{
    "info": {
        "author": "Ge Yang",
        "author_email": "yangge1987@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "Programming Language :: Python :: 3"
        ],
        "description": "TF\\_Logger, A Tensorboard Logging Utility for Non-Tensorflow Python Projects\n============================================================================\n\n.. code-block:: bash\n\n    pip install tf_logger\n\nUsage\n-----\n\n.. code-block:: python\n\n    from tf_logger import TF_Logger\n\n    logger = TF_Logger(log_directory=\"/tmp/logs/tf_logger_test/\")\n\n    logger.log(index=3, note='this is a log entry!')\n    logger.flush()\n\n    # Images\n    face = scipy.misc.face()\n    face_bw = scipy.misc.face(gray=True)\n    logger.log_image(index=4, color_image=face, black_white=face_bw)\n        image_bw = np.zeros((64, 64, 1))\n        image_bw_2 = scipy.misc.face(gray=True)[::4, ::4]\n\n    # now print a stack\n    for i in range(10):\n        logger.log_image(i, animation=[face] * 5)\n\n.. figure:: https://github.com/episodeyang/tf_logger/blob/master/figures/logging_images.png?raw=true\n   :alt: logging images using tf\\_logger\n\n   logging images using tf\\_logger\n\n    I'm planning on writing a better ML dashboard in the future.\n\nThis version of logger is integrated with ``tensorboard`` and at the\nsame time prints the data in a tabular format to your ``stdout``. - can\nsilence ``stdout`` per key (per ``logger.log`` call) - can print with\ncolor: ``logger.log(timestep, some_key=green(some_data))`` - can print\nwith custom formatting:\n``logger.log(timestep, some_key=green(some_data, percent))`` where\n``percent`` - uses the correct ``unix`` table characters (please stop\nusing ``|`` and ``+``. **Use ``\u2502``, ``\u253c`` instead**)\n\nA typical print out of this logger look like the following:\n\n.. code-block:: python\n\n    from tf_logger import TF_Logger\n\n    logger = TF_Logger(log_directory=f\"/mnt/bucket/deep_Q_learning/{datetime.now(%Y%m%d-%H%M%S.%f):}\")\n\n    logger.log_params(G=vars(G), RUN=vars(RUN), Reporting=vars(Reporting))\n\noutputs the following\n\n.. figure:: https://github.com/episodeyang/tf_logger/blob/master/figures/example_log_output.png?raw=true\n   :alt: example\\_real\\_log\\_output\n\n   example\\_real\\_log\\_output\n\nAnd the data from multiple experiments can be views with tensorboard.\n\n.. figure:: https://github.com/episodeyang/tf_logger/blob/master/figures/tensorboard_example.png?raw=true\n   :alt: tensorboard\\_example\n\n   tensorboard\\_example\n\n.. code-block:: text\n\n    \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n                  G               \n    \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n               env_name            \u2502 MountainCar-v0      \n                 seed              \u2502 None                \n          stochastic_action        \u2502 True                \n             conv_params           \u2502 None                \n             value_params          \u2502 (64,)               \n            use_layer_norm         \u2502 True                \n             buffer_size           \u2502 50000               \n          replay_batch_size        \u2502 32                  \n          prioritized_replay       \u2502 True                \n                alpha              \u2502 0.6                 \n              beta_start           \u2502 0.4                 \n               beta_end            \u2502 1.0                 \n        prioritized_replay_eps     \u2502 1e-06               \n          grad_norm_clipping       \u2502 10                  \n               double_q            \u2502 True                \n             use_dueling           \u2502 False               \n         exploration_fraction      \u2502 0.1                 \n              final_eps            \u2502 0.1                 \n             n_timesteps           \u2502 100000              \n            learning_rate          \u2502 0.001               \n                gamma              \u2502 1.0                 \n            learning_start         \u2502 1000                \n            learn_interval         \u2502 1                   \n    target_network_update_interval \u2502 500                 \n    \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n                 RUN              \n    \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n            log_directory          \u2502 /mnt/slab/krypton/machine_learning/ge_dqn/2017-11-20/162048.353909-MountainCar-v0-prioritized_replay(True)\n              checkpoint           \u2502 checkpoint.cp       \n               log_file            \u2502 output.log          \n    \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n              Reporting           \n    \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n         checkpoint_interval       \u2502 10000               \n            reward_average         \u2502 100                 \n            print_interval         \u2502 10                  \n    \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n    \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n    \u2502      timestep      \u2502        1999        \u2502\n    \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n    \u2502      episode       \u2502         10         \u2502\n    \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n    \u2502    total reward    \u2502       -200.0       \u2502\n    \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n    \u2502 total reward/mean  \u2502       -200.0       \u2502\n    \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n    \u2502  total reward/max  \u2502       -200.0       \u2502\n    \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n    \u2502time spent exploring\u2502       82.0%        \u2502\n    \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n    \u2502    replay beta     \u2502        0.41        \u2502\n    \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n\n.. code-block:: python\n\n    from tf_logger import TF_Logger\n\n    logger = TF_Logger('/mnt/slab/krypton/unitest')\n    logger.log(0, some=Color(0.1, 'yellow'))\n    logger.log(1, some=Color(0.28571, 'yellow', lambda v: f\"{v * 100:.5f}%\"))\n    logger.log(2, some=Color(0.85, 'yellow', percent))\n    logger.log(3, {\"some_var/smooth\": 10}, some=Color(0.85, 'yellow', percent))\n    logger.log(4, some=Color(10, 'yellow'))\n    logger.log_histogram(4, td_error_weights=[0, 1, 2, 3, 4, 2, 3, 4, 5])\n\ncolored output: (where the values are yellow)\n\n.. code-block:: text\n\n    \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n    \u2502        some        \u2502        0.1         \u2502\n    \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n    \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n    \u2502        some        \u2502     28.57100%      \u2502\n    \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n    \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n    \u2502        some        \u2502       85.0%        \u2502\n    \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n    \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n    \u2502  some var/smooth   \u2502         10         \u2502\n    \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n    \u2502        some        \u2502       85.0%        \u2502\n    \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n\n.. figure:: https://github.com/episodeyang/tf_logger/blob/master/figures/logger_color_output.png?raw=true\n   :alt: logger-colored-output\n\n   logger-colored-output\n\n\n",
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