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loss cel+dice: fixup
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1 changed files with 5 additions and 3 deletions
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@ -22,17 +22,19 @@ def dice_loss(y_true, y_pred):
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class LossCrossEntropyDice(tf.keras.losses.Loss):
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"""Cross-entropy loss and dice loss combined together into one nice neat package.
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Combines the two with mean.
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The ground truth labels should sparse, NOT one-hot. The predictions should be one-hot, NOT sparse.
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@source https://lars76.github.io/2018/09/27/loss-functions-for-segmentation.html#9
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"""
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def __init__(self, **kwargs):
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super(LossCrossEntropyDice, self).__init__(**kwargs)
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def call(self, y_true, y_pred):
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y_true = tf.cast(y_true, tf.float32)
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y_true = tf.one_hot(y_true, 2) # Input is sparse
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o = tf.nn.sigmoid_cross_entropy_with_logits(y_true, y_pred) + dice_loss(y_true, y_pred)
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return tf.reduce_mean(o)
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def get_config(self):
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config = super(LossCrossEntropyDice, self).get_config()
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config.update({
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