optimizer = tf.train.RMSPropOptimizer(0.01, epsilon=1.0)
inference.initialize(optimizer=optimizer)
init = tf.global_variables_initializer()
init.run()
n_epoch = 100
n_iter_per_epoch = 1000
for epoch in range(n_epoch):
avg_loss = 0.0
widgets = ["epoch #%d|" % epoch, Percentage(), Bar(), ETA()]
pbar = ProgressBar(n_iter_per_epoch, widgets=widgets)
pbar.start()
for t in range(n_iter_per_epoch):
pbar.update(t)
x_train, _ = mnist.train.next_batch(M)
info_dict = inference.update(feed_dict={x_ph: x_train})
avg_loss += info_dict['loss']
# Print a lower bound to the average marginal likelihood for an
# image.
avg_loss = avg_loss / n_iter_per_epoch
avg_loss = avg_loss / M
print("log p(x) >= {:0.3f}".format(avg_loss))
# Prior predictive check.
imgs = sess.run(x.value())
for m in range(M):
imsave("img/%d.png" % m, imgs[m].reshape(28, 28))
init = tf.global_variables_initializer()
init.run()
n_iter_per_epoch = 100
n_epoch = T // n_iter_per_epoch
for epoch in range(n_epoch):
avg_loss = 0.0
widgets = ["epoch #%d|" % epoch, Percentage(), Bar(), ETA()]
pbar = ProgressBar(n_iter_per_epoch, widgets=widgets)
pbar.start()
for t in range(n_iter_per_epoch):
pbar.update(t)
info_dict_e = inference_e.update()
info_dict_m = inference_m.update()
avg_loss += info_dict_m['loss']
print("Acceptance Rate:")
print(info_dict_e['accept_rate'])
# Print a lower bound to the average marginal likelihood for an
# image.
avg_loss = avg_loss / n_iter_per_epoch
avg_loss = avg_loss / N
print("log p(x) >= {:0.3f}".format(avg_loss))
# Prior predictive check.
imgs = x.value().eval()
for m in range(N):
imsave(os.path.join(IMG_DIR, '%d.png') % m, imgs[m].reshape(28, 28))
init = tf.initialize_all_variables()
init.run()
n_iter_per_epoch = 100
n_epoch = T // n_iter_per_epoch
for epoch in range(n_epoch):
avg_loss = 0.0
widgets = ["epoch #%d|" % epoch, Percentage(), Bar(), ETA()]
pbar = ProgressBar(n_iter_per_epoch, widgets=widgets)
pbar.start()
for t in range(n_iter_per_epoch):
pbar.update(t)
info_dict_e = inference_e.update()
info_dict_m = inference_m.update()
avg_loss += info_dict_m['loss']
print("Acceptance Rate:")
print(info_dict_e['accept_rate'])
# Print a lower bound to the average marginal likelihood for an
# image.
avg_loss = avg_loss / n_iter_per_epoch
avg_loss = avg_loss / N
print("log p(x) >= {:0.3f}".format(avg_loss))
# Prior predictive check.
imgs = x.value().eval()
for m in range(N):
imsave(os.path.join(IMG_DIR, '%d.png') % m, imgs[m].reshape(28, 28))
from __future__ import print_function, division, absolute_import
from data import get_value
import tensorflow as tf
import edward as ed
from edward.models import Beta, Bernoulli
theta = Beta(a=1.0, b=1.0)
# 100-dimensional Bernoulli
x = Bernoulli(p=tf.ones(12) * theta)
# ====== sampling from each marginal variables
theta_sample = theta.sample()
x_sample = x.sample()
print("Marginal theta samples:", get_value(theta_sample))
print("Marginal X samples:", get_value(x_sample))
# ====== sampling from the joint distribution
samples = get_value([x.value(), theta.value()])
print("From joint distribution:")
print("- X:", samples[0])
print("- theta:", samples[1])
optimizer = tf.train.RMSPropOptimizer(0.01, epsilon=1.0)
inference.initialize(optimizer=optimizer)
init = tf.initialize_all_variables()
init.run()
n_epoch = 100
n_iter_per_epoch = 1000
for epoch in range(n_epoch):
avg_loss = 0.0
widgets = ["epoch #%d|" % epoch, Percentage(), Bar(), ETA()]
pbar = ProgressBar(n_iter_per_epoch, widgets=widgets)
pbar.start()
for t in range(n_iter_per_epoch):
pbar.update(t)
x_train, _ = mnist.train.next_batch(M)
info_dict = inference.update(feed_dict={x_ph: x_train})
avg_loss += info_dict['loss']
# Print a lower bound to the average marginal likelihood for an
# image.
avg_loss = avg_loss / n_iter_per_epoch
avg_loss = avg_loss / M
print("log p(x) >= {:0.3f}".format(avg_loss))
# Prior predictive check.
imgs = sess.run(x.value())
for m in range(M):
imsave("img/%d.png" % m, imgs[m].reshape(28, 28))
