chore: import upstream snapshot with attribution

2026-07-13 12:19:01 +08:00
commit 3b90d1192f
2172 changed files with 594509 additions and 0 deletions
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+{
+ "<h1>Neural Networks with Adaptive Computation</h1>\n<p>These are neural network architectures that change the computation complexity based on the complexity of the input sample.</p>\n<ul><li>\ud83d\udea7 TODO: Adaptive Computation Time for Recurrent Neural Networks </li>\n<li><a href=\"ponder_net/index.html\">PonderNet: Learning to Ponder</a></li></ul>\n": "<h1>\u9069\u5fdc\u578b\u8a08\u7b97\u3092\u5099\u3048\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af</h1>\n<p>\u3053\u308c\u3089\u306f\u3001\u5165\u529b\u30b5\u30f3\u30d7\u30eb\u306e\u8907\u96d1\u3055\u306b\u57fa\u3065\u3044\u3066\u8a08\u7b97\u306e\u8907\u96d1\u3055\u3092\u5909\u66f4\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>\ud83d\udea7 TODO: \u30ea\u30ab\u30ec\u30f3\u30c8\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u9069\u5fdc\u578b\u8a08\u7b97\u6642\u9593</li>\n<li><a href=\"ponder_net/index.html\">PonderNet: \u719f\u8003\u3059\u308b\u3053\u3068\u3092\u5b66\u3076</a></li></ul>\n",
+ "A set of PyTorch implementations/tutorials related to adaptive computation": "\u9069\u5fdc\u578b\u8a08\u7b97\u306b\u95a2\u9023\u3059\u308bPyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306e\u30bb\u30c3\u30c8",
+ "Neural Networks with Adaptive Computation": "\u9069\u5fdc\u578b\u8a08\u7b97\u3092\u5099\u3048\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af"
+}
@@ -0,0 +1,5 @@
+{
+ "<h1>Neural Networks with Adaptive Computation</h1>\n<p>These are neural network architectures that change the computation complexity based on the complexity of the input sample.</p>\n<ul><li>\ud83d\udea7 TODO: Adaptive Computation Time for Recurrent Neural Networks </li>\n<li><a href=\"ponder_net/index.html\">PonderNet: Learning to Ponder</a></li></ul>\n": "<h1>\u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0dd3\u0d9c\u0dab\u0db1\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd</h1>\n<p>\u0db8\u0dda\u0dc0\u0dcf\u0d86\u0daf\u0dcf\u0db1 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba\u0dda \u0dc3\u0d82\u0d9a\u0dd3\u0dbb\u0dca\u0dab\u0dad\u0dca\u0dc0\u0dba \u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca\u0dc0 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0dc3\u0d82\u0d9a\u0dd3\u0dbb\u0dca\u0dab\u0dad\u0dcf\u0dc0 \u0dc0\u0dd9\u0db1\u0dc3\u0dca \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>\ud83d\udea7TODO: \u0db4\u0dd4\u0db1\u0dbb\u0dcf\u0dc0\u0dbb\u0dca\u0dad\u0db1 \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0dd3 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d9a\u0dcf\u0dbd\u0dba </li>\n<li><a href=\"ponder_net/index.html\">\u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca: \u0db8\u0dd9\u0db1\u0dd9\u0dc4\u0dd2 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a></li></ul>\n",
+ "A set of PyTorch implementations/tutorials related to adaptive computation": "\u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0dd3 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc4\u0dcf \u0dc3\u0db8\u0dca\u0db6\u0db1\u0dca\u0db0 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 Adaptive Computation": "\u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0dd3 \u0d9c\u0dab\u0db1\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd"
+}
@@ -0,0 +1,5 @@
+{
+ "<h1>Neural Networks with Adaptive Computation</h1>\n<p>These are neural network architectures that change the computation complexity based on the complexity of the input sample.</p>\n<ul><li>\ud83d\udea7 TODO: Adaptive Computation Time for Recurrent Neural Networks </li>\n<li><a href=\"ponder_net/index.html\">PonderNet: Learning to Ponder</a></li></ul>\n": "<h1>\u5177\u6709\u81ea\u9002\u5e94\u8ba1\u7b97\u7684\u795e\u7ecf\u7f51\u7edc</h1>\n<p>\u8fd9\u4e9b\u662f\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\uff0c\u6839\u636e\u8f93\u5165\u6837\u672c\u7684\u590d\u6742\u5ea6\u6765\u6539\u53d8\u8ba1\u7b97\u590d\u6742\u5ea6\u3002</p>\n<ul><li>\ud83d\udea7 TODO\uff1a\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u81ea\u9002\u5e94\u8ba1\u7b97\u65f6\u95f4</li>\n<li><a href=\"ponder_net/index.html\">PonderNet\uff1a\u5b66\u4f1a\u601d\u8003</a></li></ul>\n",
+ "A set of PyTorch implementations/tutorials related to adaptive computation": "\u4e00\u7ec4\u4e0e\u81ea\u9002\u5e94\u8ba1\u7b97\u76f8\u5173\u7684 PyTorch \u5b9e\u73b0/\u6559\u7a0b",
+ "Neural Networks with Adaptive Computation": "\u5177\u6709\u81ea\u9002\u5e94\u8ba1\u7b97\u7684\u795e\u7ecf\u7f51\u7edc"
+}
@@ -0,0 +1,15 @@
+{
+ "<h1>Parity Task</h1>\n<p>This creates data for Parity Task from the paper <a href=\"https://arxiv.org/abs/1603.08983\">Adaptive Computation Time for Recurrent Neural Networks</a>.</p>\n<p>The input of the parity task is a vector with <span translate=no>_^_0_^_</span>&#x27;s <span translate=no>_^_1_^_</span>&#x27;s and <span translate=no>_^_2_^_</span>&#x27;s. The output is the parity of <span translate=no>_^_3_^_</span>&#x27;s - one if there is an odd number of <span translate=no>_^_4_^_</span>&#x27;s and zero otherwise. The input is generated by making a random number of elements in the vector either <span translate=no>_^_5_^_</span> or <span translate=no>_^_6_^_</span>&#x27;s.</p>\n": "<h1>\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af</h1>\n<p>\u3053\u308c\u306b\u3088\u308a\u3001\u8ad6\u6587\u300c<a href=\"https://arxiv.org/abs/1603.08983\">\u30ea\u30ab\u30ec\u30f3\u30c8\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u9069\u5fdc\u7684\u8a08\u7b97\u6642\u9593</a>\u300d\u304b\u3089\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af\u306e\u30c7\u30fc\u30bf\u304c\u4f5c\u6210\u3055\u308c\u307e\u3059\u3002</p>\n<p>\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af\u306e\u5165\u529b\u306f\u3001\u3068 <span translate=no>_^_2_^_</span> s <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span> \u306e\u4ed8\u3044\u305f\u30d9\u30af\u30c8\u30eb\u3067\u3001\u51fa\u529b\u306f s <span translate=no>_^_3_^_</span> \u306e\u30d1\u30ea\u30c6\u30a3\u3067\u3059\u3002s <span translate=no>_^_4_^_</span> \u306e\u6570\u304c\u5947\u6570\u306e\u5834\u5408\u306f 1\u3001\u305d\u308c\u4ee5\u5916\u306e\u5834\u5408\u306f 0 \u3067\u3059\u3002\u5165\u529b\u306f\u3001<span translate=no>_^_5_^_</span><span translate=no>_^_6_^_</span>\u30d9\u30af\u30c8\u30eb\u5185\u306e\u30e9\u30f3\u30c0\u30e0\u306a\u6570\u306e\u8981\u7d20\u3092\u307e\u305f\u306f\u306e\u3044\u305a\u308c\u304b\u306b\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u751f\u6210\u3055\u308c\u307e\u3059\u3002</p>\n",
+ "<h3>Parity dataset</h3>\n": "<h3>\u30d1\u30ea\u30c6\u30a3\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8</h3>\n",
+ "<p> </p>\n": "<p></p>\n",
+ "<p> Generate a sample</p>\n": "<p>\u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210</p>\n",
+ "<p> Size of the dataset</p>\n": "<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30b5\u30a4\u30ba</p>\n",
+ "<p>Empty vector </p>\n": "<p>\u7a7a\u306e\u30d9\u30af\u30c8\u30eb</p>\n",
+ "<p>Fill non-zero elements with <span translate=no>_^_0_^_</span>&#x27;s and <span translate=no>_^_1_^_</span>&#x27;s </p>\n": "<p>0 \u4ee5\u5916\u306e\u8981\u7d20\u3092\u300c\u300d\u3068 <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span>\u300c\u300d\u3067\u57cb\u3081\u308b</p>\n",
+ "<p>Number of non-zero elements - a random number between <span translate=no>_^_0_^_</span> and total number of elements </p>\n": "<p>\u30bc\u30ed\u4ee5\u5916\u306e\u8981\u7d20\u306e\u6570-<span translate=no>_^_0_^_</span> \u8981\u7d20\u306e\u6570\u3068\u8981\u7d20\u6570\u306e\u9593\u306e\u30e9\u30f3\u30c0\u30e0\u306a\u6570</p>\n",
+ "<p>Randomly permute the elements </p>\n": "<p>\u8981\u7d20\u3092\u30e9\u30f3\u30c0\u30e0\u306b\u4e26\u3079\u66ff\u3048\u308b</p>\n",
+ "<p>The parity </p>\n": "<p>\u30d1\u30ea\u30c6\u30a3</p>\n",
+ "<ul><li><span translate=no>_^_0_^_</span> is the number of samples </li>\n<li><span translate=no>_^_1_^_</span> is the number of elements in the input vector</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u30b5\u30f3\u30d7\u30eb\u6570</li>\n<li><span translate=no>_^_1_^_</span>\u306f\u5165\u529b\u30d9\u30af\u30c8\u30eb\u306e\u8981\u7d20\u6570\u3067\u3059</li></ul>\n",
+ "Parity Task": "\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af",
+ "This creates data for Parity Task from the paper Adaptive Computation Time for Recurrent Neural Networks": "\u3053\u308c\u306b\u3088\u308a\u3001\u8ad6\u6587\u300c\u30ea\u30ab\u30ec\u30f3\u30c8\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u9069\u5fdc\u7684\u8a08\u7b97\u6642\u9593\u300d\u304b\u3089\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af\u306e\u30c7\u30fc\u30bf\u304c\u4f5c\u6210\u3055\u308c\u307e\u3059\u3002"
+}
@@ -0,0 +1,15 @@
+{
+ "<h1>Parity Task</h1>\n<p>This creates data for Parity Task from the paper <a href=\"https://arxiv.org/abs/1603.08983\">Adaptive Computation Time for Recurrent Neural Networks</a>.</p>\n<p>The input of the parity task is a vector with <span translate=no>_^_0_^_</span>&#x27;s <span translate=no>_^_1_^_</span>&#x27;s and <span translate=no>_^_2_^_</span>&#x27;s. The output is the parity of <span translate=no>_^_3_^_</span>&#x27;s - one if there is an odd number of <span translate=no>_^_4_^_</span>&#x27;s and zero otherwise. The input is generated by making a random number of elements in the vector either <span translate=no>_^_5_^_</span> or <span translate=no>_^_6_^_</span>&#x27;s.</p>\n": "<h1>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0dba</h1>\n<p>\u0db8\u0dd9\u0dba\u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0dc0\u0dbd\u0dd2\u0db1\u0dca Parity Task \u0dc3\u0db3\u0dc4\u0dcf \u0daf\u0dad\u0dca\u0dad \u0db1\u0dd2\u0dbb\u0dca\u0db8\u0dcf\u0dab\u0dba \u0d9a\u0dbb\u0dba\u0dd2 <a href=\"https://arxiv.org/abs/1603.08983\">\u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0dd3 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d9a\u0dcf\u0dbd\u0dba \u0db4\u0dd4\u0db1\u0dbb\u0dcf\u0dc0\u0dbb\u0dca\u0dad\u0db1 \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd \u0dc3\u0db3\u0dc4\u0dcf</a>. </p>\n<p>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0d9a\u0dbb\u0dca\u0dad\u0dc0\u0dca\u0dba\u0dba\u0dda \u0d86\u0daf\u0dcf\u0db1\u0dba \u0dba\u0db1\u0dd4 <span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u0d9c\u0dda \u0dc4\u0dcf \u0dc3\u0db8\u0d9f \u0daf\u0ddb\u0dc1\u0dd2\u0d9a\u0dba\u0d9a\u0dd2. \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba \u0dba\u0db1\u0dd4 <span translate=no>_^_3_^_</span>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0dc0\u0dba\u0dba\u0dd2 - \u0d91\u0dc4\u0dd2 \u0db1\u0db8\u0dca \u0d91\u0d9a\u0dca \u0dba\u0db1\u0dd4 \u0d94\u0dad\u0dca\u0dad\u0dda \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0\u0d9a\u0dca \u0dc0\u0db1 <span translate=no>_^_4_^_</span>\u0d85\u0dad\u0dbb \u0dc0\u0dd9\u0db1\u0dad\u0dca \u0d86\u0d9a\u0dcf\u0dbb\u0dba\u0d9a\u0dd2\u0db1\u0dca \u0dc1\u0dd4\u0db1\u0dca\u0dba \u0dc0\u0dda. \u0d86\u0daf\u0dcf\u0db1\u0dba \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dd4 \u0dbd\u0db6\u0db1\u0dca\u0db1\u0dda \u0daf\u0ddb\u0dc1\u0dd2\u0d9a\u0dba\u0dda \u0d85\u0dc4\u0db9\u0dd4 \u0db8\u0dd6\u0dbd\u0daf\u0dca\u0dbb\u0dc0\u0dca\u0dba \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0\u0d9a\u0dca <span translate=no>_^_5_^_</span> \u0dc4\u0ddd <span translate=no>_^_6_^_</span>\u0dba.</p>\n",
+ "<h3>Parity dataset</h3>\n": "<h3>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0daf\u0dad\u0dca\u0dad \u0d9a\u0da7\u0dca\u0da7\u0dbd\u0dba</h3>\n",
+ "<p> </p>\n": "<p> </p>\n",
+ "<p> Generate a sample</p>\n": "<p> \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba\u0d9a\u0dca\u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
+ "<p> Size of the dataset</p>\n": "<p> \u0daf\u0dad\u0dca\u0dad\u0dc3\u0db8\u0dd4\u0daf\u0dcf\u0dba \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba</p>\n",
+ "<p>Empty vector </p>\n": "<p>\u0dc4\u0dd2\u0dc3\u0dca\u0daf\u0ddb\u0dc1\u0dd2\u0d9a </p>\n",
+ "<p>Fill non-zero elements with <span translate=no>_^_0_^_</span>&#x27;s and <span translate=no>_^_1_^_</span>&#x27;s </p>\n": "<p><span translate=no>_^_0_^_</span>\u0d9c\u0dda \u0dc3\u0dc4 <span translate=no>_^_1_^_</span>\u0d9c\u0dda \u0dc3\u0db8\u0d9c \u0dc1\u0dd4\u0db1\u0dca\u0dba \u0db1\u0ddc\u0dc0\u0db1 \u0d85\u0d82\u0d9c \u0db4\u0dd4\u0dbb\u0dc0\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Number of non-zero elements - a random number between <span translate=no>_^_0_^_</span> and total number of elements </p>\n": "<p>\u0dc1\u0dd4\u0db1\u0dca\u0dba\u0db1\u0ddc\u0dc0\u0db1 \u0db8\u0dd6\u0dbd\u0daf\u0dca\u0dbb\u0dc0\u0dca\u0dba \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0 - \u0d85\u0dad\u0dbb \u0d85\u0dc4\u0db9\u0dd4 \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0\u0d9a\u0dca <span translate=no>_^_0_^_</span> \u0dc3\u0dc4 \u0dc3\u0db8\u0dca\u0db4\u0dd6\u0dbb\u0dca\u0dab \u0db8\u0dd6\u0dbd\u0daf\u0dca\u0dbb\u0dc0\u0dca\u0dba \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0 </p>\n",
+ "<p>Randomly permute the elements </p>\n": "<p>\u0d85\u0dc4\u0db9\u0dd4\u0dbd\u0dd9\u0dc3 \u0db8\u0dd6\u0dbd\u0daf\u0dca\u0dbb\u0dc0\u0dca\u0dba \u0dc0\u0dd2\u0d9a\u0dd8\u0dad\u0dd2 \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
+ "<p>The parity </p>\n": "<p>\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf\u0dc0\u0dba </p>\n",
+ "<ul><li><span translate=no>_^_0_^_</span> is the number of samples </li>\n<li><span translate=no>_^_1_^_</span> is the number of elements in the input vector</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span> \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0 \u0dc0\u0dda </li>\n<li><span translate=no>_^_1_^_</span> \u0d86\u0daf\u0dcf\u0db1 \u0daf\u0ddb\u0dc1\u0dd2\u0d9a\u0dba\u0dda \u0db8\u0dd6\u0dbd\u0daf\u0dca\u0dbb\u0dc0\u0dca\u0dba \u0d9c\u0dab\u0db1</li></ul>\n",
+ "Parity Task": "\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf \u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0dba",
+ "This creates data for Parity Task from the paper Adaptive Computation Time for Recurrent Neural Networks": "\u0db8\u0dd9\u0dba \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0dc0\u0dbd\u0dd2\u0db1\u0dca Parity Task \u0dc3\u0db3\u0dc4\u0dcf \u0daf\u0dad\u0dca\u0dad \u0db1\u0dd2\u0dbb\u0dca\u0db8\u0dcf\u0dab\u0dba \u0d9a\u0dbb\u0dba\u0dd2 \u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0dd3 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d9a\u0dcf\u0dbd\u0dba \u0db4\u0dd4\u0db1\u0dbb\u0dcf\u0dc0\u0dbb\u0dca\u0dad\u0db1 \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd \u0dc3\u0db3\u0dc4\u0dcf"
+}
@@ -0,0 +1,15 @@
+{
+ "<h1>Parity Task</h1>\n<p>This creates data for Parity Task from the paper <a href=\"https://arxiv.org/abs/1603.08983\">Adaptive Computation Time for Recurrent Neural Networks</a>.</p>\n<p>The input of the parity task is a vector with <span translate=no>_^_0_^_</span>&#x27;s <span translate=no>_^_1_^_</span>&#x27;s and <span translate=no>_^_2_^_</span>&#x27;s. The output is the parity of <span translate=no>_^_3_^_</span>&#x27;s - one if there is an odd number of <span translate=no>_^_4_^_</span>&#x27;s and zero otherwise. The input is generated by making a random number of elements in the vector either <span translate=no>_^_5_^_</span> or <span translate=no>_^_6_^_</span>&#x27;s.</p>\n": "<h1>\u5947\u5076\u6821\u9a8c\u4efb\u52a1</h1>\n<p>\u8fd9\u5c06\u4ece\u8bba\u6587\u300a<a href=\"https://arxiv.org/abs/1603.08983\">\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u81ea\u9002\u5e94\u8ba1\u7b97\u65f6\u95f4\u300b\u4e2d\u4e3a</a>\u5947\u5076\u6821\u9a8c\u4efb\u52a1\u521b\u5efa\u6570\u636e\u3002</p>\n<p>\u5947\u5076\u6821\u9a8c\u4efb\u52a1\u7684\u8f93\u5165\u662f\u4e00\u4e2a\u5e26\u6709<span translate=no>_^_0_^_</span>'s \u548c<span translate=no>_^_1_^_</span>'s \u7684\u5411\u91cf\u3002\u8f93\u51fa\u662f<span translate=no>_^_2_^_</span>'s \u7684<span translate=no>_^_3_^_</span>\u5947\u5076\u6821\u9a8c\u2014\u2014\u5982\u679c\u6709\uff0c\u5219\u4e3a 1\u662f\u7684\u5947\u6570<span translate=no>_^_4_^_</span>\uff0c\u5426\u5219\u4e3a\u96f6\u3002\u8f93\u5165\u662f\u901a\u8fc7\u4f7f\u77e2\u91cf\u4e2d\u7684\u968f\u673a\u6570\u91cf\u7684\u5143\u7d20\u4e3a<span translate=no>_^_5_^_</span>\u6216\u800c\u751f\u6210<span translate=no>_^_6_^_</span>\u7684\u3002</p>\n",
+ "<h3>Parity dataset</h3>\n": "<h3>\u5947\u5076\u6821\u9a8c\u6570\u636e</h3>\n",
+ "<p> </p>\n": "<p></p>\n",
+ "<p> Generate a sample</p>\n": "<p>\u751f\u6210\u6837\u672c</p>\n",
+ "<p> Size of the dataset</p>\n": "<p>\u6570\u636e\u96c6\u7684\u5927\u5c0f</p>\n",
+ "<p>Empty vector </p>\n": "<p>\u7a7a\u5411\u91cf</p>\n",
+ "<p>Fill non-zero elements with <span translate=no>_^_0_^_</span>&#x27;s and <span translate=no>_^_1_^_</span>&#x27;s </p>\n": "<p>\u7528<span translate=no>_^_0_^_</span> \u201c\u548c<span translate=no>_^_1_^_</span>\u201d \u586b\u5145\u975e\u96f6\u5143\u7d20</p>\n",
+ "<p>Number of non-zero elements - a random number between <span translate=no>_^_0_^_</span> and total number of elements </p>\n": "<p>\u975e\u96f6\u5143\u7d20\u7684\u6570\u91cf-\u4ecb\u4e8e<span translate=no>_^_0_^_</span>\u548c\u5143\u7d20\u603b\u6570\u4e4b\u95f4\u7684\u968f\u673a\u6570</p>\n",
+ "<p>Randomly permute the elements </p>\n": "<p>\u968f\u673a\u6392\u5217\u5143\u7d20</p>\n",
+ "<p>The parity </p>\n": "<p>\u5947\u5076\u6821\u9a8c</p>\n",
+ "<ul><li><span translate=no>_^_0_^_</span> is the number of samples </li>\n<li><span translate=no>_^_1_^_</span> is the number of elements in the input vector</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u6837\u672c\u7684\u6570\u91cf</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u8f93\u5165\u5411\u91cf\u4e2d\u7684\u5143\u7d20\u6570</li></ul>\n",
+ "Parity Task": "\u5947\u5076\u6821\u9a8c\u4efb\u52a1",
+ "This creates data for Parity Task from the paper Adaptive Computation Time for Recurrent Neural Networks": "\u8fd9\u5c06\u4ece\u8bba\u6587\u300a\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u81ea\u9002\u5e94\u8ba1\u7b97\u65f6\u95f4\u300b\u4e2d\u4e3a\u5947\u5076\u6821\u9a8c\u4efb\u52a1\u521b\u5efa\u6570\u636e"
+}
@@ -0,0 +1,37 @@
+{
+ "<h1><a href=\"index.html\">PonderNet</a> <a href=\"../parity.html\">Parity Task</a> Experiment</h1>\n<p>This trains a <a href=\"index.html\">PonderNet</a> on <a href=\"../parity.html\">Parity Task</a>.</p>\n": "<h1><a href=\"index.html\">PonderNet <a href=\"../parity.html\">\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af\u5b9f\u9a13</a></a></h1>\n<p><a href=\"../parity.html\">\u3053\u308c\u306f\u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af\u3067</a> <a href=\"index.html\">PonderNet</a> \u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u3059\u3002</p>\n",
+ "<p> </p>\n": "<p></p>\n",
+ "<p> Configurations with a  <a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.train_valid.SimpleTrainValidConfigs\">simple training loop</a></p>\n": "<p><a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.train_valid.SimpleTrainValidConfigs\">\u30b7\u30f3\u30d7\u30eb\u306a\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30eb\u30fc\u30d7\u306b\u3088\u308b\u69cb\u6210</a></p>\n",
+ "<p> Run the experiment</p>\n": "<p>\u5b9f\u9a13\u3092\u5b9f\u884c\u3059\u308b</p>\n",
+ "<p> This method gets called by the trainer for each batch</p>\n": "<p>\u3053\u306e\u30e1\u30bd\u30c3\u30c9\u306f\u3001\u30d0\u30c3\u30c1\u3054\u3068\u306b\u30c8\u30ec\u30fc\u30ca\u30fc\u306b\u3088\u3063\u3066\u547c\u3073\u51fa\u3055\u308c\u307e\u3059\u3002</p>\n",
+ "<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
+ "<p><span translate=no>_^_0_^_</span> for the geometric distribution <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u5e7e\u4f55\u5206\u5e03\u7528 <span translate=no>_^_1_^_</span></p>\n",
+ "<p>Accuracy calculator </p>\n": "<p>\u7cbe\u5ea6\u8a08\u7b97\u30c4\u30fc\u30eb</p>\n",
+ "<p>Batch size </p>\n": "<p>\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba</p>\n",
+ "<p>Calculate the expected number of steps taken </p>\n": "<p>\u4e88\u60f3\u3055\u308c\u308b\u6b69\u6570\u306e\u8a08\u7b97</p>\n",
+ "<p>Calculate the reconstruction loss </p>\n": "<p>\u518d\u69cb\u6210\u640d\u5931\u306e\u8a08\u7b97</p>\n",
+ "<p>Calculate the regularization loss </p>\n": "<p>\u6b63\u5247\u5316\u640d\u5931\u306e\u8a08\u7b97</p>\n",
+ "<p>Call accuracy metric </p>\n": "<p>\u901a\u8a71\u7cbe\u5ea6\u6307\u6a19</p>\n",
+ "<p>Clear gradients </p>\n": "<p>\u30af\u30ea\u30a2\u30b0\u30e9\u30c7\u30fc\u30b7\u30e7\u30f3</p>\n",
+ "<p>Clip gradients </p>\n": "<p>\u30af\u30ea\u30c3\u30d7\u30b0\u30e9\u30c7\u30fc\u30b7\u30e7\u30f3</p>\n",
+ "<p>Compute gradients </p>\n": "<p>\u52fe\u914d\u306e\u8a08\u7b97</p>\n",
+ "<p>Get the input and labels and move them to the model&#x27;s device </p>\n": "<p>\u5165\u529b\u3068\u30e9\u30d9\u30eb\u3092\u53d6\u5f97\u3057\u3066\u30e2\u30c7\u30eb\u306e\u30c7\u30d0\u30a4\u30b9\u306b\u79fb\u52d5\u3057\u307e\u3059</p>\n",
+ "<p>Gradient clipping by norm </p>\n": "<p>\u6a19\u6e96\u306b\u3088\u308b\u30b0\u30e9\u30c7\u30fc\u30b7\u30e7\u30f3\u30af\u30ea\u30c3\u30d4\u30f3\u30b0</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>Initialize the model </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u521d\u671f\u5316</p>\n",
+ "<p>Maximum number of steps <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6700\u5927\u30b9\u30c6\u30c3\u30d7\u6570 <span translate=no>_^_0_^_</span></p>\n",
+ "<p>Model </p>\n": "<p>\u30e2\u30c7\u30eb</p>\n",
+ "<p>Number of batches per epoch </p>\n": "<p>\u30a8\u30dd\u30c3\u30af\u3042\u305f\u308a\u306e\u30d0\u30c3\u30c1\u6570</p>\n",
+ "<p>Number of epochs </p>\n": "<p>\u30a8\u30dd\u30c3\u30af\u6570</p>\n",
+ "<p>Number of units in the hidden layer (state) </p>\n": "<p>\u96a0\u308c\u5c64 (\u72b6\u614b) \u5185\u306e\u30e6\u30cb\u30c3\u30c8\u6570</p>\n",
+ "<p>Optimizer </p>\n": "<p>\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc</p>\n",
+ "<p>Print indicators to screen </p>\n": "<p>\u30a4\u30f3\u30b8\u30b1\u30fc\u30bf\u3092\u753b\u9762\u306b\u5370\u5237</p>\n",
+ "<p>Regularization loss <span translate=no>_^_0_^_</span> coefficient <span translate=no>_^_1_^_</span> </p>\n": "<p>\u6b63\u5247\u5316\u640d\u5931\u4fc2\u6570 <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span></p>\n",
+ "<p>Run the model </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u5b9f\u884c</p>\n",
+ "<p>Set the model mode </p>\n": "<p>\u30e2\u30c7\u30eb\u30e2\u30fc\u30c9\u3092\u8a2d\u5b9a</p>\n",
+ "<p>The number of elements in the input vector. <em>We keep it low for demonstration; otherwise, training takes a lot of time. Although the parity task seems simple, figuring out the pattern by looking at samples is quite hard.</em> </p>\n": "<p>\u5165\u529b\u30d9\u30af\u30c8\u30eb\u306e\u8981\u7d20\u6570\u3002<em>\u30c7\u30e2\u7528\u306b\u4f4e\u304f\u8a2d\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002\u305d\u3046\u3057\u306a\u3044\u3068\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306b\u6642\u9593\u304c\u304b\u304b\u308a\u307e\u3059\u3002\u30d1\u30ea\u30c6\u30a3\u306e\u30bf\u30b9\u30af\u306f\u7c21\u5358\u305d\u3046\u306b\u898b\u3048\u307e\u3059\u304c\u3001\u30b5\u30f3\u30d7\u30eb\u3092\u898b\u3066\u30d1\u30bf\u30fc\u30f3\u3092\u7406\u89e3\u3059\u308b\u306e\u306f\u304b\u306a\u308a\u96e3\u3057\u3044\u3067\u3059</em></p>\u3002\n",
+ "<p>Training and validation loaders </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u304a\u3088\u3073\u691c\u8a3c\u30ed\u30fc\u30c0\u30fc</p>\n",
+ "<p>We need to set the metrics to calculate them for the epoch for training and validation </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3068\u691c\u8a3c\u306e\u305f\u3081\u306b\u3001\u30a8\u30dd\u30c3\u30af\u306b\u5408\u308f\u305b\u3066\u305d\u308c\u3089\u3092\u8a08\u7b97\u3059\u308b\u30e1\u30c8\u30ea\u30c3\u30af\u3092\u8a2d\u5b9a\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059</p>\n",
+ "PonderNet Parity Task Experiment": "PonderNet \u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af\u5b9f\u9a13",
+ "This trains is a PonderNet on Parity Task": "\u3053\u306e\u30c8\u30ec\u30a4\u30f3\u306f PonderNet on \u30d1\u30ea\u30c6\u30a3\u30bf\u30b9\u30af\u3067\u3059"
+}
@@ -0,0 +1,37 @@
+{
+ "<h1><a href=\"index.html\">PonderNet</a> <a href=\"../parity.html\">Parity Task</a> Experiment</h1>\n<p>This trains a <a href=\"index.html\">PonderNet</a> on <a href=\"../parity.html\">Parity Task</a>.</p>\n": "<h1><a href=\"index.html\">\u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca</a> <a href=\"../parity.html\">\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf \u0d9a\u0dcf\u0dbb\u0dca\u0dba</a> \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8</h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"../parity.html\">\u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf \u0d9a\u0dcf\u0dbb\u0dca\u0dba</a> \u0db4\u0dd2\u0dc5\u0dd2\u0db6\u0db3 <a href=\"index.html\">\u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca</a> \u0d91\u0d9a\u0d9a\u0dca \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dbb\u0dba\u0dd2. </p>\n",
+ "<p> </p>\n": "<p> </p>\n",
+ "<p> Configurations with a  <a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.train_valid.SimpleTrainValidConfigs\">simple training loop</a></p>\n": "<p> <a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.train_valid.SimpleTrainValidConfigs\">\u0dc3\u0dbb\u0dbd \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0dbd\u0dd6\u0db4\u0dba\u0d9a\u0dca</a>\u0dc3\u0dc4\u0dd2\u0dad \u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dca</p>\n",
+ "<p> Run the experiment</p>\n": "<p> \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf\u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
+ "<p> This method gets called by the trainer for each batch</p>\n": "<p> \u0db8\u0dd9\u0db8\u0d9a\u0dca\u0dbb\u0db8\u0dba \u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0d9a\u0dbb\u0dd4 \u0dc0\u0dd2\u0dc3\u0dd2\u0db1\u0dca \u0d9a\u0dd0\u0db3\u0dc0\u0db1\u0dd4 \u0dbd\u0dd0\u0db6\u0dda</p>\n",
+ "<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span> </p>\n",
+ "<p><span translate=no>_^_0_^_</span> for the geometric distribution <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span> \u0da2\u0dca\u0dba\u0dcf\u0db8\u0dd2\u0dad\u0dd2\u0d9a \u0dc0\u0dca\u0dba\u0dcf\u0db4\u0dca\u0dad\u0dd2\u0dba \u0dc3\u0db3\u0dc4\u0dcf <span translate=no>_^_1_^_</span> </p>\n",
+ "<p>Accuracy calculator </p>\n": "<p>\u0db1\u0dd2\u0dbb\u0dc0\u0daf\u0dca\u0dba\u0dad\u0dcf\u0dc0\u0dba\u0d9a\u0dd0\u0dbd\u0dca\u0d9a\u0dca\u0dba\u0dd4\u0dbd\u0dda\u0da7\u0dbb\u0dba </p>\n",
+ "<p>Batch size </p>\n": "<p>\u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca\u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba </p>\n",
+ "<p>Calculate the expected number of steps taken </p>\n": "<p>\u0d9c\u0dd9\u0db1\u0d87\u0dad\u0dd2 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Calculate the reconstruction loss </p>\n": "<p>\u0db4\u0dca\u0dbb\u0dad\u0dd2\u0dc3\u0d82\u0dc3\u0dca\u0d9a\u0dbb\u0dab\u0d85\u0dbd\u0dcf\u0db7\u0dba \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Calculate the regularization loss </p>\n": "<p>\u0db1\u0dd2\u0dba\u0dcf\u0db8\u0db1\u0dba\u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d85\u0dbd\u0dcf\u0db7\u0dba \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Call accuracy metric </p>\n": "<p>\u0d87\u0db8\u0dad\u0dd4\u0db8\u0dca\u0db1\u0dd2\u0dbb\u0dc0\u0daf\u0dca\u0dba\u0dad\u0dcf\u0dc0 \u0db8\u0dd9\u0da7\u0dca\u0dbb\u0dd2\u0d9a\u0dca </p>\n",
+ "<p>Clear gradients </p>\n": "<p>\u0d85\u0db1\u0dd4\u0d9a\u0dca\u0dbb\u0db8\u0dd2\u0d9a\u0db4\u0dd0\u0dc4\u0dd0\u0daf\u0dd2\u0dbd\u0dd2 \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Clip gradients </p>\n": "<p>\u0d9a\u0dca\u0dbd\u0dd2\u0db4\u0dca\u0d85\u0db1\u0dd4\u0d9a\u0dca\u0dbb\u0db8\u0dd2\u0d9a </p>\n",
+ "<p>Compute gradients </p>\n": "<p>\u0d85\u0db1\u0dd4\u0d9a\u0dca\u0dbb\u0db8\u0dd2\u0d9a\u0d9c\u0dab\u0db1\u0dba </p>\n",
+ "<p>Get the input and labels and move them to the model&#x27;s device </p>\n": "<p>\u0d86\u0daf\u0dcf\u0db1\u0dc3\u0dc4 \u0dbd\u0dda\u0db6\u0dbd \u0dbd\u0db6\u0dcf\u0d9c\u0dd9\u0db1 \u0d92\u0dc0\u0dcf \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0dda \u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba\u0da7 \u0d9c\u0dd9\u0db1 \u0dba\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Gradient clipping by norm </p>\n": "<p>\u0dc3\u0db8\u0dca\u0db8\u0dad\u0dba\u0d85\u0db1\u0dd4\u0dc0 \u0dc1\u0dca\u0dbb\u0dda\u0dab\u0dd2\u0dba \u0d9a\u0dca\u0dbd\u0dd2\u0db4\u0dd2\u0db1\u0dca \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 </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>Initialize the model </p>\n": "<p>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Maximum number of steps <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d8b\u0db4\u0dbb\u0dd2\u0db8\u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 <span translate=no>_^_0_^_</span> </p>\n",
+ "<p>Model </p>\n": "<p>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba </p>\n",
+ "<p>Number of batches per epoch </p>\n": "<p>\u0d91\u0d9a\u0dca\u0d8a\u0db4\u0ddd\u0da0\u0dca\u0da0\u0dba\u0d9a\u0da7 \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0d9c\u0dab\u0db1 </p>\n",
+ "<p>Number of epochs </p>\n": "<p>\u0d91\u0db4\u0ddc\u0da0\u0dca\u0d9c\u0dab\u0db1 </p>\n",
+ "<p>Number of units in the hidden layer (state) </p>\n": "<p>\u0dc3\u0dd0\u0d9f\u0dc0\u0dd4\u0dab\u0dd4\u0dc3\u0dca\u0dae\u0dbb\u0dba\u0dda \u0d92\u0d9a\u0d9a \u0d9c\u0dab\u0db1 (\u0dbb\u0dcf\u0da2\u0dca\u0dba) </p>\n",
+ "<p>Optimizer </p>\n": "<p>\u0db4\u0dca\u0dbb\u0dc1\u0dc3\u0dca\u0dad\u0d9a\u0dbb\u0dab\u0dba </p>\n",
+ "<p>Print indicators to screen </p>\n": "<p>\u0dad\u0dd2\u0dbb\u0dba\u0dc0\u0dd9\u0dad \u0daf\u0dbb\u0dca\u0dc1\u0d9a \u0db8\u0dd4\u0daf\u0dca\u0dbb\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Regularization loss <span translate=no>_^_0_^_</span> coefficient <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0db4\u0dcf\u0da9\u0dd4 <span translate=no>_^_0_^_</span> \u0dc3\u0d82\u0d9c\u0dd4\u0dab\u0d9a\u0dba \u0dc0\u0dd2\u0db0\u0dd2\u0db8\u0dad\u0dca \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 <span translate=no>_^_1_^_</span> </p>\n",
+ "<p>Run the model </p>\n": "<p>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0db0\u0dcf\u0dc0\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
+ "<p>Set the model mode </p>\n": "<p>\u0d86\u0daf\u0dbb\u0dca\u0dc1\u0db4\u0dca\u0dbb\u0d9a\u0dcf\u0dbb\u0dba \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1 </p>\n",
+ "<p>The number of elements in the input vector. <em>We keep it low for demonstration; otherwise, training takes a lot of time. Although the parity task seems simple, figuring out the pattern by looking at samples is quite hard.</em> </p>\n": "<p>\u0d86\u0daf\u0dcf\u0db1\u0daf\u0ddb\u0dc1\u0dd2\u0d9a\u0dba\u0dda \u0db8\u0dd6\u0dbd\u0daf\u0dca\u0dbb\u0dc0\u0dca\u0dba \u0d9c\u0dab\u0db1. <em>\u0db1\u0dd2\u0dbb\u0dd6\u0db4\u0dab\u0dba\u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0db4\u0dd2 \u0d91\u0dba \u0d85\u0da9\u0dd4 \u0db8\u0da7\u0dca\u0da7\u0db8\u0d9a \u0dad\u0db6\u0dcf \u0d9c\u0db1\u0dd2\u0db8\u0dd4; \u0d91\u0dc3\u0dda \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2\u0db1\u0db8\u0dca \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0dc0 \u0dc3\u0db3\u0dc4\u0dcf \u0db6\u0ddc\u0dc4\u0ddd \u0d9a\u0dcf\u0dbd\u0dba\u0d9a\u0dca \u0d9c\u0dad \u0dc0\u0dda. \u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf \u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0dba \u0dc3\u0dbb\u0dbd \u0dba\u0dd0\u0dba\u0dd2 \u0db4\u0dd9\u0db1\u0dd4\u0db1\u0daf, \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd \u0daf\u0dd9\u0dc3 \u0db6\u0dd0\u0dbd\u0dd3\u0db8\u0dd9\u0db1\u0dca \u0dbb\u0da7\u0dcf\u0dc0 \u0d85\u0dc0\u0db6\u0ddc\u0db0 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dad\u0dbb\u0db8\u0d9a\u0dca \u0d85\u0db4\u0dc4\u0dc3\u0dd4\u0dba. </em> </p>\n",
+ "<p>Training and validation loaders </p>\n": "<p>\u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0dc0\u0dc3\u0dc4 \u0dc0\u0dbd\u0d82\u0d9c\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d9a\u0dcf\u0dbb\u0d9a\u0dba\u0db1\u0dca </p>\n",
+ "<p>We need to set the metrics to calculate them for the epoch for training and validation </p>\n": "<p>\u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4\u0dc0\u0dc3\u0dc4 \u0dc0\u0dbd\u0d82\u0d9c\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d91\u0db4\u0ddd\u0da0\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d92\u0dc0\u0dcf \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0db4\u0dca\u0dbb\u0db8\u0dd2\u0dad\u0dd2\u0d9a \u0dc3\u0d9a\u0dc3\u0dca \u0d9a\u0dc5 \u0dba\u0dd4\u0dad\u0dd4\u0dba </p>\n",
+ "PonderNet Parity Task Experiment": "\u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca \u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf \u0d9a\u0dcf\u0dbb\u0dca\u0dba \u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf \u0db6\u0dd0\u0dbd\u0dd3\u0db8",
+ "This trains is a PonderNet on Parity Task": "\u0db8\u0dd9\u0db8 \u0daf\u0dd4\u0db8\u0dca\u0dbb\u0dd2\u0dba \u0dc3\u0db8\u0dcf\u0db1\u0dcf\u0dad\u0dca\u0db8\u0dad\u0dcf \u0d9a\u0dcf\u0dbb\u0dca\u0dba \u0db4\u0dd2\u0dc5\u0dd2\u0db6\u0db3 \u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca \u0dba"
+}
@@ -0,0 +1,37 @@
+{
+ "<h1><a href=\"index.html\">PonderNet</a> <a href=\"../parity.html\">Parity Task</a> Experiment</h1>\n<p>This trains a <a href=\"index.html\">PonderNet</a> on <a href=\"../parity.html\">Parity Task</a>.</p>\n": "<h1><a href=\"index.html\">PonderNet</a> <a href=\"../parity.html\">\u5947\u5076\u6821\u9a8c\u4efb\u52a1</a>\u5b9e\u9a8c</h1>\n<p>\u8fd9\u4f1a\u5728<a href=\"../parity.html\">\u5947\u5076\u6821\u9a8c\u4efb\u52a1</a>\u4e0a\u8bad\u7ec3 <a href=\"index.html\">PonderNet</a>\u3002</p>\n",
+ "<p> </p>\n": "<p></p>\n",
+ "<p> Configurations with a  <a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.train_valid.SimpleTrainValidConfigs\">simple training loop</a></p>\n": "<p>\u5e26\u6709<a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.train_valid.SimpleTrainValidConfigs\">\u7b80\u5355\u8bad\u7ec3\u5faa\u73af</a>\u7684\u914d\u7f6e</p>\n",
+ "<p> Run the experiment</p>\n": "<p>\u8fd0\u884c\u5b9e\u9a8c</p>\n",
+ "<p> This method gets called by the trainer for each batch</p>\n": "<p>\u57f9\u8bad\u5e08\u4f1a\u4e3a\u6bcf\u6279\u6b21\u8c03\u7528\u6b64\u65b9\u6cd5</p>\n",
+ "<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
+ "<p><span translate=no>_^_0_^_</span> for the geometric distribution <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u7528\u4e8e\u51e0\u4f55\u5206\u5e03<span translate=no>_^_1_^_</span></p>\n",
+ "<p>Accuracy calculator </p>\n": "<p>\u7cbe\u5ea6\u8ba1\u7b97\u5668</p>\n",
+ "<p>Batch size </p>\n": "<p>\u6279\u91cf\u5927\u5c0f</p>\n",
+ "<p>Calculate the expected number of steps taken </p>\n": "<p>\u8ba1\u7b97\u9884\u671f\u91c7\u53d6\u7684\u6b65\u6570</p>\n",
+ "<p>Calculate the reconstruction loss </p>\n": "<p>\u8ba1\u7b97\u91cd\u5efa\u635f\u5931</p>\n",
+ "<p>Calculate the regularization loss </p>\n": "<p>\u8ba1\u7b97\u6b63\u5219\u5316\u635f\u5931</p>\n",
+ "<p>Call accuracy metric </p>\n": "<p>\u547c\u53eb\u51c6\u786e\u5ea6\u6307\u6807</p>\n",
+ "<p>Clear gradients </p>\n": "<p>\u6e10\u53d8\u6e05\u6670</p>\n",
+ "<p>Clip gradients </p>\n": "<p>\u526a\u8f91\u6e10\u53d8</p>\n",
+ "<p>Compute gradients </p>\n": "<p>\u8ba1\u7b97\u68af\u5ea6</p>\n",
+ "<p>Get the input and labels and move them to the model&#x27;s device </p>\n": "<p>\u83b7\u53d6\u8f93\u5165\u548c\u6807\u7b7e\u5e76\u5c06\u5176\u79fb\u52a8\u5230\u6a21\u578b\u7684\u8bbe\u5907\u4e2d</p>\n",
+ "<p>Gradient clipping by norm </p>\n": "<p>\u6309\u89c4\u8303\u8fdb\u884c\u6e10\u53d8\u88c1\u526a</p>\n",
+ "<p>Increment step in training mode </p>\n": "<p>\u5728\u8bad\u7ec3\u6a21\u5f0f\u4e2d\u589e\u52a0\u6b65\u6570</p>\n",
+ "<p>Initialize the model </p>\n": "<p>\u521d\u59cb\u5316\u6a21\u578b</p>\n",
+ "<p>Maximum number of steps <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6700\u5927\u6b65\u6570<span translate=no>_^_0_^_</span></p>\n",
+ "<p>Model </p>\n": "<p>\u578b\u53f7</p>\n",
+ "<p>Number of batches per epoch </p>\n": "<p>\u6bcf\u4e2a\u7eaa\u5143\u7684\u6279\u6b21\u6570</p>\n",
+ "<p>Number of epochs </p>\n": "<p>\u5468\u671f\u7684\u6570\u91cf</p>\n",
+ "<p>Number of units in the hidden layer (state) </p>\n": "<p>\u9690\u85cf\u5c42\uff08\u72b6\u6001\uff09\u4e2d\u7684\u5355\u4f4d\u6570\u91cf</p>\n",
+ "<p>Optimizer </p>\n": "<p>\u4f18\u5316\u5668</p>\n",
+ "<p>Print indicators to screen </p>\n": "<p>\u5c06\u6307\u793a\u5668\u6253\u5370\u5230\u5c4f\u5e55\u4e0a</p>\n",
+ "<p>Regularization loss <span translate=no>_^_0_^_</span> coefficient <span translate=no>_^_1_^_</span> </p>\n": "<p>\u6b63\u5219\u5316\u635f\u5931<span translate=no>_^_0_^_</span>\u7cfb\u6570<span translate=no>_^_1_^_</span></p>\n",
+ "<p>Run the model </p>\n": "<p>\u8fd0\u884c\u6a21\u578b</p>\n",
+ "<p>Set the model mode </p>\n": "<p>\u8bbe\u7f6e\u6a21\u578b\u6a21\u5f0f</p>\n",
+ "<p>The number of elements in the input vector. <em>We keep it low for demonstration; otherwise, training takes a lot of time. Although the parity task seems simple, figuring out the pattern by looking at samples is quite hard.</em> </p>\n": "<p>\u8f93\u5165\u5411\u91cf\u4e2d\u7684\u5143\u7d20\u6570\u3002<em>\u6211\u4eec\u5c06\u5176\u4fdd\u6301\u5728\u8f83\u4f4e\u7684\u6c34\u5e73\u4ee5\u8fdb\u884c\u6f14\u793a\uff1b\u5426\u5219\uff0c\u8bad\u7ec3\u4f1a\u82b1\u8d39\u5f88\u591a\u65f6\u95f4\u3002\u5c3d\u7ba1\u5947\u5076\u6821\u9a8c\u4efb\u52a1\u770b\u8d77\u6765\u5f88\u7b80\u5355\uff0c\u4f46\u901a\u8fc7\u67e5\u770b\u6837\u672c\u6765\u627e\u51fa\u6a21\u5f0f\u76f8\u5f53\u56f0\u96be\u3002</em></p>\n",
+ "<p>Training and validation loaders </p>\n": "<p>\u8bad\u7ec3\u548c\u9a8c\u8bc1\u88c5\u8f7d\u673a</p>\n",
+ "<p>We need to set the metrics to calculate them for the epoch for training and validation </p>\n": "<p>\u6211\u4eec\u9700\u8981\u8bbe\u7f6e\u6307\u6807\u6765\u8ba1\u7b97\u8bad\u7ec3\u548c\u9a8c\u8bc1\u65f6\u671f\u7684\u6307\u6807</p>\n",
+ "PonderNet Parity Task Experiment": "PonderNet \u5947\u5076\u6821\u9a8c\u4efb\u52a1\u5b9e\u9a8c",
+ "This trains is a PonderNet on Parity Task": "\u8fd9\u5217\u706b\u8f66\u662f\u5947\u5076\u6821\u9a8c\u4efb\u52a1\u4e0a\u7684 PonderNet"
+}
@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/2107.05407\">PonderNet: Learning to Ponder</a>.</p>\n<p>PonderNet adapts the computation based on the input. It changes the number of steps to take on a recurrent network based on the input. PonderNet learns this with end-to-end gradient descent. </p>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: \u719f\u8003\u3059\u308b\u3053\u3068\u3092\u5b66\u3076</a></h1>\n<p>\u3053\u308c\u306f\u3001\u8ad6\u6587\u300c<a href=\"https://arxiv.org/abs/2107.05407\">PonderNet: \u719f\u8003\u3092\u5b66\u307c\u3046</a>\u300d<a href=\"https://pytorch.org\">\u3092PyTorch\u3067\u5b9f\u88c5\u3057\u305f\u3082\u306e\u3067\u3059</a>\u3002</p>\n<p>PonderNet \u306f\u5165\u529b\u306b\u57fa\u3065\u3044\u3066\u8a08\u7b97\u3092\u8abf\u6574\u3057\u307e\u3059\u3002\u5165\u529b\u306b\u57fa\u3065\u3044\u3066\u30ea\u30ab\u30ec\u30f3\u30c8\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3067\u5b9f\u884c\u3059\u308b\u30b9\u30c6\u30c3\u30d7\u306e\u6570\u3092\u5909\u66f4\u3057\u307e\u3059\u3002PonderNet\u306f\u3053\u308c\u3092\u7aef\u304b\u3089\u7aef\u307e\u3067\u306e\u52fe\u914d\u964d\u4e0b\u6cd5\u3067\u5b66\u7fd2\u3057\u307e\u3059</p>\u3002\n",
+ "PonderNet: Learning to Ponder": "PonderNet: \u719f\u8003\u3059\u308b\u3053\u3068\u3092\u5b66\u3076"
+}
@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/2107.05407\">PonderNet: Learning to Ponder</a>.</p>\n<p>PonderNet adapts the computation based on the input. It changes the number of steps to take on a recurrent network based on the input. PonderNet learns this with end-to-end gradient descent. </p>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">\u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca: \u0db8\u0dd9\u0db1\u0dd9\u0dc4\u0dd2 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a></h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"https://arxiv.org/abs/2107.05407\">PonderNet \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 <a href=\"https://pytorch.org\">PyTorch</a> \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8: \u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca \u0dc0\u0dd9\u0dad \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a> . </p>\n<p>PonderNet\u0d86\u0daf\u0dcf\u0db1\u0dba \u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca\u0dc0 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0dba\u0dd2. \u0d86\u0daf\u0dcf\u0db1\u0dba \u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca\u0dc0 \u0db4\u0dd4\u0db1\u0dbb\u0dcf\u0dc0\u0dbb\u0dca\u0dad\u0db1 \u0da2\u0dcf\u0dbd\u0dba\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9c\u0dad \u0dba\u0dd4\u0dad\u0dd4 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0d91\u0dba \u0dc0\u0dd9\u0db1\u0dc3\u0dca \u0d9a\u0dbb\u0dba\u0dd2. \u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db8\u0dd9\u0dba \u0d89\u0d9c\u0dd9\u0db1 \u0d9c\u0db1\u0dca\u0db1\u0dda \u0d85\u0dc0\u0dc3\u0dcf\u0db1\u0dba \u0dc3\u0dd2\u0da7 \u0d85\u0dc0\u0dc3\u0dcf\u0db1\u0dba \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0dc0\u0dd6 \u0dc1\u0dca\u0dbb\u0dda\u0dab\u0dd2\u0dba\u0dda \u0dc3\u0db8\u0dca\u0db7\u0dc0\u0dba\u0d9a\u0dca \u0dc3\u0db8\u0d9f\u0dba. </p>\n",
+ "PonderNet: Learning to Ponder": "\u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca: \u0db8\u0dd9\u0db1\u0dd9\u0dc4\u0dd2 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8"
+}
@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/2107.05407\">PonderNet: Learning to Ponder</a>.</p>\n<p>PonderNet adapts the computation based on the input. It changes the number of steps to take on a recurrent network based on the input. PonderNet learns this with end-to-end gradient descent. </p>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet\uff1a\u5b66\u4f1a\u601d\u8003</a></h1>\n<p>\u8fd9\u662f <a href=\"https://pytorch.org\">P <a href=\"https://arxiv.org/abs/2107.05407\">onderNet\uff1a\u5b66\u4f1a\u601d\u8003</a>\u8bba\u6587\u7684 PyTorch</a> \u5b9e\u73b0\u3002</p>\n<p>PonderNet \u6839\u636e\u8f93\u5165\u8c03\u6574\u8ba1\u7b97\u3002\u5b83\u4f1a\u6839\u636e\u8f93\u5165\u66f4\u6539\u5faa\u73af\u7f51\u7edc\u4e0a\u8981\u6267\u884c\u7684\u6b65\u9aa4\u6570\u3002PonderNet \u901a\u8fc7\u7aef\u5230\u7aef\u68af\u5ea6\u4e0b\u964d\u6765\u5b66\u4e60\u8fd9\u4e00\u70b9\u3002</p>\n",
+ "PonderNet: Learning to Ponder": "PonderNet\uff1a\u5b66\u4f1a\u601d\u8003"
+}
@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/adaptive_computation/index.html\">Neural Networks with Adaptive Computation</a></h1>\n<p>These are neural network architectures that change the computation complexity based on the complexity of the input sample.</p>\n<ul><li>\ud83d\udea7 TODO: Adaptive Computation Time for Recurrent Neural Networks </li>\n<li><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a> </li></ul>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/index.html\">\u9069\u5fdc\u578b\u8a08\u7b97\u3092\u5099\u3048\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af</a></h1>\n<p>\u3053\u308c\u3089\u306f\u3001\u5165\u529b\u30b5\u30f3\u30d7\u30eb\u306e\u8907\u96d1\u3055\u306b\u57fa\u3065\u3044\u3066\u8a08\u7b97\u306e\u8907\u96d1\u3055\u3092\u5909\u66f4\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>\ud83d\udea7 TODO: \u30ea\u30ab\u30ec\u30f3\u30c8\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u9069\u5fdc\u578b\u8a08\u7b97\u6642\u9593</li>\n<li><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: \u719f\u8003\u3059\u308b\u3053\u3068\u3092\u5b66\u3076</a></li></ul>\n",
+ "Neural Networks with Adaptive Computation": "\u9069\u5fdc\u578b\u8a08\u7b97\u3092\u5099\u3048\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af"
+}
@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/adaptive_computation/index.html\">Neural Networks with Adaptive Computation</a></h1>\n<p>These are neural network architectures that change the computation complexity based on the complexity of the input sample.</p>\n<ul><li>\ud83d\udea7 TODO: Adaptive Computation Time for Recurrent Neural Networks </li>\n<li><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a> </li></ul>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/index.html\">\u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0dd3 \u0d9c\u0dab\u0db1\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd</a></h1>\n<p>\u0db8\u0dda\u0dc0\u0dcf\u0d86\u0daf\u0dcf\u0db1 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba\u0dda \u0dc3\u0d82\u0d9a\u0dd3\u0dbb\u0dca\u0dab\u0dad\u0dca\u0dc0\u0dba \u0db8\u0dad \u0db4\u0daf\u0db1\u0db8\u0dca\u0dc0 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0dc3\u0d82\u0d9a\u0dd3\u0dbb\u0dca\u0dab\u0dad\u0dcf\u0dc0 \u0dc0\u0dd9\u0db1\u0dc3\u0dca \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>\ud83d\udea7TODO: \u0db4\u0dd4\u0db1\u0dbb\u0dcf\u0dc0\u0dbb\u0dca\u0dad\u0db1 \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0dd3 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d9a\u0dcf\u0dbd\u0dba </li>\n<li><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">\u0db4\u0ddc\u0db1\u0dca\u0da9\u0dbb\u0dca\u0db1\u0dd9\u0da7\u0dca: \u0db8\u0dd9\u0db1\u0dd9\u0dc4\u0dd2 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a> </li></ul>\n",
+ "Neural Networks with Adaptive Computation": "\u0d85\u0db1\u0dd4\u0dc0\u0dbb\u0dca\u0dad\u0dd3 \u0d9c\u0dab\u0db1\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd"
+}
@@ -0,0 +1,4 @@
+{
+ "<h1><a href=\"https://nn.labml.ai/adaptive_computation/index.html\">Neural Networks with Adaptive Computation</a></h1>\n<p>These are neural network architectures that change the computation complexity based on the complexity of the input sample.</p>\n<ul><li>\ud83d\udea7 TODO: Adaptive Computation Time for Recurrent Neural Networks </li>\n<li><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet: Learning to Ponder</a> </li></ul>\n": "<h1><a href=\"https://nn.labml.ai/adaptive_computation/index.html\">\u5177\u6709\u81ea\u9002\u5e94\u8ba1\u7b97\u7684\u795e\u7ecf\u7f51\u7edc</a></h1>\n<p>\u8fd9\u4e9b\u662f\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\uff0c\u6839\u636e\u8f93\u5165\u6837\u672c\u7684\u590d\u6742\u5ea6\u6765\u6539\u53d8\u8ba1\u7b97\u590d\u6742\u5ea6\u3002</p>\n<ul><li>\ud83d\udea7 TODO\uff1a\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u81ea\u9002\u5e94\u8ba1\u7b97\u65f6\u95f4</li>\n<li><a href=\"https://nn.labml.ai/adaptive_computation/ponder_net/index.html\">PonderNet\uff1a\u5b66\u4f1a\u601d\u8003</a></li></ul>\n",
+ "Neural Networks with Adaptive Computation": "\u5177\u6709\u81ea\u9002\u5e94\u8ba1\u7b97\u7684\u795e\u7ecf\u7f51\u7edc"
+}