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        "author": "Matthew Rahtz",
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        "description": "# Easy TensorFlow Logging\n\n[![CircleCI](https://circleci.com/gh/mrahtz/easy-tf-log.svg?style=shield)](https://circleci.com/gh/mrahtz/easy-tf-log)\n\nAre you prototyping something and want to be able to _magically_ graph some value\nwithout going through all the usual steps to set up TensorFlow logging properly?\n\n`easy_tf_log` is a simple module to do just that.\n\n```\nfrom easy_tf_log import tflog\n```\n\nthen you can do\n\n```\nfor i in range(10):\n    tflog('really_interesting_variable_name', i)\n```\n\nand you'll find a directory `logs` that you can point TensorBoard to\n\n`$ tensorboard --logdir logs`\n\nto get\n\n![](https://github.com/mrahtz/easy-tf-log/blob/master/tensorboard_screenshot.png)\n\nSee [`demo.py`](demo.py) for a full demo.\n\nBased on logging code from OpenAI's [baselines](https://github.com/openai/baselines).\n\n## Installation\n\n`pip install easy-tf-log`\n\nNote that TensorFlow must be installed separately.\n\n## Documentation\n\n`easy-tf-log` supports logging using either a global logger or an instantiated logger object.\n\nThe global logger is good for very quick prototypes, but for anything more complicated,\nyou'll probably want to instantiate your own `Logger` object.\n\n### Global logger\n\n* `easy_tf_log.tflog(key, value, step=None)`\n  * Logs `value` (int or float) under the name `key` (string).\n  * `step` (int) sets the step associated with `value` explicitly.\n    If not specified, the step will increment on each call.\n* `easy_tf_log.set_dir(log_dir)`\n  * Sets the global logger to log to the specified directory.\n  * `log_dir` can be an absolute or a relative path.\n* `easy_tf_log.set_writer(writer)`\n  * Sets the global logger to log using the specified `tf.summary.FileWriter` instance.\n\nBy default (i.e. if `set_dir` is not called), the global logger logs to a `logs` directory\nautomatically created in the working directory.\n\n### Logger object\n\n* `logger = easy_tf_log.Logger(log_dir=None, writer=None)`\n  * Create a `Logger`.\n  * `log_dir`: an absolute of relative path specifying the directory to log to.\n  * `writer`: an existing `tf.summary.FileWriter` instance to use for logging.\n  * If neither `log_dir` nor `writer` are specified, the logger will log to a `logs` directory in the\n    working directory. If both are specified, the constructor will raise a `ValueError`.\n* `logger.log_key_value(key, value, step=None)`\n  * See `tflog`.\n* `logger.log_list_stats(key, values_list)`\n  * Log the minimum, maximum, mean, and standard deviation of `values_list` (a list of ints or floats).\n* `logger.measure_rate(key, value)`\n  * Log the rate at which `value` (int or float) changes per second.\n  * The first call internally stores the time of the first value;\n    the second call logs the change between the second value and the first value divided by the\n    time between the calls; etc.\n* `logger.set_dir(log_dir)`\n  * See `easy_tf_log.set_dir(log_dir)`.\n* `logger.set_writer(writer)`\n  * See `easy_tf_log.set_writer(writer)`.\n* `logger.close()`\n  * Flush logs and close the log file handle.\n\n\n",
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