sbrl-github/research-rainfallradar
1
0
Fork 0
mirror of https://github.com/sbrl/research-rainfallradar synced 2024-11-22 01:12:59 +00:00
Code Issues Projects Releases Packages Wiki Activity

loss cel+dice: fixup

Browse source
This commit is contained in:
Starbeamrainbowlabs 2023-01-13 18:21:11 +00:00
parent 0f0b691b5d
commit 37d1598b0b
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
1 changed files with 5 additions and 3 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

8
aimodel/src/lib/ai/components/LossCrossEntropyDice.py
View file

@ -22,17 +22,19 @@ def dice_loss(y_true, y_pred):
class LossCrossEntropyDice(tf.keras.losses.Loss): class LossCrossEntropyDice(tf.keras.losses.Loss):
"""Cross-entropy loss and dice loss combined together into one nice neat package. """Cross-entropy loss and dice loss combined together into one nice neat package.
Combines the two with mean. Combines the two with mean.
The ground truth labels should sparse, NOT one-hot. The predictions should be one-hot, NOT sparse.
@source https://lars76.github.io/2018/09/27/loss-functions-for-segmentation.html#9 @source https://lars76.github.io/2018/09/27/loss-functions-for-segmentation.html#9
""" """
def __init__(self, **kwargs): def __init__(self, **kwargs):
super(LossCrossEntropyDice, self).__init__(**kwargs) super(LossCrossEntropyDice, self).__init__(**kwargs)
def call(self, y_true, y_pred): def call(self, y_true, y_pred):
y_true = tf.cast(y_true, tf.float32) y_true = tf.cast(y_true, tf.float32)
y_true = tf.one_hot(y_true, 2) # Input is sparse
o = tf.nn.sigmoid_cross_entropy_with_logits(y_true, y_pred) + dice_loss(y_true, y_pred) o = tf.nn.sigmoid_cross_entropy_with_logits(y_true, y_pred) + dice_loss(y_true, y_pred)
return tf.reduce_mean(o) return tf.reduce_mean(o)
def get_config(self): def get_config(self):
config = super(LossCrossEntropyDice, self).get_config() config = super(LossCrossEntropyDice, self).get_config()
config.update({ config.update({