{"id":19156177,"url":"https://github.com/kyegomez/liqudnet","last_synced_at":"2025-04-06T04:09:16.691Z","repository":{"id":205710626,"uuid":"714876405","full_name":"kyegomez/LiqudNet","owner":"kyegomez","description":"Implementation of Liquid Nets in Pytorch","archived":false,"fork":false,"pushed_at":"2025-01-27T17:40:00.000Z","size":2281,"stargazers_count":59,"open_issues_count":1,"forks_count":9,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-30T03:04:01.248Z","etag":null,"topics":["artificial-intelligence","attention-is-all-you-need","attention-mechanism","liquidnets","machine-learning","recurrent-neural-network","recurrent-neural-networks"],"latest_commit_sha":null,"homepage":"https://discord.gg/GYbXvDGevY","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kyegomez.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null},"funding":{"github":["kyegomez"],"patreon":null,"open_collective":null,"ko_fi":null,"tidelift":null,"community_bridge":null,"liberapay":null,"issuehunt":null,"otechie":null,"lfx_crowdfunding":null,"custom":null}},"created_at":"2023-11-06T02:49:52.000Z","updated_at":"2025-03-06T12:39:54.000Z","dependencies_parsed_at":"2024-11-09T08:35:38.810Z","dependency_job_id":"d7e27732-8074-40e5-81cd-b931af7c86a6","html_url":"https://github.com/kyegomez/LiqudNet","commit_stats":null,"previous_names":["kyegomez/liqudnet"],"tags_count":0,"template":false,"template_full_name":"kyegomez/Python-Package-Template","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FLiqudNet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FLiqudNet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FLiqudNet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FLiqudNet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kyegomez","download_url":"https://codeload.github.com/kyegomez/LiqudNet/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247430869,"owners_count":20937874,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","attention-is-all-you-need","attention-mechanism","liquidnets","machine-learning","recurrent-neural-network","recurrent-neural-networks"],"created_at":"2024-11-09T08:33:30.669Z","updated_at":"2025-04-06T04:09:16.649Z","avatar_url":"https://github.com/kyegomez.png","language":"Python","funding_links":["https://github.com/sponsors/kyegomez"],"categories":[],"sub_categories":[],"readme":"[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)\n\n# LiquidNet\nThis is a simple implementation of the Liquid net official repo translated into pytorch for simplicity. [Find the original repo here:](https://github.com/raminmh/liquid_time_constant_networks)\n\n## Install\n`pip install liquidnet`\n\n## Usage\n```python\nimport torch\nfrom liquidnet.main import LiquidNet\n\n# Create an LiquidNet with a specified number of units\nnum_units = 64\nltc_cell = LiquidNet(num_units)\n\n# Generate random input data with batch size 4 and input size 32\nbatch_size = 4\ninput_size = 32\ninputs = torch.randn(batch_size, input_size)\n\n# Initialize the cell state (hidden state)\ninitial_state = torch.zeros(batch_size, num_units)\n\n# Forward pass through the LiquidNet\noutputs, final_state = ltc_cell(inputs, initial_state)\n\n# Print the shape of outputs and final_state\nprint(\"Outputs shape:\", outputs.shape)\nprint(\"Final state shape:\", final_state.shape)\n\n```\n\n## `VisionLiquidNet`\n- Simple model with 2 convolutions with 2 max pools, alot of room for improvement\n\n```python\nimport torch \nfrom liquidnet.vision_liquidnet import VisionLiquidNet\n\n# Random Input Image\nx = torch.randn(4, 3, 32, 32)\n\n# Create a VisionLiquidNet with a specified number of units\nmodel = VisionLiquidNet(64, 10)\n\n# Forward pass through the VisionLiquidNet\nprint(model(x).shape)\n\n\n```\n\n\n# Citation\n```bibtex\n@article{DBLP:journals/corr/abs-2006-04439,\n  author       = {Ramin M. Hasani and\n                  Mathias Lechner and\n                  Alexander Amini and\n                  Daniela Rus and\n                  Radu Grosu},\n  title        = {Liquid Time-constant Networks},\n  journal      = {CoRR},\n  volume       = {abs/2006.04439},\n  year         = {2020},\n  url          = {https://arxiv.org/abs/2006.04439},\n  eprinttype    = {arXiv},\n  eprint       = {2006.04439},\n  timestamp    = {Fri, 12 Jun 2020 14:02:57 +0200},\n  biburl       = {https://dblp.org/rec/journals/corr/abs-2006-04439.bib},\n  bibsource    = {dblp computer science bibliography, https://dblp.org}\n}\n\n```\n\n\n# License\nMIT\n\n\n# Todo:\n- [ ] Implement LiquidNet for vision and train on CIFAR\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkyegomez%2Fliqudnet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkyegomez%2Fliqudnet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkyegomez%2Fliqudnet/lists"}