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 visual

Answer 1

Finally there is some official documentation about "Logging arbitrary image data" with an example of matplotlib created images.
They write:

In the code below, you'll log the first 25 images as a nice grid using matplotlib's subplot() function. You'll then view the grid in TensorBoard:

# Clear out prior logging data.
!rm -rf logs/plots

logdir = "logs/plots/" + datetime.now().strftime("%Y%m%d-%H%M%S")
file_writer = tf.summary.create_file_writer(logdir)

def plot_to_image(figure):
  """Converts the matplotlib plot specified by 'figure' to a PNG image and
  returns it. The supplied figure is closed and inaccessible after this call."""
  # Save the plot to a PNG in memory.
  buf = io.BytesIO()
  plt.savefig(buf, format='png')
  # Closing the figure prevents it from being displayed directly inside
  # the notebook.
  plt.close(figure)
  buf.seek(0)
  # Convert PNG buffer to TF image
  image = tf.image.decode_png(buf.getvalue(), channels=4)
  # Add the batch dimension
  image = tf.expand_dims(image, 0)
  return image

def image_grid():
  """Return a 5x5 grid of the MNIST images as a matplotlib figure."""
  # Create a figure to contain the plot.
  figure = plt.figure(figsize=(10,10))
  for i in range(25):
    # Start next subplot.
    plt.subplot(5, 5, i + 1, title=class_names[train_labels[i]])
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
  
  return figure

# Prepare the plot
figure = image_grid()
# Convert to image and log
with file_writer.as_default():
  tf.summary.image("Training data", plot_to_image(figure), step=0)

%tensorboard --logdir logs/plots