def solve(global_step):
"""add solver to losses"""
# learning reate
lr = _configure_learning_rate(82783, global_step)
optimizer = _configure_optimizer(lr)
tf.summary.scalar('learning_rate', lr)
# compute and apply gradient
losses = tf.get_collection(tf.GraphKeys.LOSSES)
regular_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
regular_loss = tf.add_n(regular_losses)
out_loss = tf.add_n(losses)
total_loss = tf.add_n(losses + regular_losses)
tf.summary.scalar('total_loss', total_loss)
tf.summary.scalar('out_loss', out_loss)
tf.summary.scalar('regular_loss', regular_loss)
### add the center loss into the summary
update_ops = []
variables_to_train = _get_variables_to_train()
# update_op = optimizer.minimize(total_loss)
gradients = optimizer.compute_gradients(total_loss,
var_list=variables_to_train)
grad_updates = optimizer.apply_gradients(gradients,
global_step=global_step)
update_ops.append(grad_updates)
# update moving mean and variance
if FLAGS.update_bn:
update_bns = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
update_bn = tf.group(*update_bns)
update_ops.append(update_bn)
return tf.group(*update_ops)
def solve(global_step):
"""add solver to losses"""
# learning reate
lr = _configure_learning_rate(82783, global_step)
optimizer = _configure_optimizer(lr)
tf.summary.scalar('learning_rate', lr)
# compute and apply gradient
losses = tf.get_collection(tf.GraphKeys.LOSSES)
regular_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
regular_loss = tf.add_n(regular_losses)
out_loss = tf.add_n(losses)
total_loss = tf.add_n(losses + regular_losses)
tf.summary.scalar('total_loss', total_loss)
tf.summary.scalar('out_loss', out_loss)
tf.summary.scalar('regular_loss', regular_loss)
update_ops = []
variables_to_train = _get_variables_to_train()
# update_op = optimizer.minimize(total_loss)
gradients = optimizer.compute_gradients(total_loss, var_list=variables_to_train)
grad_updates = optimizer.apply_gradients(gradients,
global_step=global_step)
update_ops.append(grad_updates)
# update moving mean and variance
if FLAGS.update_bn:
update_bns = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
update_bn = tf.group(*update_bns)
update_ops.append(update_bn)
return tf.group(*update_ops)
## optimization
learning_rate = _configure_learning_rate(82783, global_step)
optimizer = _configure_optimizer(learning_rate)
summaries.add(tf.summary.scalar('learning_rate', learning_rate))
for loss in tf.get_collection(tf.GraphKeys.LOSSES):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
loss = tf.get_collection(tf.GraphKeys.LOSSES)
regular_loss = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n(loss + regular_loss)
reg_loss = tf.add_n(regular_loss)
summaries.add(tf.summary.scalar('total_loss', total_loss))
summaries.add(tf.summary.scalar('regular_loss', reg_loss))
variables_to_train = _get_variables_to_train()
update_op = optimizer.minimize(total_loss)
# gradients = optimizer.compute_gradients(total_loss, var_list=variables_to_train)
# grad_updates = optimizer.apply_gradients(gradients,
# global_step=global_step)
# update_op = tf.group(grad_updates)
# summary_op = tf.summary.merge(list(summaries), name='summary_op')
summary_op = tf.summary.merge_all()
logdir = os.path.join(FLAGS.train_dir,
strftime('%Y%m%d%H%M%S', gmtime()))
if not os.path.exists(logdir):
os.makedirs(logdir)
summary_writer = tf.summary.FileWriter(logdir, graph=sess.graph)
init_op = tf.group(tf.global_variables_initializer(),
