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sbn_rws.py
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sbn_rws.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import time
import tensorflow as tf
from tensorflow.contrib import layers
from six.moves import range, zip
import numpy as np
import zhusuan as zs
from examples import conf
from examples.utils import dataset
@zs.reuse('model')
def sbn(observed, n, n_x, n_h, n_particles):
with zs.BayesianNet(observed=observed) as model:
h3_logits = tf.zeros([n, n_h])
h3 = zs.Bernoulli('h3', h3_logits, n_samples=n_particles,
group_ndims=1, dtype=tf.float32)
h2_logits = layers.fully_connected(h3, n_h, activation_fn=None)
h2 = zs.Bernoulli('h2', h2_logits, group_ndims=1, dtype=tf.float32)
h1_logits = layers.fully_connected(h2, n_h, activation_fn=None)
h1 = zs.Bernoulli('h1', h1_logits, group_ndims=1, dtype=tf.float32)
x_logits = layers.fully_connected(h1, n_x, activation_fn=None)
x = zs.Bernoulli('x', x_logits, group_ndims=1)
return model
@zs.reuse('proposal')
def q_net(x, n_h, n_particles):
with zs.BayesianNet() as proposal:
h1_logits = layers.fully_connected(tf.to_float(x), n_h,
activation_fn=None)
h1 = zs.Bernoulli('h1', h1_logits, n_samples=n_particles,
group_ndims=1, dtype=tf.float32)
h2_logits = layers.fully_connected(h1, n_h, activation_fn=None)
h2 = zs.Bernoulli('h2', h2_logits, group_ndims=1, dtype=tf.float32)
h3_logits = layers.fully_connected(h2, n_h, activation_fn=None)
h3 = zs.Bernoulli('h3', h3_logits, group_ndims=1, dtype=tf.float32)
return proposal
if __name__ == "__main__":
tf.set_random_seed(1237)
# Load MNIST
data_path = os.path.join(conf.data_dir, 'mnist.pkl.gz')
x_train, t_train, x_valid, t_valid, x_test, t_test = \
dataset.load_mnist_realval(data_path)
x_train = np.vstack([x_train, x_valid]).astype('float32')
np.random.seed(1234)
x_test = np.random.binomial(1, x_test, size=x_test.shape).astype('float32')
n_x = x_train.shape[1]
# Define model parameters
n_h = 200
# Define training/evaluation parameters
lb_samples = 10
ll_samples = 1000
epochs = 3000
batch_size = 24
test_batch_size = 100
iters = x_train.shape[0] // batch_size
test_iters = x_test.shape[0] // test_batch_size
test_freq = 10
learning_rate = 0.001
anneal_lr_freq = 200
anneal_lr_rate = 0.75
# Build the computation graph
n_particles = tf.placeholder(tf.int32, shape=[], name='n_particles')
x_orig = tf.placeholder(tf.float32, shape=[None, n_x], name='x')
x_bin = tf.cast(tf.less(tf.random_uniform(tf.shape(x_orig), 0, 1), x_orig),
tf.int32)
x = tf.placeholder(tf.int32, shape=[None, n_x], name='x')
x_obs = tf.tile(tf.expand_dims(x, 0), [n_particles, 1, 1])
n = tf.shape(x)[0]
h_names = ['h' + str(i + 1) for i in range(3)]
def log_joint(observed):
model = sbn(observed, n, n_x, n_h, n_particles)
log_phs = model.local_log_prob(h_names)
log_px_h1 = model.local_log_prob('x')
return tf.add_n(log_phs) + log_px_h1
proposal = q_net(x, n_h, n_particles)
qh_outputs = proposal.query(h_names, outputs=True, local_log_prob=True)
latent = dict(zip(h_names, qh_outputs))
learning_rate_ph = tf.placeholder(tf.float32, shape=[], name='lr')
optimizer = tf.train.AdamOptimizer(learning_rate_ph, epsilon=1e-4)
# learning model parameters
lower_bound = tf.reduce_mean(
zs.variational.importance_weighted_objective(
log_joint, observed={'x': x_obs}, latent=latent, axis=0))
model_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='model')
model_grads = optimizer.compute_gradients(-lower_bound, model_params)
# adapting the proposal
klpq_obj = zs.variational.klpq(
log_joint, observed={'x': x_obs}, latent=latent, axis=0)
klpq_cost = tf.reduce_mean(klpq_obj.rws())
proposal_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='proposal')
klpq_grads = optimizer.compute_gradients(klpq_cost, proposal_params)
infer_op = optimizer.apply_gradients(model_grads + klpq_grads)
# Run the inference
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(1, epochs + 1):
time_epoch = -time.time()
if epoch % anneal_lr_freq == 0:
learning_rate *= anneal_lr_rate
np.random.shuffle(x_train)
lbs = []
for t in range(iters):
x_batch = x_train[t * batch_size:(t + 1) * batch_size]
x_batch_bin = sess.run(x_bin, feed_dict={x_orig: x_batch})
_, lb = sess.run([infer_op, lower_bound],
feed_dict={x: x_batch_bin,
learning_rate_ph: learning_rate,
n_particles: lb_samples})
lbs.append(lb)
time_epoch += time.time()
print('Epoch {} ({:.1f}s): Lower bound = {}'.format(
epoch, time_epoch, np.mean(lbs)))
if epoch % test_freq == 0:
time_test = -time.time()
test_lbs = []
test_lls = []
for t in range(test_iters):
test_x_batch = x_test[
t * test_batch_size: (t + 1) * test_batch_size]
test_lb = sess.run(lower_bound,
feed_dict={x: test_x_batch,
n_particles: lb_samples})
test_ll = sess.run(lower_bound,
feed_dict={x: test_x_batch,
n_particles: ll_samples})
test_lbs.append(test_lb)
test_lls.append(test_ll)
time_test += time.time()
print('>>> TEST ({:.1f}s)'.format(time_test))
print('>> Test lower bound = {}'.format(np.mean(test_lbs)))
print('>> Test log likelihood = {}'.format(np.mean(test_lls)))