def main(_):
ed.set_seed(42)
# DATA. MNIST batches are fed at training time.
(x_train, _), (x_test, _) = mnist(FLAGS.data_dir)
x_train_generator = generator(x_train, FLAGS.M)
# MODEL
# Define a subgraph of the full model, corresponding to a minibatch of
# size M.
z = Normal(loc=tf.zeros([FLAGS.M, FLAGS.d]),
scale=tf.ones([FLAGS.M, FLAGS.d]))
hidden = tf.layers.dense(z, 256, activation=tf.nn.relu)
x = Bernoulli(logits=tf.layers.dense(hidden, 28 * 28))
# INFERENCE
# Define a subgraph of the variational model, corresponding to a
# minibatch of size M.
x_ph = tf.placeholder(tf.int32, [FLAGS.M, 28 * 28])
hidden = tf.layers.dense(tf.cast(x_ph, tf.float32),
256,
activation=tf.nn.relu)
qz = Normal(loc=tf.layers.dense(hidden, FLAGS.d),
scale=tf.layers.dense(hidden,
FLAGS.d,
activation=tf.nn.softplus))
# Bind p(x, z) and q(z | x) to the same TensorFlow placeholder for x.
inference = ed.KLqp({z: qz}, data={x: x_ph})
optimizer = tf.train.RMSPropOptimizer(0.01, epsilon=1.0)
inference.initialize(optimizer=optimizer)
tf.global_variables_initializer().run()
n_iter_per_epoch = x_train.shape[0] // FLAGS.M
for epoch in range(1, FLAGS.n_epoch + 1):
print("Epoch: {0}".format(epoch))
avg_loss = 0.0
pbar = Progbar(n_iter_per_epoch)
for t in range(1, n_iter_per_epoch + 1):
pbar.update(t)
x_batch = next(x_train_generator)
info_dict = inference.update(feed_dict={x_ph: x_batch})
avg_loss += info_dict['loss']
# Print a lower bound to the average marginal likelihood for an
# image.
avg_loss /= n_iter_per_epoch
avg_loss /= FLAGS.M
print("-log p(x) <= {:0.3f}".format(avg_loss))
# Prior predictive check.
images = x.eval()
for m in range(FLAGS.M):
imsave(
os.path.join(FLAGS.out_dir, '%d.png') % m,
images[m].reshape(28, 28))
def main(_):
ed.set_seed(42)
# DATA. MNIST batches are fed at training time.
(x_train, _), (x_test, _) = mnist(FLAGS.data_dir)
x_train_generator = generator(x_train, FLAGS.M)
# MODEL
# Define a subgraph of the full model, corresponding to a minibatch of
# size M.
z = Normal(loc=tf.zeros([FLAGS.M, FLAGS.d]),
scale=tf.ones([FLAGS.M, FLAGS.d]))
hidden = tf.layers.dense(z, 256, activation=tf.nn.relu)
x = Bernoulli(logits=tf.layers.dense(hidden, 28 * 28))
# INFERENCE
# Define a subgraph of the variational model, corresponding to a
# minibatch of size M.
x_ph = tf.placeholder(tf.int32, [FLAGS.M, 28 * 28])
hidden = tf.layers.dense(tf.cast(x_ph, tf.float32), 256,
activation=tf.nn.relu)
qz = Normal(loc=tf.layers.dense(hidden, FLAGS.d),
scale=tf.layers.dense(
hidden, FLAGS.d, activation=tf.nn.softplus))
# Bind p(x, z) and q(z | x) to the same TensorFlow placeholder for x.
inference = ed.KLqp({z: qz}, data={x: x_ph})
optimizer = tf.train.RMSPropOptimizer(0.01, epsilon=1.0)
inference.initialize(optimizer=optimizer)
tf.global_variables_initializer().run()
n_iter_per_epoch = x_train.shape[0] // FLAGS.M
for epoch in range(1, FLAGS.n_epoch + 1):
print("Epoch: {0}".format(epoch))
avg_loss = 0.0
pbar = Progbar(n_iter_per_epoch)
for t in range(1, n_iter_per_epoch + 1):
pbar.update(t)
x_batch = next(x_train_generator)
info_dict = inference.update(feed_dict={x_ph: x_batch})
avg_loss += info_dict['loss']
# Print a lower bound to the average marginal likelihood for an
# image.
avg_loss /= n_iter_per_epoch
avg_loss /= FLAGS.M
print("-log p(x) <= {:0.3f}".format(avg_loss))
# Prior predictive check.
images = x.eval()
for m in range(FLAGS.M):
imsave(os.path.join(FLAGS.out_dir, '%d.png') % m,
images[m].reshape(28, 28))
# Bind p(x, z) and q(z | x) to the same TensorFlow placeholder for x.
inference = ed.KLqp({z: qz}, data={x: x_ph})
optimizer = tf.train.RMSPropOptimizer(0.01, epsilon=1.0)
inference.initialize(optimizer=optimizer)
tf.global_variables_initializer().run()
n_epoch = 100
n_iter_per_epoch = x_train.shape[0] // M
for epoch in range(1, n_epoch + 1):
print("Epoch: {0}".format(epoch))
avg_loss = 0.0
pbar = Progbar(n_iter_per_epoch)
for t in range(1, n_iter_per_epoch + 1):
pbar.update(t)
x_batch = next(x_train_generator)
info_dict = inference.update(feed_dict={x_ph: x_batch})
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.
images = x.eval()
for m in range(M):
imsave(os.path.join(out_dir, '%d.png') % m, images[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.eval()
for m in range(N):
imsave(os.path.join(IMG_DIR, '%d.png') % m, imgs[m].reshape(28, 28))
import tensorflow as tf
import edward as ed
from edward.models import Bernoulli, Beta, Binomial
import matplotlib.pyplot as plt
import seaborn as sns
##Single coin weight inference
##Model:
theta = Beta(1.0, 1.0)
x = Bernoulli(probs=theta)
##Sampling:
with tf.Session() as sess:
for i in range(10):
print(x.eval())
##Observations:
data = 1
##Infer:
qtheta = Beta(tf.Variable(1.0), tf.Variable(1.0))
inference = ed.KLqp({theta: qtheta}, {x: data})
inference.run()
##Results:
qtheta_samples = qtheta.sample(1000).eval()
print(qtheta_samples.mean())
plt.hist(qtheta_samples)
plt.show()
if(log_enabled):
pbar = Progbar(n_iter_per_epoch)
for t in range(1, n_iter_per_epoch + 1):
if(log_enabled):
pbar.update(t)
x_batch = next(x_train_generator)
info_dict = inference.update(feed_dict={x_ph: x_batch})
avg_loss += info_dict['loss']/d
# 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))
saver.save(sess, out_model+"/model.%05d.ckpt"%(epoch))
if np.isnan(avg_loss):
print("[ERR0R]")
break
idx = np.random.randint(M, size=16)
samples = x.eval()
samples = samples[idx, ]
fig = plot(samples)
plt.savefig(os.path.join(out_dir, '{}.png').format(
str(i).zfill(3)), bbox_inches='tight')
plt.close(fig)
i+=1