我正在尝试在用 Keras（TF 后端）编写的不同 convnet 中 实现NiftyNet中实现的 Generalized Dice Loss 。
def generalised_dice_loss(prediction,
ground_truth,
weight_map=None,
type_weight='Square'):
"""
Function to calculate the Generalised Dice Loss defined in
Sudre, C. et. al. (2017) Generalised Dice overlap as a deep learning
loss function for highly unbalanced segmentations. DLMIA 2017
:param prediction: the logits
:param ground_truth: the segmentation ground truth
:param weight_map:
:param type_weight: type of weighting allowed between labels (choice
between Square (square of inverse of volume),
Simple (inverse of volume) and Uniform (no weighting))
:return: the loss
"""
prediction = tf.cast(prediction, tf.float32)
if len(ground_truth.shape) == len(prediction.shape):
ground_truth = ground_truth[..., -1]
one_hot = labels_to_one_hot(ground_truth, tf.shape(prediction)[-1])
if weight_map is not None:
n_classes = prediction.shape[1].value
weight_map_nclasses = tf.reshape(
tf.tile(weight_map, [n_classes]), prediction.get_shape())
ref_vol = tf.sparse_reduce_sum(
weight_map_nclasses * one_hot, reduction_axes=[0])
intersect = tf.sparse_reduce_sum(
weight_map_nclasses * one_hot * prediction, reduction_axes=[0])
seg_vol = tf.reduce_sum(
tf.multiply(weight_map_nclasses, prediction), 0)
else:
ref_vol = tf.sparse_reduce_sum(one_hot, reduction_axes=[0])
intersect = tf.sparse_reduce_sum(one_hot * prediction,
reduction_axes=[0])
seg_vol = tf.reduce_sum(prediction, 0)
if type_weight == 'Square':
weights = tf.reciprocal(tf.square(ref_vol))
elif type_weight == 'Simple':
weights = tf.reciprocal(ref_vol)
elif type_weight == 'Uniform':
weights = tf.ones_like(ref_vol)
else:
raise ValueError("The variable type_weight \"{}\""
"is not defined.".format(type_weight))
new_weights = tf.where(tf.is_inf(weights), tf.zeros_like(weights), weights)
weights = tf.where(tf.is_inf(weights), tf.ones_like(weights) *
tf.reduce_max(new_weights), weights)
generalised_dice_numerator = \
2 * tf.reduce_sum(tf.multiply(weights, intersect))
generalised_dice_denominator = \
tf.reduce_sum(tf.multiply(weights, seg_vol + ref_vol)) + 1e-6
generalised_dice_score = \
generalised_dice_numerator / generalised_dice_denominator
return 1 - generalised_dice_score
但是，当我尝试使用此损失函数训练模型时，会出现以下错误：
ValueError：一个操作没有梯度。请确保您的所有操作都定义了渐变（即可微分）。没有梯度的常见操作：K.argmax、K.round、K.eval。
我尝试实现的任何其他损失函数都不会发生这种情况。有人知道如何解决这个问题吗？据我所知，代码中不存在不可微的运算符。