tensorboard-easy 0.2.3

Usage

You can use the logger as a context manager:

from tensorboard_easy import Logger
import numpy as np

with Logger('/path/to/logs/folder/') as log:
    log.log_scalar('my_scalar', 100, step=1)
    log.log_image('my_images', np.random.rand(3, 20, 20), step=1)

or you can close the logger explicitly:

log = Logger('/some/other/logs')
log.log_text('my_text', "Let's throw in some text", 0)
log.log_text('my_text', [['Some', 'tensor'], ['with', 'text!']], 1)

log.log_histogram('my_histogram', np.random.rand(500), step=0)
log.close()

It supports scalars, images, text and histograms.

You can also create functions, that write to a specific tag and automatically increase the step:

with Logger('/path/to/logs/folder/') as log:
    write_loss = log.make_log_scalar('loss')
    for i in range(1, 100):
        write_loss(1 / i)

Installation

It can be installed via pip:

pip install tensorboard-easy

The tensorflow or tensorflow-tensorboard packages are not required, however you will need one of them to visualize your logs.