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        "author": "Nick Maina",
        "author_email": "nick.maina1@gmail.com",
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            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.0",
            "Programming Language :: Python :: 3.1",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# keras-plugins\r\n\r\n## Callbacks\r\n\r\n### Telegram Callback\r\n\r\nNotify levels available:\r\n1) on_train_begin,\r\n2) on_train_end,\r\n3) on_batch_begin,\r\n4) on_batch_end,\r\n5) on_epoch_begin,\r\n6) on_epoch_end\r\n  \r\n##### How to use\r\n##### Installation\r\n```\r\npip install kerasplugins\r\n```\r\n\r\n```python\r\nfrom kerasplugins import callbacks\r\n\r\n#Notify can either be a list, dict or set\r\nnotify = { \r\n  'on_batch_end',  # sends BATCH END: Loss 0.50 Accuracy: 0.75\r\n  'on_epoch_end'   # sends EPOCH END: Loss 0.43 Accuracy: 0.81\r\n}\r\n\r\n# msg is the initial message\r\nmsg = \"Predicting Bitcoin Price\"\r\n\r\ntelegram = callbacks.TelegramNotify(<token>, <chat_id>, msg=msg, notify=notify)\r\n\r\n# channel is \"#general\" by default\r\nslack = callbacks.SlackNotify(<slack_token>, <channel>, msg=msg, notify=notify)\r\n\r\nmodel.fit(X_train, Y_train, validation_data=[X_test, Y_test], batch_size=256, epochs=10, callbacks=[telegram, slack])\r\n```\r\n\r\n## Coming Soon\r\n1) Ability to stop training remotely\r\n",
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