https://github.com/GPflow/GPflow
Revision 6e57626e7f567f601a51a771b0a7be75e105f577 authored by st-- on 01 October 2019, 19:13:50 UTC, committed by Artem Artemev on 01 October 2019, 19:13:50 UTC
1 parent 705380d
Tip revision: 6e57626e7f567f601a51a771b0a7be75e105f577 authored by st-- on 01 October 2019, 19:13:50 UTC
GPflow 2.0 notebook update: GPs for big data (#1048)
GPflow 2.0 notebook update: GPs for big data (#1048)
Tip revision: 6e57626
intro_to_gpflow2_plotting.py
import io
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
def summary_matplotlib_image(figures, step, fmt="png"):
for name, fig in figures.items():
buf = io.BytesIO()
fig.savefig(buf, format=fmt, bbox_inches='tight')
buf.seek(0)
image = buf.getvalue()
image = tf.image.decode_image(buf.getvalue(), channels=4)
image = tf.expand_dims(image, 0)
tf.summary.image(name=name, data=image, step=step)
def plotting_regression(X, Y, xx, mean, var, samples):
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111)
ax.plot(xx, mean, 'C0', lw=2)
ax.fill_between(xx[:, 0],
mean[:, 0] - 1.96 * np.sqrt(var[:, 0]),
mean[:, 0] + 1.96 * np.sqrt(var[:, 0]),
color='C0',
alpha=0.2)
ax.plot(X, Y, 'kx')
ax.plot(xx, samples[:, :, 0].numpy().T, 'C0', linewidth=.5)
ax.set_ylim(-2., +2.)
ax.set_xlim(0, 10)
plt.close()
return fig
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