def main():
data = ed.Data(np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1]))
model = BetaBernoulli()
variational = Variational()
variational.add(Beta())
# mean-field variational inference.
inference = ed.MFVI(model, variational, data)
inference.run(n_iter=10000)
def __init__(self, model, data=Data(), transform=tf.identity):
if hasattr(model, 'num_vars'):
variational = Variational()
variational.add(PointMass(model.num_vars, transform))
else:
variational = Variational()
variational.add(PointMass(0, transform))
VariationalInference.__init__(self, model, variational, data)
def _test(self, sess, data, n_minibatch, x=None, is_file=False):
model = NormalModel()
variational = Variational()
variational.add(Normal())
inference = ed.MFVI(model, variational, data)
inference.initialize(n_minibatch=n_minibatch)
if x is not None:
# Placeholder setting.
# Check data is same as data fed to it.
feed_dict = {inference.data['x']: x}
# avoid directly fetching placeholder
data_id = {
k: tf.identity(v)
for k, v in six.iteritems(inference.data)
}
val = sess.run(data_id, feed_dict)
assert np.all(val['x'] == x)
elif is_file:
# File reader setting.
# Check data varies by session run.
val = sess.run(inference.data)
val_1 = sess.run(inference.data)
assert not np.all(val['x'] == val_1['x'])
elif n_minibatch is None:
# Preloaded full setting.
# Check data is full data.
val = sess.run(inference.data)
assert np.all(val['x'] == data['x'])
elif n_minibatch == 1:
# Preloaded batch setting, with n_minibatch=1.
# Check data is randomly shuffled.
assert not np.all([
sess.run(inference.data)['x'] == data['x'][i]
for i in range(10)
])
else:
# Preloaded batch setting.
# Check data is randomly shuffled.
val = sess.run(inference.data)
assert not np.all(val['x'] == data['x'][:n_minibatch])
# Check data varies by session run.
val_1 = sess.run(inference.data)
assert not np.all(val['x'] == val_1['x'])
inference.finalize()
def __init__(self, model, data=None, params=None):
with tf.variable_scope("variational"):
if hasattr(model, 'n_vars'):
variational = Variational()
variational.add(PointMass(model.n_vars, params))
else:
variational = Variational()
variational.add(PointMass(0))
super(MAP, self).__init__(model, variational, data)
def _test(data, n_data, x=None, is_file=False):
sess = ed.get_session()
model = NormalModel()
variational = Variational()
variational.add(Normal())
inference = ed.MFVI(model, variational, data)
inference.initialize(n_data=n_data)
if x is not None:
# Placeholder setting.
# Check data is same as data fed to it.
feed_dict = {inference.data['x']: x}
# avoid directly fetching placeholder
data_id = {k: tf.identity(v) for k,v in
six.iteritems(inference.data)}
val = sess.run(data_id, feed_dict)
assert np.all(val['x'] == x)
elif is_file:
# File reader setting.
# Check data varies by session run.
val = sess.run(inference.data)
val_1 = sess.run(inference.data)
assert not np.all(val['x'] == val_1['x'])
elif n_data is None:
# Preloaded full setting.
# Check data is full data.
val = sess.run(inference.data)
assert np.all(val['x'] == data['x'])
else:
# Preloaded batch setting.
# Check data is randomly shuffled.
val = sess.run(inference.data)
assert not np.all(val['x'] == data['x'][:n_data])
# Check data varies by session run.
val_1 = sess.run(inference.data)
assert not np.all(val['x'] == val_1['x'])
inference.finalize()
sess.close()
del sess
tf.reset_default_graph()
def _test(self, sess, data, n_minibatch, x=None, is_file=False):
model = NormalModel()
variational = Variational()
variational.add(Normal())
inference = ed.MFVI(model, variational, data)
inference.initialize(n_minibatch=n_minibatch)
if x is not None:
# Placeholder setting.
# Check data is same as data fed to it.
feed_dict = {inference.data['x']: x}
# avoid directly fetching placeholder
data_id = {k: tf.identity(v) for k,v in
six.iteritems(inference.data)}
val = sess.run(data_id, feed_dict)
assert np.all(val['x'] == x)
elif is_file:
# File reader setting.
# Check data varies by session run.
val = sess.run(inference.data)
val_1 = sess.run(inference.data)
assert not np.all(val['x'] == val_1['x'])
elif n_minibatch is None:
# Preloaded full setting.
# Check data is full data.
val = sess.run(inference.data)
assert np.all(val['x'] == data['x'])
elif n_minibatch == 1:
# Preloaded batch setting, with n_minibatch=1.
# Check data is randomly shuffled.
assert not np.all([sess.run(inference.data)['x'] == data['x'][i] for i in range(10)])
else:
# Preloaded batch setting.
# Check data is randomly shuffled.
val = sess.run(inference.data)
assert not np.all(val['x'] == data['x'][:n_minibatch])
# Check data varies by session run.
val_1 = sess.run(inference.data)
assert not np.all(val['x'] == val_1['x'])
inference.finalize()
def __init__(self, model, data=Data(), transform=tf.identity):
if hasattr(model, 'num_vars'):
variational = Variational()
variational.add(PointMass(model.num_vars, transform))
else:
variational = Variational()
variational.add(PointMass(0, transform))
VariationalInference.__init__(self, model, variational, data)
def __init__(self, model, data=Data(), params=None):
if hasattr(model, 'num_vars'):
variational = Variational()
variational.add(PointMass(model.num_vars, params))
else:
variational = Variational()
variational.add(PointMass(0))
VariationalInference.__init__(self, model, variational, data)
stds = [[0.1, 0.1], [0.1, 0.1]]
x = np.zeros((N, 2), dtype=np.float32)
for n in range(N):
k = np.argmax(np.random.multinomial(1, pi))
x[n, :] = np.random.multivariate_normal(mus[k], np.diag(stds[k]))
return {'x': x}
ed.set_seed(42)
data = build_toy_dataset(500)
plt.scatter(data['x'][:, 0], data['x'][:, 1])
plt.axis([-3, 3, -3, 3])
plt.title("Simulated dataset")
plt.show()
model = MixtureGaussian(K=2, D=2)
variational = Variational()
variational.add(Dirichlet(model.K))
variational.add(Normal(model.K*model.D))
variational.add(InvGamma(model.K*model.D))
inference = ed.MFVI(model, variational, data)
inference.run(n_iter=4000, n_samples=50, n_minibatch=10)
clusters = np.argmax(ed.evaluate('log_likelihood', model, variational, data), axis=0)
plt.scatter(data['x'][:, 0], data['x'][:, 1], c=clusters, cmap=cm.bwr)
plt.axis([-3, 3, -3, 3])
plt.title("Predicted cluster assignments")
plt.show()
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import edward as ed
import tensorflow as tf
from edward.models import Variational, Bernoulli
from edward.stats import bernoulli
class BernoulliPosterior:
"""
p(x, z) = p(z) = p(z | x) = Bernoulli(z; p)
"""
def __init__(self, p):
self.p = p
def log_prob(self, xs, zs):
return bernoulli.logpmf(zs, p)
ed.set_seed(42)
p = tf.constant(0.6)
model = BernoulliPosterior(p)
variational = Variational()
variational.add(Bernoulli())
inference = ed.MFVI(model, variational)
inference.run(n_iter=10000)
def build_toy_dataset(n_data=40, noise_std=0.1):
ed.set_seed(0)
x = np.concatenate([np.linspace(0, 2, num=n_data/2),
np.linspace(6, 8, num=n_data/2)])
y = 0.075*x + norm.rvs(0, noise_std, size=n_data)
x = (x - 4.0) / 4.0
x = x.reshape((n_data, 1))
y = y.reshape((n_data, 1))
data = np.concatenate((y, x), axis=1) # n_data x 2
data = tf.constant(data, dtype=tf.float32)
return ed.Data(data)
ed.set_seed(42)
model = LinearModel()
variational = Variational()
variational.add(Normal(model.num_vars))
data = build_toy_dataset()
# Set up figure
fig = plt.figure(figsize=(8,8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.ion()
plt.show(block=False)
sess = ed.get_session()
inference = ed.MFVI(model, variational, data)
inference.initialize(n_minibatch=5, n_print=5)
for t in range(250):
loss = inference.update()
if t % inference.n_print == 0:
ed.set_seed(42)
model = NormalBernoulli(num_vars=10)
# We use the variational model
# q(z | x) = prod_{n=1}^N q(z_n | x)
# = prod_{n=1}^n Normal(z_n | loc, scale = phi(x_n))
# It is a distribution of the latent variables z_n for each data
# point x_n. We use neural_network() to globally parameterize the local
# variational factors q(z_n | x).
# We also do data subsampling during inference. Therefore we only need
# to explicitly represent the corresponding variational factors for a
# mini-batch,
# q(z_{batch} | x) = prod_{m=1}^{n_data} Normal(z_m | loc, scale = phi(x))
x_ph = tf.placeholder(tf.float32, [FLAGS.n_data, 28 * 28])
loc, scale = neural_network(x_ph)
variational = Variational()
variational.add(Normal(model.num_vars * FLAGS.n_data, loc=loc, scale=scale))
if not os.path.exists(FLAGS.data_directory):
os.makedirs(FLAGS.data_directory)
mnist = input_data.read_data_sets(FLAGS.data_directory, one_hot=True)
# data uses placeholder in order to build inference's computational
# graph. np.arrays of data are fed in during computation.
x = tf.placeholder(tf.float32, [FLAGS.n_data, 28 * 28])
data = ed.Data(x)
sess = ed.get_session()
inference = ed.MFVI(model, variational, data)
with tf.variable_scope("model") as scope:
Normal.mapping = mapping
class Data:
def __init__(self, data):
self.mnist = data
def sample(self, size):
x_batch, _ = mnist.train.next_batch(size)
return x_batch
ed.set_seed(42)
model = NormalBernoulli(FLAGS.num_vars)
variational = Variational()
variational.add(Normal(FLAGS.num_vars))
if not os.path.exists(FLAGS.data_directory):
os.makedirs(FLAGS.data_directory)
mnist = input_data.read_data_sets(FLAGS.data_directory, one_hot=True)
data = Data(mnist)
inference = ed.VAE(model, variational, data)
sess = inference.initialize(n_data=FLAGS.n_data)
with tf.variable_scope("model", reuse=True) as scope:
p_rep = model.sample_prior([FLAGS.n_data, FLAGS.num_vars])
for epoch in range(FLAGS.n_epoch):
avg_loss = 0.0
ed.set_seed(42)
model = NormalBernoulli(n_vars=10)
# Use the variational model
# q(z | x) = prod_{n=1}^n Normal(z_n | loc, scale = neural_network(x_n))
# It is a distribution of the latent variables z_n for each data
# point x_n. We use neural_network() to globally parameterize the local
# variational factors q(z_n | x).
# We also do data subsampling during inference. Therefore we only need
# to explicitly represent the variational factors for a mini-batch,
# q(z_{batch} | x) = prod_{m=1}^{n_data} Normal(z_m | loc, scale = neural_network(x_m))
x_ph = tf.placeholder(tf.float32, [N_MINIBATCH, 28 * 28])
loc, scale = neural_network(x_ph)
variational = Variational()
variational.add(Normal(model.num_vars * N_MINIBATCH, loc=loc, scale=scale))
# MNIST batches are fed at training time.
if not os.path.exists(DATA_DIR):
os.makedirs(DATA_DIR)
mnist = input_data.read_data_sets(DATA_DIR, one_hot=True)
x = tf.placeholder(tf.float32, [N_MINIBATCH, 28 * 28])
data = {'x': x}
sess = ed.get_session()
inference = ed.MFVI(model, variational, data)
with tf.variable_scope("model") as scope:
inference.initialize(optimizer="PrettyTensor")
with tf.variable_scope("model", reuse=True) as scope:
def __init__(self, model, data=Data(), params=None):
with tf.variable_scope("variational"):
variational = Variational()
variational.add(PointMass(model.num_vars, params))
VariationalInference.__init__(self, model, variational, data)
Posterior: (1-dimensional) Normal
Variational model
Likelihood: Mean-field Normal
"""
import edward as ed
import tensorflow as tf
from edward.models import Variational, Normal
from edward.stats import norm
class NormalPosterior:
"""
p(x, z) = p(z) = p(z | x) = Normal(z; mu, std)
"""
def __init__(self, mu, std):
self.mu = mu
self.std = std
def log_prob(self, xs, zs):
return norm.logpdf(zs, self.mu, self.std)
ed.set_seed(42)
mu = tf.constant(1.0)
std = tf.constant(1.0)
model = NormalPosterior(mu, std)
variational = Variational()
variational.add(Normal())
inference = ed.MFVI(model, variational)
inference.run(n_iter=10000)
return [mean, stddev]
ed.set_seed(42)
model = NormalBernoulli(num_vars=10)
# We use the variational model
# q(z | x) = prod_{n=1}^N q(z_n | x)
# = prod_{n=1}^n Normal(z_n | mu, sigma = phi(x_n))
# It is a distribution of the latent variables z_n for each data
# point x_n. We use mapping() to globally parameterize the local
# variational factors q(z_n | x).
# We also do data subsampling during inference. Therefore we only need
# to explicitly represent the corresponding variational factors for a
# mini-batch,
# q(z_{batch} | x) = prod_{m=1}^{n_data} Normal(z_m | mu, sigma = phi(x))
variational = Variational()
Normal.mapping = mapping
Normal.num_local_vars = model.num_vars
variational.add(Normal(model.num_vars * FLAGS.n_data))
if not os.path.exists(FLAGS.data_directory):
os.makedirs(FLAGS.data_directory)
mnist = input_data.read_data_sets(FLAGS.data_directory, one_hot=True)
# data uses placeholder in order to build inference's computational
# graph. np.arrays of data are fed in during computation.
x = tf.placeholder(tf.float32, [FLAGS.n_data, 28 * 28])
data = ed.Data(x)
inference = ed.MFVI(model, variational, data)
ed.set_seed(42)
model = NormalBernoulli(num_vars=10)
# Use the variational model
# q(z | x) = prod_{n=1}^n Normal(z_n | loc, scale = neural_network(x_n))
# It is a distribution of the latent variables z_n for each data
# point x_n. We use neural_network() to globally parameterize the local
# variational factors q(z_n | x).
# We also do data subsampling during inference. Therefore we only need
# to explicitly represent the variational factors for a mini-batch,
# q(z_{batch} | x) = prod_{m=1}^{n_data} Normal(z_m | loc, scale = neural_network(x_m))
x_ph = tf.placeholder(tf.float32, [N_DATA, 28 * 28])
loc, scale = neural_network(x_ph)
variational = Variational()
variational.add(Normal(model.num_vars * N_DATA, loc=loc, scale=scale))
# MNIST batches are fed at training time.
if not os.path.exists(DATA_DIR):
os.makedirs(DATA_DIR)
mnist = input_data.read_data_sets(DATA_DIR, one_hot=True)
x = tf.placeholder(tf.float32, [N_DATA, 28 * 28])
data = {'x': x}
sess = ed.get_session()
inference = ed.MFVI(model, variational, data)
with tf.variable_scope("model") as scope:
inference.initialize(optimizer="PrettyTensor")
with tf.variable_scope("model", reuse=True) as scope:
ed.set_seed(0)
D = 1
x = np.linspace(-3, 3, num=n_data)
y = np.tanh(x) + norm.rvs(0, noise_std, size=n_data)
y[y < 0.5] = 0
y[y >= 0.5] = 1
x = (x - 4.0) / 4.0
x = x.reshape((n_data, D))
y = y.reshape((n_data, 1))
data = np.concatenate((y, x), axis=1) # n_data x (D+1)
data = tf.constant(data, dtype=tf.float32)
return ed.Data(data)
ed.set_seed(42)
model = HierarchicalLogistic(weight_dim=[1,1])
variational = Variational()
variational.add(Normal(model.num_vars))
data = build_toy_dataset()
# Set up figure
fig = plt.figure(figsize=(8,8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.ion()
plt.show(block=False)
inference = ed.MFVI(model, variational, data)
inference.initialize(n_print=5)
sess = ed.get_session()
for t in range(600):
loss = inference.update()
if t % inference.n_print == 0:
of 100,000 samples (!).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import edward as ed
import tensorflow as tf
from edward.models import Variational, Bernoulli
from edward.stats import bernoulli
class BernoulliModel:
"""p(x, z) = p(z) = p(z | x) = Bernoulli(z; p)"""
def __init__(self, p):
self.p = p
def log_prob(self, xs, zs):
return bernoulli.logpmf(zs, p)
ed.set_seed(42)
p = tf.constant(0.6)
model = BernoulliModel(p)
variational = Variational()
variational.add(Bernoulli())
inference = ed.MFVI(model, variational)
inference.run(n_samples=int(1e5))
log_prior = dirichlet.logpdf(pi, self.alpha)
log_prior += tf.reduce_sum(norm.logpdf(mus, 0, np.sqrt(self.c)), 1)
log_prior += tf.reduce_sum(invgamma.logpdf(sigmas, self.a, self.b), 1)
# Loop over each mini-batch zs[b,:]
log_lik = []
n_minibatch = get_dims(zs)[0]
for s in range(n_minibatch):
log_lik_z = N*tf.reduce_sum(tf.log(pi), 1)
for k in range(self.K):
log_lik_z += tf.reduce_sum(multivariate_normal.logpdf(xs,
mus[s, (k*self.D):((k+1)*self.D)],
sigmas[s, (k*self.D):((k+1)*self.D)]))
log_lik += [log_lik_z]
return log_prior + tf.pack(log_lik)
ed.set_seed(42)
x = np.loadtxt('data/mixture_data.txt', dtype='float32', delimiter=',')
data = ed.Data(tf.constant(x, dtype=tf.float32))
model = MixtureGaussian(K=2, D=2)
variational = Variational()
variational.add(Dirichlet([1, model.K]))
variational.add(Normal(model.K*model.D))
variational.add(InvGamma(model.K*model.D))
inference = ed.MFVI(model, variational, data)
inference.run(n_iter=500, n_minibatch=5, n_data=5)
Prior: Beta
Likelihood: Bernoulli
Variational model
Likelihood: Mean-field Beta
"""
import edward as ed
from edward.models import Variational, Beta
model_code = """
data {
int<lower=0> N;
int<lower=0,upper=1> y[N];
}
parameters {
real<lower=0,upper=1> theta;
}
model {
theta ~ beta(1.0, 1.0);
for (n in 1:N)
y[n] ~ bernoulli(theta);
}
"""
ed.set_seed(42)
model = ed.StanModel(model_code=model_code)
variational = Variational()
variational.add(Beta())
data = ed.Data(dict(N=10, y=[0, 1, 0, 0, 0, 0, 0, 0, 0, 1]))
inference = ed.MFVI(model, variational, data)
inference.run(n_iter=10000)
def __init__(self):
self.n_vars = 1
def log_prob(self, xs, zs):
log_prior = beta.logpdf(zs, a=1.0, b=1.0)
log_lik = tf.pack([
tf.reduce_sum(bernoulli.logpmf(xs['x'], z)) for z in tf.unpack(zs)
])
return log_lik + log_prior
def sample_likelihood(self, zs, n):
"""x | z ~ p(x | z)"""
out = []
for s in range(zs.shape[0]):
out += [{'x': bernoulli.rvs(zs[s, :], size=n).reshape((n, ))}]
return out
ed.set_seed(42)
model = BetaBernoulli()
variational = Variational()
variational.add(Beta(model.n_vars))
data = {'x': np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 1])}
inference = ed.MFVI(model, variational, data)
inference.run(n_iter=200)
T = lambda x, z=None: tf.reduce_mean(tf.cast(x['x'], tf.float32))
print(ed.ppc(model, variational, data, T))
p(x, z) = Bernoulli(x | z) * Beta(z | 1, 1)
"""
def __init__(self):
self.num_vars = 1
def log_prob(self, xs, zs):
log_prior = beta.logpdf(zs, a=1.0, b=1.0)
log_lik = tf.pack([tf.reduce_sum(bernoulli.logpmf(xs, z))
for z in tf.unpack(zs)])
return log_lik + log_prior
def sample_likelihood(self, zs, size):
"""x | z ~ p(x | z)"""
out = np.zeros((zs.shape[0], size))
for s in range(zs.shape[0]):
out[s,:] = bernoulli.rvs(zs[s,:], size=size).reshape((size,))
return out
ed.set_seed(42)
model = BetaBernoulli()
variational = Variational()
variational.add(Beta(model.num_vars))
data = ed.Data(tf.constant((0, 1, 0, 0, 0, 0, 0, 0, 0, 1), dtype=tf.float32))
inference = ed.MFVI(model, variational, data)
inference.run(n_iter=200)
T = lambda y, z=None: tf.reduce_mean(y)
print(ed.ppc(model, variational, data, T))
log_prior = dirichlet.logpdf(pi, self.alpha)
log_prior += tf.reduce_sum(norm.logpdf(mus, 0, np.sqrt(self.c)), 1)
log_prior += tf.reduce_sum(invgamma.logpdf(sigmas, self.a, self.b), 1)
# Loop over each mini-batch zs[b,:]
log_lik = []
n_minibatch = get_dims(zs[0])[0]
for s in range(n_minibatch):
log_lik_z = N*tf.reduce_sum(tf.log(pi), 1)
for k in range(self.K):
log_lik_z += tf.reduce_sum(multivariate_normal.logpdf(xs,
mus[s, (k*self.D):((k+1)*self.D)],
sigmas[s, (k*self.D):((k+1)*self.D)]))
log_lik += [log_lik_z]
return log_prior + tf.pack(log_lik)
ed.set_seed(42)
x = np.loadtxt('data/mixture_data.txt', dtype='float32', delimiter=',')
data = ed.Data(tf.constant(x, dtype=tf.float32))
model = MixtureGaussian(K=2, D=2)
variational = Variational()
variational.add(Dirichlet(model.K))
variational.add(Normal(model.K*model.D))
variational.add(InvGamma(model.K*model.D))
inference = ed.MFVI(model, variational, data)
inference.run(n_iter=500, n_minibatch=5, n_data=5)
ed.set_seed(42)
model = NormalBernoulli(num_vars=10)
# We use the variational model
# q(z | x) = prod_{n=1}^N q(z_n | x)
# = prod_{n=1}^n Normal(z_n | mu, sigma = phi(x_n))
# It is a distribution of the latent variables z_n for each data
# point x_n. We use mapping() to globally parameterize the local
# variational factors q(z_n | x).
# We also do data subsampling during inference. Therefore we only need
# to explicitly represent the corresponding variational factors for a
# mini-batch,
# q(z_{batch} | x) = prod_{m=1}^{n_data} Normal(z_m | mu, sigma = phi(x))
variational = Variational()
Normal.mapping = mapping
Normal.num_local_vars = model.num_vars
variational.add(Normal(model.num_vars * FLAGS.n_data))
if not os.path.exists(FLAGS.data_directory):
os.makedirs(FLAGS.data_directory)
mnist = input_data.read_data_sets(FLAGS.data_directory, one_hot=True)
# data uses placeholder in order to build inference's computational
# graph. np.arrays of data are fed in during computation.
x = tf.placeholder(tf.float32, [FLAGS.n_data, 28 * 28])
data = ed.Data(x)
sess = ed.get_session()
D = 1
x = np.linspace(-3, 3, num=n_data)
y = np.tanh(x) + norm.rvs(0, noise_std, size=n_data)
y[y < 0.5] = 0
y[y >= 0.5] = 1
x = (x - 4.0) / 4.0
x = x.reshape((n_data, D))
y = y.reshape((n_data, 1))
data = np.concatenate((y, x), axis=1) # n_data x (D+1)
data = tf.constant(data, dtype=tf.float32)
return ed.Data(data)
ed.set_seed(42)
model = HierarchicalLogistic(weight_dim=[1, 1])
variational = Variational()
variational.add(Normal(model.num_vars))
data = build_toy_dataset()
# Set up figure
fig = plt.figure(figsize=(8, 8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.ion()
plt.show(block=False)
inference = ed.MFVI(model, variational, data)
inference.initialize(n_print=5)
sess = ed.get_session()
for t in range(600):
loss = inference.update()
if t % inference.n_print == 0:
ed.set_seed(42)
model = NormalBernoulli(n_vars=10)
# Use the variational model
# q(z | x) = prod_{n=1}^n Normal(z_n | loc, scale = neural_network(x_n))
# It is a distribution of the latent variables z_n for each data
# point x_n. We use neural_network() to globally parameterize the local
# variational factors q(z_n | x).
# We also do data subsampling during inference. Therefore we only need
# to explicitly represent the variational factors for a mini-batch,
# q(z_{batch} | x) = prod_{m=1}^{n_data} Normal(z_m | loc, scale = neural_network(x_m))
x_ph = tf.placeholder(tf.float32, [N_MINIBATCH, 28 * 28])
loc, scale = neural_network(x_ph)
variational = Variational()
variational.add(Normal(model.n_vars * N_MINIBATCH, loc=loc, scale=scale))
# MNIST batches are fed at training time.
if not os.path.exists(DATA_DIR):
os.makedirs(DATA_DIR)
mnist = input_data.read_data_sets(DATA_DIR, one_hot=True)
x = tf.placeholder(tf.float32, [N_MINIBATCH, 28 * 28])
data = {'x': x}
sess = ed.get_session()
inference = ed.MFVI(model, variational, data)
with tf.variable_scope("model") as scope:
inference.initialize(optimizer="PrettyTensor")
with tf.variable_scope("model", reuse=True) as scope:
def build_toy_dataset(N=40, noise_std=0.1):
ed.set_seed(0)
x = np.concatenate(
[np.linspace(0, 2, num=N / 2),
np.linspace(6, 8, num=N / 2)])
y = 0.075 * x + norm.rvs(0, noise_std, size=N)
x = (x - 4.0) / 4.0
x = x.reshape((N, 1))
return {'x': x, 'y': y}
ed.set_seed(42)
model = LinearModel()
variational = Variational()
variational.add(Normal(model.n_vars))
data = build_toy_dataset()
# Set up figure
fig = plt.figure(figsize=(8, 8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.ion()
plt.show(block=False)
sess = ed.get_session()
inference = ed.MFVI(model, variational, data)
inference.initialize(n_samples=5, n_print=5)
for t in range(250):
loss = inference.update()
if t % inference.n_print == 0:
import tensorflow as tf
from edward.models import Variational, Normal
from edward.stats import norm
class NormalPosterior:
"""
p(x, z) = p(z) = p(z | x) = Normal(z; mu, std)
"""
def __init__(self, mu, std):
self.mu = mu
self.std = std
def log_prob(self, xs, zs):
return norm.logpdf(zs, self.mu, self.std)
ed.set_seed(42)
mu = tf.constant(1.0)
std = tf.constant(1.0)
model = NormalPosterior(mu, std)
variational = Variational()
variational.add(Normal())
inference = ed.MFVI(model, variational)
inference.initialize()
for t in range(1000):
loss = inference.update()
inference.print_progress(t, loss)
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import edward as ed
import numpy as np
import tensorflow as tf
from edward.models import Variational, Beta
from edward.stats import bernoulli, beta
class BetaBernoulli:
"""p(x, z) = Bernoulli(x | z) * Beta(z | 1, 1)"""
def log_prob(self, xs, zs):
log_prior = beta.logpdf(zs, a=1.0, b=1.0)
log_lik = tf.pack([tf.reduce_sum(bernoulli.logpmf(xs['x'], z))
for z in tf.unpack(zs)])
return log_lik + log_prior
ed.set_seed(42)
model = BetaBernoulli()
variational = Variational()
variational.add(Beta())
data = {'x': np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 1])}
inference = ed.MFVI(model, variational, data)
inference.run(n_iter=10000)
return [mean, stddev]
Normal.mapping = mapping
class Data:
def __init__(self, data):
self.mnist = data
def sample(self, size):
x_batch, _ = mnist.train.next_batch(size)
return x_batch
ed.set_seed(42)
model = NormalBernoulli(FLAGS.num_vars)
variational = Variational()
variational.add(Normal(FLAGS.num_vars))
if not os.path.exists(FLAGS.data_directory):
os.makedirs(FLAGS.data_directory)
mnist = input_data.read_data_sets(FLAGS.data_directory, one_hot=True)
data = Data(mnist)
inference = ed.VAE(model, variational, data)
sess = inference.initialize(n_data=FLAGS.n_data)
with tf.variable_scope("model", reuse=True) as scope:
p_rep = model.sample_prior([FLAGS.n_data, FLAGS.num_vars])
for epoch in range(FLAGS.n_epoch):
avg_loss = 0.0
