{"id":13454897,"url":"https://github.com/tflearn/tflearn","last_synced_at":"2025-05-10T00:42:03.421Z","repository":{"id":47437412,"uuid":"55147386","full_name":"tflearn/tflearn","owner":"tflearn","description":"Deep learning library featuring a higher-level API for TensorFlow.","archived":false,"fork":false,"pushed_at":"2024-05-06T11:34:20.000Z","size":1589,"stargazers_count":9617,"open_issues_count":580,"forks_count":2407,"subscribers_count":454,"default_branch":"master","last_synced_at":"2025-05-08T20:56:10.814Z","etag":null,"topics":["data-science","deep-learning","machine-learning","neural-network","tensorflow","tflearn"],"latest_commit_sha":null,"homepage":"http://tflearn.org","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tflearn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"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}},"created_at":"2016-03-31T12:05:53.000Z","updated_at":"2025-05-08T18:53:55.000Z","dependencies_parsed_at":"2024-06-18T13:43:00.981Z","dependency_job_id":null,"html_url":"https://github.com/tflearn/tflearn","commit_stats":{"total_commits":516,"total_committers":132,"mean_commits":3.909090909090909,"dds":0.5329457364341086,"last_synced_commit":"db5176773299b67a2a75c5889fb2aba7fd0fea8a"},"previous_names":[],"tags_count":8,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tflearn%2Ftflearn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tflearn%2Ftflearn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tflearn%2Ftflearn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tflearn%2Ftflearn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tflearn","download_url":"https://codeload.github.com/tflearn/tflearn/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253157192,"owners_count":21863072,"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":["data-science","deep-learning","machine-learning","neural-network","tensorflow","tflearn"],"created_at":"2024-07-31T08:00:59.095Z","updated_at":"2025-05-08T22:29:01.434Z","avatar_url":"https://github.com/tflearn.png","language":"Python","readme":"[![Build Status](https://travis-ci.org/tflearn/tflearn.svg?branch=master)](https://travis-ci.org/tflearn/tflearn)\n[![PyPI version](https://badge.fury.io/py/tflearn.svg)](https://badge.fury.io/py/tflearn)\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)\n[![Join the chat at https://gitter.im/einsteinsci/betterbeginnings](https://badges.gitter.im/tflearn/tflearn.svg)](https://gitter.im/tflearn/tflearn?utm_source=badge\u0026utm_medium=badge\u0026utm_campaign=pr-badge\u0026utm_content=badge)\n\n# TFLearn: Deep learning library featuring a higher-level API for TensorFlow.\n\nTFlearn is a modular and transparent deep learning library built on top of Tensorflow.  It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it.\n\nTFLearn features include:\n\n- Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples.\n- Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics...\n- Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn.\n- Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs and optimizers.\n- Easy and beautiful graph visualization, with details about weights, gradients, activations and more...\n- Effortless device placement for using multiple CPU/GPU.\n\nThe high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques.\n\nNote: Latest TFLearn (v0.5) is only compatible with TensorFlow v2.0 and over.\n\n## Overview\n```python\n# Classification\ntflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5)\n\nnet = tflearn.input_data(shape=[None, 784])\nnet = tflearn.fully_connected(net, 64)\nnet = tflearn.dropout(net, 0.5)\nnet = tflearn.fully_connected(net, 10, activation='softmax')\nnet = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy')\n\nmodel = tflearn.DNN(net)\nmodel.fit(X, Y)\n```\n\n```python\n# Sequence Generation\nnet = tflearn.input_data(shape=[None, 100, 5000])\nnet = tflearn.lstm(net, 64)\nnet = tflearn.dropout(net, 0.5)\nnet = tflearn.fully_connected(net, 5000, activation='softmax')\nnet = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy')\n\nmodel = tflearn.SequenceGenerator(net, dictionary=idx, seq_maxlen=100)\nmodel.fit(X, Y)\nmodel.generate(50, temperature=1.0)\n```\n\nThere are many more examples available *[here](http://tflearn.org/examples)*.\n\n## Compatibility\nTFLearn is based on the original tensorflow v1 graph API. When using TFLearn, make sure to import tensorflow that way:\n```\nimport tflearn\nimport tensorflow.compat.v1 as tf\n```\n\n## Installation\n\n**TensorFlow Installation**\n\nTFLearn requires Tensorflow (version 2.0+) to be installed.\n\nTo install TensorFlow, simply run:\n```\npip install tensorflow\n```\nor, with GPU-support:\n```\npip install tensorflow-gpu\n```\n\nFor more details see *[TensorFlow installation instructions](https://www.tensorflow.org/install)*\n\n**TFLearn Installation**\n\nTo install TFLearn, the easiest way is to run\n\nFor the bleeding edge version (recommended):\n```python\npip install git+https://github.com/tflearn/tflearn.git\n```\nFor the latest stable version:\n```python\npip install tflearn\n```\nOtherwise, you can also install from source by running (from source folder):\n```python\npython setup.py install\n```\n\n- For more details, please see the *[Installation Guide](http://tflearn.org/installation)*.\n\n## Getting Started\n\nSee *[Getting Started with TFLearn](http://tflearn.org/getting_started)* to learn about TFLearn basic functionalities or start browsing *[TFLearn Tutorials](http://tflearn.org/tutorials)*.\n\n## Examples\n\nThere are many neural network implementation available, see *[Examples](http://tflearn.org/examples)*.\n\n## Documentation\n\n[http://tflearn.org/doc_index](http://tflearn.org/doc_index)\n\n## Model Visualization\n\n**Graph**\n\n![Graph Visualization](docs/templates/img/graph.png)\n\n**Loss \u0026 Accuracy (multiple runs)**\n\n![Loss Visualization](docs/templates/img/loss_acc.png)\n\n**Layers**\n\n![Layers Visualization](docs/templates/img/layer_visualization.png)\n\n## Contributions\n\nThis is the first release of TFLearn, if you find any bug, please report it in the GitHub issues section.\n\nImprovements and requests for new features are more than welcome! Do not hesitate to twist and tweak TFLearn, and send pull-requests.\n\nFor more info: *[Contribute to TFLearn](http://tflearn.org/contributions)*.\n\n## License\n\nMIT License\n","funding_links":[],"categories":["Libraries","The Data Science Toolbox","Python","Deep Learning","Deep Learning Framework","库","机器学习框架","Tensor Flow","Uncategorized","Source code","Machine Learning"],"sub_categories":["Deep Learning Packages","TensorFlow","High-Level DL APIs","General-Purpose Machine Learning","微信群","Automated Machine Learning","Uncategorized","Chat","Reinforcement Learning"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftflearn%2Ftflearn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftflearn%2Ftflearn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftflearn%2Ftflearn/lists"}