def define_eval_metrics(self):
"""
Evaluate the model on the current batch
"""
# Dice
with tf.variable_scope('Dice'):
self.dice = dice_coe(output=self.sup_pred_mask_oh,
target=self.sup_output_data)
with tf.variable_scope('Dice_3channels'):
self.dice_3chs = dice_coe(output=self.sup_pred_mask_oh[..., 1:],
target=self.sup_output_data[..., 1:])
def define_eval_metrics(self):
"""
Evaluate the model on the current batch
"""
with tf.variable_scope('Dice_sup'):
self.dice_sup = dice_coe(output=self.sup_pred_mask_oh[..., 1:],
target=self.acdc_sup_output_mask[..., 1:])
def dice_loss(output, target, axis=(1, 2, 3), smooth=1e-12):
""" Returns Soft Sørensen–Dice loss """
return 1.0 - dice_coe(output, target, axis=axis, smooth=smooth)