# START
print_term('Starting session...', run_id)
sess = tf.Session()
K.set_session(sess)
print_term('Started session...', run_id)
# Build the network and the various operations
print_term('Building network...', run_id)
col = Colorization(256)
fwd_col = Feedforward_Colorization(256)
ref = Refinement()
opt_operations = training_pipeline(col, fwd_col, ref, learning_rate,
batch_size)
evaluations_ops = evaluation_pipeline(col, fwd_col, ref, val_number_of_images)
train_col_writer, train_fwd_writer, train_ref_writer, val_col_writer, val_fwd_writer, val_ref_writer = metrics_system(
run_id, sess)
saver, checkpoint_paths, latest_checkpoint = checkpointing_system(run_id)
print_term('Built network', run_id)
with sess.as_default():
# tf.summary.merge_all()
# writer = tf.summary.FileWriter('./graphs', sess.graph)
# Initialize
print_term('Initializing variables...', run_id)
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
print_term('Initialized variables', run_id)
# Coordinate the loading of image files.
print_term('Coordinating loaded image files...', run_id)
from colorization import Colorization
from colorization.training_utils import evaluation_pipeline, \
checkpointing_system, plot_evaluation, metrics_system
# PARAMETERS
run_id = 'run{}'.format(1)
number_of_images = 200
# START
sess = tf.Session()
K.set_session(sess)
# Build the network and the various operations
col = Colorization(256)
evaluations_ops = evaluation_pipeline(col, number_of_images)
summary_writer = metrics_system(run_id, sess)
saver, checkpoint_paths, latest_checkpoint = checkpointing_system(run_id)
with sess.as_default():
# Initialize
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# Restore
if latest_checkpoint is not None:
print('Restoring from: {}'.format(latest_checkpoint))
saver.restore(sess, latest_checkpoint)
print(' done!')