def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
if FLAGS.debug:
for grad, var in grads:
utils.add_gradient_summary(grad. var)
return optimizer.apply_gradients(grads)
def _training(self, global_step):
"""
Setup the training phase with Adam
:param global_step: global step of training
"""
optimizer = tf.train.AdamOptimizer(self.lr)
grads = optimizer.compute_gradients(self.loss_op)
for grad, var in grads:
tf_utils.add_gradient_summary(grad, var, collections=['train'])
return optimizer.apply_gradients(grads, global_step=global_step)
tf.summary.scalar('mean_iou', mean_IOU)
tf.summary.scalar('reg_loss', reg_loss)
# tf.summary.scalar('learning_rate', decay_learning_rate)
with tf.name_scope('summary_ious'):
add_iou_summary(IOUs, pascal_voc_classes)
with tf.name_scope('summary_vars'):
for weight in weight_vars:
add_var_summary(weight)
for bias in bias_vars:
add_var_summary(bias)
with tf.name_scope('summary_grads'):
for grad, var in weight_grads:
add_gradient_summary(grad, var)
for grad, var in bias_grads:
add_gradient_summary(grad, var)
# with tf.name_scope('summary_activations'):
# for activations in endpoints.keys():
# add_activation_summary(endpoints[activations])
merge_summary = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir, sess.graph)
saver = tf.train.Saver(max_to_keep=3)
# initialize
ckpt = None
if FLAGS.last_ckpt is not None:
ckpt = tf.train.latest_checkpoint(FLAGS.last_ckpt)