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Yes, you can do NAS (neural architecture search) quite easily.\"}]]]}]]}]]}],[\"$\",\"li\",null,{\"className\":\"leading-normal flex items-start\",\"children\":[[\"$\",\"div\",null,{\"className\":\"text-base leading-normal mr-1 flex min-h-[1lh] min-w-6 items-center justify-center text-tint\",\"children\":[\"$\",\"div\",null,{\"className\":\"before:font-var before:content-[--pseudoBefore--content]\",\"style\":{\"--pseudoBefore--content\":\"'•'\",\"--font-family\":\"Arial\",\"fontSize\":\"min(1.5em, 24px)\",\"lineHeight\":1}}]}],[\"$\",\"div\",null,{\"className\":\"flex min-w-0 flex-1 flex-col space-y-2\",\"children\":[[\"$\",\"p\",null,{\"className\":\"w-full decoration-primary/6 max-w-3xl min-h-[1lh] flip-heading-hash [\u0026:is(h2)\u003ediv]:mt-0 [\u0026:is(h3)\u003ediv]:mt-0 [\u0026:is(h4)\u003ediv]:mt-0 mx-0\",\"children\":[[[\"$\",\"$47\",\"0\",{\"children\":\"sometimes we wish to compose models together, e.g. a hybrid network with a conv net and MLP for bi-modal inputs, joined together in the middle with another MLP, and split to multiple outputs for multi-modal controls.\"}]]]}]]}]]}],[\"$\",\"li\",null,{\"className\":\"leading-normal flex items-start\",\"children\":[[\"$\",\"div\",null,{\"className\":\"text-base leading-normal mr-1 flex min-h-[1lh] min-w-6 items-center justify-center text-tint\",\"children\":[\"$\",\"div\",null,{\"className\":\"before:font-var before:content-[--pseudoBefore--content]\",\"style\":{\"--pseudoBefore--content\":\"'•'\",\"--font-family\":\"Arial\",\"fontSize\":\"min(1.5em, 24px)\",\"lineHeight\":1}}]}],[\"$\",\"div\",null,{\"className\":\"flex min-w-0 flex-1 flex-col space-y-2\",\"children\":[[\"$\",\"p\",null,{\"className\":\"w-full decoration-primary/6 max-w-3xl min-h-[1lh] flip-heading-hash [\u0026:is(h2)\u003ediv]:mt-0 [\u0026:is(h3)\u003ediv]:mt-0 [\u0026:is(h4)\u003ediv]:mt-0 mx-0\",\"children\":[[[\"$\",\"$47\",\"0\",{\"children\":\"The composed networks is always a DAG. This means it can be specified via a JSON/YAML structure too (this can be proven mathematically, but it's not why we're here).\"}]]]}]]}]]}]]}],[\"$\",\"p\",null,{\"className\":\"mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0\",\"children\":[[[\"$\",\"$47\""])</script><script>self.__next_f.push([1,",\"0\",{\"children\":\"These essentially formed the design requirement for TorchArc. Additionally, I've also added a \"}],[\"$\",\"$47\",\"1\",{\"children\":[\"$\",\"code\",null,{\"className\":\"py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]\",\"children\":\"carry_forward\"}]}],[\"$\",\"$47\",\"2\",{\"children\":\" method which accepts a TensorTuple input, forward-pass any tensors in it by name-matching, and carry any unused tensors in the output. 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1))\",\"marks\":[]}],\"key\":\"385c88f856d54626b107d21cf16ba905\"}],\"key\":\"ade93102ec0d44b99bf6b8dad320bdc1\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (5): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\",\"marks\":[]}],\"key\":\"4e9b4eaedc0c4d16ac5ab83877d99543\"}],\"key\":\"231891a2e81b44b9a4ddd737d142e4af\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (6): ReLU()\",\"marks\":[]}],\"key\":\"e59356e68fce4cd0adb4d07e5198b86c\"}],\"key\":\"922eeacbdb7046daa238f4f846324b68\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (7): Dropou"])</script><script>self.__next_f.push([1,"t2d(p=0.2, inplace=False)\",\"marks\":[]}],\"key\":\"e40691e9ab0d4ab49bd3b142db0daca1\"}],\"key\":\"bec9cc981bcb4dcc826a0d7a07330962\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\")\",\"marks\":[]}],\"key\":\"4598185fcbfa4deead1a64862e5bb694\"}],\"key\":\"6ffce8c979464f3d8d15a3ebf0eedf6e\"}],\"key\":\"5303f27f493b48ec93aae991bfd01e01\"},\"style\":[\"mx-auto w-full decoration-primary/6\",\"max-w-3xl\",\"flip-heading-hash\"],\"inlines\":[]}]\n51:[\"$\",\"$L5d\",null,{\"block\":{\"object\":\"block\",\"type\":\"code\",\"isVoid\":false,\"data\":{\"syntax\":\"python\"},\"nodes\":[{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"arc = {\",\"marks\":[]}],\"key\":\"5da12a0baf5448d487a4722fbdffe0d2\"}],\"key\":\"555986ec42f7437694767301d9258994\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'type': 'Linear',\",\"marks\":[]}],\"key\":\"179c648a066d400ebe51cc97d5de9992\"}],\"key\":\"ba382acf4c18482c8f09aa2ba5ca2524\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'in_features': 8,\",\"marks\":[]}],\"key\":\"81ad91c6d9d543289c42c5956a4ec1ff\"}],\"key\":\"f1c9f3dc5ffd4bec88c76fe43780155f\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'layers': [64, 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torcharc.build(arc)\",\"marks\":[]}],\"key\":\"a455a9e516c14a53b8c791b75aaff940\"}],\"key\":\"23749aa4941241e6ba141d524b53e67e\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"\",\"marks\":[]}],\"key\":\"e38c5337d7964fc295007ae964213329\"}],\"key\":\"2440d6c926cf40409d5b2b029682f404\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"batch_size = 16\",\"ma"])</script><script>self.__next_f.push([1,"rks\":[]}],\"key\":\"03220791cc114dbb8c4bf129da669563\"}],\"key\":\"e473bfa559c5429f8c3396946af77ffc\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"x = torch.rand([batch_size, arc['in_features']])\",\"marks\":[]}],\"key\":\"b4d7abcfc64a43d78b57695a30064dc3\"}],\"key\":\"538f134dbd9846f597a80e7228f09df1\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"y = model(x)\",\"marks\":[]}],\"key\":\"754652e348794812829707dbc140d9a5\"}],\"key\":\"ea4accb59240429d8a59e5c07793092b\"}],\"key\":\"3224a5aae9c64a29a700391544c71aaa\"},\"style\":[\"mx-auto w-full decoration-primary/6\",\"max-w-3xl\",\"flip-heading-hash\"],\"inlines\":[]}]\n52:[\"$\",\"$L5d\",null,{\"block\":{\"object\":\"block\",\"type\":\"code\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"Sequential(\",\"marks\":[]}],\"key\":\"f03a852e6884480a9fd03e11ce88023d\"}],\"key\":\"efb5551571354621a616d2d2d681e612\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (0): Linear(in_features=8, out_features=64, bias=True)\",\"marks\":[]}],\"key\":\"2a54899296ac46f8a18e9dada73179b4\"}],\"key\":\"1ac391e4e1894a93b612cc020f32ddd0\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\",\"marks\":[]}],\"key\":\"71683b16f8b64312bef3e278b114643b\"}],\"key\":\"b97ca4fdedaa4a008d17776d15ddeed7\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (2): ReLU()\",\"marks\":[]}],\"key\":\"95c8bb57dfb44bcd9bc501e892e5f647\"}],\"key\":\"3967d8a50b4b4461a5e7e5825ce970d4\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (3): Dropout(p=0.2, inplace=False)\",\"marks\":[]}],\"key\":\"6d4f9552c0974ae29d23385691e9c921\"}],\"key\":\"8f50a11c1be04a16804a521c51cfa694\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (4): Linear(in_features=64, out_features=32, bias=True)\",\"marks\":[]}],\"key\":\"1988f5d0da4a4912a86c8c894c1025b7\"}],\"key\":\"fd3b91ce5a414d61a369f3b7d83097dc\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\",\"marks\":[]}],\"key\":\"111c19484f384abab6abd8d43254e19f\"}],\"key\":\"d4a5b46017c54f35ac966a7d17284ae2\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (6): ReLU()\",\"marks\":[]}],\"key\":\"dbaa39ed6fb04bb394bd7b807a5d17bf\"}],\"key\":\"3632b2a7ff994f2e9313462518811d2e\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (7): Dropout(p=0.2, inplace=False)\",\"marks\":[]}],\"key\":\"bda9b82a96214391b51780aa52cebf66\"}],\"key\":\"5bc3947844674aa8997b26e749f691fc\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\")\",\"marks\":[]}],\"key\":\"78242b2d2f87497cabf405c4764b174d\"}],\"key\":\"2e4c1b0d015444c495b9b58e34de0dbf\"}],\"key\":\"6033d17a070042cc81fb32bc495a1915\"},\"style\":[\"mx-auto w-full decoration-primary/6\",\"max-w-3xl\",\"flip-heading-hash\"],\"inlines\":[]}]\n53:[\"$\",\"$L5d\",null,{\"block\":{\"object\":\"block\",\"type\":\"code\",\"isVoid\":false,\"data\":{\"syntax\":\"python\"},\"nodes\":[{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"arc = {\",\"marks\":[]}],\"key\":\"750457d0021441438e08552c84d964c8\"}],\"key\":\"5bb2fcd93df34008a30da68cb7e0210c\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'type': 'TSTransformer',\",\"mar"])</script><script>self.__next_f.push([1,"ks\":[]}],\"key\":\"a68d10a5866f4a8caf3ee0e4ed4fab70\"}],\"key\":\"04479ca8792f49cca2bb449691edf568\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'd_model': 64,\",\"marks\":[]}],\"key\":\"5b543e5937c6440b9de301b7f86c0e37\"}],\"key\":\"b63f27c2b4704fd096838efdc89904f2\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'nhead': 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torcharc.build(arc)\",\"marks\":[]}],\"key\":\"e310a8c5117747d693da9ab4997977f8\"}],\"key\":\"09e63f3c1e104fdd9a1029b3183973fd\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"\",\"marks\":[]}],\"key\":\"a71dd5744aa2400fa115efa549833413\"}],\"key\":\"28a5b65a496d4444a8d2bb8b74d88013\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"seq_len = 32\",\"marks\":[]}],\"key\":\"0b573501fc1f48ada23d118db2e6bcfd\"}],\"key\":\"09b5d3f7bf604b4d9babb3a6c474a68f\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"x = torch.rand([seq_len, arc['in_channels']])\",\"marks\":[]}],\"key\":\"296db151e98745349165b8b17390feb6\"}],\"key\":\"4e859f73b6194cc4a036c06bdb4dd482\"}],\"key\":\"1df16460c6d94cd7be7906780b45fb95\"},\"style\":[\"mx-auto w-full decoration-primary/6\",\"max-w-3xl\",\"flip-heading-hash\"],\"inlines\":[]}]\n54:[\"$\",\"$L5d\",null,{\"block\":{\"object\":\"block\",\"type\":\"code\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"TSTransformer(\",\"marks\":[]}],\"key\":\"70ce1a9f008240f59c2366f5ec41a7b4\"}],\"key\":\"bbc32d068a7046e2a06374d623baf115\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (in_embedding): Linear(in_features=1, out_features=64, 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'FiLMMerge',\",\"marks\":[]}],\"key\":\"931ac4429bbd4740a1041e2391c2ccf4\"}],\"key\":\"d5800844015448fcae3c8707c4e93046\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'in_names': ['image', 'vector'],\",\"marks\":[]}],\"key\":\"24d7c373bab8451f9eb780560a42237f\"}],\"key\":\"82036996f1a04a0eb2e10ebf9d062f8d\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'names': {'feature': 'image', 'conditioner': 'vector'},\",\"marks\":[]}],\"key\":\"e63f005db494463eadbaeb303967dd92\"}],\"key\":\"1c215fc60f7740f898a7dcf8e62c886a\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" },\",\"marks\":[]}],\"key\":\"22a42a041bf84affaab2b65cc94c"])</script><script>self.__next_f.push([1,"0f54\"}],\"key\":\"6b8974ba72e94b63849b5aa115d1bdd4\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'Flatten': {\",\"marks\":[]}],\"key\":\"9369dd231960476da50d7f6a25c5c99b\"}],\"key\":\"0093c3e06b544259b270d0bd96161e19\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'type': 'Flatten'\",\"marks\":[]}],\"key\":\"a8d42630579f4054a7a87a1963fe3c52\"}],\"key\":\"e464a45241ca41358b05d2ffb25c239f\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" },\",\"marks\":[]}],\"key\":\"c150cce06d52431fad1404be93c9a3f8\"}],\"key\":\"51e3bcdfb05848eda2f2dad0e41ef5df\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'Linear': {\",\"marks\":[]}],\"key\":\"63f656ee2b724205bef24126acaff45c\"}],\"key\":\"f4b6bec13d5842f8aae6261727b02517\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'type': 'Linear',\",\"marks\":[]}],\"key\":\"bddc77059fbb4da7a201853476513716\"}],\"key\":\"616153013f6f4190b5ad9d535f995df5\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'layers': [64, 32],\",\"marks\":[]}],\"key\":\"0255f7a997cf4e5bb557d95fcb08cfe4\"}],\"key\":\"608bc1c0f6a44a8b85346ef5bd62433c\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'batch_norm': True,\",\"marks\":[]}],\"key\":\"b80e4aa559be4fecaa6575ca8edb742d\"}],\"key\":\"04c82cb7cce7453da3c0d6ec5d58e5c3\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'activation': 'ReLU',\",\"marks\":[]}],\"key\":\"a91edb99f3274306850d587231ff2771\"}],\"key\":\"858687863b1a48cc87f196618c671b9d\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'dropout': 0.2,\",\"marks\":[]}],\"key\":\"f906ead58a51429d98c65805e995c5a8\"}],\"key\":\"09f8360a5b1d4735b76ef767291bfc34\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'init': 'kaiming_uniform_',\",\"marks\":[]}],\"key\":\"7de29f1eaec841c6b7e1097f4120a808\"}],\"key\":\"1cd6341bc6db467cacd2373c710aaa40\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" },\",\"marks\":[]}],\"key\":\"95e453049c2d4a678ac17c7a7020874c\"}],\"key\":\"5ee540f9a6c445a3a047bd8d07e73212\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'out': {\",\"marks\":[]}],\"key\":\"858a0df64bf14a69b7232ee6849acc1a\"}],\"key\":\"924bd7de0cbc470487962401d90ab73e\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'type': 'Linear',\",\"marks\":[]}],\"key\":\"60222f4c025d4579bf1ccdd4718c95b4\"}],\"key\":\"81bb22d0c99b4bc1a42b6bbd1440dad2\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'out_features': 8,\",\"marks\":[]}],\"key\":\"3ce80b0788a944dcabc71143f487a7d2\"}],\"key\":\"3eed3068c17a4b8496a4c85d4de43d3a\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" },\",\"marks\":[]}],\"key\":\"e9438e0a813f4f0188335934071d8ab8\"}],\"key\":\"ff3376d7e0d6425baea383d5b71f4dff\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'fork': {\",\"marks\":[]}],\"key\":\"ecb6576fcbf3482e80975dda82c8af4d\"}],\"key\":\"9a4be73426ed4157883953e3ed28cb25\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'type': 'SplitFork',\",\"marks\":["])</script><script>self.__next_f.push([1,"]}],\"key\":\"c9f3d6d07a174631b39d3203db3811c2\"}],\"key\":\"246a2c1dd1674390be1cf485cdf89874\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" 'shapes': {'mean': [4], 'std': [4]},\",\"marks\":[]}],\"key\":\"81a130f4ae55436aaf07ab7aebd73fc1\"}],\"key\":\"d075cef4a2e341eaab9f3499c6aae4dd\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" }\",\"marks\":[]}],\"key\":\"9aedfb161ac04a74a9aa5dccc3de8890\"}],\"key\":\"b2fe5552b51044d28510246b3fc9b1b3\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"}\",\"marks\":[]}],\"key\":\"17525a92a6d84a28a4aff0ffd74f1d88\"}],\"key\":\"544ff6cf20e94eaab0ab2ee000e3d74e\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"model = torcharc.build(arc)\",\"marks\":[]}],\"key\":\"52f404ab509c46b88c1e7ea94c87824b\"}],\"key\":\"920d6ce34d1a4a4e902220f76f072e1a\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"\",\"marks\":[]}],\"key\":\"e4c2a7eb76ba485898e99a146f6394f3\"}],\"key\":\"5761adb0c519427588e78dbea73635ce\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"batch_size = 16\",\"marks\":[]}],\"key\":\"6856f0a4067a46beba91dda1892bcedb\"}],\"key\":\"6768c54e43be4f90bd07fcc71b7c9735\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"dag_in_shape = arc['dag_in_shape']\",\"marks\":[]}],\"key\":\"3118f717ea3147f28d6f14d1478a9474\"}],\"key\":\"5869dec8d1e8457aa62d86c87a594b22\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"xs = {'image': torch.rand([batch_size, *dag_in_shape['image']]), 'vector': torch.rand([batch_size, *dag_in_shape['vector']])}\",\"marks\":[]}],\"key\":\"23db59d6a8f645ae8fc4b73588a91c60\"}],\"key\":\"1af9d519e8e143cea8e31297961842e8\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"# returns dict of Tensors if output is multi-modal, Tensor otherwise\",\"marks\":[]}],\"key\":\"5ee08a4b115945fc883fc318eacf4013\"}],\"key\":\"5cc27999eb9a42b78d63a7e487f6d75c\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"ys = model(xs)\",\"marks\":[]}],\"key\":\"f128f1c576984536afbb0a3343fe60f2\"}],\"key\":\"25c258bea85d4534a6608e141741badf\"}],\"key\":\"bc3eee8427f1444ca16364d7ab76b6bf\"},\"style\":[\"mx-auto w-full decoration-primary/6\",\"max-w-3xl\",\"flip-heading-hash\"],\"inlines\":[]}]\n56:[\"$\",\"$L5d\",null,{\"block\":{\"object\":\"block\",\"type\":\"code\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\"DAGNet(\",\"marks\":[]}],\"key\":\"8d125d2299a84f5facb746dbcd64975a\"}],\"key\":\"5858f2773981490b96306b39752b6de1\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (module_dict): ModuleDict(\",\"marks\":[]}],\"key\":\"ea37dd895bd1415e9efcb11abac824b7\"}],\"key\":\"9ade8050c25944509d53d1e3d8f38f58\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (image): Sequential(\",\"marks\":[]}],\"key\":\"2c2323ff62bc4e83b17859eace813684\"}],\"key\":\"b29a0b2406104ee3a2a8ae597c197726\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (0): Conv2d(3, 16, kernel_size=(4, 4), stride=(2, 2))\",\"marks\":[]}],\"key\":\"e6aa56454d1843b9b9229eb58cdd79f6\"}],\"key\":\"8445b87c299748f0a5084f8354a5eccb\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running"])</script><script>self.__next_f.push([1,"_stats=True)\",\"marks\":[]}],\"key\":\"f5700f739622439387619ad30056c0a3\"}],\"key\":\"8024b34999ea4ea4866129a41c23d59d\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (2): ReLU()\",\"marks\":[]}],\"key\":\"8630808d9c4c42039b2a160de84b2ffa\"}],\"key\":\"c23b4a93cfc745628d13a8db10e96614\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (3): Dropout2d(p=0.2, inplace=False)\",\"marks\":[]}],\"key\":\"99f451bfd2cf48339765bdff48bc51a9\"}],\"key\":\"e6e74e204e924893b450d85d73b14faf\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (4): Conv2d(16, 16, kernel_size=(4, 4), stride=(1, 1))\",\"marks\":[]}],\"key\":\"ec568726284f4c64b4e2f39ff3485700\"}],\"key\":\"f705e0aaf4a944cf972f0db364fcc0e3\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (5): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\",\"marks\":[]}],\"key\":\"5779180e6d2b4918b463b7397603d4af\"}],\"key\":\"3a30be37adbe464c98806d7bed49ad92\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (6): ReLU()\",\"marks\":[]}],\"key\":\"cda55351736b4e80a390d52eeb9613de\"}],\"key\":\"2e29eb59eeda43469a3280fe2b4fe702\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (7): Dropout2d(p=0.2, inplace=False)\",\"marks\":[]}],\"key\":\"501db5e4c12a475c933952ee44da0df4\"}],\"key\":\"133e273168704950bfd14a0aa7e97ab9\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" )\",\"marks\":[]}],\"key\":\"7aae14b5cdbf4f52a3cfccf01ddc8348\"}],\"key\":\"06ab0f6cfb9d4bd3a237b563c02d3bee\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (merge): FiLMMerge(\",\"marks\":[]}],\"key\":\"a31c4b05fb9b4e3e8b45a10b518e4098\"}],\"key\":\"2afacd5177934750a59d1a3ecfec056e\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (conditioner_scale): Linear(in_features=8, out_features=16, bias=True)\",\"marks\":[]}],\"key\":\"82d74793ca674a75bad991cdbe04df70\"}],\"key\":\"4d8f4833f81b478a84d5b80d7b4dcfe7\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (conditioner_shift): Linear(in_features=8, out_features=16, bias=True)\",\"marks\":[]}],\"key\":\"9e0cf239b239408bb7019d39b8c3dafa\"}],\"key\":\"5f34d9f6bfa44857bb98cf9580cd0279\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" )\",\"marks\":[]}],\"key\":\"34670cab96ec443fb05680d2f5c4c961\"}],\"key\":\"57d882f3421b438da223e3f5bdca18eb\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (Flatten): Flatten()\",\"marks\":[]}],\"key\":\"acf2fabec81e490b8e1eb2d5d6359d2e\"}],\"key\":\"e8284377bced47bd91534d3fc5a02550\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (Linear): Sequential(\",\"marks\":[]}],\"key\":\"f9e577e05d98411a957f6c21045b2c46\"}],\"key\":\"c7ca4151fddc4e919672cd9337827b5c\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (0): Linear(in_features=576, out_features=64, bias=True)\",\"marks\":[]}],\"key\":\"fee621e6866e4da8a5610527c60088b4\"}],\"key\":\"e12e84005fa04b13b9ae3cf020ca64a9\"},{\"object\":\"block\",\"type\":\"code-line\",\"isVoid\":false,\"data\":{},\"nodes\":[{\"object\":\"text\",\"leaves\":[{\"object\":\"leaf\",\"text\":\" (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, 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theme-gradient-tint:bg-gradient-tint theme-gradient:bg-gradient-primary theme-muted:bg-tint-subtle pb-4 page-api-block:xl:max-2xl:hidden page-api-block:xl:max-2xl:pb-0 page-api-block:xl:max-2xl:group-hover/aside:flex page-api-block:xl:max-2xl:bg-transparent"><div class="" data-visual-test="removed"></div></div></aside><main class="relative min-w-0 flex-1 py-8 lg:px-12 break-anywhere page-api-block:xl:max-2xl:pr-0 page-api-block:mx-auto page-api-block:max-w-screen-2xl page-default-width page-has-toc"><header class="max-w-3xl mx-auto mb-6 space-y-3 page-api-block:ml-0"><nav><ol class="flex flex-wrap items-center gap-2"><li><a class="decoration-[max(0.07em,1px)] underline-offset-2 links-accent:underline-offset-4 links-default:decoration-primary/6 links-default:text-primary-subtle links-default:hover:text-primary-strong links-default:contrast-more:text-primary links-default:contrast-more:hover:text-primary-strong links-accent:decoration-primary-subtle links-accent:hover:decoration-[3px] links-accent:hover:[text-decoration-skip-ink:none] transition-all duration-100 no-underline hover:underline text-xs tracking-wide font-semibold uppercase flex items-center gap-1.5 contrast-more:underline contrast-more:decoration-current" href="/blog/ml">ML</a></li></ol></nav><h1 class="text-4xl font-bold flex items-center gap-4">TorchArc</h1><p class="text-lg text-tint">2020/06/20</p></header><div class="grid [&>*+*]:mt-5 whitespace-pre-wrap"><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">Building modular PyTorch models for my projects in the past years has prompted me to use a config-based approach to define model architecture. Over time I have iteratively refined the method, and recently I felt it has become sufficiently mature to be open sourced.</p><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">The project is known as <a class="underline decoration-[max(0.07em,1px)] underline-offset-2 links-accent:underline-offset-4 links-default:decoration-primary/6 links-default:text-primary-subtle links-default:hover:text-primary-strong links-default:contrast-more:text-primary links-default:contrast-more:hover:text-primary-strong links-accent:decoration-primary-subtle links-accent:hover:decoration-[3px] links-accent:hover:[text-decoration-skip-ink:none] transition-all duration-100" href="https://github.com/kengz/torcharc">TorchArc</a>: Build PyTorch networks by specifying architectures. You can install it from pip:</p><div class="group/codeblock grid grid-flow-col mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div class="flex items-center justify-start gap-2 text-sm [grid-area:1/1]"></div><button class="z-[2] mt-2 mr-2 self-start justify-self-end rounded-md bg-transparent p-1 text-tint text-xs leading-none opacity-0 ring-1 ring-tint backdrop-blur-md transition-opacity duration-75 [grid-area:2/1] hover:ring-tint-hover group-hover/codeblock:opacity-[1] print:hidden">Copy</button><pre class="hide-scroll relative overflow-auto bg-tint theme-gradient:bg-tint-12/1 ring-tint-subtle [grid-area:2/1] rounded-md straight-corners:rounded-sm"><code id=":Rsd9uu8qfkvfa:" class="inline-grid min-w-full grid-cols-[auto_1fr] p-2 [count-reset:line]"><span class="highlight-line"><span class="highlight-line-content">pip install torcharc</span></span></code></pre></div><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">My experience from building quite a lot of models for DL and RL (which can have unconventional architecture) has resulted in the following observations:</p><ul class="min-w-0 space-y-2 mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><li class="leading-normal flex items-start"><div class="text-base leading-normal mr-1 flex min-h-[1lh] min-w-6 items-center justify-center text-tint"><div class="before:font-var before:content-[--pseudoBefore--content]" style="--pseudoBefore--content:'•';--font-family:Arial;font-size:min(1.5em, 24px);line-height:1"></div></div><div class="flex min-w-0 flex-1 flex-col space-y-2"><p class="w-full decoration-primary/6 max-w-3xl min-h-[1lh] flip-heading-hash [&:is(h2)>div]:mt-0 [&:is(h3)>div]:mt-0 [&:is(h4)>div]:mt-0 mx-0">most models are built with common components and hyperparameters - layers, width, activation, norm, dropout, init, etc. These can be specified via a config with the structure of a JSON/YAML.</p></div></li><li class="leading-normal flex items-start"><div class="text-base leading-normal mr-1 flex min-h-[1lh] min-w-6 items-center justify-center text-tint"><div class="before:font-var before:content-[--pseudoBefore--content]" style="--pseudoBefore--content:'•';--font-family:Arial;font-size:min(1.5em, 24px);line-height:1"></div></div><div class="flex min-w-0 flex-1 flex-col space-y-2"><p class="w-full decoration-primary/6 max-w-3xl min-h-[1lh] flip-heading-hash [&:is(h2)>div]:mt-0 [&:is(h3)>div]:mt-0 [&:is(h4)>div]:mt-0 mx-0">using config-based architecture frees us from frequent hard-code changes, while also immediately allow for hyperparameter optimization on the entire architecture. Yes, you can do NAS (neural architecture search) quite easily.</p></div></li><li class="leading-normal flex items-start"><div class="text-base leading-normal mr-1 flex min-h-[1lh] min-w-6 items-center justify-center text-tint"><div class="before:font-var before:content-[--pseudoBefore--content]" style="--pseudoBefore--content:'•';--font-family:Arial;font-size:min(1.5em, 24px);line-height:1"></div></div><div class="flex min-w-0 flex-1 flex-col space-y-2"><p class="w-full decoration-primary/6 max-w-3xl min-h-[1lh] flip-heading-hash [&:is(h2)>div]:mt-0 [&:is(h3)>div]:mt-0 [&:is(h4)>div]:mt-0 mx-0">sometimes we wish to compose models together, e.g. a hybrid network with a conv net and MLP for bi-modal inputs, joined together in the middle with another MLP, and split to multiple outputs for multi-modal controls.</p></div></li><li class="leading-normal flex items-start"><div class="text-base leading-normal mr-1 flex min-h-[1lh] min-w-6 items-center justify-center text-tint"><div class="before:font-var before:content-[--pseudoBefore--content]" style="--pseudoBefore--content:'•';--font-family:Arial;font-size:min(1.5em, 24px);line-height:1"></div></div><div class="flex min-w-0 flex-1 flex-col space-y-2"><p class="w-full decoration-primary/6 max-w-3xl min-h-[1lh] flip-heading-hash [&:is(h2)>div]:mt-0 [&:is(h3)>div]:mt-0 [&:is(h4)>div]:mt-0 mx-0">The composed networks is always a DAG. This means it can be specified via a JSON/YAML structure too (this can be proven mathematically, but it's not why we're here).</p></div></li></ul><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">These essentially formed the design requirement for TorchArc. Additionally, I've also added a <code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">carry_forward</code> method which accepts a TensorTuple input, forward-pass any tensors in it by name-matching, and carry any unused tensors in the output. This allows a multi-modal input to be carried and forward-passed all the way until the output.</p><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">Let's jump straight into TorchArc.</p><h2 id="example-usage" class="text-3xl font-semibold heading group relative grid mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div class="hash grid grid-area-1-1 relative -ml-6 w-7 border-0 opacity-0 group-hover:opacity-[0] group-focus:opacity-[0] md:group-hover:md:opacity-[1] md:group-focus:md:opacity-[1] mt-[1em]"><a href="#example-usage" aria-label="Direct link to heading" class="inline-flex h-full items-start leading-tight"><svg style="mask-image:url(https://ka-p.fontawesome.com/releases/v6.6.0/svgs/regular/hashtag.svg?v=2&token=a463935e93);mask-repeat:no-repeat;mask-position:center" class="gb-icon w-3.5 h-[1em] mt-0.5 transition-colors text-transparent group-hover:text-tint-subtle contrast-more:group-hover:text-tint-strong lg:w-4"></svg></a></div><div class="grid-area-1-1 z-[1] justify-self-start text-left leading-tight mt-[1em]">Example Usage</div></h2><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">Given just the architecture, <code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">torcharc</code> can build generic DAG (directed acyclic graph) of nn modules, which consists of:</p><ul class="min-w-0 space-y-2 mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><li class="leading-normal flex items-start"><div class="text-base leading-normal mr-1 flex min-h-[1lh] min-w-6 items-center justify-center text-tint"><div class="before:font-var before:content-[--pseudoBefore--content]" style="--pseudoBefore--content:'•';--font-family:Arial;font-size:min(1.5em, 24px);line-height:1"></div></div><div class="flex min-w-0 flex-1 flex-col space-y-2"><p class="w-full decoration-primary/6 max-w-3xl min-h-[1lh] flip-heading-hash [&:is(h2)>div]:mt-0 [&:is(h3)>div]:mt-0 [&:is(h4)>div]:mt-0 mx-0">single-input-output modules: <code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">Conv1d, Conv2d, Conv3d, Linear, PTTSTransformer, TSTransformer</code> or any other valid nn.Module</p></div></li><li class="leading-normal flex items-start"><div class="text-base leading-normal mr-1 flex min-h-[1lh] min-w-6 items-center justify-center text-tint"><div class="before:font-var before:content-[--pseudoBefore--content]" style="--pseudoBefore--content:'•';--font-family:Arial;font-size:min(1.5em, 24px);line-height:1"></div></div><div class="flex min-w-0 flex-1 flex-col space-y-2"><p class="w-full decoration-primary/6 max-w-3xl min-h-[1lh] flip-heading-hash [&:is(h2)>div]:mt-0 [&:is(h3)>div]:mt-0 [&:is(h4)>div]:mt-0 mx-0">fork modules: <code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">ReuseFork, SplitFork</code></p></div></li><li class="leading-normal flex items-start"><div class="text-base leading-normal mr-1 flex min-h-[1lh] min-w-6 items-center justify-center text-tint"><div class="before:font-var before:content-[--pseudoBefore--content]" style="--pseudoBefore--content:'•';--font-family:Arial;font-size:min(1.5em, 24px);line-height:1"></div></div><div class="flex min-w-0 flex-1 flex-col space-y-2"><p class="w-full decoration-primary/6 max-w-3xl min-h-[1lh] flip-heading-hash [&:is(h2)>div]:mt-0 [&:is(h3)>div]:mt-0 [&:is(h4)>div]:mt-0 mx-0">merge modules: <code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">ConcatMerge, FiLMMerge</code></p></div></li></ul><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">The custom modules are defined in <a class="underline decoration-[max(0.07em,1px)] underline-offset-2 links-accent:underline-offset-4 links-default:decoration-primary/6 links-default:text-primary-subtle links-default:hover:text-primary-strong links-default:contrast-more:text-primary links-default:contrast-more:hover:text-primary-strong links-accent:decoration-primary-subtle links-accent:hover:decoration-[3px] links-accent:hover:[text-decoration-skip-ink:none] transition-all duration-100" href="https://github.com/kengz/torcharc/tree/master/torcharc/module"><code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">torcharc/module</code></a>, registered in <a class="underline decoration-[max(0.07em,1px)] underline-offset-2 links-accent:underline-offset-4 links-default:decoration-primary/6 links-default:text-primary-subtle links-default:hover:text-primary-strong links-default:contrast-more:text-primary links-default:contrast-more:hover:text-primary-strong links-accent:decoration-primary-subtle links-accent:hover:decoration-[3px] links-accent:hover:[text-decoration-skip-ink:none] transition-all duration-100" href="https://github.com/kengz/torcharc/blob/master/torcharc/module_builder.py"><code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">torcharc/module_builder.py</code></a>.</p><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">The full examples of architecture references are in <a class="underline decoration-[max(0.07em,1px)] underline-offset-2 links-accent:underline-offset-4 links-default:decoration-primary/6 links-default:text-primary-subtle links-default:hover:text-primary-strong links-default:contrast-more:text-primary links-default:contrast-more:hover:text-primary-strong links-accent:decoration-primary-subtle links-accent:hover:decoration-[3px] links-accent:hover:[text-decoration-skip-ink:none] transition-all duration-100" href="https://github.com/kengz/torcharc/blob/master/torcharc/arc_ref.py"><code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">torcharc/arc_ref.py</code></a>, and full functional examples are in <a class="underline decoration-[max(0.07em,1px)] underline-offset-2 links-accent:underline-offset-4 links-default:decoration-primary/6 links-default:text-primary-subtle links-default:hover:text-primary-strong links-default:contrast-more:text-primary links-default:contrast-more:hover:text-primary-strong links-accent:decoration-primary-subtle links-accent:hover:decoration-[3px] links-accent:hover:[text-decoration-skip-ink:none] transition-all duration-100" href="https://github.com/kengz/torcharc/tree/master/test/module"><code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">test/module/</code></a>. Below we walk through some main examples.</p><h3 id="convnet" class="text-2xl font-semibold heading group relative grid mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div class="hash grid grid-area-1-1 relative -ml-6 w-7 border-0 opacity-0 group-hover:opacity-[0] group-focus:opacity-[0] md:group-hover:md:opacity-[1] md:group-focus:md:opacity-[1] mt-[0.75em]"><a href="#convnet" aria-label="Direct link to heading" class="inline-flex h-full items-start leading-snug"><svg style="mask-image:url(https://ka-p.fontawesome.com/releases/v6.6.0/svgs/regular/hashtag.svg?v=2&token=a463935e93);mask-repeat:no-repeat;mask-position:center" class="gb-icon w-3.5 h-[1em] mt-0.5 transition-colors text-transparent group-hover:text-tint-subtle contrast-more:group-hover:text-tint-strong lg:w-4"></svg></a></div><div class="grid-area-1-1 z-[1] justify-self-start text-left leading-snug mt-[0.75em]">ConvNet</div></h3><div class="rounded-lg straight-corners:rounded-sm ring-1 ring-inset ring-tint-subtle flex overflow-hidden flex-col mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div role="tablist" class="group/tabs inline-flex flex-row self-stretch after:flex-[1] after:bg-tint-12/1 [&:has(button.active-tab:last-of-type):after]:rounded-bl-md"><button role="tab" aria-selected="true" aria-controls="arc" id="tab-arc" class="[&:has(+_.active-tab)]:rounded-br-md [.active-tab_+_&]:rounded-bl-md [.active-tab_+_:after]:rounded-br-md inline-block text-sm px-3.5 py-2 transition-[color] font-[500] relative after:transition-colors after:border-r after:absolute after:left-[unset] after:right-0 after:border-tint after:top-[15%] after:h-[70%] after:w-[1px] last:after:border-transparent hover:text-tint-strong truncate max-w-full shrink-0 active-tab text-tint-strong bg-transparent after:[&.active-tab]:border-transparent after:[:has(+_&.active-tab)]:border-transparent after:[:has(&_+)]:border-transparent">arc</button><button role="tab" aria-selected="false" aria-controls="model" id="tab-model" class="[&:has(+_.active-tab)]:rounded-br-md [.active-tab_+_&]:rounded-bl-md [.active-tab_+_:after]:rounded-br-md inline-block text-sm px-3.5 py-2 transition-[color] font-[500] relative after:transition-colors after:border-r after:absolute after:left-[unset] after:right-0 after:border-tint after:top-[15%] after:h-[70%] after:w-[1px] last:after:border-transparent text-tint bg-tint-12/1 hover:text-tint-strong truncate max-w-full">model</button></div><div role="tabpanel" id="arc" aria-labelledby="tab-arc" class="p-4"><div class="w-full space-y-4"><div class="group/codeblock grid grid-flow-col mx-auto w-full decoration-primary/6 max-w-3xl flip-heading-hash"><div class="flex items-center justify-start gap-2 text-sm [grid-area:1/1]"></div><button class="z-[2] mt-2 mr-2 self-start justify-self-end rounded-md bg-transparent p-1 text-tint text-xs leading-none opacity-0 ring-1 ring-tint backdrop-blur-md transition-opacity duration-75 [grid-area:2/1] hover:ring-tint-hover group-hover/codeblock:opacity-[1] print:hidden">Copy</button><pre class="hide-scroll relative overflow-auto bg-tint theme-gradient:bg-tint-12/1 ring-tint-subtle [grid-area:2/1] rounded-md straight-corners:rounded-sm"><code id=":R5jkd9uu8qfkvfa:" class="inline-grid min-w-full grid-cols-[auto_1fr] p-2 [count-reset:line]"><span class="highlight-line"><span class="highlight-line-content">import torcharc </span></span><span class="highlight-line"><span class="highlight-line-content"> </span></span><span class="highlight-line"><span class="highlight-line-content"> </span></span><span class="highlight-line"><span class="highlight-line-content">arc = { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'Conv2d', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'in_shape': [3, 20, 20], </span></span><span class="highlight-line"><span class="highlight-line-content"> 'layers': [ </span></span><span class="highlight-line"><span class="highlight-line-content"> [16, 4, 2, 0, 1], </span></span><span class="highlight-line"><span class="highlight-line-content"> [16, 4, 1, 0, 1] </span></span><span class="highlight-line"><span class="highlight-line-content"> ], </span></span><span class="highlight-line"><span class="highlight-line-content"> 'batch_norm': True, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'activation': 'ReLU', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'dropout': 0.2, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'init': 'kaiming_uniform_', </span></span><span class="highlight-line"><span class="highlight-line-content">} </span></span><span class="highlight-line"><span class="highlight-line-content">model = torcharc.build(arc) </span></span><span class="highlight-line"><span class="highlight-line-content"> </span></span><span class="highlight-line"><span class="highlight-line-content">batch_size = 16 </span></span><span class="highlight-line"><span class="highlight-line-content">x = torch.rand([batch_size, *arc['in_shape']]) </span></span><span class="highlight-line"><span class="highlight-line-content">y = model(x)</span></span></code></pre></div></div></div><div role="tabpanel" id="model" aria-labelledby="tab-model" class="p-4 hidden"><div class="w-full space-y-4"><div class="group/codeblock grid grid-flow-col mx-auto w-full decoration-primary/6 max-w-3xl flip-heading-hash"><div class="flex items-center justify-start gap-2 text-sm [grid-area:1/1]"></div><button class="z-[2] mt-2 mr-2 self-start justify-self-end rounded-md bg-transparent p-1 text-tint text-xs leading-none opacity-0 ring-1 ring-tint backdrop-blur-md transition-opacity duration-75 [grid-area:2/1] hover:ring-tint-hover group-hover/codeblock:opacity-[1] print:hidden">Copy</button><pre class="hide-scroll relative overflow-auto bg-tint theme-gradient:bg-tint-12/1 ring-tint-subtle [grid-area:2/1] rounded-md straight-corners:rounded-sm"><code id=":R6jkd9uu8qfkvfa:" class="inline-grid min-w-full grid-cols-[auto_1fr] p-2 [count-reset:line]"><span class="highlight-line"><span class="highlight-line-content">Sequential( </span></span><span class="highlight-line"><span class="highlight-line-content"> (0): Conv2d(3, 16, kernel_size=(4, 4), stride=(2, 2)) </span></span><span class="highlight-line"><span class="highlight-line-content"> (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (2): ReLU() </span></span><span class="highlight-line"><span class="highlight-line-content"> (3): Dropout2d(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> (4): Conv2d(16, 16, kernel_size=(4, 4), stride=(1, 1)) </span></span><span class="highlight-line"><span class="highlight-line-content"> (5): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (6): ReLU() </span></span><span class="highlight-line"><span class="highlight-line-content"> (7): Dropout2d(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content">)</span></span></code></pre></div></div></div></div><h3 id="mlp" class="text-2xl font-semibold heading group relative grid mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div class="hash grid grid-area-1-1 relative -ml-6 w-7 border-0 opacity-0 group-hover:opacity-[0] group-focus:opacity-[0] md:group-hover:md:opacity-[1] md:group-focus:md:opacity-[1] mt-[0.75em]"><a href="#mlp" aria-label="Direct link to heading" class="inline-flex h-full items-start leading-snug"><svg style="mask-image:url(https://ka-p.fontawesome.com/releases/v6.6.0/svgs/regular/hashtag.svg?v=2&token=a463935e93);mask-repeat:no-repeat;mask-position:center" class="gb-icon w-3.5 h-[1em] mt-0.5 transition-colors text-transparent group-hover:text-tint-subtle contrast-more:group-hover:text-tint-strong lg:w-4"></svg></a></div><div class="grid-area-1-1 z-[1] justify-self-start text-left leading-snug mt-[0.75em]">MLP</div></h3><div class="rounded-lg straight-corners:rounded-sm ring-1 ring-inset ring-tint-subtle flex overflow-hidden flex-col mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div role="tablist" class="group/tabs inline-flex flex-row self-stretch after:flex-[1] after:bg-tint-12/1 [&:has(button.active-tab:last-of-type):after]:rounded-bl-md"><button role="tab" aria-selected="true" aria-controls="arc-1" id="tab-arc-1" class="[&:has(+_.active-tab)]:rounded-br-md [.active-tab_+_&]:rounded-bl-md [.active-tab_+_:after]:rounded-br-md inline-block text-sm px-3.5 py-2 transition-[color] font-[500] relative after:transition-colors after:border-r after:absolute after:left-[unset] after:right-0 after:border-tint after:top-[15%] after:h-[70%] after:w-[1px] last:after:border-transparent hover:text-tint-strong truncate max-w-full shrink-0 active-tab text-tint-strong bg-transparent after:[&.active-tab]:border-transparent after:[:has(+_&.active-tab)]:border-transparent after:[:has(&_+)]:border-transparent">arc</button><button role="tab" aria-selected="false" aria-controls="model-1" id="tab-model-1" class="[&:has(+_.active-tab)]:rounded-br-md [.active-tab_+_&]:rounded-bl-md [.active-tab_+_:after]:rounded-br-md inline-block text-sm px-3.5 py-2 transition-[color] font-[500] relative after:transition-colors after:border-r after:absolute after:left-[unset] after:right-0 after:border-tint after:top-[15%] after:h-[70%] after:w-[1px] last:after:border-transparent text-tint bg-tint-12/1 hover:text-tint-strong truncate max-w-full">model</button></div><div role="tabpanel" id="arc-1" aria-labelledby="tab-arc-1" class="p-4"><div class="w-full space-y-4"><div class="group/codeblock grid grid-flow-col mx-auto w-full decoration-primary/6 max-w-3xl flip-heading-hash"><div class="flex items-center justify-start gap-2 text-sm [grid-area:1/1]"></div><button class="z-[2] mt-2 mr-2 self-start justify-self-end rounded-md bg-transparent p-1 text-tint text-xs leading-none opacity-0 ring-1 ring-tint backdrop-blur-md transition-opacity duration-75 [grid-area:2/1] hover:ring-tint-hover group-hover/codeblock:opacity-[1] print:hidden">Copy</button><pre class="hide-scroll relative overflow-auto bg-tint theme-gradient:bg-tint-12/1 ring-tint-subtle [grid-area:2/1] rounded-md straight-corners:rounded-sm"><code id=":R5k4d9uu8qfkvfa:" class="inline-grid min-w-full grid-cols-[auto_1fr] p-2 [count-reset:line]"><span class="highlight-line"><span class="highlight-line-content">arc = { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'Linear', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'in_features': 8, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'layers': [64, 32], </span></span><span class="highlight-line"><span class="highlight-line-content"> 'batch_norm': True, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'activation': 'ReLU', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'dropout': 0.2, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'init': { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'normal_', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'std': 0.01, </span></span><span class="highlight-line"><span class="highlight-line-content"> }, </span></span><span class="highlight-line"><span class="highlight-line-content">} </span></span><span class="highlight-line"><span class="highlight-line-content">model = torcharc.build(arc) </span></span><span class="highlight-line"><span class="highlight-line-content"> </span></span><span class="highlight-line"><span class="highlight-line-content">batch_size = 16 </span></span><span class="highlight-line"><span class="highlight-line-content">x = torch.rand([batch_size, arc['in_features']]) </span></span><span class="highlight-line"><span class="highlight-line-content">y = model(x)</span></span></code></pre></div></div></div><div role="tabpanel" id="model-1" aria-labelledby="tab-model-1" class="p-4 hidden"><div class="w-full space-y-4"><div class="group/codeblock grid grid-flow-col mx-auto w-full decoration-primary/6 max-w-3xl flip-heading-hash"><div class="flex items-center justify-start gap-2 text-sm [grid-area:1/1]"></div><button class="z-[2] mt-2 mr-2 self-start justify-self-end rounded-md bg-transparent p-1 text-tint text-xs leading-none opacity-0 ring-1 ring-tint backdrop-blur-md transition-opacity duration-75 [grid-area:2/1] hover:ring-tint-hover group-hover/codeblock:opacity-[1] print:hidden">Copy</button><pre class="hide-scroll relative overflow-auto bg-tint theme-gradient:bg-tint-12/1 ring-tint-subtle [grid-area:2/1] rounded-md straight-corners:rounded-sm"><code id=":R6k4d9uu8qfkvfa:" class="inline-grid min-w-full grid-cols-[auto_1fr] p-2 [count-reset:line]"><span class="highlight-line"><span class="highlight-line-content">Sequential( </span></span><span class="highlight-line"><span class="highlight-line-content"> (0): Linear(in_features=8, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (2): ReLU() </span></span><span class="highlight-line"><span class="highlight-line-content"> (3): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> (4): Linear(in_features=64, out_features=32, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (6): ReLU() </span></span><span class="highlight-line"><span class="highlight-line-content"> (7): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content">)</span></span></code></pre></div></div></div></div><h3 id="time-series-transformer" class="text-2xl font-semibold heading group relative grid mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div class="hash grid grid-area-1-1 relative -ml-6 w-7 border-0 opacity-0 group-hover:opacity-[0] group-focus:opacity-[0] md:group-hover:md:opacity-[1] md:group-focus:md:opacity-[1] mt-[0.75em]"><a href="#time-series-transformer" aria-label="Direct link to heading" class="inline-flex h-full items-start leading-snug"><svg style="mask-image:url(https://ka-p.fontawesome.com/releases/v6.6.0/svgs/regular/hashtag.svg?v=2&token=a463935e93);mask-repeat:no-repeat;mask-position:center" class="gb-icon w-3.5 h-[1em] mt-0.5 transition-colors text-transparent group-hover:text-tint-subtle contrast-more:group-hover:text-tint-strong lg:w-4"></svg></a></div><div class="grid-area-1-1 z-[1] justify-self-start text-left leading-snug mt-[0.75em]">Time-Series Transformer</div></h3><div class="rounded-lg straight-corners:rounded-sm ring-1 ring-inset ring-tint-subtle flex overflow-hidden flex-col mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div role="tablist" class="group/tabs inline-flex flex-row self-stretch after:flex-[1] after:bg-tint-12/1 [&:has(button.active-tab:last-of-type):after]:rounded-bl-md"><button role="tab" aria-selected="true" aria-controls="arc-2" id="tab-arc-2" class="[&:has(+_.active-tab)]:rounded-br-md [.active-tab_+_&]:rounded-bl-md [.active-tab_+_:after]:rounded-br-md inline-block text-sm px-3.5 py-2 transition-[color] font-[500] relative after:transition-colors after:border-r after:absolute after:left-[unset] after:right-0 after:border-tint after:top-[15%] after:h-[70%] after:w-[1px] last:after:border-transparent hover:text-tint-strong truncate max-w-full shrink-0 active-tab text-tint-strong bg-transparent after:[&.active-tab]:border-transparent after:[:has(+_&.active-tab)]:border-transparent after:[:has(&_+)]:border-transparent">arc</button><button role="tab" aria-selected="false" aria-controls="model-2" id="tab-model-2" class="[&:has(+_.active-tab)]:rounded-br-md [.active-tab_+_&]:rounded-bl-md [.active-tab_+_:after]:rounded-br-md inline-block text-sm px-3.5 py-2 transition-[color] font-[500] relative after:transition-colors after:border-r after:absolute after:left-[unset] after:right-0 after:border-tint after:top-[15%] after:h-[70%] after:w-[1px] last:after:border-transparent text-tint bg-tint-12/1 hover:text-tint-strong truncate max-w-full">model</button></div><div role="tabpanel" id="arc-2" aria-labelledby="tab-arc-2" class="p-4"><div class="w-full space-y-4"><div class="group/codeblock grid grid-flow-col mx-auto w-full decoration-primary/6 max-w-3xl flip-heading-hash"><div class="flex items-center justify-start gap-2 text-sm [grid-area:1/1]"></div><button class="z-[2] mt-2 mr-2 self-start justify-self-end rounded-md bg-transparent p-1 text-tint text-xs leading-none opacity-0 ring-1 ring-tint backdrop-blur-md transition-opacity duration-75 [grid-area:2/1] hover:ring-tint-hover group-hover/codeblock:opacity-[1] print:hidden">Copy</button><pre class="hide-scroll relative overflow-auto bg-tint theme-gradient:bg-tint-12/1 ring-tint-subtle [grid-area:2/1] rounded-md straight-corners:rounded-sm"><code id=":R5kkd9uu8qfkvfa:" class="inline-grid min-w-full grid-cols-[auto_1fr] p-2 [count-reset:line]"><span class="highlight-line"><span class="highlight-line-content">arc = { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'TSTransformer', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'd_model': 64, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'nhead': 8, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'num_encoder_layers': 4, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'num_decoder_layers': 4, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'dropout': 0.2, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'dim_feedforward': 2048, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'activation': 'relu', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'in_embedding': 'Linear', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'pe': 'sinusoid', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'attention_size': None, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'in_channels': 1, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'out_channels': 1, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'q': 8, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'v': 8, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'chunk_mode': None, </span></span><span class="highlight-line"><span class="highlight-line-content">} </span></span><span class="highlight-line"><span class="highlight-line-content">model = torcharc.build(arc) </span></span><span class="highlight-line"><span class="highlight-line-content"> </span></span><span class="highlight-line"><span class="highlight-line-content">seq_len = 32 </span></span><span class="highlight-line"><span class="highlight-line-content">x = torch.rand([seq_len, arc['in_channels']])</span></span></code></pre></div></div></div><div role="tabpanel" id="model-2" aria-labelledby="tab-model-2" class="p-4 hidden"><div class="w-full space-y-4"><div class="group/codeblock grid grid-flow-col mx-auto w-full decoration-primary/6 max-w-3xl flip-heading-hash"><div class="flex items-center justify-start gap-2 text-sm [grid-area:1/1]"></div><button class="z-[2] mt-2 mr-2 self-start justify-self-end rounded-md bg-transparent p-1 text-tint text-xs leading-none opacity-0 ring-1 ring-tint backdrop-blur-md transition-opacity duration-75 [grid-area:2/1] hover:ring-tint-hover group-hover/codeblock:opacity-[1] print:hidden">Copy</button><pre class="hide-scroll relative overflow-auto bg-tint theme-gradient:bg-tint-12/1 ring-tint-subtle [grid-area:2/1] rounded-md straight-corners:rounded-sm"><code id=":R6kkd9uu8qfkvfa:" class="inline-grid min-w-full grid-cols-[auto_1fr] p-2 [count-reset:line]"><span class="highlight-line"><span class="highlight-line-content">TSTransformer( </span></span><span class="highlight-line"><span class="highlight-line-content"> (in_embedding): Linear(in_features=1, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (pe): SinusoidPE( </span></span><span class="highlight-line"><span class="highlight-line-content"> (dropout): Dropout(p=0.1, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (encoders): Sequential( </span></span><span class="highlight-line"><span class="highlight-line-content"> (0): Encoder( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_selfAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_feedForward): PositionwiseFeedForward( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear1): Linear(in_features=64, out_features=2048, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear2): Linear(in_features=2048, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_dropout): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (1): Encoder( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_selfAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_feedForward): PositionwiseFeedForward( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear1): Linear(in_features=64, out_features=2048, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear2): Linear(in_features=2048, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_dropout): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (2): Encoder( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_selfAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_feedForward): PositionwiseFeedForward( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear1): Linear(in_features=64, out_features=2048, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear2): Linear(in_features=2048, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_dropout): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (3): Encoder( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_selfAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_feedForward): PositionwiseFeedForward( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear1): Linear(in_features=64, out_features=2048, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear2): Linear(in_features=2048, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_dropout): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (decoders): ModuleList( </span></span><span class="highlight-line"><span class="highlight-line-content"> (0): Decoder( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_selfAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_encoderDecoderAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_feedForward): PositionwiseFeedForward( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear1): Linear(in_features=64, out_features=2048, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear2): Linear(in_features=2048, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm3): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_dropout): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (1): Decoder( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_selfAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_encoderDecoderAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_feedForward): PositionwiseFeedForward( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear1): Linear(in_features=64, out_features=2048, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear2): Linear(in_features=2048, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm3): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_dropout): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (2): Decoder( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_selfAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_encoderDecoderAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_feedForward): PositionwiseFeedForward( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear1): Linear(in_features=64, out_features=2048, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear2): Linear(in_features=2048, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm3): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_dropout): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (3): Decoder( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_selfAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_encoderDecoderAttention): MultiHeadAttention( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_q): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_k): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_v): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_W_o): Linear(in_features=64, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_feedForward): PositionwiseFeedForward( </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear1): Linear(in_features=64, out_features=2048, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_linear2): Linear(in_features=2048, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_layerNorm3): LayerNorm((64,), eps=1e-05, elementwise_affine=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (_dropout): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (out_linear): Linear(in_features=64, out_features=1, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content">)</span></span></code></pre></div></div></div></div><h3 id="dag-hydra" class="text-2xl font-semibold heading group relative grid mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div class="hash grid grid-area-1-1 relative -ml-6 w-7 border-0 opacity-0 group-hover:opacity-[0] group-focus:opacity-[0] md:group-hover:md:opacity-[1] md:group-focus:md:opacity-[1] mt-[0.75em]"><a href="#dag-hydra" aria-label="Direct link to heading" class="inline-flex h-full items-start leading-snug"><svg style="mask-image:url(https://ka-p.fontawesome.com/releases/v6.6.0/svgs/regular/hashtag.svg?v=2&token=a463935e93);mask-repeat:no-repeat;mask-position:center" class="gb-icon w-3.5 h-[1em] mt-0.5 transition-colors text-transparent group-hover:text-tint-subtle contrast-more:group-hover:text-tint-strong lg:w-4"></svg></a></div><div class="grid-area-1-1 z-[1] justify-self-start text-left leading-snug mt-[0.75em]">DAG: Hydra</div></h3><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">Ultimately, we can build a generic DAG network using the modules linked by the fork and merge modules. The example below shows HydraNet - a network with multiple inputs and multiple outputs.</p><div class="rounded-lg straight-corners:rounded-sm ring-1 ring-inset ring-tint-subtle flex overflow-hidden flex-col mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div role="tablist" class="group/tabs inline-flex flex-row self-stretch after:flex-[1] after:bg-tint-12/1 [&:has(button.active-tab:last-of-type):after]:rounded-bl-md"><button role="tab" aria-selected="true" aria-controls="arc-3" id="tab-arc-3" class="[&:has(+_.active-tab)]:rounded-br-md [.active-tab_+_&]:rounded-bl-md [.active-tab_+_:after]:rounded-br-md inline-block text-sm px-3.5 py-2 transition-[color] font-[500] relative after:transition-colors after:border-r after:absolute after:left-[unset] after:right-0 after:border-tint after:top-[15%] after:h-[70%] after:w-[1px] last:after:border-transparent hover:text-tint-strong truncate max-w-full shrink-0 active-tab text-tint-strong bg-transparent after:[&.active-tab]:border-transparent after:[:has(+_&.active-tab)]:border-transparent after:[:has(&_+)]:border-transparent">arc</button><button role="tab" aria-selected="false" aria-controls="model-3" id="tab-model-3" class="[&:has(+_.active-tab)]:rounded-br-md [.active-tab_+_&]:rounded-bl-md [.active-tab_+_:after]:rounded-br-md inline-block text-sm px-3.5 py-2 transition-[color] font-[500] relative after:transition-colors after:border-r after:absolute after:left-[unset] after:right-0 after:border-tint after:top-[15%] after:h-[70%] after:w-[1px] last:after:border-transparent text-tint bg-tint-12/1 hover:text-tint-strong truncate max-w-full">model</button></div><div role="tabpanel" id="arc-3" aria-labelledby="tab-arc-3" class="p-4"><div class="w-full space-y-4"><div class="group/codeblock grid grid-flow-col mx-auto w-full decoration-primary/6 max-w-3xl flip-heading-hash"><div class="flex items-center justify-start gap-2 text-sm [grid-area:1/1]"></div><button class="z-[2] mt-2 mr-2 self-start justify-self-end rounded-md bg-transparent p-1 text-tint text-xs leading-none opacity-0 ring-1 ring-tint backdrop-blur-md transition-opacity duration-75 [grid-area:2/1] hover:ring-tint-hover group-hover/codeblock:opacity-[1] print:hidden">Copy</button><pre class="hide-scroll relative overflow-auto bg-tint theme-gradient:bg-tint-12/1 ring-tint-subtle [grid-area:2/1] rounded-md straight-corners:rounded-sm"><code id=":R5lcd9uu8qfkvfa:" class="inline-grid min-w-full grid-cols-[auto_1fr] p-2 [count-reset:line]"><span class="highlight-line"><span class="highlight-line-content">arc = { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'dag_in_shape': {'image': [3, 20, 20], 'vector': [8]}, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'image': { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'Conv2d', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'in_names': ['image'], </span></span><span class="highlight-line"><span class="highlight-line-content"> 'layers': [ </span></span><span class="highlight-line"><span class="highlight-line-content"> [16, 4, 2, 0, 1], </span></span><span class="highlight-line"><span class="highlight-line-content"> [16, 4, 1, 0, 1] </span></span><span class="highlight-line"><span class="highlight-line-content"> ], </span></span><span class="highlight-line"><span class="highlight-line-content"> 'batch_norm': True, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'activation': 'ReLU', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'dropout': 0.2, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'init': 'kaiming_uniform_', </span></span><span class="highlight-line"><span class="highlight-line-content"> }, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'merge': { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'FiLMMerge', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'in_names': ['image', 'vector'], </span></span><span class="highlight-line"><span class="highlight-line-content"> 'names': {'feature': 'image', 'conditioner': 'vector'}, </span></span><span class="highlight-line"><span class="highlight-line-content"> }, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'Flatten': { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'Flatten' </span></span><span class="highlight-line"><span class="highlight-line-content"> }, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'Linear': { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'Linear', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'layers': [64, 32], </span></span><span class="highlight-line"><span class="highlight-line-content"> 'batch_norm': True, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'activation': 'ReLU', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'dropout': 0.2, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'init': 'kaiming_uniform_', </span></span><span class="highlight-line"><span class="highlight-line-content"> }, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'out': { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'Linear', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'out_features': 8, </span></span><span class="highlight-line"><span class="highlight-line-content"> }, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'fork': { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'SplitFork', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'shapes': {'mean': [4], 'std': [4]}, </span></span><span class="highlight-line"><span class="highlight-line-content"> } </span></span><span class="highlight-line"><span class="highlight-line-content">} </span></span><span class="highlight-line"><span class="highlight-line-content">model = torcharc.build(arc) </span></span><span class="highlight-line"><span class="highlight-line-content"> </span></span><span class="highlight-line"><span class="highlight-line-content">batch_size = 16 </span></span><span class="highlight-line"><span class="highlight-line-content">dag_in_shape = arc['dag_in_shape'] </span></span><span class="highlight-line"><span class="highlight-line-content">xs = {'image': torch.rand([batch_size, *dag_in_shape['image']]), 'vector': torch.rand([batch_size, *dag_in_shape['vector']])} </span></span><span class="highlight-line"><span class="highlight-line-content"># returns dict of Tensors if output is multi-modal, Tensor otherwise </span></span><span class="highlight-line"><span class="highlight-line-content">ys = model(xs)</span></span></code></pre></div></div></div><div role="tabpanel" id="model-3" aria-labelledby="tab-model-3" class="p-4 hidden"><div class="w-full space-y-4"><div class="group/codeblock grid grid-flow-col mx-auto w-full decoration-primary/6 max-w-3xl flip-heading-hash"><div class="flex items-center justify-start gap-2 text-sm [grid-area:1/1]"></div><button class="z-[2] mt-2 mr-2 self-start justify-self-end rounded-md bg-transparent p-1 text-tint text-xs leading-none opacity-0 ring-1 ring-tint backdrop-blur-md transition-opacity duration-75 [grid-area:2/1] hover:ring-tint-hover group-hover/codeblock:opacity-[1] print:hidden">Copy</button><pre class="hide-scroll relative overflow-auto bg-tint theme-gradient:bg-tint-12/1 ring-tint-subtle [grid-area:2/1] rounded-md straight-corners:rounded-sm"><code id=":R6lcd9uu8qfkvfa:" class="inline-grid min-w-full grid-cols-[auto_1fr] p-2 [count-reset:line]"><span class="highlight-line"><span class="highlight-line-content">DAGNet( </span></span><span class="highlight-line"><span class="highlight-line-content"> (module_dict): ModuleDict( </span></span><span class="highlight-line"><span class="highlight-line-content"> (image): Sequential( </span></span><span class="highlight-line"><span class="highlight-line-content"> (0): Conv2d(3, 16, kernel_size=(4, 4), stride=(2, 2)) </span></span><span class="highlight-line"><span class="highlight-line-content"> (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (2): ReLU() </span></span><span class="highlight-line"><span class="highlight-line-content"> (3): Dropout2d(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> (4): Conv2d(16, 16, kernel_size=(4, 4), stride=(1, 1)) </span></span><span class="highlight-line"><span class="highlight-line-content"> (5): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (6): ReLU() </span></span><span class="highlight-line"><span class="highlight-line-content"> (7): Dropout2d(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (merge): FiLMMerge( </span></span><span class="highlight-line"><span class="highlight-line-content"> (conditioner_scale): Linear(in_features=8, out_features=16, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (conditioner_shift): Linear(in_features=8, out_features=16, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (Flatten): Flatten() </span></span><span class="highlight-line"><span class="highlight-line-content"> (Linear): Sequential( </span></span><span class="highlight-line"><span class="highlight-line-content"> (0): Linear(in_features=576, out_features=64, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (2): ReLU() </span></span><span class="highlight-line"><span class="highlight-line-content"> (3): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> (4): Linear(in_features=64, out_features=32, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (5): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (6): ReLU() </span></span><span class="highlight-line"><span class="highlight-line-content"> (7): Dropout(p=0.2, inplace=False) </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content"> (out): Linear(in_features=32, out_features=8, bias=True) </span></span><span class="highlight-line"><span class="highlight-line-content"> (fork): SplitFork() </span></span><span class="highlight-line"><span class="highlight-line-content"> ) </span></span><span class="highlight-line"><span class="highlight-line-content">)</span></span></code></pre></div></div></div></div><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">The DAG module accepts a <code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">dict</code> (example below) as input, and the module selects its input by matching its own name in the arc and the <code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">in_name</code>, then carry forward the output together with any unconsumed inputs.</p><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">For example, the input <code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">xs</code> with keys <code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">image, vector</code> passes through the first <code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">image</code> module, and the output becomes <code class="py-[1px] px-1.5 min-w-[1.625rem] justify-center items-center ring-1 ring-inset ring-tint bg-tint rounded text-[.875em] leading-[calc(max(1.20em,1.25rem))]">{'image': image_module(xs.image), 'vector': xs.vector}</code>. This is then passed through the remainder of the modules in the arc as declared.</p><h3 id="optimizer" class="text-2xl font-semibold heading group relative grid mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div class="hash grid grid-area-1-1 relative -ml-6 w-7 border-0 opacity-0 group-hover:opacity-[0] group-focus:opacity-[0] md:group-hover:md:opacity-[1] md:group-focus:md:opacity-[1] mt-[0.75em]"><a href="#optimizer" aria-label="Direct link to heading" class="inline-flex h-full items-start leading-snug"><svg style="mask-image:url(https://ka-p.fontawesome.com/releases/v6.6.0/svgs/regular/hashtag.svg?v=2&token=a463935e93);mask-repeat:no-repeat;mask-position:center" class="gb-icon w-3.5 h-[1em] mt-0.5 transition-colors text-transparent group-hover:text-tint-subtle contrast-more:group-hover:text-tint-strong lg:w-4"></svg></a></div><div class="grid-area-1-1 z-[1] justify-self-start text-left leading-snug mt-[0.75em]">Optimizer</div></h3><p class="mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0">TorchArc also provides convenience method to construct optimizer in the same config-driven manner.</p><div class="rounded-lg straight-corners:rounded-sm ring-1 ring-inset ring-tint-subtle flex overflow-hidden flex-col mx-auto w-full decoration-primary/6 max-w-3xl page-api-block:ml-0"><div role="tablist" class="group/tabs inline-flex flex-row self-stretch after:flex-[1] after:bg-tint-12/1 [&:has(button.active-tab:last-of-type):after]:rounded-bl-md"><button role="tab" aria-selected="true" aria-controls="arc-4" id="tab-arc-4" class="[&:has(+_.active-tab)]:rounded-br-md [.active-tab_+_&]:rounded-bl-md [.active-tab_+_:after]:rounded-br-md inline-block text-sm px-3.5 py-2 transition-[color] font-[500] relative after:transition-colors after:border-r after:absolute after:left-[unset] after:right-0 after:border-tint after:top-[15%] after:h-[70%] after:w-[1px] last:after:border-transparent hover:text-tint-strong truncate max-w-full shrink-0 active-tab text-tint-strong bg-transparent after:[&.active-tab]:border-transparent after:[:has(+_&.active-tab)]:border-transparent after:[:has(&_+)]:border-transparent">arc</button><button role="tab" aria-selected="false" aria-controls="second-tab" id="tab-second-tab" class="[&:has(+_.active-tab)]:rounded-br-md [.active-tab_+_&]:rounded-bl-md [.active-tab_+_:after]:rounded-br-md inline-block text-sm px-3.5 py-2 transition-[color] font-[500] relative after:transition-colors after:border-r after:absolute after:left-[unset] after:right-0 after:border-tint after:top-[15%] after:h-[70%] after:w-[1px] last:after:border-transparent text-tint bg-tint-12/1 hover:text-tint-strong truncate max-w-full">Second Tab</button></div><div role="tabpanel" id="arc-4" aria-labelledby="tab-arc-4" class="p-4"><div class="w-full space-y-4"><div class="group/codeblock grid grid-flow-col mx-auto w-full decoration-primary/6 max-w-3xl flip-heading-hash"><div class="flex items-center justify-start gap-2 text-sm [grid-area:1/1]"></div><button class="z-[2] mt-2 mr-2 self-start justify-self-end rounded-md bg-transparent p-1 text-tint text-xs leading-none opacity-0 ring-1 ring-tint backdrop-blur-md transition-opacity duration-75 [grid-area:2/1] hover:ring-tint-hover group-hover/codeblock:opacity-[1] print:hidden">Copy</button><pre class="hide-scroll relative overflow-auto bg-tint theme-gradient:bg-tint-12/1 ring-tint-subtle [grid-area:2/1] rounded-md straight-corners:rounded-sm"><code id=":R5mkd9uu8qfkvfa:" class="inline-grid min-w-full grid-cols-[auto_1fr] p-2 [count-reset:line]"><span class="highlight-line"><span class="highlight-line-content">import torcharc </span></span><span class="highlight-line"><span class="highlight-line-content"> </span></span><span class="highlight-line"><span class="highlight-line-content"> </span></span><span class="highlight-line"><span class="highlight-line-content">arc = { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'Linear', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'in_features': 8, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'layers': [64, 32], </span></span><span class="highlight-line"><span class="highlight-line-content"> 'batch_norm': True, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'activation': 'ReLU', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'dropout': 0.2, </span></span><span class="highlight-line"><span class="highlight-line-content"> 'init': { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'normal_', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'std': 0.01, </span></span><span class="highlight-line"><span class="highlight-line-content"> }, </span></span><span class="highlight-line"><span class="highlight-line-content">} </span></span><span class="highlight-line"><span class="highlight-line-content">optim_spec = { </span></span><span class="highlight-line"><span class="highlight-line-content"> 'type': 'Adam', </span></span><span class="highlight-line"><span class="highlight-line-content"> 'lr': 0.001, </span></span><span class="highlight-line"><span class="highlight-line-content">} </span></span><span class="highlight-line"><span class="highlight-line-content"> </span></span><span class="highlight-line"><span class="highlight-line-content">model = torcharc.build(arc) </span></span><span class="highlight-line"><span class="highlight-line-content">optimizer = torcharc.build_optimizer(optim_spec, model)</span></span></code></pre></div></div></div><div role="tabpanel" id="second-tab" aria-labelledby="tab-second-tab" class="p-4 hidden"><div class="w-full space-y-4"><div class="group/codeblock grid grid-flow-col mx-auto w-full decoration-primary/6 max-w-3xl flip-heading-hash"><div class="flex items-center justify-start gap-2 text-sm [grid-area:1/1]"></div><button class="z-[2] mt-2 mr-2 self-start justify-self-end rounded-md bg-transparent p-1 text-tint text-xs leading-none opacity-0 ring-1 ring-tint backdrop-blur-md transition-opacity duration-75 [grid-area:2/1] hover:ring-tint-hover group-hover/codeblock:opacity-[1] print:hidden">Copy</button><pre class="hide-scroll relative overflow-auto bg-tint theme-gradient:bg-tint-12/1 ring-tint-subtle [grid-area:2/1] rounded-md straight-corners:rounded-sm"><code id=":R6mkd9uu8qfkvfa:" class="inline-grid min-w-full grid-cols-[auto_1fr] p-2 [count-reset:line]"><span class="highlight-line"><span class="highlight-line-content">Adam ( </span></span><span class="highlight-line"><span class="highlight-line-content">Parameter Group 0 </span></span><span class="highlight-line"><span class="highlight-line-content"> amsgrad: False </span></span><span class="highlight-line"><span class="highlight-line-content"> betas: (0.9, 0.999) </span></span><span class="highlight-line"><span class="highlight-line-content"> eps: 1e-08 </span></span><span class="highlight-line"><span class="highlight-line-content"> lr: 0.001 </span></span><span class="highlight-line"><span class="highlight-line-content"> weight_decay: 0 </span></span><span class="highlight-line"><span class="highlight-line-content">)</span></span></code></pre></div></div></div></div></div><div class="flex flex-col md:flex-row mt-6 gap-2 max-w-3xl mx-auto page-api-block:ml-0 text-tint"><a class="group text-sm p-2.5 flex gap-4 flex-1 flex-row-reverse items-center pl-4 border border-tint-subtle rounded straight-corners:rounded-none hover:border-primary text-pretty md:p-4 md:text-base" href="/blog/ml/running-wandb-on-kubernetes"><span class="flex flex-col flex-1 text-right"><span class="text-xs">Previous</span><span class="text-tint-strong group-hover:text-primary line-clamp-2">Running WandB on Kubernetes</span></span><svg 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