A Python wrapper for visualizing data in Tensorboard

This package aims to leverage the visualization tool Tensorboard for monitoring iterative procedures, such as update step in machine learning algorithm (implemented in packages other than Tensorflow, ex. Theano or PyTorch). The package provides python APIs to log data as Tensorflow tf.Summary objects into Tensorflow event files. The event files can be visualized in Tensorboard.

Note this package DOES NOT support visualization of the network graph.

Installation

Clone and navigate into the repository, and then execute the command:

python setup.py

This package depends on Tensorflow and TensorBoard. Installing Tensorflow through pip should include TensorBoard.

Usage

Logging and visualizing data with this package takes three steps:

Create an TFSummary object with a given directory.
Log data with the TFSummary object in iterative steps.
Launch a TensorBoard server with the same directory in Step 1.

Instantiating TFSummary

The TFSummary object is designed to be similar to the Tensorflow FileWriter object. The constructor of TFSummary takes a logdir argument, i.e., the directory where all of the data will be written. In addition, the TFSummary takes an optional argument name that specifies the name of event file. An event is a collection of summaries of scalars or images.

from pytensorboard import TFSummary

tfs_training = TFSummary('path_to_log_dir', name='training')
tfs_validation = TFSummary('path_to_log_dir', name='validation')

Logging with TFSummary

Logging data is primarily achieved via the high-level API: TFSummary.add(global_step=None[, tag=value[, ...]]). The argument global_step is generally used to specify the index of step or epoch during training. TFSummary.add takes variable number of keyword arguments in the format of tag=value:

for i in xrange(1000):
    # iterative step, ex. gradient descent or evolutional step
    ...
    # log the progress every 100 steps
    if i % 100 == 0:
        tfs_training.add(global_step=i, error=training_err, loss=loss)
        tfs_validation.add(global_step=i, error=valid_error)

The value of a keyword argument in TFSummary.add can be a scalar or an "image". Note an "image" here can be a matplotlib.Figure.figure object or simply a 2-D numpy array. The latter will be rendered as a heatmap by matplotlib.pyplot.imshow.

import numpy
import matplotlib.pyplot as plt

noise = numpy.random.random((100, 100))
fig = plt.figure()
plt.plot(numpy.range(100), numpy.random.random(100))

tfs_validation.add(global_step=i, error=valid_error, result=fig, noise=noise)

Launching TensorBoard

Follow the instruction on the official Tensorflow website for launching TensorBoard.

Documentation

Constructor

TFSummary:

High-Level API:

TFSummary.add: add multiple scalars or images identified by different tags.

Low-Level APIs

TFSummary.add_scalars: add a single scalar.
TFSummary.add_scalar: add multiple scalars under the same tag.
TFSummary.add_images: add a single image.
TFSummary.add_image: add multiple images under the same tag.

Acknowledgements

Based on this Gist by Michael Gygli .

chungheng/pytensorboard