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@ -31,7 +31,7 @@ class LossCrossEntropyDice(tf.keras.losses.Loss):
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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.cast(tf.one_hot(y_true, 2), dtype=tf.int32) # Input is sparse
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y_true = tf.one_hot(tf.cast(y_true, dtype=tf.int32), 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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