def loss_func(self):
with tf.name_scope('Loss'):
self.y_prob = tf.nn.softmax(self.logits, axis=-1)
y_one_hot = tf.one_hot(self.labels_pl,
depth=self.conf.num_cls,
axis=3,
name='y_one_hot')
if self.conf.weighted_loss:
loss = weighted_cross_entropy(y_one_hot,
self.logits,
self.conf.num_cls,
data=self.conf.data)
else:
if self.conf.loss_type == 'cross-entropy':
with tf.name_scope('cross_entropy'):
loss = cross_entropy(y_one_hot, self.logits,
self.conf.num_cls)
elif self.conf.loss_type == 'dice':
with tf.name_scope('dice_coefficient'):
loss = dice_coeff(y_one_hot, self.logits)
with tf.name_scope('total'):
if self.conf.use_reg:
with tf.name_scope('L2_loss'):
l2_loss = tf.reduce_sum(self.conf.lmbda * tf.stack([
tf.nn.l2_loss(v)
for v in tf.get_collection('weights')
]))
self.total_loss = loss + l2_loss
else:
self.total_loss = loss
self.mean_loss, self.mean_loss_op = tf.metrics.mean(
self.total_loss)
def loss_func(self):
with tf.name_scope('Loss'):
self.y_prob = tf.nn.softmax(self.logits, axis=-1)
with tf.name_scope('cross_entropy'):
loss = cross_entropy(self.labels_pl, self.logits)
with tf.name_scope('total'):
if self.conf.use_reg:
with tf.name_scope('L2_loss'):
l2_loss = tf.reduce_sum(self.conf.lmbda * tf.stack([
tf.nn.l2_loss(v)
for v in tf.get_collection('weights')
]))
self.total_loss = loss + l2_loss
else:
self.total_loss = loss
self.mean_loss, self.mean_loss_op = tf.metrics.mean(
self.total_loss)