'summary_train_op', 'summary_test_op', 'summary_epoch_train_op']
tensors = [loss, accuracy, train_op, global_step, image_place, label_place, dropout_param, summary_train_op,
summary_test_op, summary_epoch_train_op]
tensors_dictionary = dict(zip(tensors_key, tensors))
############################################
############ Run the Session ###############
############################################
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(graph=graph, config=session_conf)
with sess.as_default():
saver = tf.train.Saver(max_to_keep=FLAGS.max_num_checkpoint)
sess.run(tf.global_variables_initializer())
###################################################
############ Training / Evaluation ###############
###################################################
train_evaluation.train(sess=sess, saver=saver, tensors=tensors_dictionary, data=data,
train_dir=FLAGS.train_dir,
finetuning=FLAGS.fine_tuning, online_test=FLAGS.online_test,
num_epochs=FLAGS.num_epochs, checkpoint_dir=FLAGS.checkpoint_dir,
batch_size=FLAGS.batch_size)
# Test in the end of experiment.
train_evaluation.evaluation(sess=sess, saver=saver, tensors=tensors_dictionary, data=data,
checkpoint_dir=FLAGS.checkpoint_dir)
tensors_dictionary = dict(zip(tensors_key, tensors))
############################################
############ Run the Session ###############
############################################
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(graph=graph, config=session_conf)
with sess.as_default():
# Run the saver.
# 'max_to_keep' flag determines the maximum number of models that the tensorflow save and keep. default by TensorFlow = 5.
saver = tf.train.Saver(max_to_keep=FLAGS.max_num_checkpoint)
# Initialize all variables
sess.run(tf.global_variables_initializer())
###################################################
############ Training / Evaluation ###############
###################################################
train_evaluation.train(sess=sess, saver=saver, tensors=tensors_dictionary, data=data,
train_dir=FLAGS.train_dir,
finetuning=FLAGS.fine_tuning, online_test=FLAGS.online_test,
num_epochs=FLAGS.num_epochs, checkpoint_dir=FLAGS.checkpoint_dir,
batch_size=FLAGS.batch_size)
# Test in the end of experiment.
train_evaluation.evaluation(sess=sess, saver=saver, tensors=tensors_dictionary, data=data,
checkpoint_dir=FLAGS.checkpoint_dir)