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train_beta_vae.py
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train_beta_vae.py
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import tensorflow as tf
import numpy as np
import os
import sys
from random import shuffle
import argparse
from utils.train_utils import *
from model import create_vae_with_elbo_loss
config = tf.ConfigProto(device_count = {'GPU': 1})
parser = argparse.ArgumentParser()
parser.add_argument('logdir', type=str,
help='Directory to store checkpoints and summaries')
parser.add_argument('train_dir', type=str,
help='Directory with training data')
parser.add_argument('test_dir', type=str,
help='Directory with test data')
args = parser.parse_args()
batch_size = 128
segment_length = 20
segment_channels = 80
encoder_units = 256
decoder_units = 256
num_epochs = 1200
num_latents = 32
beta = 8.0
segment = tf.placeholder(
tf.float32, shape=(batch_size, segment_length, segment_channels))
total_loss, outputs = create_vae_with_elbo_loss(
segment, segment_channels,
encoder_units, decoder_units, num_latents,
beta)
outs = outputs['encoder_outs']
z_prior = outputs['z_prior']
z = outputs['z_posterior_sample']
segment_pred = outputs['x_reconst_mean']
optimizer = tf.train.AdamOptimizer()
updates = optimizer.minimize(total_loss)
loss_sum = tf.summary.scalar("total_loss", total_loss)
random_segment_sum = plot_segments(segment_pred, "random_segments")
reconst_segment_sum = plot_segments(
tf.concat((segment, segment_pred), axis=2),
"reconstructed_segments")
outs = tf.expand_dims(outs[:1], -1)
outs_sum = tf.summary.image("encoder_outs", outs)
train_ds = create_batched_dataset(args.train_dir, batch_size, segment_channels,
segment_length, shuffle=False)
test_ds = create_batched_dataset(args.test_dir, batch_size, segment_channels,
segment_length, shuffle=False).repeat()
train_iterator = tf.data.Iterator.from_structure(train_ds.output_types,
train_ds.output_shapes)
next_train_batch = train_iterator.get_next()
next_test_batch = test_ds.make_one_shot_iterator().get_next()
training_init_op = train_iterator.make_initializer(train_ds)
global_i = tf.get_variable("global_i", dtype=tf.int32, trainable=False,
initializer=0)
inc_global_i = tf.assign_add(global_i, 1)
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
load(saver, sess, args.logdir)
summary_writer = tf.summary.FileWriter(args.logdir, sess.graph)
test_loss_ = np.inf
train_loss_ = np.inf
for epoch in range(num_epochs):
# Initialize an iterator over the training dataset.
sess.run(training_init_op)
while True:
sess.run(inc_global_i)
i_ = sess.run(global_i)
try:
if i_ % 100 != 0:
train_seg_ = sess.run(next_train_batch)
train_loss_, _ = sess.run(
[total_loss, updates], {segment: train_seg_})
else:
test_seg_, z_prior_ = sess.run(
[next_test_batch, z_prior])
test_loss_, loss_sum_, reconst_segment_sum_, outs_sum_ = (
sess.run([total_loss, loss_sum,
reconst_segment_sum, outs_sum],
{segment: test_seg_}))
random_segment_sum_ = sess.run(
random_segment_sum, {z: z_prior_})
summary_writer.add_summary(loss_sum_, i_)
summary_writer.add_summary(reconst_segment_sum_, i_)
summary_writer.add_summary(random_segment_sum_, i_)
summary_writer.add_summary(outs_sum_, i_)
if i_ % 10000 == 0:
save(saver, sess, args.logdir, i_)
print("Epoch: {}, step: {}, train_loss: {:.2f}, test_loss: {:.2f}"
.format(epoch, i_, train_loss_, test_loss_))
except tf.errors.OutOfRangeError:
break
except KeyboardInterrupt:
save(saver, sess, args.logdir, i_)
sess.close()
sys.exit()