scale=tf.nn.softplus(tf.Variable(tf.random_normal([K, D]))))
qsigma = InverseGamma(concentration=tf.nn.softplus(
tf.Variable(tf.random_normal([K, D]))),
rate=tf.nn.softplus(tf.Variable(tf.random_normal([K,
D]))))
qc = Categorical(logits=tf.Variable(tf.zeros([N, K])))
inference = ed.KLqp(latent_vars={
pi: qpi,
mu: qmu,
sigma: qsigma,
c: qc
},
data={x: x_data})
inference.initialize(n_iter=10000, n_samples=200)
sess = ed.get_session()
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
info_dict = inference.update()
inference.print_progress(info_dict)
t = info_dict['t']
if t == 1 or t % inference.n_print == 0:
qpi_mean, qmu_mean, qsigma_mean = \
sess.run([qpi.mean(), qmu.mean(), qsigma.mean()])
print('\nInferred membership probabilities: {}'.format(qpi_mean))
print('Inferred cluster means: {}'.format(qmu_mean))
print('Inferred sigmas: {}'.format(qsigma_mean))
import tensorflow as tf
from edward.models import InverseGamma, Normal
N = 1000
# Data generation (known mean)
mu = 7.0
sigma = 0.55
xn_data = np.random.normal(mu, sigma, N)
print('sigma={}'.format(sigma))
# Prior definition
alpha = tf.Variable(0.9, dtype=tf.float32, trainable=False)
beta = tf.Variable(0.5, dtype=tf.float32, trainable=False)
# Posterior inference
# Probabilistic model
ig = InverseGamma(alpha, beta)
xn = Normal(mu, tf.ones([N]) * tf.sqrt(ig))
# Variational model
qig = InverseGamma(tf.nn.softplus(tf.Variable(tf.random_normal([]))),
tf.nn.softplus(tf.Variable(tf.random_normal([]))))
# Inference
inference = ed.KLqp({ig: qig}, data={xn: xn_data})
inference.run(n_iter=2000, n_samples=150)
sess = ed.get_session()
print('Inferred sigma={}'.format(sess.run(tf.sqrt(qig.mean()))))
