How to Display Custom Images in Tensorboard (e.g. Matplotlib Plots)?

Question

The Image Dashboard section of the Tensorboard ReadMe says:

Since the image dashboard supports arbitrary pngs, you can use this to embed custom visualizations (e.g. matplotlib scatterplots) into TensorBoard.

I see how a pyplot image could be written to file, read back in as a tensor, and then used with tf.image_summary() to write it to TensorBoard, but this statement from the readme suggests there is a more direct way. Is there? If so, is there any further documentation and/or examples of how to do this efficiently?

Answer 1

It is quite easy to do if you have the image in a memory buffer. Below, I show an example, where a pyplot is saved to a buffer and then converted to a TF image representation which is then sent to an image summary.

import io import matplotlib.pyplot as plt import tensorflow as tf   def gen_plot():     """Create a pyplot plot and save to buffer."""     plt.figure()     plt.plot([1, 2])     plt.title("test")     buf = io.BytesIO()     plt.savefig(buf, format='png')     buf.seek(0)     return buf   # Prepare the plot plot_buf = gen_plot()  # Convert PNG buffer to TF image image = tf.image.decode_png(plot_buf.getvalue(), channels=4)  # Add the batch dimension image = tf.expand_dims(image, 0)  # Add image summary summary_op = tf.summary.image("plot", image)  # Session with tf.Session() as sess:     # Run     summary = sess.run(summary_op)     # Write summary     writer = tf.train.SummaryWriter('./logs')     writer.add_summary(summary)     writer.close() 

This gives the following TensorBoard visualization: