The Image Dashboard section of the Tensorboard ReadMe says:
Since the image dashboard supports arbitrary pngs, you can use this to embed custom visual
Next script does not use intermediate RGB/PNG encoding. It also fixes the issue with additional operation construction during execution, single summary is reused.
Size of the figure is expected to remain the same during execution
Solution that works:
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
def get_figure():
fig = plt.figure(num=0, figsize=(6, 4), dpi=300)
fig.clf()
return fig
def fig2rgb_array(fig, expand=True):
fig.canvas.draw()
buf = fig.canvas.tostring_rgb()
ncols, nrows = fig.canvas.get_width_height()
shape = (nrows, ncols, 3) if not expand else (1, nrows, ncols, 3)
return np.fromstring(buf, dtype=np.uint8).reshape(shape)
def figure_to_summary(fig):
image = fig2rgb_array(fig)
summary_writer.add_summary(
vis_summary.eval(feed_dict={vis_placeholder: image}))
if __name__ == '__main__':
# construct graph
x = tf.Variable(initial_value=tf.random_uniform((2, 10)))
inc = x.assign(x + 1)
# construct summary
fig = get_figure()
vis_placeholder = tf.placeholder(tf.uint8, fig2rgb_array(fig).shape)
vis_summary = tf.summary.image('custom', vis_placeholder)
with tf.Session() as sess:
tf.global_variables_initializer().run()
summary_writer = tf.summary.FileWriter('./tmp', sess.graph)
for i in range(100):
# execute step
_, values = sess.run([inc, x])
# draw on the plot
fig = get_figure()
plt.subplot('111').scatter(values[0], values[1])
# save the summary
figure_to_summary(fig)