\u5916\u306e\u30a8\u30d3\u30c7\u30f3\u30b9\u306b\u5909\u63db\u3059\u308b\u30e2\u30b8\u30e5\u30fc\u30eb</p>\n",
"<p>Move data to the device </p>\n": "<p>\u30c7\u30fc\u30bf\u3092\u30c7\u30d0\u30a4\u30b9\u306b\u79fb\u52d5</p>\n",
"<p>One-hot coded targets </p>\n": "<p>\u30ef\u30f3\u30db\u30c3\u30c8\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u30bf\u30fc\u30b2\u30c3\u30c8</p>\n",
"<p>ReLU activation </p>\n": "<p>ReLU \u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30b7\u30e7\u30f3</p>\n",
"<p>ReLU to calculate evidence </p>\n": "<p>\u30a8\u30d3\u30c7\u30f3\u30b9\u306e\u8a08\u7b97\u306b\u306f ReLU</p>\n",
"<p>Save the tracked metrics </p>\n": "<p>\u8ffd\u8de1\u3057\u305f\u30e1\u30c8\u30ea\u30af\u30b9\u3092\u4fdd\u5b58\u3059\u308b</p>\n",
"<p>Second <span translate=no>_^_0_^_</span> convolution layer </p>\n": "<p>2 <span translate=no>_^_0_^_</span> \u756a\u76ee\u306e\u7573\u307f\u8fbc\u307f\u5c64</p>\n",
"<p>Set tracker configurations </p>\n": "<p>\u30c8\u30e9\u30c3\u30ab\u30fc\u69cb\u6210\u3092\u8a2d\u5b9a</p>\n",
"<p>Softplus to calculate evidence </p>\n": "<p>\u8a3c\u62e0\u8a08\u7b97\u7528\u30bd\u30d5\u30c8\u30d7\u30e9\u30b9</p>\n",
"<p>Start the experiment and run the training loop </p>\n": "<p>\u5b9f\u9a13\u3092\u958b\u59cb\u3057\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30eb\u30fc\u30d7\u3092\u5b9f\u884c\u3057\u307e\u3059</p>\n",
"<p>Take optimizer step </p>\n": "<p>\u6700\u9069\u5316\u306e\u4e00\u6b69\u3092\u8e0f\u307f\u51fa\u3059</p>\n",
"<p>Total loss </p>\n": "<p>\u7dcf\u640d\u5931</p>\n",
"<p>Track statistics </p>\n": "<p>\u30c8\u30e9\u30c3\u30af\u7d71\u8a08</p>\n",
"<p>Train the model </p>\n": "<p>\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0</p>\n",
"<p>Training/Evaluation mode </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0/\u8a55\u4fa1\u30e2\u30fc\u30c9</p>\n",
"<p>Update global step (number of samples processed) when in training mode </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30e2\u30fc\u30c9\u6642\u306b\u30b0\u30ed\u30fc\u30d0\u30eb\u30b9\u30c6\u30c3\u30d7 (\u51e6\u7406\u3055\u308c\u305f\u30b5\u30f3\u30d7\u30eb\u6570) \u3092\u66f4\u65b0</p>\n",
"<ul><li><span translate=no>_^_0_^_</span> is the batch of MNIST images of shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u306eMNIST\u753b\u50cf\u306e\u30d0\u30c3\u30c1\u3067\u3059 <span translate=no>_^_1_^_</span></li></ul>\n",
"Evidential Deep Learning to Quantify Classification Uncertainty Experiment": "\u5206\u985e\u4e0d\u78ba\u5b9a\u6027\u5b9f\u9a13\u3092\u5b9a\u91cf\u5316\u3059\u308b\u305f\u3081\u306e\u30a8\u30d3\u30c7\u30f3\u30b7\u30e3\u30eb\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0",
"This trains is EDL model on MNIST": "\u3053\u306e\u5217\u8eca\u306fMNIST\u306eEDL\u30e2\u30c7\u30eb\u3067\u3059"
}
translate_cache/uncertainty/evidence/experiment.si.json
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{
"<h1><a href=\"index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a> Experiment</h1>\n<p>This trains a model based on <a href=\"index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a> on MNIST dataset.</p>\n": "<h1><a href=\"index.html\">\u0dc0\u0dbb\u0dca\u0d9c\u0dd3\u0d9a\u0dbb\u0dab \u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a> </h1>\n<p>MNIST\u0daf\u0dad\u0dca\u0dad \u0d9a\u0da7\u0dca\u0da7\u0dbd\u0dba\u0dda <a href=\"index.html\">\u0dc0\u0dbb\u0dca\u0d9c\u0dd3\u0d9a\u0dbb\u0dab \u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf\u0dc0 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0dc3\u0dcf\u0d9a\u0dca\u0dc2\u0dd2 \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a> \u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca \u0dc0\u0dd6 \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0d9a\u0dca \u0db8\u0dd9\u0dba \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dbb\u0dba\u0dd2. </p>\n",
"<h2>Configurations</h2>\n<p>We use <a href=\"../../experiments/mnist.html#MNISTConfigs\"><span translate=no>_^_0_^_</span></a> configurations.</p>\n": "<h2>\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dca</h2>\n<p>\u0d85\u0db4\u0dd2 <a href=\"../../experiments/mnist.html#MNISTConfigs\"><span translate=no>_^_0_^_</span></a> \u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd4. </p>\n",
"<h2>LeNet based model fro MNIST classification</h2>\n": "<h2>Lenet\u0db4\u0daf\u0db1\u0db8\u0dca \u0d9a\u0dbb\u0d9c\u0dad\u0dca \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0dc3\u0dd2\u0da7 MNIST \u0dc0\u0dbb\u0dca\u0d9c\u0dd3\u0d9a\u0dbb\u0dab\u0dba</h2>\n",
"<h3>Create model</h3>\n": "<h3>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</h3>\n",
"<h3>Initialization</h3>\n": "<h3>\u0d86\u0dbb\u0db8\u0dca\u0db7\u0d9a\u0d9a\u0dbb\u0dab\u0dba</h3>\n",
"<h3>KL Divergence Loss Coefficient Schedule</h3>\n": "<h3>KL\u0d85\u0db4\u0dc3\u0dbb\u0db1\u0dba \u0db4\u0dcf\u0da9\u0dd4 \u0dc3\u0d82\u0d9c\u0dd4\u0dab\u0d9a \u0d8b\u0db4\u0dbd\u0dda\u0d9b\u0db1\u0dba</h3>\n",
"<h3>Training or validation step</h3>\n": "<h3>\u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0dc0\u0dc4\u0ddd \u0dc0\u0dbd\u0d82\u0d9c\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0db4\u0dd2\u0dba\u0dc0\u0dbb</h3>\n",
"<p> </p>\n": "<p> </p>\n",
"<p>&#x27;loss_func&#x27;: &#x27;max_likelihood_loss&#x27;, &#x27;loss_func&#x27;: &#x27;cross_entropy_bayes_risk&#x27;, </p>\n": "<p>'loss_func':' max_likelihood_loss ',' \u0d85\u0dc4\u0dd2\u0db8\u0dd2_func ':' cross_entropy_bayes_risk ', </p>\n",
"<p><a href=\"index.html#CrossEntropyBayesRisk\">Cross Entropy Bayes Risk</a> </p>\n": "<p><a href=\"index.html#CrossEntropyBayesRisk\">\u0d9a\u0dd4\u0dbb\u0dd4\u0dc3 \u0d91\u0db1\u0dca\u0da7\u0dca\u0dbb\u0ddc\u0db4\u0dd2 \u0db6\u0dda\u0dc3\u0dca \u0d85\u0dc0\u0daf\u0dcf\u0db1\u0db8\u0dca</a> </p>\n",
"<p><a href=\"index.html#KLDivergenceLoss\">KL Divergence regularization</a> </p>\n": "<p><a href=\"index.html#KLDivergenceLoss\">KL \u0d85\u0db4\u0dc3\u0dbb\u0db1\u0dba \u0dc0\u0dd2\u0db0\u0dd2\u0db8\u0dad\u0dca</a> </p>\n",
"<p><a href=\"index.html#MaximumLikelihoodLoss\">Maximum Likelihood Loss</a> </p>\n": "<p><a href=\"index.html#MaximumLikelihoodLoss\">\u0d8b\u0db4\u0dbb\u0dd2\u0db8 \u0dc3\u0db8\u0dca\u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf\u0dc0 \u0db1\u0dd0\u0dad\u0dd2\u0dc0\u0dd3\u0db8</a> </p>\n",
"<p><a href=\"index.html#SquaredErrorBayesRisk\">Squared Error Bayes Risk</a> </p>\n": "<p><a href=\"index.html#SquaredErrorBayesRisk\">\u0dc0\u0dbb\u0dca\u0d9c \u0daf\u0ddd\u0dc2 \u0d85\u0dc0\u0daf\u0dcf\u0db1\u0db8\u0dca \u0db6\u0dda\u0dc3\u0dca</a> </p>\n",
"<p><a href=\"index.html#TrackStatistics\">Stats module</a> for tracking </p>\n": "<p>\u0dbd\u0dd4\u0dc4\u0dd4\u0db6\u0dd0\u0db3\u0dd3\u0db8\u0dc3\u0db3\u0dc4\u0dcf<a href=\"index.html#TrackStatistics\">\u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0db1 \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd\u0dba</a> </p>\n",
"<p><span translate=no>_^_0_^_</span> max-pooling </p>\n": "<p><span translate=no>_^_0_^_</span> \u0d8b\u0db4\u0dbb\u0dd2\u0db8 \u0dad\u0da7\u0dcf\u0d9a </p>\n",
"<p>Apply dropout </p>\n": "<p>\u0d85\u0dad\u0dc4\u0dd0\u0dbb\u0daf\u0dd0\u0db8\u0dd3\u0db8 \u0dba\u0ddc\u0daf\u0db1\u0dca\u0db1 </p>\n",
"<p>Apply final layer and return </p>\n": "<p>\u0d85\u0dc0\u0dc3\u0dcf\u0db1\u0dc3\u0dca\u0dae\u0dbb\u0dba \u0dba\u0ddc\u0daf\u0db1\u0dca\u0db1 \u0dc3\u0dc4 \u0d86\u0db4\u0dc3\u0dd4 \u0dba\u0db1\u0dca\u0db1 </p>\n",
"<p>Apply first convolution and max pooling. The result has shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db4\u0dc5\u0db8\u0dd4\u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd3\u0db8 \u0dc3\u0dc4 \u0d8b\u0db4\u0dbb\u0dd2\u0db8 \u0dad\u0da7\u0dcf\u0d9a \u0dba\u0ddc\u0daf\u0db1\u0dca\u0db1. \u0db4\u0dca\u0dbb\u0dad\u0dd2 result \u0dbd\u0dba \u0dc4\u0dd0\u0da9\u0dba \u0d87\u0dad <span translate=no>_^_0_^_</span> </p>\n",
"<p>Apply hidden layer </p>\n": "<p>\u0dc3\u0dd0\u0d9f\u0dc0\u0dd4\u0dab\u0dd4\u0dc3\u0dca\u0dae\u0dbb\u0dba \u0dba\u0ddc\u0daf\u0db1\u0dca\u0db1 </p>\n",
"<p>Apply second convolution and max pooling. The result has shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0daf\u0dd9\u0dc0\u0db1\u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd3\u0db8 \u0dc3\u0dc4 \u0d8b\u0db4\u0dbb\u0dd2\u0db8 \u0dad\u0da7\u0dcf\u0d9a \u0dba\u0ddc\u0daf\u0db1\u0dca\u0db1. \u0db4\u0dca\u0dbb\u0dad\u0dd2 result \u0dbd\u0dba \u0dc4\u0dd0\u0da9\u0dba \u0d87\u0dad <span translate=no>_^_0_^_</span> </p>\n",
"<p>Calculate KL Divergence regularization loss </p>\n": "<p>KL\u0d85\u0db4\u0dc3\u0dbb\u0db1\u0dba \u0dc0\u0dd2\u0db0\u0dd2\u0db8\u0dad\u0dca \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d85\u0dbd\u0dcf\u0db7\u0dba \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<p>Calculate gradients </p>\n": "<p>\u0d85\u0db1\u0dd4\u0d9a\u0dca\u0dbb\u0db8\u0dd2\u0d9a\u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<p>Calculate loss </p>\n": "<p>\u0d85\u0dbd\u0dcf\u0db7\u0dba\u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<p>Clear the gradients </p>\n": "<p>\u0d85\u0db1\u0dd4\u0d9a\u0dca\u0dbb\u0db8\u0dd2\u0d9a\u0d89\u0dc0\u0dad\u0dca </p>\n",
"<p>Create a <a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.schedule.Piecewise\">relative piecewise schedule</a> </p>\n": "<p><a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.schedule.Piecewise\">\u0dc3\u0dcf\u0db4\u0dda\u0d9a\u0dca\u0dc2 \u0d9a\u0dd1\u0dbd\u0dd2 \u0d9a\u0dcf\u0dbd\u0dc3\u0da7\u0dc4\u0db1\u0d9a\u0dca \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</a> </p>\n",
"<p>Create configurations </p>\n": "<p>\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca\u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1 </p>\n",
"<p>Create experiment </p>\n": "<p>\u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf\u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1 </p>\n",
"<p>Dropout </p>\n": "<p>\u0dc4\u0dd0\u0dbd\u0dd3\u0db8 </p>\n",
"<p>Dropout for the hidden layer </p>\n": "<p>\u0dc3\u0dd0\u0d9f\u0dc0\u0dd4\u0dab\u0dd4\u0dc3\u0dca\u0dad\u0dbb\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0dad\u0dc4\u0dd0\u0dbb \u0daf\u0dd0\u0db8\u0dd3\u0db8 </p>\n",
"<p>Final fully connected layer to output evidence for <span translate=no>_^_0_^_</span> classes. The ReLU or Softplus activation is applied to this outside the model to get the non-negative evidence </p>\n": "<p><span translate=no>_^_0_^_</span> \u0db4\u0db1\u0dca\u0dad\u0dd2 \u0dc3\u0db3\u0dc4\u0dcf \u0db1\u0dd2\u0db8\u0dd0\u0dc0\u0dd4\u0db8\u0dca \u0dc3\u0dcf\u0d9a\u0dca\u0dc2\u0dd2 \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0db4\u0dd6\u0dbb\u0dca\u0dab \u0dc3\u0db8\u0dca\u0db6\u0db1\u0dca\u0db0\u0dd2\u0dad \u0dc3\u0dca\u0dae\u0dbb\u0dba. Negative \u0dab\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0db1\u0ddc\u0dc0\u0db1 \u0dc3\u0dcf\u0d9a\u0dca\u0dc2\u0dd2 \u0dbd\u0db6\u0dcf \u0d9c\u0dd0\u0db1\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0dd9\u0db1\u0dca \u0db4\u0dd2\u0da7\u0dad RelU \u0dc4\u0ddd Softplus \u0dc3\u0d9a\u0dca\u0dbb\u0dd2\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0db8\u0dda \u0dc3\u0db3\u0dc4\u0dcf \u0dba\u0ddc\u0daf\u0db1\u0dd4 \u0dbd\u0dd0\u0db6\u0dda </p>\n",
"<p>First <span translate=no>_^_0_^_</span> convolution layer </p>\n": "<p>\u0db4\u0dc5\u0db8\u0dd4 <span translate=no>_^_0_^_</span> \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd4\u0dab\u0dd4 \u0dc3\u0dca\u0dae\u0dbb\u0dba </p>\n",
"<p>First fully-connected layer that maps to <span translate=no>_^_0_^_</span> features </p>\n": "<p><span translate=no>_^_0_^_</span> \u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca \u0d9c\u0dad \u0d9a\u0dbb\u0db1 \u0db4\u0dc5\u0db8\u0dd4 \u0db4\u0dd6\u0dbb\u0dca\u0dab \u0dc3\u0db8\u0dca\u0db6\u0db1\u0dca\u0db0\u0dd2\u0dad \u0dc3\u0dca\u0dae\u0dbb\u0dba </p>\n",
"<p>Flatten the tensor to shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0da7\u0dd9\u0db1\u0dca\u0dc3\u0dbb\u0dba\u0dc4\u0dd0\u0da9\u0dba\u0da7 \u0dc3\u0db8\u0dad\u0dbd\u0dcf \u0d9a\u0dbb\u0db1\u0dca\u0db1 <span translate=no>_^_0_^_</span> </p>\n",
"<p>Get evidences <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc3\u0dcf\u0d9a\u0dca\u0dc2\u0dd2\u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1 <span translate=no>_^_0_^_</span> </p>\n",
"<p>Get model outputs </p>\n": "<p>\u0d86\u0daf\u0dbb\u0dca\u0dc1\u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba\u0db1\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1 </p>\n",
"<p>KL Divergence loss coefficient <span translate=no>_^_0_^_</span> </p>\n": "<p>KL\u0d85\u0db4\u0dc3\u0dbb\u0db1\u0dba \u0db4\u0dcf\u0da9\u0dd4 \u0dc3\u0d82\u0d9c\u0dd4\u0dab\u0d9a\u0dba <span translate=no>_^_0_^_</span> </p>\n",
"<p>KL Divergence regularization coefficient schedule </p>\n": "<p>KL\u0d85\u0db4\u0dc3\u0dbb\u0db1\u0dba \u0dc0\u0dd2\u0db0\u0dd2\u0db8\u0dad\u0dca \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0dc3\u0d82\u0d9c\u0dd4\u0dab\u0d9a\u0dba \u0d9a\u0dcf\u0dbd\u0dc3\u0da7\u0dc4\u0db1 </p>\n",
"<p>Load configurations </p>\n": "<p>\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca\u0db4\u0dd6\u0dbb\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<p>Module to convert the model output to non-zero evidences </p>\n": "<p>\u0d86\u0daf\u0dbb\u0dca\u0dc1\u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba \u0dc1\u0dd4\u0db1\u0dca\u0dba \u0db1\u0ddc\u0dc0\u0db1 \u0dc3\u0dcf\u0d9a\u0dca\u0dc2\u0dd2 \u0db6\u0dc0\u0da7 \u0db4\u0dbb\u0dd2\u0dc0\u0dbb\u0dca\u0dad\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd\u0dba </p>\n",
"<p>Move data to the device </p>\n": "<p>\u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba\u0dc0\u0dd9\u0dad \u0daf\u0dad\u0dca\u0dad \u0d9c\u0dd9\u0db1\u0dba\u0db1\u0dca\u0db1 </p>\n",
"<p>One-hot coded targets </p>\n": "<p>\u0d91\u0d9a\u0dca-\u0d8b\u0dab\u0dd4\u0dc3\u0dd4\u0db8\u0dca\u0d9a\u0dda\u0dad \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0d89\u0dbd\u0d9a\u0dca\u0d9a </p>\n",
"<p>ReLU activation </p>\n": "<p>Relu\u0dc3\u0d9a\u0dca\u0dbb\u0dd2\u0dba </p>\n",
"<p>ReLU to calculate evidence </p>\n": "<p>\u0dc3\u0dcf\u0d9a\u0dca\u0dc2\u0dd2\u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 RELU </p>\n",
"<p>Save the tracked metrics </p>\n": "<p>\u0dbd\u0dd4\u0dc4\u0dd4\u0db6\u0dd0\u0db3\u0d87\u0dad\u0dd2 \u0db4\u0dca\u0dbb\u0db8\u0dd2\u0dad\u0dd2\u0d9a \u0dc3\u0dd4\u0dbb\u0d9a\u0dd2\u0db1\u0dca\u0db1 </p>\n",
"<p>Second <span translate=no>_^_0_^_</span> convolution layer </p>\n": "<p>\u0daf\u0dd9\u0dc0\u0db1 <span translate=no>_^_0_^_</span> \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd4\u0dab\u0dd4 \u0dc3\u0dca\u0dae\u0dbb\u0dba </p>\n",
"<p>Set tracker configurations </p>\n": "<p>\u0da7\u0dca\u0dbb\u0dd0\u0d9a\u0dbb\u0dca\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1 </p>\n",
"<p>Softplus to calculate evidence </p>\n": "<p>\u0dc3\u0dcf\u0d9a\u0dca\u0dc2\u0dd2\u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0dc3\u0ddc\u0dc6\u0dca\u0da7\u0dca\u0db4\u0dca\u0dbd\u0dc3\u0dca </p>\n",
"<p>Start the experiment and run the training loop </p>\n": "<p>\u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf\u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0dbd\u0dd6\u0db4\u0dba \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<p>Take optimizer step </p>\n": "<p>\u0db4\u0dca\u0dbb\u0dc1\u0dc3\u0dca\u0dad\u0dd2\u0d9a\u0dbb\u0dab\u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0db1\u0dca\u0db1 </p>\n",
"<p>Total loss </p>\n": "<p>\u0db8\u0dd4\u0dc5\u0dd4\u0d85\u0dbd\u0dcf\u0db7\u0dba </p>\n",
"<p>Track statistics </p>\n": "<p>\u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dbd\u0dda\u0d9b\u0db1\u0db1\u0dd2\u0dbb\u0dd3\u0d9a\u0dca\u0dc2\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<p>Train the model </p>\n": "<p>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<p>Training/Evaluation mode </p>\n": "<p>\u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0dc0/\u0d87\u0d9c\u0dba\u0dd3\u0db8\u0dca\u0db8\u0dcf\u0daf\u0dd2\u0dbd\u0dd2\u0dba </p>\n",
"<p>Update global step (number of samples processed) when in training mode </p>\n": "<p>\u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0db4\u0dca\u0dbb\u0d9a\u0dcf\u0dbb\u0dba\u0dda\u0daf\u0dd3 \u0d9c\u0ddd\u0dbd\u0dd3\u0dba \u0db4\u0dd2\u0dba\u0dc0\u0dbb (\u0dc3\u0dd0\u0d9a\u0dc3\u0dd6 \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd \u0d9c\u0dab\u0db1) \u0dba\u0dcf\u0dc0\u0dad\u0dca\u0d9a\u0dcf\u0dbd\u0dd3\u0db1 \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
"<ul><li><span translate=no>_^_0_^_</span> is the batch of MNIST images of shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span> \u0dc4\u0dd0\u0da9\u0dba\u0dda MNIST \u0dbb\u0dd6\u0db4 \u0d9a\u0dcf\u0dab\u0dca\u0da9\u0dba\u0dba\u0dd2 <span translate=no>_^_1_^_</span></li></ul>\n",
"Evidential Deep Learning to Quantify Classification Uncertainty Experiment": "\u0dc0\u0dbb\u0dca\u0d9c\u0dd3\u0d9a\u0dbb\u0dab \u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8",
"This trains is EDL model on MNIST": "\u0db8\u0dd9\u0db8 \u0daf\u0dd4\u0db8\u0dca\u0dbb\u0dd2\u0dba MNIST \u0dc4\u0dd2 EDL \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0dba\u0dd2"
}
translate_cache/uncertainty/evidence/experiment.zh.json
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{
"<h1><a href=\"index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a> Experiment</h1>\n<p>This trains a model based on <a href=\"index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a> on MNIST dataset.</p>\n": "<h1>\u57fa\u4e8e<a href=\"index.html\">\u8bc1\u636e\u7684\u6df1\u5ea6\u5b66\u4e60\u91cf\u5316\u5206\u7c7b\u4e0d\u786e\u5b9a\u6027</a>\u5b9e\u9a8c</h1>\n<p>\u8fd9\u5c06\u8bad\u7ec3\u4e00\u4e2a\u57fa\u4e8e<a href=\"index.html\">\u8bc1\u636e\u6df1\u5ea6\u5b66\u4e60\u7684\u6a21\u578b\uff0c\u4ee5\u91cf\u5316MNIST\u6570\u636e\u96c6\u4e0a\u7684\u5206\u7c7b\u4e0d\u786e\u5b9a\u6027</a>\u3002</p>\n",
"<h2>Configurations</h2>\n<p>We use <a href=\"../../experiments/mnist.html#MNISTConfigs\"><span translate=no>_^_0_^_</span></a> configurations.</p>\n": "<h2>\u914d\u7f6e</h2>\n<p>\u6211\u4eec\u4f7f\u7528<a href=\"../../experiments/mnist.html#MNISTConfigs\"><span translate=no>_^_0_^_</span></a>\u914d\u7f6e\u3002</p>\n",
"<h2>LeNet based model fro MNIST classification</h2>\n": "<h2>\u57fa\u4e8e LeNET \u7684 MINST \u5206\u7c7b\u6a21\u578b</h2>\n",
"<h3>Create model</h3>\n": "<h3>\u521b\u5efa\u6a21\u578b</h3>\n",
"<h3>Initialization</h3>\n": "<h3>\u521d\u59cb\u5316</h3>\n",
"<h3>KL Divergence Loss Coefficient Schedule</h3>\n": "<h3>KL \u80cc\u79bb\u635f\u5931\u7cfb\u6570\u65f6\u95f4\u8868</h3>\n",
"<h3>Training or validation step</h3>\n": "<h3>\u57f9\u8bad\u6216\u9a8c\u8bc1\u6b65\u9aa4</h3>\n",
"<p> </p>\n": "<p></p>\n",
"<p>&#x27;loss_func&#x27;: &#x27;max_likelihood_loss&#x27;, &#x27;loss_func&#x27;: &#x27;cross_entropy_bayes_risk&#x27;, </p>\n": "<p>'loss_func'\uff1a'max_imilihood_loss'\uff0c'loss_func'\uff1a'cross_entropy_bayes_risk '\uff0c</p>\n",
"<p><a href=\"index.html#CrossEntropyBayesRisk\">Cross Entropy Bayes Risk</a> </p>\n": "<p><a href=\"index.html#CrossEntropyBayesRisk\">\u4ea4\u53c9\u71b5\u8d1d\u53f6\u65af\u98ce\u9669</a></p>\n",
"<p><a href=\"index.html#KLDivergenceLoss\">KL Divergence regularization</a> </p>\n": "<p><a href=\"index.html#KLDivergenceLoss\">KL \u5206\u6b67\u6b63\u5219\u5316</a></p>\n",
"<p><a href=\"index.html#MaximumLikelihoodLoss\">Maximum Likelihood Loss</a> </p>\n": "<p><a href=\"index.html#MaximumLikelihoodLoss\">\u6700\u5927\u4f3c\u7136\u635f\u5931</a></p>\n",
"<p><a href=\"index.html#SquaredErrorBayesRisk\">Squared Error Bayes Risk</a> </p>\n": "<p><a href=\"index.html#SquaredErrorBayesRisk\">\u5e73\u65b9\u8bef\u5dee\u8d1d\u53f6\u65af\u98ce\u9669</a></p>\n",
"<p><a href=\"index.html#TrackStatistics\">Stats module</a> for tracking </p>\n": "<p>\u7528\u4e8e\u8ddf\u8e2a\u7684<a href=\"index.html#TrackStatistics\">\u7edf\u8ba1\u6a21\u5757</a></p>\n",
"<p><span translate=no>_^_0_^_</span> max-pooling </p>\n": "<p><span translate=no>_^_0_^_</span>max-pooling</p>\n",
"<p>Apply dropout </p>\n": "<p>\u7533\u8bf7\u9000\u5b66</p>\n",
"<p>Apply final layer and return </p>\n": "<p>\u5e94\u7528\u6700\u540e\u4e00\u5c42\u7136\u540e\u8fd4\u56de</p>\n",
"<p>Apply first convolution and max pooling. The result has shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5e94\u7528\u7b2c\u4e00\u4e2a\u5377\u79ef\u548c\u6700\u5927\u6c60\u3002\u7ed3\u679c\u6709\u5f62\u72b6<span translate=no>_^_0_^_</span></p>\n",
"<p>Apply hidden layer </p>\n": "<p>\u5e94\u7528\u9690\u85cf\u5c42</p>\n",
"<p>Apply second convolution and max pooling. The result has shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5e94\u7528\u7b2c\u4e8c\u4e2a\u5377\u79ef\u548c\u6700\u5927\u6c60\u3002\u7ed3\u679c\u6709\u5f62\u72b6<span translate=no>_^_0_^_</span></p>\n",
"<p>Calculate KL Divergence regularization loss </p>\n": "<p>\u8ba1\u7b97 KL \u80cc\u79bb\u6b63\u5219\u5316\u635f\u5931</p>\n",
"<p>Calculate gradients </p>\n": "<p>\u8ba1\u7b97\u68af\u5ea6</p>\n",
"<p>Calculate loss </p>\n": "<p>\u8ba1\u7b97\u635f\u5931</p>\n",
"<p>Clear the gradients </p>\n": "<p>\u6e05\u9664\u6e10\u53d8</p>\n",
"<p>Create a <a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.schedule.Piecewise\">relative piecewise schedule</a> </p>\n": "<p>\u521b\u5efa<a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.schedule.Piecewise\">\u76f8\u5bf9\u7684\u5206\u6bb5\u65f6\u95f4\u8868</a></p>\n",
"<p>Create configurations </p>\n": "<p>\u521b\u5efa\u914d\u7f6e</p>\n",
"<p>Create experiment </p>\n": "<p>\u521b\u5efa\u5b9e\u9a8c</p>\n",
"<p>Dropout </p>\n": "<p>\u8f8d\u5b66</p>\n",
"<p>Dropout for the hidden layer </p>\n": "<p>\u9690\u85cf\u56fe\u5c42\u7684\u9000\u51fa</p>\n",
"<p>Final fully connected layer to output evidence for <span translate=no>_^_0_^_</span> classes. The ReLU or Softplus activation is applied to this outside the model to get the non-negative evidence </p>\n": "<p>\u6700\u540e\u4e00\u4e2a\u5b8c\u5168\u8fde\u63a5\u7684\u5c42\uff0c\u7528\u4e8e\u8f93\u51fa<span translate=no>_^_0_^_</span>\u8bfe\u5802\u8bc1\u636e\u3002RelU \u6216 Softplus \u6fc0\u6d3b\u5728\u6a21\u578b\u4e4b\u5916\u5e94\u7528\u4e8e\u6b64\uff0c\u4ee5\u83b7\u5f97\u975e\u8d1f\u9762\u8bc1\u636e</p>\n",
"<p>First <span translate=no>_^_0_^_</span> convolution layer </p>\n": "<p>\u7b2c\u4e00\u4e2a<span translate=no>_^_0_^_</span>\u5377\u79ef\u5c42</p>\n",
"<p>First fully-connected layer that maps to <span translate=no>_^_0_^_</span> features </p>\n": "<p>\u7b2c\u4e00\u4e2a\u6620\u5c04\u5230\u8981\u7d20\u7684\u5b8c\u5168\u8fde\u63a5\u7684<span translate=no>_^_0_^_</span>\u56fe\u5c42</p>\n",
"<p>Flatten the tensor to shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5c06\u5f20\u91cf\u5c55\u5e73\u6210\u5f62\u72b6<span translate=no>_^_0_^_</span></p>\n",
"<p>Get evidences <span translate=no>_^_0_^_</span> </p>\n": "<p>\u83b7\u53d6\u8bc1\u636e<span translate=no>_^_0_^_</span></p>\n",
"<p>Get model outputs </p>\n": "<p>\u83b7\u53d6\u6a21\u578b\u8f93\u51fa</p>\n",
"<p>KL Divergence loss coefficient <span translate=no>_^_0_^_</span> </p>\n": "<p>KL \u80cc\u79bb\u635f\u5931\u7cfb\u6570<span translate=no>_^_0_^_</span></p>\n",
"<p>KL Divergence regularization coefficient schedule </p>\n": "<p>KL \u53d1\u6563\u6b63\u5219\u5316\u7cfb\u6570\u65f6\u95f4\u8868</p>\n",
"<p>Load configurations </p>\n": "<p>\u88c5\u8f7d\u914d\u7f6e</p>\n",
"<p>Module to convert the model output to non-zero evidences </p>\n": "<p>\u7528\u4e8e\u5c06\u6a21\u578b\u8f93\u51fa\u8f6c\u6362\u4e3a\u975e\u96f6\u8bc1\u636e\u7684\u6a21\u5757</p>\n",
"<p>Move data to the device </p>\n": "<p>\u5c06\u6570\u636e\u79fb\u52a8\u5230\u8bbe\u5907</p>\n",
"<p>One-hot coded targets </p>\n": "<p>\u4e00\u70ed\u7f16\u7801\u76ee\u6807</p>\n",
"<p>ReLU activation </p>\n": "<p>\u6fc0\u6d3b ReLU</p>\n",
"<p>ReLU to calculate evidence </p>\n": "<p>RelU \u6765\u8ba1\u7b97\u8bc1\u636e</p>\n",
"<p>Save the tracked metrics </p>\n": "<p>\u4fdd\u5b58\u8ddf\u8e2a\u7684\u6307\u6807</p>\n",
"<p>Second <span translate=no>_^_0_^_</span> convolution layer </p>\n": "<p>\u7b2c\u4e8c\u4e2a<span translate=no>_^_0_^_</span>\u5377\u79ef\u5c42</p>\n",
"<p>Set tracker configurations </p>\n": "<p>\u8bbe\u7f6e\u8ddf\u8e2a\u5668\u914d\u7f6e</p>\n",
"<p>Softplus to calculate evidence </p>\n": "<p>Softplus \u7528\u4e8e\u8ba1\u7b97\u8bc1\u636e</p>\n",
"<p>Start the experiment and run the training loop </p>\n": "<p>\u5f00\u59cb\u5b9e\u9a8c\u5e76\u8fd0\u884c\u8bad\u7ec3\u5faa\u73af</p>\n",
"<p>Take optimizer step </p>\n": "<p>\u91c7\u53d6\u4f18\u5316\u5668\u6b65\u9aa4</p>\n",
"<p>Total loss </p>\n": "<p>\u603b\u4e8f\u635f</p>\n",
"<p>Track statistics </p>\n": "<p>\u8ffd\u8e2a\u7edf\u8ba1\u6570\u636e</p>\n",
"<p>Train the model </p>\n": "<p>\u8bad\u7ec3\u6a21\u578b</p>\n",
"<p>Training/Evaluation mode </p>\n": "<p>\u8bad\u7ec3/\u8bc4\u4f30\u6a21\u5f0f</p>\n",
"<p>Update global step (number of samples processed) when in training mode </p>\n": "<p>\u5728\u8bad\u7ec3\u6a21\u5f0f\u4e0b\u66f4\u65b0\u5168\u5c40\u6b65\u957f\uff08\u5904\u7406\u7684\u6837\u672c\u6570\uff09</p>\n",
"<ul><li><span translate=no>_^_0_^_</span> is the batch of MNIST images of shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u4e00\u6279 MNIST \u5f62\u72b6\u7684\u56fe\u50cf<span translate=no>_^_1_^_</span></li></ul>\n",
"Evidential Deep Learning to Quantify Classification Uncertainty Experiment": "\u57fa\u4e8e\u8bc1\u636e\u7684\u6df1\u5ea6\u5b66\u4e60\u91cf\u5316\u5206\u7c7b\u4e0d\u786e\u5b9a\u6027\u5b9e\u9a8c",
"This trains is EDL model on MNIST": "\u8fd9\u5217\u706b\u8f66\u662f MNIST \u4e0a\u7684 EDL \u578b\u53f7"
}
translate_cache/uncertainty/evidence/readme.ja.json
+4
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{
"<h1><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/1806.01768\">Evidential Deep Learning to Quantify Classification Uncertainty</a>.</p>\n<p>Here is the <a href=\"https://nn.labml.ai/uncertainty/evidence/experiment.html\">training code <span translate=no>_^_0_^_</span></a> to train a model on MNIST dataset. </p>\n": "<h1><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">\u5206\u985e\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u5b9a\u91cf\u5316\u3059\u308b\u30a8\u30d3\u30c7\u30f3\u30b7\u30e3\u30eb\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0</a></h1>\n<p>\u3053\u308c\u306f\u3001<a href=\"https://arxiv.org/abs/1806.01768\">\u5206\u985e\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u5b9a\u91cf\u5316\u3059\u308b\u305f\u3081\u306e\u8ad6\u6587\u300c\u30a8\u30d3\u30c7\u30f3\u30b7\u30e3\u30eb\u30fb\u30c7\u30a3\u30fc\u30d7\u30fb\u30e9\u30fc\u30cb\u30f3\u30b0\u300d<a href=\"https://pytorch.org\">\u3092PyTorch\u3067\u5b9f\u88c5\u3057\u305f\u3082\u306e\u3067\u3059</a></a>\u3002</p>\n<p>\u3053\u308c\u306f\u3001<a href=\"https://nn.labml.ai/uncertainty/evidence/experiment.html\"><span translate=no>_^_0_^_</span>MNIST\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u305f\u3081\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30b3\u30fc\u30c9\u3067\u3059</a>\u3002</p>\n",
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translate_cache/uncertainty/evidence/readme.si.json
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{
"<h1><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/1806.01768\">Evidential Deep Learning to Quantify Classification Uncertainty</a>.</p>\n<p>Here is the <a href=\"https://nn.labml.ai/uncertainty/evidence/experiment.html\">training code <span translate=no>_^_0_^_</span></a> to train a model on MNIST dataset.</p>\n<p><a href=\"https://app.labml.ai/run/f82b2bfc01ba11ecbb2aa16a33570106\"><span translate=no>_^_1_^_</span></a> </p>\n": "<h1><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">\u0dc0\u0dbb\u0dca\u0d9c\u0dd3\u0d9a\u0dbb\u0dab \u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf\u0dc0 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a></h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"https://arxiv.org/abs/1806.01768\">\u0dc0\u0dbb\u0dca\u0d9c\u0dd3\u0d9a\u0dbb\u0dab \u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf\u0dc0 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0db4\u0dd0\u0dc4\u0dd0\u0daf\u0dd2\u0dbd\u0dd2 \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0d89\u0d9c\u0dd9\u0db1\u0dd4\u0db8\u0dca \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8</a> <a href=\"https://pytorch.org\">PyTorch</a> \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dba\u0dd2. </p>\n<p>MNIST\u0daf\u0dad\u0dca\u0dad \u0d9a\u0da7\u0dca\u0da7\u0dbd\u0dba\u0dda \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0d9a\u0dca <a href=\"https://nn.labml.ai/uncertainty/evidence/experiment.html\">\u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 <span translate=no>_^_0_^_</span>\u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dda\u0dad\u0dba</a> \u0db8\u0dd9\u0db1\u0dca\u0db1. </p>\n<p><a href=\"https://app.labml.ai/run/f82b2bfc01ba11ecbb2aa16a33570106\"><span translate=no>_^_1_^_</span></a> </p>\n",
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translate_cache/uncertainty/evidence/readme.zh.json
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{
"<h1><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/1806.01768\">Evidential Deep Learning to Quantify Classification Uncertainty</a>.</p>\n<p>Here is the <a href=\"https://nn.labml.ai/uncertainty/evidence/experiment.html\">training code <span translate=no>_^_0_^_</span></a> to train a model on MNIST dataset. </p>\n": "<h1><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">\u7528\u8bc1\u636e\u6df1\u5ea6\u5b66\u4e60\u6765\u91cf\u5316\u5206\u7c7b\u4e0d\u786e\u5b9a\u6027</a></h1>\n<p>\u8fd9\u662f P <a href=\"https://pytorch.org\">yTorch</a> \u5bf9\u300a<a href=\"https://arxiv.org/abs/1806.01768\">\u91cf\u5316\u5206\u7c7b\u4e0d\u786e\u5b9a\u6027\u7684\u8bc1\u636e\u6df1\u5ea6\u5b66\u4e60</a>\u300b\u8bba\u6587\u7684\u5b9e\u73b0\u3002</p>\n<p>\u8fd9\u662f\u5728 MNIST \u6570\u636e\u96c6\u4e0a<a href=\"https://nn.labml.ai/uncertainty/evidence/experiment.html\">\u8bad\u7ec3\u6a21\u578b\u7684\u8bad\u7ec3\u4ee3\u7801<span translate=no>_^_0_^_</span></a>\u3002</p>\n",
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{
"<h1><a href=\"https://nn.labml.ai/uncertainty/index.html\">Neural Networks with Uncertainty Estimation</a></h1>\n<p>These are neural network architectures that estimate the uncertainty of the predictions.</p>\n<ul><li><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a> </li></ul>\n": "<h1><a href=\"https://nn.labml.ai/uncertainty/index.html\">\u4e0d\u78ba\u5b9f\u6027\u63a8\u5b9a\u3092\u7528\u3044\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af</a></h1>\n<p>\u3053\u308c\u3089\u306f\u3001\u4e88\u6e2c\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u63a8\u5b9a\u3059\u308b\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3067\u3059\u3002</p>\n<ul><li><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">\u5206\u985e\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u5b9a\u91cf\u5316\u3059\u308b\u30a8\u30d3\u30c7\u30f3\u30b7\u30e3\u30eb\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0</a></li></ul>\n",
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}
translate_cache/uncertainty/readme.si.json
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{
"<h1><a href=\"https://nn.labml.ai/uncertainty/index.html\">Neural Networks with Uncertainty Estimation</a></h1>\n<p>These are neural network architectures that estimate the uncertainty of the predictions.</p>\n<ul><li><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a> </li></ul>\n": "<h1><a href=\"https://nn.labml.ai/uncertainty/index.html\">\u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf \u0d87\u0dc3\u0dca\u0dad\u0db8\u0dda\u0db1\u0dca\u0dad\u0dd4 \u0dc3\u0dc4\u0dd2\u0dad \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd</a></h1>\n<p>\u0db8\u0dda\u0dc0\u0dcf\u0db4\u0dd4\u0dbb\u0ddd\u0d9a\u0dae\u0db1\u0dc0\u0dbd \u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf\u0dc0 \u0dad\u0d9a\u0dca\u0dc3\u0dda\u0dbb\u0dd4 \u0d9a\u0dbb\u0db1 \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd \u0d9c\u0dd8\u0dc4 \u0db1\u0dd2\u0dbb\u0dca\u0db8\u0dcf\u0dab \u0dc0\u0dda. </p>\n<ul><li><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">\u0dc0\u0dbb\u0dca\u0d9c\u0dd3\u0d9a\u0dbb\u0dab \u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf\u0dc0 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a> </li></ul>\n",
"Neural Networks with Uncertainty Estimation": "\u0d85\u0dc0\u0dd2\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dad\u0dcf \u0d87\u0dc3\u0dca\u0dad\u0db8\u0dda\u0db1\u0dca\u0dad\u0dd4 \u0dc3\u0dc4\u0dd2\u0dad \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd"
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translate_cache/uncertainty/readme.zh.json
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{
"<h1><a href=\"https://nn.labml.ai/uncertainty/index.html\">Neural Networks with Uncertainty Estimation</a></h1>\n<p>These are neural network architectures that estimate the uncertainty of the predictions.</p>\n<ul><li><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">Evidential Deep Learning to Quantify Classification Uncertainty</a> </li></ul>\n": "<h1><a href=\"https://nn.labml.ai/uncertainty/index.html\">\u5177\u6709\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\u7684\u795e\u7ecf\u7f51\u7edc</a></h1>\n<p>\u8fd9\u4e9b\u662f\u4f30\u8ba1\u9884\u6d4b\u4e0d\u786e\u5b9a\u6027\u7684\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\u3002</p>\n<ul><li><a href=\"https://nn.labml.ai/uncertainty/evidence/index.html\">\u7528\u4e8e\u91cf\u5316\u5206\u7c7b\u4e0d\u786e\u5b9a\u6027\u7684\u8bc1\u636e\u6027\u6df1\u5ea6\u5b66\u4e60</a></li></ul>\n",
"Neural Networks with Uncertainty Estimation": "\u5177\u6709\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\u7684\u795e\u7ecf\u7f51\u7edc"
}