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A print and debugging utility that makes your error printouts look nice

Project description

pip install tf_logger

Usage

from tf_logger import TF_Logger

logger = TF_Logger(log_directory="/tmp/logs/tf_logger_test/")

logger.log(index=3, note='this is a log entry!')
logger.flush()

# Images
face = scipy.misc.face()
face_bw = scipy.misc.face(gray=True)
logger.log_image(index=4, color_image=face, black_white=face_bw)
    image_bw = np.zeros((64, 64, 1))
    image_bw_2 = scipy.misc.face(gray=True)[::4, ::4]

# now print a stack
for i in range(10):
    logger.log_image(i, animation=[face] * 5)
logging images using tf\_logger

logging images using tf_logger

I’m planning on writing a better ML dashboard in the future.

This version of logger is integrated with tensorboard and at the same time prints the data in a tabular format to your stdout. - can silence stdout per key (per logger.log call) - can print with color: logger.log(timestep, some_key=green(some_data)) - can print with custom formatting: logger.log(timestep, some_key=green(some_data, percent)) where percent - uses the correct unix table characters (please stop using | and +. Use ``│``, ``┼`` instead)

A typical print out of this logger look like the following:

from tf_logger import TF_Logger

logger = TF_Logger(log_directory=f"/mnt/bucket/deep_Q_learning/{datetime.now(%Y%m%d-%H%M%S.%f):}")

logger.log_params(G=vars(G), RUN=vars(RUN), Reporting=vars(Reporting))

outputs the following

example\_real\_log\_output

example_real_log_output

And the data from multiple experiments can be views with tensorboard.

tensorboard\_example

tensorboard_example

═════════════════════════════════════════════════════
              G
───────────────────────────────┬─────────────────────
           env_name            │ MountainCar-v0
             seed              │ None
      stochastic_action        │ True
         conv_params           │ None
         value_params          │ (64,)
        use_layer_norm         │ True
         buffer_size           │ 50000
      replay_batch_size        │ 32
      prioritized_replay       │ True
            alpha              │ 0.6
          beta_start           │ 0.4
           beta_end            │ 1.0
    prioritized_replay_eps     │ 1e-06
      grad_norm_clipping       │ 10
           double_q            │ True
         use_dueling           │ False
     exploration_fraction      │ 0.1
          final_eps            │ 0.1
         n_timesteps           │ 100000
        learning_rate          │ 0.001
            gamma              │ 1.0
        learning_start         │ 1000
        learn_interval         │ 1
target_network_update_interval │ 500
═══════════════════════════════╧═════════════════════
             RUN
───────────────────────────────┬─────────────────────
        log_directory          │ /mnt/slab/krypton/machine_learning/ge_dqn/2017-11-20/162048.353909-MountainCar-v0-prioritized_replay(True)
          checkpoint           │ checkpoint.cp
           log_file            │ output.log
═══════════════════════════════╧═════════════════════
          Reporting
───────────────────────────────┬─────────────────────
     checkpoint_interval       │ 10000
        reward_average         │ 100
        print_interval         │ 10
═══════════════════════════════╧═════════════════════
╒════════════════════╤════════════════════╕
│      timestep      │        1999        │
├────────────────────┼────────────────────┤
│      episode       │         10         │
├────────────────────┼────────────────────┤
│    total reward    │       -200.0       │
├────────────────────┼────────────────────┤
│ total reward/mean  │       -200.0       │
├────────────────────┼────────────────────┤
│  total reward/max  │       -200.0       │
├────────────────────┼────────────────────┤
│time spent exploring│       82.0%        │
├────────────────────┼────────────────────┤
│    replay beta     │        0.41        │
╘════════════════════╧════════════════════╛
from tf_logger import TF_Logger

logger = TF_Logger('/mnt/slab/krypton/unitest')
logger.log(0, some=Color(0.1, 'yellow'))
logger.log(1, some=Color(0.28571, 'yellow', lambda v: f"{v * 100:.5f}%"))
logger.log(2, some=Color(0.85, 'yellow', percent))
logger.log(3, {"some_var/smooth": 10}, some=Color(0.85, 'yellow', percent))
logger.log(4, some=Color(10, 'yellow'))
logger.log_histogram(4, td_error_weights=[0, 1, 2, 3, 4, 2, 3, 4, 5])

colored output: (where the values are yellow)

╒════════════════════╤════════════════════╕
│        some        │        0.1         │
╘════════════════════╧════════════════════╛
╒════════════════════╤════════════════════╕
│        some        │     28.57100%      │
╘════════════════════╧════════════════════╛
╒════════════════════╤════════════════════╕
│        some        │       85.0%        │
╘════════════════════╧════════════════════╛
╒════════════════════╤════════════════════╕
│  some var/smooth   │         10         │
├────────────────────┼────────────────────┤
│        some        │       85.0%        │
╘════════════════════╧════════════════════╛
logger-colored-output

logger-colored-output

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