def _Train():
params = dict()
params['batch_size'] = FLAGS.batch_size
params['seq_len'] = FLAGS.sequence_length
params['image_size'] = FLAGS.image_size
params['is_training'] = True
params['norm_scale'] = FLAGS.norm_scale
params['scale'] = FLAGS.scale
params['learning_rate'] = FLAGS.learning_rate
params['l2_loss'] = FLAGS.l2_loss
params['reconstr_loss'] = FLAGS.reconstr_loss
params['kl_loss'] = FLAGS.kl_loss
train_dir = os.path.join(FLAGS.log_root, 'train')
images = reader.ReadInput(FLAGS.data_filepattern,
shuffle=True,
params=params)
images *= params['scale']
# Increase the value makes training much faster.
image_diff_list = reader.SequenceToImageAndDiff(images)
model = cross_conv_model.CrossConvModel(image_diff_list, params)
model.Build()
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph())
summary_writer = tf.summary.FileWriter(train_dir)
sv = tf.train.Supervisor(logdir=FLAGS.log_root,
summary_op=None,
is_chief=True,
save_model_secs=60,
global_step=model.global_step)
sess = sv.prepare_or_wait_for_session(
FLAGS.master, config=tf.ConfigProto(allow_soft_placement=True))
total_loss = 0.0
step = 0
sample_z_mean = np.zeros(model.z_mean.get_shape().as_list())
sample_z_stddev_log = np.zeros(model.z_stddev_log.get_shape().as_list())
sample_step = 0
while True:
_, loss_val, total_steps, summaries, z_mean, z_stddev_log = sess.run([
model.train_op, model.loss, model.global_step, model.summary_op,
model.z_mean, model.z_stddev_log
])
sample_z_mean += z_mean
sample_z_stddev_log += z_stddev_log
total_loss += loss_val
step += 1
sample_step += 1
if step % 100 == 0:
summary_writer.add_summary(summaries, total_steps)
sys.stderr.write('step: %d, loss: %f\n' %
(total_steps, total_loss / step))
total_loss = 0.0
step = 0
# Sampled z is used for eval.
# It seems 10k is better than 1k. Maybe try 100k next?
if sample_step % 10000 == 0:
with tf.gfile.Open(os.path.join(FLAGS.log_root, 'z_mean.npy'),
'w') as f:
np.save(f, sample_z_mean / sample_step)
with tf.gfile.Open(
os.path.join(FLAGS.log_root, 'z_stddev_log.npy'),
'w') as f:
np.save(f, sample_z_stddev_log / sample_step)
sample_z_mean = np.zeros(model.z_mean.get_shape().as_list())
sample_z_stddev_log = np.zeros(
model.z_stddev_log.get_shape().as_list())
sample_step = 0
def _Eval():
params = dict()
params['batch_size'] = FLAGS.batch_size
params['seq_len'] = FLAGS.sequence_length
params['image_size'] = FLAGS.image_size
params['is_training'] = False
params['norm_scale'] = FLAGS.norm_scale
params['scale'] = FLAGS.scale
params['l2_loss'] = FLAGS.l2_loss
params['reconstr_loss'] = FLAGS.reconstr_loss
params['kl_loss'] = FLAGS.kl_loss
eval_dir = os.path.join(FLAGS.log_root, 'eval')
images = reader.ReadInput(FLAGS.data_filepattern,
shuffle=False,
params=params)
images *= params['scale']
# Increase the value makes training much faster.
image_diff_list = reader.SequenceToImageAndDiff(images)
model = cross_conv_model.CrossConvModel(image_diff_list, params)
model.Build()
summary_writer = tf.summary.FileWriter(eval_dir)
saver = tf.train.Saver()
sess = tf.Session('', config=tf.ConfigProto(allow_soft_placement=True))
tf.train.start_queue_runners(sess)
while True:
time.sleep(60)
try:
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
except tf.errors.OutOfRangeError as e:
sys.stderr.write('Cannot restore checkpoint: %s\n' % e)
continue
if not (ckpt_state and ckpt_state.model_checkpoint_path):
sys.stderr.write('No model to eval yet at %s\n' % FLAGS.log_root)
continue
sys.stderr.write('Loading checkpoint %s\n' %
ckpt_state.model_checkpoint_path)
saver.restore(sess, ckpt_state.model_checkpoint_path)
# Use the empirical distribution of z from training set.
if not tf.gfile.Exists(os.path.join(FLAGS.log_root, 'z_mean.npy')):
sys.stderr.write('No z at %s\n' % FLAGS.log_root)
continue
with tf.gfile.Open(os.path.join(FLAGS.log_root, 'z_mean.npy')) as f:
sample_z_mean = np.load(io.BytesIO(f.read()))
with tf.gfile.Open(os.path.join(FLAGS.log_root,
'z_stddev_log.npy')) as f:
sample_z_stddev_log = np.load(io.BytesIO(f.read()))
total_loss = 0.0
for _ in xrange(FLAGS.eval_batch_count):
loss_val, total_steps, summaries = sess.run(
[model.loss, model.global_step, model.summary_op],
feed_dict={
model.z_mean: sample_z_mean,
model.z_stddev_log: sample_z_stddev_log
})
total_loss += loss_val
summary_writer.add_summary(summaries, total_steps)
sys.stderr.write('steps: %d, loss: %f\n' %
(total_steps, total_loss / FLAGS.eval_batch_count))
