chore: import upstream snapshot with attribution

translate_cache/gan/original/__init__.ja.json

@@ -0,0 +1,13 @@
+{
+ "<h1>Generative Adversarial Networks (GAN)</h1>\n<p>This is an implementation of <a href=\"https://arxiv.org/abs/1406.2661\">Generative Adversarial Networks</a>.</p>\n<p>The generator, <span translate=no>_^_0_^_</span> generates samples that match the distribution of data, while the discriminator, <span translate=no>_^_1_^_</span> gives the probability that <span translate=no>_^_2_^_</span> came from data rather than <span translate=no>_^_3_^_</span>.</p>\n<p>We train <span translate=no>_^_4_^_</span> and <span translate=no>_^_5_^_</span> simultaneously on a two-player min-max game with value function <span translate=no>_^_6_^_</span>.</p>\n<p><span translate=no>_^_7_^_</span></p>\n<p><span translate=no>_^_8_^_</span> is the probability distribution over data, whilst <span translate=no>_^_9_^_</span> probability distribution of <span translate=no>_^_10_^_</span>, which is set to gaussian noise.</p>\n<p>This file defines the loss functions. <a href=\"experiment.html\">Here</a> is an MNIST example with two multilayer perceptron for the generator and discriminator.</p>\n": "<h1>\u30b8\u30a7\u30cd\u30ec\u30fc\u30c6\u30a3\u30d6\u30fb\u30a2\u30c9\u30d0\u30fc\u30b5\u30ea\u30a2\u30eb\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af (GAN)</h1>\n<p><a href=\"https://arxiv.org/abs/1406.2661\">\u3053\u308c\u306f\u30b8\u30a7\u30cd\u30ec\u30fc\u30c6\u30a3\u30d6\u30fb\u30a2\u30c9\u30d0\u30fc\u30b5\u30ea\u30a2\u30eb\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u5b9f\u88c5\u3067\u3059</a>\u3002</p>\n<p><span translate=no>_^_0_^_</span>\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u30fc\u306f\u30c7\u30fc\u30bf\u306e\u5206\u5e03\u306b\u4e00\u81f4\u3059\u308b\u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210\u3057\u3001<span translate=no>_^_1_^_</span>\u30c7\u30a3\u30b9\u30af\u30ea\u30df\u30cd\u30fc\u30bf\u30fc\u306f\u30c7\u30fc\u30bf\u304b\u3089\u5f97\u3089\u308c\u308b\u78ba\u7387\u3067\u306f\u306a\u304f\u3001<span translate=no>_^_2_^_</span>\u30c7\u30fc\u30bf\u304b\u3089\u5f97\u3089\u308c\u308b\u78ba\u7387\u3092\u8fd4\u3057\u307e\u3059\u3002<span translate=no>_^_3_^_</span></p>\n<p><span translate=no>_^_4_^_</span><span translate=no>_^_5_^_</span>\u30d0\u30ea\u30e5\u30fc\u6a5f\u80fd\u3092\u5099\u3048\u305f2\u4eba\u7528\u306e\u30df\u30cb\u30de\u30c3\u30af\u30b9\u30b2\u30fc\u30e0\u3067\u540c\u6642\u306b\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u3059\u3002<span translate=no>_^_6_^_</span></p>\n<p><span translate=no>_^_7_^_</span></p>\n<p><span translate=no>_^_8_^_</span>\u306f\u30c7\u30fc\u30bf\u5168\u4f53\u306e\u78ba\u7387\u5206\u5e03\u3067<span translate=no>_^_10_^_</span>\u3001<span translate=no>_^_9_^_</span>\u306e\u78ba\u7387\u5206\u5e03\u306f\u30ac\u30a6\u30b9\u30ce\u30a4\u30ba\u306b\u8a2d\u5b9a\u3055\u308c\u307e\u3059\u3002</p>\n<p>\u3053\u306e\u30d5\u30a1\u30a4\u30eb\u306f\u640d\u5931\u95a2\u6570\u3092\u5b9a\u7fa9\u3057\u307e\u3059\u3002<a href=\"experiment.html\">\u3053\u308c\u306f</a>\u3001\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u30fc\u3068\u30c7\u30a3\u30b9\u30af\u30ea\u30df\u30cd\u30fc\u30bf\u30fc\u306b2\u3064\u306e\u591a\u5c64\u30d1\u30fc\u30bb\u30d7\u30c8\u30ed\u30f3\u3092\u4f7f\u3063\u305fMNIST\u306e\u4f8b\u3067\u3059</p>\u3002\n",
+ "<h2>Discriminator Loss</h2>\n<p>Discriminator should <strong>ascend</strong> on the gradient,</p>\n<p><span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span> is the mini-batch size and <span translate=no>_^_2_^_</span> is used to index samples in the mini-batch. <span translate=no>_^_3_^_</span> are samples from <span translate=no>_^_4_^_</span> and <span translate=no>_^_5_^_</span> are samples from <span translate=no>_^_6_^_</span>.</p>\n": "<h2>\u30c7\u30a3\u30b9\u30af\u30ea\u30df\u30cd\u30fc\u30bf\u30fc\u30ed\u30b9</h2>\n<p><strong>\u30c7\u30a3\u30b9\u30af\u30ea\u30df\u30cd\u30fc\u30bf\u30fc\u306f\u52fe\u914d\u306e\u4e0a\u3092\u5411\u3044\u3066\u3044\u308b\u306f\u305a\u3067\u3059\u304c</strong></p>\n<p><span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span>\u306f\u30df\u30cb\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u3067\u3001<span translate=no>_^_2_^_</span>\u30df\u30cb\u30d0\u30c3\u30c1\u5185\u306e\u30b5\u30f3\u30d7\u30eb\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<span translate=no>_^_3_^_</span><span translate=no>_^_4_^_</span><span translate=no>_^_5_^_</span>\u304b\u3089\u306e\u30b5\u30f3\u30d7\u30eb\u3067\u3042\u308a\u3001<span translate=no>_^_6_^_</span>\u304b\u3089\u306e\u30b5\u30f3\u30d7\u30eb\u3067\u3059\u3002</p>\n",
+ "<h2>Generator Loss</h2>\n<p>Generator should <strong>descend</strong> on the gradient,</p>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h2>\u767a\u96fb\u6a5f\u640d\u5931</h2>\n<p><strong>\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u306f\u52fe\u914d\u306b\u6cbf\u3063\u3066\u4e0b\u964d\u3059\u308b\u306f\u305a\u3067\u3059\u304c</strong>\u3001</p>\n<p><span translate=no>_^_0_^_</span></p>\n",
+ "<p> <span translate=no>_^_0_^_</span> are logits from <span translate=no>_^_1_^_</span> and <span translate=no>_^_2_^_</span> are logits from <span translate=no>_^_3_^_</span></p>\n": "<p><span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u5143\u306e\u30ed\u30b8\u30c3\u30c8\u3068\u5143\u306e\u30ed\u30b8\u30c3\u30c8 <span translate=no>_^_3_^_</span></p>\n",
+ "<p> Create smoothed labels</p>\n": "<p>\u306a\u3081\u3089\u304b\u306a\u30e9\u30d9\u30eb\u3092\u4f5c\u6210</p>\n",
+ "<p>Labels are registered as buffered and persistence is set to <span translate=no>_^_0_^_</span>. </p>\n": "<p>\u30e9\u30d9\u30eb\u306f\u30d0\u30c3\u30d5\u30a1\u30ea\u30f3\u30b0\u3055\u308c\u3066\u767b\u9332\u3055\u308c\u3001\u30d1\u30fc\u30b7\u30b9\u30bf\u30f3\u30b9\u306f\u306b\u8a2d\u5b9a\u3055\u308c\u307e\u3059\u3002<span translate=no>_^_0_^_</span></p>\n",
+ "<p>We use PyTorch Binary Cross Entropy Loss, which is <span translate=no>_^_0_^_</span>, where <span translate=no>_^_1_^_</span> are the labels and <span translate=no>_^_2_^_</span> are the predictions. <em>Note the negative sign</em>. We use labels equal to <span translate=no>_^_3_^_</span> for <span translate=no>_^_4_^_</span> from <span translate=no>_^_5_^_</span> and labels equal to <span translate=no>_^_6_^_</span> for <span translate=no>_^_7_^_</span> from <span translate=no>_^_8_^_</span> Then descending on the sum of these is the same as ascending on the above gradient.</p>\n<p><span translate=no>_^_9_^_</span> combines softmax and binary cross entropy loss. </p>\n": "<p>PyTorch\u306e\u30d0\u30a4\u30ca\u30ea\u30af\u30ed\u30b9\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u640d\u5931\u3092\u4f7f\u3044\u307e\u3059\u3002\u3064\u307e\u308a<span translate=no>_^_0_^_</span>\u3001<span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u30e9\u30d9\u30eb\u306f\u3069\u3053\u3067\u4e88\u6e2c\u306f\u3069\u3053\u3067\u3059\u304b\u3002<em>\u30de\u30a4\u30ca\u30b9\u8a18\u53f7\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044</em>\u3002for from <span translate=no>_^_5_^_</span> \u3068\u540c\u3058\u30e9\u30d9\u30eb\u3068 <span translate=no>_^_3_^_</span> for <span translate=no>_^_4_^_</span> from <span translate=no>_^_6_^_</span> \u306b\u7b49\u3057\u3044\u30e9\u30d9\u30eb\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u3053\u308c\u3089\u306e\u5408\u8a08\u3067\u964d\u9806\u306b\u306a\u308b\u3068\u3001\u4e0a\u8a18\u306e\u52fe\u914d\u3067\u6607\u9806\u306b\u306a\u308b\u306e\u3068\u540c\u3058\u306b\u306a\u308a\u307e\u3059</p>\u3002<span translate=no>_^_7_^_</span> <span translate=no>_^_8_^_</span>\n<p><span translate=no>_^_9_^_</span>\u30bd\u30d5\u30c8\u30de\u30c3\u30af\u30b9\u3068\u30d0\u30a4\u30ca\u30ea\u30af\u30ed\u30b9\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u640d\u5931\u3092\u7d44\u307f\u5408\u308f\u305b\u305f\u3082\u306e\u3067\u3059\u3002</p>\n",
+ "<p>We use label smoothing because it seems to work better in some cases </p>\n": "<p>\u30e9\u30d9\u30eb\u30b9\u30e0\u30fc\u30b8\u30f3\u30b0\u3092\u4f7f\u7528\u3059\u308b\u306e\u306f\u3001\u5834\u5408\u306b\u3088\u3063\u3066\u306f\u3046\u307e\u304f\u3044\u304f\u3068\u601d\u308f\u308c\u308b\u305f\u3081\u3067\u3059\u3002</p>\n",
+ "<p>We use labels equal to <span translate=no>_^_0_^_</span> for <span translate=no>_^_1_^_</span> from <span translate=no>_^_2_^_</span> Then descending on this loss is the same as descending on the above gradient. </p>\n": "<p><span translate=no>_^_0_^_</span>for <span translate=no>_^_1_^_</span> \u3068\u7b49\u3057\u3044\u30e9\u30d9\u30eb\u3092\u4f7f\u3044\u307e\u3059\u3002<span translate=no>_^_2_^_</span>\u3053\u306e\u640d\u5931\u3067\u964d\u9806\u3092\u964d\u9806\u3059\u308b\u3068\u3001\u4e0a\u306e\u52fe\u914d\u3067\u964d\u9806\u306b\u306a\u308b\u306e\u3068\u540c\u3058\u306b\u306a\u308a\u307e\u3059\u3002</p>\n",
+ "A simple PyTorch implementation/tutorial of Generative Adversarial Networks (GAN) loss functions.": "\u30b8\u30a7\u30cd\u30ec\u30fc\u30c6\u30a3\u30d6\u30fb\u30a2\u30c9\u30d0\u30fc\u30b5\u30ea\u30a2\u30eb\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08GAN\uff09\u640d\u5931\u95a2\u6570\u306e\u7c21\u5358\u306aPyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3002",
+ "Generative Adversarial Networks (GAN)": "\u30b8\u30a7\u30cd\u30ec\u30fc\u30c6\u30a3\u30d6\u30fb\u30a2\u30c9\u30d0\u30fc\u30b5\u30ea\u30a2\u30eb\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af (GAN)"
+}

translate_cache/gan/original/__init__.si.json

@@ -0,0 +1,13 @@
+{
+ "<h1>Generative Adversarial Networks (GAN)</h1>\n<p>This is an implementation of <a href=\"https://arxiv.org/abs/1406.2661\">Generative Adversarial Networks</a>.</p>\n<p>The generator, <span translate=no>_^_0_^_</span> generates samples that match the distribution of data, while the discriminator, <span translate=no>_^_1_^_</span> gives the probability that <span translate=no>_^_2_^_</span> came from data rather than <span translate=no>_^_3_^_</span>.</p>\n<p>We train <span translate=no>_^_4_^_</span> and <span translate=no>_^_5_^_</span> simultaneously on a two-player min-max game with value function <span translate=no>_^_6_^_</span>.</p>\n<p><span translate=no>_^_7_^_</span></p>\n<p><span translate=no>_^_8_^_</span> is the probability distribution over data, whilst <span translate=no>_^_9_^_</span> probability distribution of <span translate=no>_^_10_^_</span>, which is set to gaussian noise.</p>\n<p>This file defines the loss functions. <a href=\"experiment.html\">Here</a> is an MNIST example with two multilayer perceptron for the generator and discriminator.</p>\n": "<h1>\u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a\u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd (GAN)</h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"https://arxiv.org/abs/1406.2661\">Generative Aversarial Network</a>\u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dba\u0dd2. </p>\n<p>\u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a\u0dba\u0db1\u0dca\u0dad\u0dca\u0dbb\u0dba, \u0daf\u0dad\u0dca\u0dad \u0db6\u0dd9\u0daf\u0dcf \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0da7 \u0d9c\u0dd0\u0dbd\u0db4\u0dd9\u0db1 \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd <span translate=no>_^_0_^_</span> \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1 \u0d85\u0dad\u0dbb \u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0d9a\u0db8\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dcf, \u0dc0\u0da9\u0dcf \u0daf\u0dad\u0dca\u0dad \u0dc0\u0dbd\u0dd2\u0db1\u0dca <span translate=no>_^_2_^_</span> \u0db4\u0dd0\u0db8\u0dd2\u0dab\u0dd2 \u0dc3\u0db8\u0dca\u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf\u0dc0 <span translate=no>_^_1_^_</span> \u0dbd\u0db6\u0dcf \u0daf\u0dd9\u0dba\u0dd2 <span translate=no>_^_3_^_</span>. </p>\n<p>\u0dc0\u0da7\u0dd2\u0db1\u0dcf\u0d9a\u0db8\u0dca\u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0d9a\u0dcf\u0dbb\u0dd2\u0dad\u0dca\u0dc0\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dca\u0dbb\u0dd3\u0da9\u0d9a \u0daf\u0dd9\u0d9a\u0d9a \u0db8\u0dd2\u0db1\u0dd2-\u0db8\u0dd0\u0d9a\u0dca\u0dc3\u0dca \u0d9a\u0dca\u0dbb\u0dd3\u0da9\u0dcf\u0dc0\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0db4\u0dd2 \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 <span translate=no>_^_4_^_</span> \u0d9a\u0dbb\u0db8\u0dd4 <span translate=no>_^_6_^_</span>. <span translate=no>_^_5_^_</span> </p>\n<p><span translate=no>_^_7_^_</span></p>\n<p><span translate=no>_^_8_^_</span> \u0dba\u0db1\u0dd4 \u0daf\u0dad\u0dca\u0dad \u0dc0\u0dbd\u0da7 \u0dc0\u0da9\u0dcf \u0dc3\u0db8\u0dca\u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0db6\u0dd9\u0daf\u0dcf \u0dc4\u0dd0\u0dbb\u0dd3\u0db8 \u0dc0\u0db1 \u0d85\u0dad\u0dbb <span translate=no>_^_9_^_</span> \u0dc3\u0db8\u0dca\u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dca\u0dba\u0dcf\u0db4\u0dca\u0dad\u0dd2\u0dba <span translate=no>_^_10_^_</span>\u0dc0\u0db1 \u0d85\u0dad\u0dbb \u0d91\u0dba \u0d9c\u0dc0\u0dd4\u0dc3\u0dd2\u0dba\u0dcf\u0db1\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba\u0da7 \u0dc3\u0d9a\u0dc3\u0dcf \u0d87\u0dad. </p>\n<p>\u0db8\u0dd9\u0db8\u0d9c\u0ddc\u0db1\u0dd4\u0dc0 \u0db4\u0dcf\u0da9\u0dd4 \u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0dba\u0db1\u0dca \u0d85\u0dbb\u0dca\u0dae \u0daf\u0d9a\u0dca\u0dc0\u0dba\u0dd2. <a href=\"experiment.html\">\u0db8\u0dd9\u0db1\u0dca\u0db1</a> \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0dba\u0db1\u0dca\u0dad\u0dca\u0dbb\u0dba \u0dc3\u0dc4 \u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0d9a\u0db8\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dcf \u0dc3\u0db3\u0dc4\u0dcf \u0db6\u0dc4\u0dd4 \u0dc3\u0dca\u0dae\u0dbb perceptron \u0daf\u0dd9\u0d9a\u0d9a\u0dca \u0dc3\u0dc4\u0dd2\u0dad MNIST \u0d8b\u0daf\u0dcf\u0dc4\u0dbb\u0dab\u0dba\u0d9a\u0dd2. </p>\n",
+ "<h2>Discriminator Loss</h2>\n<p>Discriminator should <strong>ascend</strong> on the gradient,</p>\n<p><span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span> is the mini-batch size and <span translate=no>_^_2_^_</span> is used to index samples in the mini-batch. <span translate=no>_^_3_^_</span> are samples from <span translate=no>_^_4_^_</span> and <span translate=no>_^_5_^_</span> are samples from <span translate=no>_^_6_^_</span>.</p>\n": "<h2>\u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0d9a\u0db8\u0dca\u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dcf\u0d9c\u0dda \u0db4\u0dcf\u0da9\u0dd4\u0dc0</h2>\n<p>\u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0d9a\u0db8\u0dca\u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dcf \u0dc1\u0dca\u0dbb\u0dda\u0dab\u0dd2\u0dba <strong>\u0db8\u0dad\u0da7 \u0db1\u0dd0\u0d9c\u0dca\u0dc0\u0dd2\u0dba</strong> \u0dba\u0dd4\u0dad\u0dd4\u0dba,</p>\n<p><span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span> \u0d9a\u0dd4\u0da9\u0dcf \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba <span translate=no>_^_2_^_</span> \u0dc0\u0db1 \u0d85\u0dad\u0dbb \u0d9a\u0dd4\u0da9\u0dcf \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dda \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd \u0daf\u0dbb\u0dca\u0dc1\u0d9a\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0dba\u0dd2. <span translate=no>_^_3_^_</span> \u0dc0\u0dd9\u0dad\u0dd2\u0db1\u0dca \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd \u0dc0\u0db1 <span translate=no>_^_4_^_</span> \u0d85\u0dad\u0dbb <span translate=no>_^_5_^_</span> \u0d92\u0dc0\u0dcf \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd <span translate=no>_^_6_^_</span>\u0dc0\u0dda. </p>\n",
+ "<h2>Generator Loss</h2>\n<p>Generator should <strong>descend</strong> on the gradient,</p>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h2>\u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a\u0db1\u0dd0\u0dad\u0dd2\u0dc0\u0dd3\u0db8</h2>\n<p>\u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a\u0dba\u0db1\u0dca\u0dad\u0dca\u0dbb\u0dba \u0d85\u0db1\u0dd4\u0d9a\u0dca\u0dbb\u0db8\u0dd2\u0d9a \u0db8\u0dad\u0da7 <strong>\u0db6\u0dd0\u0dc3</strong> \u0dba\u0dcf \u0dba\u0dd4\u0dad\u0dd4\u0dba,</p>\n<p><span translate=no>_^_0_^_</span></p>\n",
+ "<p> <span translate=no>_^_0_^_</span> are logits from <span translate=no>_^_1_^_</span> and <span translate=no>_^_2_^_</span> are logits from <span translate=no>_^_3_^_</span></p>\n": "<p> <span translate=no>_^_0_^_</span> \u0dc3\u0dd2\u0da7 \u0db4\u0dd2\u0dc0\u0dd2\u0dc3\u0dd4\u0db8\u0dca <span translate=no>_^_2_^_</span> \u0dc0\u0db1 <span translate=no>_^_1_^_</span> \u0d85\u0dad\u0dbb \u0dc3\u0dd2\u0da7 \u0db4\u0dd2\u0dc0\u0dd2\u0dc3\u0dd4\u0db8\u0dca \u0dc0\u0dda <span translate=no>_^_3_^_</span></p>\n",
+ "<p> Create smoothed labels</p>\n": "<p> \u0dc3\u0dd4\u0db8\u0da7\u0dbd\u0dda\u0db6\u0dbd \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</p>\n",
+ "<p>Labels are registered as buffered and persistence is set to <span translate=no>_^_0_^_</span>. </p>\n": "<p>\u0dbd\u0dda\u0db6\u0dbd\u0db6\u0dc6\u0dbb\u0dca \u0dbd\u0dd9\u0dc3 \u0dbd\u0dd2\u0dba\u0dcf\u0db4\u0daf\u0dd2\u0d82\u0da0\u0dd2 \u0d9a\u0dbb \u0d87\u0dad\u0dd2 \u0d85\u0dad\u0dbb \u0db1\u0ddc\u0db4\u0dc3\u0dd4\u0db6\u0da7 \u0d8b\u0dad\u0dca\u0dc3\u0dcf\u0dc4\u0dba \u0dc3\u0d9a\u0dc3\u0dcf <span translate=no>_^_0_^_</span>\u0d87\u0dad. </p>\n",
+ "<p>We use PyTorch Binary Cross Entropy Loss, which is <span translate=no>_^_0_^_</span>, where <span translate=no>_^_1_^_</span> are the labels and <span translate=no>_^_2_^_</span> are the predictions. <em>Note the negative sign</em>. We use labels equal to <span translate=no>_^_3_^_</span> for <span translate=no>_^_4_^_</span> from <span translate=no>_^_5_^_</span> and labels equal to <span translate=no>_^_6_^_</span> for <span translate=no>_^_7_^_</span> from <span translate=no>_^_8_^_</span> Then descending on the sum of these is the same as ascending on the above gradient.</p>\n<p><span translate=no>_^_9_^_</span> combines softmax and binary cross entropy loss. </p>\n": "<p>\u0d85\u0db4\u0dd2\u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd4 PyTorch \u0daf\u0dca\u0dc0\u0dd2\u0db8\u0dba \u0d9a\u0dca\u0dbb\u0ddc\u0dc3\u0dca \u0d91\u0db1\u0dca\u0da7\u0dca\u0dbb\u0ddc\u0db4\u0dd2 \u0db1\u0dd0\u0dad\u0dd2\u0dc0\u0dd3\u0db8 <span translate=no>_^_0_^_</span>, \u0d91\u0db1\u0db8\u0dca \u0dbd\u0dda\u0db6\u0dbd\u0dca \u0dc3\u0dc4 <span translate=no>_^_2_^_</span> \u0d85\u0db1\u0dcf\u0dc0\u0dd0\u0d9a\u0dd2 <span translate=no>_^_1_^_</span> \u0d9a\u0ddc\u0dc4\u0dda\u0daf \u0dba\u0db1\u0dca\u0db1\u0dba\u0dd2. <em>\u0dc3\u0dd8\u0dab\u0dbd\u0d9a\u0dd4\u0dab \u0dc3\u0da7\u0dc4\u0db1\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</em>. \u0d85\u0db4\u0dd2 \u0dc3\u0dd2\u0da7 \u0dc3\u0db8\u0dcf\u0db1 \u0dbd\u0dda\u0db6\u0dbd\u0dca \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf <span translate=no>_^_3_^_</span> \u0d9a\u0dbb\u0db1 <span translate=no>_^_5_^_</span> \u0d85\u0dad\u0dbb <span translate=no>_^_4_^_</span> <span translate=no>_^_7_^_</span> \u0dc3\u0dd2\u0da7 <span translate=no>_^_6_^_</span> \u0dc3\u0db8\u0dcf\u0db1 \u0dbd\u0dda\u0db6\u0dbd\u0dca <span translate=no>_^_8_^_</span> \u0d91\u0dc0\u0dd2\u0da7 \u0db8\u0dda\u0dc0\u0dcf\u0dba\u0dda \u0d91\u0d9a\u0dad\u0dd4\u0dc0 \u0db8\u0dad\u0da7 \u0db6\u0dd0\u0dc3 \u0dba\u0dcf\u0db8 \u0d89\u0dc4\u0dad \u0dc1\u0dca\u0dbb\u0dda\u0dab\u0dd3\u0dba \u0db8\u0dad \u0db1\u0dd0\u0d9f\u0dd3\u0db8 \u0dc4\u0dcf \u0dc3\u0db8\u0dcf\u0db1 \u0dc0\u0dda. </p>\n<p><span translate=no>_^_9_^_</span> \u0dc3\u0ddc\u0dc6\u0dca\u0da7\u0dca\u0db8\u0dd0\u0d9a\u0dca\u0dc3\u0dca \u0dc3\u0dc4 \u0daf\u0dca\u0dc0\u0dd2\u0db8\u0dba \u0dc4\u0dbb\u0dc3\u0dca \u0d91\u0db1\u0dca\u0da7\u0dca\u0dbb\u0ddc\u0db4\u0dd2 \u0db1\u0dd0\u0dad\u0dd2\u0dc0\u0dd3\u0db8 \u0d92\u0d9a\u0dcf\u0db6\u0daf\u0dca\u0db0 \u0d9a\u0dbb\u0dba\u0dd2. </p>\n",
+ "<p>We use label smoothing because it seems to work better in some cases </p>\n": "<p>\u0d85\u0db4\u0dd2\u0dbd\u0dda\u0db6\u0dbd\u0dca \u0dc3\u0dd4\u0db8\u0da7\u0db1\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dda \u0d91\u0dba \u0dc3\u0db8\u0dc4\u0dbb \u0d85\u0dc0\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbd\u0daf\u0dd3 \u0dc0\u0da9\u0dcf \u0dc4\u0ddc\u0db3\u0dd2\u0db1\u0dca \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf \u0d9a\u0dbb\u0db1 \u0db6\u0dc0 \u0db4\u0dd9\u0db1\u0dd9\u0db1 \u0db6\u0dd0\u0dc0\u0dd2\u0db1\u0dd2 </p>\n",
+ "<p>We use labels equal to <span translate=no>_^_0_^_</span> for <span translate=no>_^_1_^_</span> from <span translate=no>_^_2_^_</span> Then descending on this loss is the same as descending on the above gradient. </p>\n": "<p>\u0d85\u0db4\u0dd2\u0dc3\u0db8\u0dcf\u0db1 \u0dbd\u0dda\u0db6\u0dbd\u0dca \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd4 <span translate=no>_^_1_^_</span> \u0dc3\u0dd2\u0da7 <span translate=no>_^_2_^_</span> \u0d91\u0dc0\u0dd2\u0da7 \u0db8\u0dd9\u0db8 \u0d85\u0dbd\u0dcf\u0db7\u0dba \u0db8\u0dad \u0db6\u0dd0\u0dc3 \u0dba\u0dcf\u0db8 \u0d89\u0dc4\u0dad \u0dc1\u0dca\u0dbb\u0dda\u0dab\u0dd2\u0dba\u0dd9\u0db1\u0dca \u0db6\u0dd0\u0dc3 \u0dba\u0dcf\u0db8 \u0dc4\u0dcf \u0dc3\u0db8\u0dcf\u0db1 \u0dc0\u0dda. <span translate=no>_^_0_^_</span> </p>\n",
+ "A simple PyTorch implementation/tutorial of Generative Adversarial Networks (GAN) loss functions.": "\u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd (GAN) \u0db4\u0dcf\u0da9\u0dd4 \u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0dba\u0db1\u0dca \u0db4\u0dd2\u0dc5\u0dd2\u0db6\u0db3 \u0dc3\u0dbb\u0dbd PyTorch \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8/\u0db1\u0dd2\u0db6\u0db1\u0dca\u0db0\u0db1\u0dba.",
+ "Generative Adversarial Networks (GAN)": "\u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd (GAN)"
+}

translate_cache/gan/original/__init__.zh.json

@@ -0,0 +1,13 @@
+{
+ "<h1>Generative Adversarial Networks (GAN)</h1>\n<p>This is an implementation of <a href=\"https://arxiv.org/abs/1406.2661\">Generative Adversarial Networks</a>.</p>\n<p>The generator, <span translate=no>_^_0_^_</span> generates samples that match the distribution of data, while the discriminator, <span translate=no>_^_1_^_</span> gives the probability that <span translate=no>_^_2_^_</span> came from data rather than <span translate=no>_^_3_^_</span>.</p>\n<p>We train <span translate=no>_^_4_^_</span> and <span translate=no>_^_5_^_</span> simultaneously on a two-player min-max game with value function <span translate=no>_^_6_^_</span>.</p>\n<p><span translate=no>_^_7_^_</span></p>\n<p><span translate=no>_^_8_^_</span> is the probability distribution over data, whilst <span translate=no>_^_9_^_</span> probability distribution of <span translate=no>_^_10_^_</span>, which is set to gaussian noise.</p>\n<p>This file defines the loss functions. <a href=\"experiment.html\">Here</a> is an MNIST example with two multilayer perceptron for the generator and discriminator.</p>\n": "<h1>\u751f\u6210\u5bf9\u6297\u7f51\u7edc (GAN)</h1>\n<p>\u8fd9\u662f<a href=\"https://arxiv.org/abs/1406.2661\">\u751f\u6210\u5bf9\u6297\u7f51\u7edc</a>\u7684\u5b9e\u73b0\u3002</p>\n\u751f\u6210@@ <p>\u5668<span translate=no>_^_0_^_</span>\u751f\u6210\u4e0e\u6570\u636e\u5206\u5e03\u76f8\u5339\u914d\u7684\u6837\u672c\uff0c\u800c\u9274\u522b\u5668\u5219<span translate=no>_^_1_^_</span>\u7ed9\u51fa\u6765\u81ea\u6570\u636e\u800c\u4e0d\u662f<span translate=no>_^_2_^_</span>\u6765\u81ea\u6570\u636e\u7684\u6982\u7387<span translate=no>_^_3_^_</span>\u3002</p>\n<p>\u6211\u4eec\u5728\u5177\u6709\u503c\u529f\u80fd\u7684\u53cc\u4eba\u6700\u5c0f\u6700\u5927\u6e38\u620f\u4e2d<span translate=no>_^_5_^_</span>\u540c\u65f6\u8fdb\u884c\u8bad\u7ec3<span translate=no>_^_4_^_</span><span translate=no>_^_6_^_</span>\u3002</p>\n<p><span translate=no>_^_7_^_</span></p>\n<p><span translate=no>_^_8_^_</span>\u662f\u6570\u636e\u7684\u6982\u7387\u5206\u5e03\uff0c\u800c<span translate=no>_^_9_^_</span>\u6982\u7387\u5206<span translate=no>_^_10_^_</span>\u5e03\u5219\u8bbe\u7f6e\u4e3a\u9ad8\u65af\u566a\u58f0\u3002</p>\n<p>\u8fd9\u4e2a\u6587\u4ef6\u5b9a\u4e49\u4e86\u635f\u5931\u51fd\u6570\u3002<a href=\"experiment.html\">\u8fd9\u662f</a>\u4e00\u4e2a MNIST \u793a\u4f8b\uff0c\u5176\u4e2d\u5305\u542b\u4e24\u4e2a\u7528\u4e8e\u751f\u6210\u5668\u548c\u9274\u522b\u5668\u7684\u591a\u5c42\u611f\u77e5\u5668\u3002</p>\n",
+ "<h2>Discriminator Loss</h2>\n<p>Discriminator should <strong>ascend</strong> on the gradient,</p>\n<p><span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span> is the mini-batch size and <span translate=no>_^_2_^_</span> is used to index samples in the mini-batch. <span translate=no>_^_3_^_</span> are samples from <span translate=no>_^_4_^_</span> and <span translate=no>_^_5_^_</span> are samples from <span translate=no>_^_6_^_</span>.</p>\n": "<h2>\u9274\u522b\u5668\u4e22\u5931</h2>\n<p>\u9274\u522b\u5668\u5e94\u8be5\u5728\u68af\u5ea6\u4e0a<strong>\u5347</strong>\uff0c</p>\n<p><span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span>\u662f\u5fae\u578b\u6279\u6b21\u5927\u5c0f\uff0c<span translate=no>_^_2_^_</span>\u7528\u4e8e\u7d22\u5f15\u5fae\u578b\u6279\u6b21\u4e2d\u7684\u6837\u672c\u3002<span translate=no>_^_3_^_</span>\u662f\u6765\u81ea\u7684\u6837\u672c<span translate=no>_^_4_^_</span>\uff0c<span translate=no>_^_5_^_</span>\u4e5f\u662f\u6765\u81ea\u7684\u6837\u672c<span translate=no>_^_6_^_</span>\u3002</p>\n",
+ "<h2>Generator Loss</h2>\n<p>Generator should <strong>descend</strong> on the gradient,</p>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h2>\u53d1\u7535\u673a\u635f\u5931</h2>\n<p>\u53d1\u7535\u673a\u5e94\u8be5<strong>\u4e0b\u964d\u5230</strong>\u68af\u5ea6\u4e0a\uff0c</p>\n<p><span translate=no>_^_0_^_</span></p>\n",
+ "<p> <span translate=no>_^_0_^_</span> are logits from <span translate=no>_^_1_^_</span> and <span translate=no>_^_2_^_</span> are logits from <span translate=no>_^_3_^_</span></p>\n": "<p><span translate=no>_^_0_^_</span>\u662f logits \u6765\u81ea<span translate=no>_^_1_^_</span>\uff0c<span translate=no>_^_2_^_</span>logits \u6765\u81ea<span translate=no>_^_3_^_</span></p>\n",
+ "<p> Create smoothed labels</p>\n": "<p>\u521b\u5efa\u7ecf\u8fc7\u5e73\u6ed1\u5904\u7406\u7684\u6807\u6ce8</p>\n",
+ "<p>Labels are registered as buffered and persistence is set to <span translate=no>_^_0_^_</span>. </p>\n": "<p>\u6807\u7b7e\u6ce8\u518c\u4e3a\u7f13\u51b2\u533a\uff0c\u5e76\u5c06\u6301\u4e45\u6027\u8bbe\u7f6e\u4e3a<span translate=no>_^_0_^_</span>\u3002</p>\n",
+ "<p>We use PyTorch Binary Cross Entropy Loss, which is <span translate=no>_^_0_^_</span>, where <span translate=no>_^_1_^_</span> are the labels and <span translate=no>_^_2_^_</span> are the predictions. <em>Note the negative sign</em>. We use labels equal to <span translate=no>_^_3_^_</span> for <span translate=no>_^_4_^_</span> from <span translate=no>_^_5_^_</span> and labels equal to <span translate=no>_^_6_^_</span> for <span translate=no>_^_7_^_</span> from <span translate=no>_^_8_^_</span> Then descending on the sum of these is the same as ascending on the above gradient.</p>\n<p><span translate=no>_^_9_^_</span> combines softmax and binary cross entropy loss. </p>\n": "<p>\u6211\u4eec\u4f7f\u7528 PyTorch \u4e8c\u8fdb\u5236\u4ea4\u53c9\u71b5\u635f\u5931<span translate=no>_^_0_^_</span>\uff0c\u4e5f\u5c31\u662f\u8bf4\uff0c\u6807\u7b7e\u5728<span translate=no>_^_1_^_</span>\u54ea\u91cc\uff0c\u9884\u6d4b\u5728<span translate=no>_^_2_^_</span>\u54ea\u91cc\u3002<em>\u6ce8\u610f\u8d1f\u53f7</em>\u3002\u6211\u4eec\u4f7f\u7528\u7b49\u4e8e for fro<span translate=no>_^_3_^_</span> m<span translate=no>_^_4_^_</span> \u7684\u6807\u7b7e<span translate=no>_^_5_^_</span>\u548c\u7b49\u4e8e f<span translate=no>_^_6_^_</span> or from<span translate=no>_^_7_^_</span> \u7684\u6807\u7b7e<span translate=no>_^_8_^_</span>\u7136\u540e\u6309\u8fd9\u4e9b\u603b\u548c\u964d\u5e8f\u4e0e\u4e0a\u9762\u7684\u68af\u5ea6\u4e0a\u5347\u76f8\u540c\u3002</p>\n<p><span translate=no>_^_9_^_</span>\u7ed3\u5408\u4e86 softmax \u548c\u4e8c\u8fdb\u5236\u4ea4\u53c9\u71b5\u635f\u5931\u3002</p>\n",
+ "<p>We use label smoothing because it seems to work better in some cases </p>\n": "<p>\u6211\u4eec\u4f7f\u7528\u6807\u7b7e\u5e73\u6ed1\uff0c\u56e0\u4e3a\u5b83\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u6548\u679c\u66f4\u597d</p>\n",
+ "<p>We use labels equal to <span translate=no>_^_0_^_</span> for <span translate=no>_^_1_^_</span> from <span translate=no>_^_2_^_</span> Then descending on this loss is the same as descending on the above gradient. </p>\n": "<p>\u6211\u4eec\u4f7f\u7528\u7b49\u4e8e f<span translate=no>_^_0_^_</span> or fro<span translate=no>_^_1_^_</span> m \u7684\u6807\u7b7e\uff0c<span translate=no>_^_2_^_</span>\u7136\u540e\u5728\u6b64\u635f\u5931\u4e0a\u964d\u5e8f\u4e0e\u4e0a\u9762\u68af\u5ea6\u4e0a\u7684\u964d\u5e8f\u76f8\u540c\u3002</p>\n",
+ "A simple PyTorch implementation/tutorial of Generative Adversarial Networks (GAN) loss functions.": "\u751f\u6210\u5bf9\u6297\u7f51\u7edc\uff08GAN\uff09\u635f\u5931\u51fd\u6570\u7684\u7b80\u5355PyTorch\u5b9e\u73b0/\u6559\u7a0b\u3002",
+ "Generative Adversarial Networks (GAN)": "\u751f\u6210\u5bf9\u6297\u7f51\u7edc (GAN)"
+}

translate_cache/gan/original/experiment.ja.json

@@ -0,0 +1,22 @@
+{
+ "<h1>Generative Adversarial Networks experiment with MNIST</h1>\n": "<h1>MNIST\u306b\u3088\u308b\u6575\u5bfe\u7684\u751f\u6210\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u5b9f\u9a13</h1>\n",
+ "<h2>Configurations</h2>\n<p>This extends MNIST configurations to get the data loaders and Training and validation loop configurations to simplify our implementation.</p>\n": "<h2>\u30b3\u30f3\u30d5\u30a3\u30ae\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3</h2>\n<p>\u3053\u308c\u306b\u3088\u308a\u3001MNIST \u306e\u69cb\u6210\u304c\u62e1\u5f35\u3055\u308c\u3001\u30c7\u30fc\u30bf\u30ed\u30fc\u30c0\u30fc\u3084\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u304a\u3088\u3073\u691c\u8a3c\u30eb\u30fc\u30d7\u306e\u69cb\u6210\u304c\u53ef\u80fd\u306b\u306a\u308a\u3001\u5b9f\u88c5\u304c\u7c21\u5358\u306b\u306a\u308a\u307e\u3059\u3002</p>\n",
+ "<h3>Simple MLP Discriminator</h3>\n<p>This has three linear layers of decreasing size with <span translate=no>_^_0_^_</span> activations. The final layer has a single output that gives the logit of whether input is real or fake. You can get the probability by calculating the sigmoid of it.</p>\n": "<h3>\u30b7\u30f3\u30d7\u30eb\u306a MLP \u30c7\u30a3\u30b9\u30af\u30ea\u30df\u30cd\u30fc\u30bf\u30fc</h3>\n<p>\u3053\u308c\u306b\u306f\u3001<span translate=no>_^_0_^_</span>\u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30b7\u30e7\u30f3\u3092\u884c\u3046\u3068\u30b5\u30a4\u30ba\u304c\u5c0f\u3055\u304f\u306a\u308b3\u3064\u306e\u7dda\u5f62\u30ec\u30a4\u30e4\u30fc\u304c\u3042\u308a\u307e\u3059\u3002\u6700\u5f8c\u306e\u30ec\u30a4\u30e4\u30fc\u306b\u306f\u3001\u5165\u529b\u304c\u672c\u7269\u304b\u507d\u7269\u304b\u3092\u30ed\u30b8\u30c3\u30c8\u3067\u793a\u3059\u51fa\u529b\u304c 1 \u3064\u3042\u308a\u307e\u3059\u3002\u78ba\u7387\u306f\u3001\u305d\u306e\u30b7\u30b0\u30e2\u30a4\u30c9\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3067\u6c42\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059</p>\u3002\n",
+ "<h3>Simple MLP Generator</h3>\n<p>This has three linear layers of increasing size with <span translate=no>_^_0_^_</span> activations. The final layer has a <span translate=no>_^_1_^_</span> activation.</p>\n": "<h3>\u30b7\u30f3\u30d7\u30eb\u306a MLP \u30b8\u30a7\u30cd\u30ec\u30fc\u30bf</h3>\n<p>\u3053\u308c\u306b\u306f\u3001<span translate=no>_^_0_^_</span>\u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30b7\u30e7\u30f3\u3092\u884c\u3046\u3068\u30b5\u30a4\u30ba\u304c\u5927\u304d\u304f\u306a\u308b3\u3064\u306e\u7dda\u5f62\u30ec\u30a4\u30e4\u30fc\u304c\u3042\u308a\u307e\u3059\u3002<span translate=no>_^_1_^_</span>\u6700\u5f8c\u306e\u30ec\u30a4\u30e4\u30fc\u306b\u306f\u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30b7\u30e7\u30f3\u304c\u3042\u308a\u307e\u3059\u3002</p>\n",
+ "<p> <span translate=no>_^_0_^_</span></p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
+ "<p> Calculate discriminator loss</p>\n": "<p>\u30c7\u30a3\u30b9\u30af\u30ea\u30df\u30cd\u30fc\u30bf\u30fc\u640d\u5931\u306e\u8a08\u7b97</p>\n",
+ "<p> Calculate generator loss</p>\n": "<p>\u767a\u96fb\u6a5f\u640d\u5931\u306e\u8a08\u7b97</p>\n",
+ "<p> Initializations</p>\n": "<p>\u521d\u671f\u5316</p>\n",
+ "<p> Take a training step</p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306e\u4e00\u6b69\u3092\u8e0f\u307f\u51fa\u3059</p>\n",
+ "<p>Get MNIST images </p>\n": "<p>MNIST \u306e\u753b\u50cf\u3092\u53d6\u5f97</p>\n",
+ "<p>Get discriminator loss </p>\n": "<p>\u30c7\u30a3\u30b9\u30af\u30ea\u30df\u30cd\u30fc\u30bf\u30fc\u640d\u5931\u3092\u53d6\u5f97</p>\n",
+ "<p>Increment step in training mode </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30e2\u30fc\u30c9\u3067\u306e\u30a4\u30f3\u30af\u30ea\u30e1\u30f3\u30c8\u30b9\u30c6\u30c3\u30d7</p>\n",
+ "<p>Log stuff </p>\n": "<p>\u30ed\u30b0\u306e\u3082\u306e</p>\n",
+ "<p>Set model states </p>\n": "<p>\u30e2\u30c7\u30eb\u72b6\u614b\u306e\u8a2d\u5b9a</p>\n",
+ "<p>Setting exponent decay rate for first moment of gradient, <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> is important. Default of <span translate=no>_^_2_^_</span> fails. </p>\n": "<p>\u52fe\u914d\u306e\u6700\u521d\u306e\u77ac\u9593\u306b\u6307\u6570\u6e1b\u8870\u7387\u3092\u8a2d\u5b9a\u3059\u308b\u3053\u3068\u306f\u91cd\u8981\u3067\u3059\u3002<span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u30c7\u30d5\u30a9\u30eb\u30c8\u306f\u5931\u6557\u3067\u3059\u3002</p>\n",
+ "<p>Train </p>\n": "<p>\u5217\u8eca</p>\n",
+ "<p>Train the discriminator </p>\n": "<p>\u30c7\u30a3\u30b9\u30af\u30ea\u30df\u30cd\u30fc\u30bf\u30fc\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0</p>\n",
+ "<p>Train the generator once in every <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u3092\u6bce\u56de 1 \u56de\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u3059 <span translate=no>_^_0_^_</span></p>\n",
+ "Generative Adversarial Networks experiment with MNIST": "MNIST\u306b\u3088\u308b\u6575\u5bfe\u7684\u751f\u6210\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u5b9f\u9a13",
+ "This experiment generates MNIST images using multi-layer perceptron.": "\u3053\u306e\u5b9f\u9a13\u3067\u306f\u3001\u591a\u5c64\u30d1\u30fc\u30bb\u30d7\u30c8\u30ed\u30f3\u3092\u4f7f\u7528\u3057\u3066MNIST\u753b\u50cf\u3092\u751f\u6210\u3057\u307e\u3059\u3002"
+}

translate_cache/gan/original/experiment.si.json

@@ -0,0 +1,22 @@
+{
+ "<h1>Generative Adversarial Networks experiment with MNIST</h1>\n": "<h1>\u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a\u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd MNIST \u0dc3\u0db8\u0d9f \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8</h1>\n",
+ "<h2>Configurations</h2>\n<p>This extends MNIST configurations to get the data loaders and Training and validation loop configurations to simplify our implementation.</p>\n": "<h2>\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dca</h2>\n<p>\u0d85\u0db4\u0d9c\u0dda\u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0dbb\u0dbd \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0daf\u0dad\u0dca\u0dad \u0db4\u0dd0\u0da7\u0dc0\u0dd4\u0db8\u0dca \u0dc3\u0dc4 \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0dc3\u0dc4 \u0dc0\u0dbd\u0d82\u0d9c\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0dbd\u0dd6\u0db4 \u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0dd0\u0db1\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0db8\u0dd9\u0dba MNIST \u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca \u0db4\u0dd4\u0dc5\u0dd4\u0dbd\u0dca \u0d9a\u0dbb\u0dba\u0dd2. </p>\n",
+ "<h3>Simple MLP Discriminator</h3>\n<p>This has three linear layers of decreasing size with <span translate=no>_^_0_^_</span> activations. The final layer has a single output that gives the logit of whether input is real or fake. You can get the probability by calculating the sigmoid of it.</p>\n": "<h3>\u0dc3\u0dbb\u0dbd\u0d91\u0db8\u0dca\u0d91\u0dbd\u0dca\u0db4\u0dd3 \u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0d9a\u0db8\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dcf</h3>\n<p>\u0db8\u0dd9\u0dba <span translate=no>_^_0_^_</span> \u0dc3\u0d9a\u0dca\u0dbb\u0dd2\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dca \u0dc3\u0db8\u0d9f \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0d85\u0da9\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0dbb\u0dda\u0d9b\u0dd3\u0dba \u0dc3\u0dca\u0dae\u0dbb \u0dad\u0dd4\u0db1\u0d9a\u0dca \u0d87\u0dad. \u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0dc3\u0dca\u0dae\u0dbb\u0dba\u0da7 \u0dad\u0db1\u0dd2 \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba\u0d9a\u0dca \u0d87\u0dad\u0dd2 \u0d85\u0dad\u0dbb \u0d91\u0db8\u0d9f\u0dd2\u0db1\u0dca \u0d86\u0daf\u0dcf\u0db1\u0dba \u0dc3\u0dd0\u0db6\u0dd1 \u0dc4\u0ddd \u0dc0\u0dca\u0dba\u0dcf\u0da2 \u0daf \u0dba\u0db1\u0dca\u0db1 \u0db4\u0dd2\u0dc5\u0dd2\u0db6\u0db3 \u0db4\u0dd2\u0dc0\u0dd2\u0dc3\u0dd4\u0db8 \u0dbd\u0db6\u0dcf \u0daf\u0dda. \u0d91\u0dba \u0dc3\u0dd2\u0d9c\u0dca\u0db8\u0ddd\u0dba\u0dd2\u0da9\u0dca \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dd9\u0db1\u0dca \u0d94\u0db6\u0da7 \u0dc3\u0db8\u0dca\u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf\u0dc0 \u0dbd\u0db6\u0dcf \u0d9c\u0dad \u0dc4\u0dd0\u0d9a\u0dd2\u0dba. </p>\n",
+ "<h3>Simple MLP Generator</h3>\n<p>This has three linear layers of increasing size with <span translate=no>_^_0_^_</span> activations. The final layer has a <span translate=no>_^_1_^_</span> activation.</p>\n": "<h3>\u0dc3\u0dbb\u0dbd\u0d91\u0db8\u0dca\u0d91\u0dbd\u0dca\u0db4\u0dd3 \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0dba\u0db1\u0dca\u0dad\u0dca\u0dbb\u0dba</h3>\n<p>\u0db8\u0dd9\u0dba <span translate=no>_^_0_^_</span> \u0dc3\u0d9a\u0dca\u0dbb\u0dd2\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dca \u0dc3\u0db8\u0d9f \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dc0\u0dd0\u0da9\u0dd2 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0dbb\u0dda\u0d9b\u0dd3\u0dba \u0dc3\u0dca\u0dae\u0dbb \u0dad\u0dd4\u0db1\u0d9a\u0dca \u0d87\u0dad. \u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0dc3\u0dca\u0dae\u0dbb\u0dba <span translate=no>_^_1_^_</span> \u0dc3\u0d9a\u0dca\u0dbb\u0dd2\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0d9a\u0dca \u0d87\u0dad. </p>\n",
+ "<p> <span translate=no>_^_0_^_</span></p>\n": "<p> <span translate=no>_^_0_^_</span></p>\n",
+ "<p> Calculate discriminator loss</p>\n": "<p> \u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0d9a\u0db8\u0dca\u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dcf\u0d9c\u0dda \u0db4\u0dcf\u0da9\u0dd4\u0dc0 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
+ "<p> Calculate generator loss</p>\n": "<p> \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a\u0d85\u0dbd\u0dcf\u0db7\u0dba \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
+ "<p> Initializations</p>\n": "<p> \u0d86\u0dbb\u0db8\u0dca\u0db7\u0d9a\u0d9a\u0dbb\u0dab\u0dba</p>\n",
+ "<p> Take a training step</p>\n": "<p> \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0db4\u0dd2\u0dba\u0dc0\u0dbb\u0d9a\u0dca \u0d9c\u0db1\u0dca\u0db1</p>\n",
+ "<p>Get MNIST images </p>\n": "<p>MNIST\u0dbb\u0dd6\u0db4 \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Get discriminator loss </p>\n": "<p>\u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0d9a\u0db8\u0dca\u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dcf\u0d9c\u0dda \u0db4\u0dcf\u0da9\u0dd4\u0dc0 \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Increment step in training mode </p>\n": "<p>\u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0db8\u0dcf\u0daf\u0dd2\u0dbd\u0dd2\u0dba\u0dda \u0dc0\u0dbb\u0dca\u0db0\u0d9a \u0db4\u0dd2\u0dba\u0dc0\u0dbb </p>\n",
+ "<p>Log stuff </p>\n": "<p>\u0dbd\u0ddc\u0d9c\u0dca\u0daf\u0dda\u0dc0\u0dbd\u0dca </p>\n",
+ "<p>Set model states </p>\n": "<p>\u0d86\u0daf\u0dbb\u0dca\u0dc1\u0dad\u0dad\u0dca\u0dc0\u0dba\u0db1\u0dca \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Setting exponent decay rate for first moment of gradient, <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> is important. Default of <span translate=no>_^_2_^_</span> fails. </p>\n": "<p>\u0dc1\u0dca\u0dbb\u0dda\u0dab\u0dd2\u0dba\u0dda\u0db4\u0dc5\u0db8\u0dd4 \u0db8\u0ddc\u0dc4\u0ddc\u0dad \u0dc3\u0db3\u0dc4\u0dcf on \u0dcf\u0dad\u0dd3\u0dba \u0d9a\u0dca\u0dc2\u0dba \u0dc0\u0dd3\u0db8\u0dda \u0d85\u0db1\u0dd4\u0db4\u0dcf\u0dad\u0dba \u0dc3\u0dd0\u0d9a\u0dc3\u0dd3\u0db8 \u0dc0\u0dd0\u0daf\u0d9c\u0dad\u0dca <span translate=no>_^_1_^_</span> \u0dc0\u0dda. <span translate=no>_^_0_^_</span> <span translate=no>_^_2_^_</span> \u0d85\u0dc3\u0db8\u0dad\u0dca \u0dc0\u0dd3\u0db8\u0dda \u0db4\u0dd9\u0dbb\u0db1\u0dd2\u0db8\u0dd2. </p>\n",
+ "<p>Train </p>\n": "<p>\u0daf\u0dd4\u0db8\u0dca\u0dbb\u0dd2\u0dba </p>\n",
+ "<p>Train the discriminator </p>\n": "<p>\u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0d9a\u0db8\u0dca\u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dcf \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Train the generator once in every <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc3\u0dd1\u0db8\u0dc0\u0dd2\u0da7\u0db8 \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0dba\u0db1\u0dca\u0dad\u0dca\u0dbb\u0dba \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dbb\u0db1\u0dca\u0db1 <span translate=no>_^_0_^_</span> </p>\n",
+ "Generative Adversarial Networks experiment with MNIST": "\u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd MNIST \u0dc3\u0db8\u0d9f \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8",
+ "This experiment generates MNIST images using multi-layer perceptron.": "\u0db8\u0dd9\u0db8 \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0db6\u0dc4\u0dd4 \u0dc3\u0dca\u0dae\u0dbb perceptron \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf MNIST \u0dbb\u0dd6\u0db4 \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0dba\u0dd2."
+}

translate_cache/gan/original/experiment.zh.json

@@ -0,0 +1,22 @@
+{
+ "<h1>Generative Adversarial Networks experiment with MNIST</h1>\n": "<h1>\u4f7f\u7528 MNIST \u8fdb\u884c\u751f\u6210\u5bf9\u6297\u7f51\u7edc\u5b9e\u9a8c</h1>\n",
+ "<h2>Configurations</h2>\n<p>This extends MNIST configurations to get the data loaders and Training and validation loop configurations to simplify our implementation.</p>\n": "<h2>\u914d\u7f6e</h2>\n<p>\u8fd9\u6269\u5c55\u4e86 MNIST \u914d\u7f6e\u4ee5\u83b7\u53d6\u6570\u636e\u52a0\u8f7d\u5668\u4ee5\u53ca\u8bad\u7ec3\u548c\u9a8c\u8bc1\u5faa\u73af\u914d\u7f6e\uff0c\u4ece\u800c\u7b80\u5316\u4e86\u6211\u4eec\u7684\u5b9e\u73b0\u3002</p>\n",
+ "<h3>Simple MLP Discriminator</h3>\n<p>This has three linear layers of decreasing size with <span translate=no>_^_0_^_</span> activations. The final layer has a single output that gives the logit of whether input is real or fake. You can get the probability by calculating the sigmoid of it.</p>\n": "<h3>\u7b80\u5355\u7684 MLP \u9274\u522b\u5668</h3>\n<p>\u5b83\u6709\u4e09\u4e2a\u7ebf\u6027\u5c42\uff0c\u968f\u7740<span translate=no>_^_0_^_</span>\u6fc0\u6d3b\u7684\u5927\u5c0f\u9010\u6e10\u51cf\u5c0f\u3002\u6700\u540e\u4e00\u5c42\u6709\u4e00\u4e2a\u5355\u72ec\u7684\u8f93\u51fa\uff0c\u5b83\u7ed9\u51fa\u4e86\u8f93\u5165\u662f\u771f\u5b9e\u8fd8\u662f\u5047\u7684 logit\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u5b83\u7684\u4e59\u72b6\u7ed3\u80a0\u6765\u83b7\u5f97\u6982\u7387\u3002</p>\n",
+ "<h3>Simple MLP Generator</h3>\n<p>This has three linear layers of increasing size with <span translate=no>_^_0_^_</span> activations. The final layer has a <span translate=no>_^_1_^_</span> activation.</p>\n": "<h3>\u7b80\u5355\u7684 MLP \u751f\u6210\u5668</h3>\n<p>\u5b83\u6709\u4e09\u4e2a\u7ebf\u6027\u5c42\uff0c\u968f\u7740<span translate=no>_^_0_^_</span>\u6fc0\u6d3b\u7684\u5927\u5c0f\u4e0d\u65ad\u589e\u52a0\u3002\u6700\u540e\u4e00\u5c42\u5df2\u6fc0<span translate=no>_^_1_^_</span>\u6d3b\u3002</p>\n",
+ "<p> <span translate=no>_^_0_^_</span></p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
+ "<p> Calculate discriminator loss</p>\n": "<p>\u8ba1\u7b97\u9274\u522b\u5668\u635f\u5931</p>\n",
+ "<p> Calculate generator loss</p>\n": "<p>\u8ba1\u7b97\u53d1\u7535\u673a\u635f\u8017</p>\n",
+ "<p> Initializations</p>\n": "<p>\u521d\u59cb\u5316</p>\n",
+ "<p> Take a training step</p>\n": "<p>\u8fc8\u51fa\u8bad\u7ec3\u4e00\u6b65</p>\n",
+ "<p>Get MNIST images </p>\n": "<p>\u83b7\u53d6 MNIST \u56fe\u7247</p>\n",
+ "<p>Get discriminator loss </p>\n": "<p>\u83b7\u5f97\u9274\u522b\u5668\u635f\u5931</p>\n",
+ "<p>Increment step in training mode </p>\n": "<p>\u5728\u8bad\u7ec3\u6a21\u5f0f\u4e2d\u589e\u52a0\u6b65\u6570</p>\n",
+ "<p>Log stuff </p>\n": "<p>\u65e5\u5fd7\u7684\u4e1c\u897f</p>\n",
+ "<p>Set model states </p>\n": "<p>\u8bbe\u7f6e\u6a21\u578b\u72b6\u6001</p>\n",
+ "<p>Setting exponent decay rate for first moment of gradient, <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> is important. Default of <span translate=no>_^_2_^_</span> fails. </p>\n": "<p>\u8bbe\u7f6e\u68af\u5ea6\u7b2c\u4e00\u65f6\u523b\u7684\u6307\u6570\u8870\u51cf\u7387<span translate=no>_^_1_^_</span>\u975e\u5e38\u91cd\u8981\u3002<span translate=no>_^_0_^_</span>\u9ed8\u8ba4\u4e3a<span translate=no>_^_2_^_</span>\u5931\u8d25\u3002</p>\n",
+ "<p>Train </p>\n": "<p>\u706b\u8f66</p>\n",
+ "<p>Train the discriminator </p>\n": "<p>\u8bad\u7ec3\u9274\u522b\u5668</p>\n",
+ "<p>Train the generator once in every <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6bcf\u9694\u4e00\u6b21\u8bad\u7ec3\u53d1\u7535\u673a<span translate=no>_^_0_^_</span></p>\n",
+ "Generative Adversarial Networks experiment with MNIST": "\u4f7f\u7528 MNIST \u8fdb\u884c\u751f\u6210\u5bf9\u6297\u7f51\u7edc\u5b9e\u9a8c",
+ "This experiment generates MNIST images using multi-layer perceptron.": "\u8be5\u5b9e\u9a8c\u4f7f\u7528\u591a\u5c42\u611f\u77e5\u5668\u751f\u6210 MNIST \u56fe\u50cf\u3002"
+}

translate_cache/gan/original/readme.ja.json

@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/gan/original/index.html\">Generative Adversarial Networks - GAN</a></h1>\n<p>This is an annotated implementation of <a href=\"https://arxiv.org/abs/1406.2661\">Generative Adversarial Networks</a>. </p>\n": "<h1><a href=\"https://nn.labml.ai/gan/original/index.html\">\u30b8\u30a7\u30cd\u30ec\u30fc\u30c6\u30a3\u30d6\u30fb\u30a2\u30c9\u30d0\u30fc\u30b5\u30ea\u30a2\u30eb\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af-GAN</a></h1>\n<p>\u3053\u308c\u306f\u3001<a href=\"https://arxiv.org/abs/1406.2661\">\u30b8\u30a7\u30cd\u30ec\u30fc\u30c6\u30a3\u30d6\u30fb\u30a2\u30c9\u30d0\u30fc\u30b5\u30ea\u30a2\u30eb\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u6ce8\u91c8\u4ed8\u304d\u5b9f\u88c5\u3067\u3059</a>\u3002</p>\n",
+ "Generative Adversarial Networks - GAN": "\u30b8\u30a7\u30cd\u30ec\u30fc\u30c6\u30a3\u30d6\u30fb\u30a2\u30c9\u30d0\u30fc\u30b5\u30ea\u30a2\u30eb\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af-GAN"
+}

translate_cache/gan/original/readme.si.json

@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/gan/original/index.html\">Generative Adversarial Networks - GAN</a></h1>\n<p>This is an annotated implementation of <a href=\"https://arxiv.org/abs/1406.2661\">Generative Adversarial Networks</a>. </p>\n": "<h1><a href=\"https://nn.labml.ai/gan/original/index.html\">\u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd - GAN</a></h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"https://arxiv.org/abs/1406.2661\">Generative Aversarial Network</a>\u0dc4\u0dd2 \u0dc0\u0dd2\u0dc0\u0dbb\u0dab\u0dba \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0dc0\u0dd3\u0db8\u0d9a\u0dd2. </p>\n",
+ "Generative Adversarial Networks - GAN": "\u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0d85\u0dc4\u0dd2\u0dad\u0d9a\u0dbb \u0da2\u0dcf\u0dbd - GAN"
+}

translate_cache/gan/original/readme.zh.json

@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/gan/original/index.html\">Generative Adversarial Networks - GAN</a></h1>\n<p>This is an annotated implementation of <a href=\"https://arxiv.org/abs/1406.2661\">Generative Adversarial Networks</a>. </p>\n": "<h1><a href=\"https://nn.labml.ai/gan/original/index.html\">\u751f\u6210\u5f0f\u5bf9\u6297\u7f51\u7edc-GAN</a></h1>\n<p>\u8fd9\u662f<a href=\"https://arxiv.org/abs/1406.2661\">\u751f\u6210\u5bf9\u6297\u7f51\u7edc</a>\u7684\u5e26\u6ce8\u91ca\u7684\u5b9e\u73b0\u3002</p>\n",
+ "Generative Adversarial Networks - GAN": "\u751f\u6210\u5f0f\u5bf9\u6297\u7f51\u7edc-GAN"
+}