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vae_conv.py
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vae_conv.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 six.moves import range
import numpy as np
import zhusuan as zs
from examples import conf
from examples.utils import dataset
from examples.utils import save_image_collections, conv2d_transpose
def deconv_resnet_block(input_, out_shape, resize=False):
if not resize:
lx_z = conv2d_transpose(input_, out_shape, kernel_size=(3, 3),
stride=(1, 1))
lx_z = conv2d_transpose(lx_z, out_shape, kernel_size=(3, 3),
stride=(1, 1), activation_fn=None)
lx_z += input_
else:
lx_z = conv2d_transpose(input_, input_.get_shape().as_list()[1:],
kernel_size=(3, 3), stride=(1, 1))
lx_z = conv2d_transpose(lx_z, out_shape, kernel_size=(3, 3),
stride=(2, 2), activation_fn=None)
residual = conv2d_transpose(input_, out_shape, kernel_size=(3, 3),
stride=(2, 2), activation_fn=None)
lx_z += residual
lx_z = tf.nn.relu(lx_z)
return lx_z
def conv_resnet_block(input_, out_channel, resize=False):
if not resize:
lz_x = tf.layers.conv2d(input_, out_channel, 3, padding="same",
activation=tf.nn.relu)
lz_x = tf.layers.conv2d(lz_x, out_channel, 3, padding="same")
lz_x += input_
else:
lz_x = tf.layers.conv2d(input_, out_channel, 3, strides=(2, 2),
padding="same", activation=tf.nn.relu)
lz_x = tf.layers.conv2d(lz_x, out_channel, 3, padding="same")
residual = tf.layers.conv2d(input_, out_channel, 3, strides=(2, 2),
padding="same")
lz_x += residual
lz_x = tf.nn.relu(lz_x)
return lz_x
@zs.meta_bayesian_net(scope="gen", reuse_variables=True)
def build_gen(n, x_dim, z_dim, n_particles, nf=16):
bn = zs.BayesianNet()
z_mean = tf.zeros([n, z_dim])
z = bn.normal("z", z_mean, std=1., group_ndims=1, n_samples=n_particles)
lx_z = tf.layers.dense(z, 7 * 7 * nf * 2, activation=tf.nn.relu)
lx_z = tf.reshape(lx_z, [-1, 7, 7, nf * 2])
lx_z = deconv_resnet_block(lx_z, [7, 7, nf * 2])
lx_z = deconv_resnet_block(lx_z, [14, 14, nf * 2], resize=True)
lx_z = deconv_resnet_block(lx_z, [14, 14, nf * 2])
lx_z = deconv_resnet_block(lx_z, [28, 28, nf], resize=True)
lx_z = deconv_resnet_block(lx_z, [28, 28, nf])
lx_z = conv2d_transpose(lx_z, [28, 28, 1], kernel_size=(3, 3),
stride=(1, 1), activation_fn=None)
x_logits = tf.reshape(lx_z, [n_particles, -1, x_dim])
bn.deterministic("x_mean", tf.sigmoid(x_logits))
bn.bernoulli("x", x_logits, group_ndims=1)
return bn
@zs.reuse_variables(scope="q_net")
def build_q_net(x, z_dim, n_particles, nf=16):
bn = zs.BayesianNet()
lz_x = 2 * tf.cast(x, tf.float32) - 1
lz_x = tf.reshape(lz_x, [-1, 28, 28, 1])
lz_x = tf.layers.conv2d(lz_x, nf, 3, padding="same", activation=tf.nn.relu)
lz_x = conv_resnet_block(lz_x, nf)
lz_x = conv_resnet_block(lz_x, nf * 2, resize=True)
lz_x = conv_resnet_block(lz_x, nf * 2)
lz_x = conv_resnet_block(lz_x, nf * 2, resize=True)
lz_x = conv_resnet_block(lz_x, nf * 2)
lz_x = tf.layers.flatten(lz_x)
lz_x = tf.layers.dense(lz_x, 500, activation=tf.nn.relu)
z_mean = tf.layers.dense(lz_x, z_dim)
z_logstd = tf.layers.dense(lz_x, z_dim)
bn.normal("z", z_mean, logstd=z_logstd, group_ndims=1,
n_samples=n_particles)
return bn
def main():
tf.set_random_seed(1234)
np.random.seed(1234)
# 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])
x_test = np.random.binomial(1, x_test, size=x_test.shape)
x_dim = x_train.shape[1]
# Define model parameters
z_dim = 32
# Build the computation graph
n_particles = tf.placeholder(tf.int32, shape=[], name="n_particles")
x_input = tf.placeholder(tf.float32, shape=[None, x_dim])
x = tf.cast(tf.random_uniform(tf.shape(x_input)) <= x_input, tf.int32)
n = tf.placeholder(tf.int32, shape=[], name="n")
model = build_gen(n, x_dim, z_dim, n_particles)
variational = build_q_net(x, z_dim, n_particles)
lower_bound = zs.variational.elbo(
model, {"x": x}, variational=variational, axis=0)
cost = tf.reduce_mean(lower_bound.sgvb())
lower_bound = tf.reduce_mean(lower_bound)
optimizer = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5)
infer_op = optimizer.minimize(cost)
# Generate images
x_gen = tf.reshape(model.observe()["x_mean"], [-1, 28, 28, 1])
# Define training/evaluation parameters
epochs = 3000
batch_size = 128
iters = x_train.shape[0] // batch_size
save_freq = 10
test_freq = 10
test_batch_size = 400
test_iters = x_test.shape[0] // test_batch_size
result_path = "results/vae_conv"
# 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()
np.random.shuffle(x_train)
lbs = []
for t in range(iters):
x_batch = x_train[t * batch_size:(t + 1) * batch_size]
_, lb = sess.run([infer_op, lower_bound],
feed_dict={x_input: x_batch,
n_particles: 1,
n: batch_size})
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 = []
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: 1,
n: test_batch_size})
test_lbs.append(test_lb)
time_test += time.time()
print(">>> TEST ({:.1f}s)".format(time_test))
print(">> Test lower bound = {}".format(np.mean(test_lbs)))
if epoch % save_freq == 0:
print("Saving images...")
images = sess.run(x_gen, feed_dict={n: 100, n_particles: 1})
name = os.path.join(result_path,
"vae.epoch.{}.png".format(epoch))
save_image_collections(images, name)
if __name__ == "__main__":
main()