{"id":26651144,"url":"https://github.com/hypergan/hyperchamber","last_synced_at":"2025-04-11T04:51:30.671Z","repository":{"id":62569945,"uuid":"58599443","full_name":"HyperGAN/hyperchamber","owner":"HyperGAN","description":"Optimize your hyperparameters","archived":false,"fork":false,"pushed_at":"2020-08-17T04:36:47.000Z","size":146,"stargazers_count":14,"open_issues_count":1,"forks_count":4,"subscribers_count":6,"default_branch":"master","last_synced_at":"2024-04-14T01:49:53.187Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/HyperGAN.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-05-12T02:13:56.000Z","updated_at":"2020-08-17T04:36:49.000Z","dependencies_parsed_at":"2022-11-03T19:09:08.359Z","dependency_job_id":null,"html_url":"https://github.com/HyperGAN/hyperchamber","commit_stats":null,"previous_names":["255bits/hyperchamber"],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HyperGAN%2Fhyperchamber","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HyperGAN%2Fhyperchamber/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HyperGAN%2Fhyperchamber/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HyperGAN%2Fhyperchamber/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/HyperGAN","download_url":"https://codeload.github.com/HyperGAN/hyperchamber/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248345289,"owners_count":21088243,"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":[],"created_at":"2025-03-25T02:56:29.797Z","updated_at":"2025-04-11T04:51:30.656Z","avatar_url":"https://github.com/HyperGAN.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# hyperchamber\nRandom search your hyper parameters.\n\n# Changelog\n\n## 0.2.x\n\n* feature: access Config variables with '.' notation\n\n## 0.2\n\n* local save/load\n\n## 0.1\n\n* initial pip release\n\nYou set a list of options that define your hyperparams:\n```python\nimport hyperchamber as hc\n\nhc.set('learning_rate', [0.1, 0.2, 0.5])\nconfig = hc.random_config() # =\u003e { 'learning_rate' : 0.2 }\n```\n\n## Examples\n\n* logistic regression classifier on MNIST [code](examples/track.py)\n\n  Based on a simple tensorflow example. We find the best learning rate from a small set of options.\n\n* Finding a better network architecture for MNIST [code](examples/mnist.py)\n\n  Uses hyperparameter tuning to find the best performing MNIST fully connected deep network configuration.\n\n  Our search space of options here is now 720 options.  Note we only have 2 variables.  This search space expands exponentially with new options to search.\n\n\n# Installation\n\n## Developer mode\n\n```\n  python setup.py develop\n```\n\n# API\n\n```python\n  import hyperchamber as hc\n```\n```python\n  hc.set(name, values)\n```\n\nSets a hyperparameter to values.  \n\n* If values is an array, config[name] will be set to one element in that array.\n* If values is a scalar, config[name] will always be set to that scalar\n\n```python\n  hc.configs(n)\n```\nReturns up to n configs of the form {name:value} for each hyperparameter.\n\n\n```python\n\thc.save(config, filename)\n```\nSaves the config to a file.\n\n```python\n\thc.load(filename)\n```\nLoad a configuration from file\n\n```python\n\thc.load_or_create_config(filename, config)\n```\nLoad a configuration from file if that file exists.  Otherwise save `config` to that file.  `config` is assumed to be a Dictionary.\n\n\n\n```python\n  hc.record(filename, config)\n```\nStore the cost of a config's training results. \n\n\n```python\n  hc.top(sort_by)\n```\n\nReturn the top results across all recorded results\n\nExample:\n\n```python\n  def by_cost(x):\n    config, result =x\n    return result['cost']\n  for config, result in hc.top(by_cost): \n    print(config, result)\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhypergan%2Fhyperchamber","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhypergan%2Fhyperchamber","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhypergan%2Fhyperchamber/lists"}