deep_visu

Visualization and experiments framework for training Deep learning and Machine learning algorithms.

Help to keep track of experiments by saving the results and configurations on single html file.

Features

Visualization for real time signals
Written in Python 3
Based on Bokeh

Requirements

Python 3
Bokeh

Use

Run bokeh in a separated terminal

~$ bokeh serve

Run the Python script

~$ python3 example_signals.py

How to use it in Python script

# Import the plot
from LogPlotBokeh import LogPlot

# Create an instance
log = LogPlot(name="Title",
        properties=["Reward", "Avr_Reward"],
        properties_telemetry=["speedX", "steer"],
        output_path="."
        )

# Feed data to both plots and the text log
episode_progres = {"Reward":10, "Avr_Reward":20}
log.progress.add(x=i, y=episode_progres)

# telemetry
telemetry = {"speedX":10, "accel":20}
log.telemetry.add(x=i, y=telemetry)

# Add some text to the log
log.terminal("Model  parameters %d" % i)

# Close
log.close()

Todos

Improve performance
Remove the use of Bokeh client

License

GPL v3

Free Software