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)
# 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)
checkpointing_system, plot_evaluation, metrics_system # PARAMETERS run_id = 'run{}'.format(1) val_number_of_images = 100 batch_size = 10 batches = val_number_of_images // batch_size costs = [] # START sess = tf.Session() K.set_session(sess) # Build the network and the various operations col = Colorization(256) evaluations_ops = evaluation_pipeline(col, batch_size) 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()) # Coordinate the loading of image files. 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))