我正在keras中进行语义分段，并试图修改categorical_crossentropy损失，以便损失是类加权的 .
这是我的代码：
def class_weighted_categorical_crossentropy(output, target, from_logits=False):
"""Categorical crossentropy between an output tensor and a target tensor.
parameter = TrainingParameters()
# create ones array with shape of target tensor
# multiply class weight array with inverse class_accuracies for each label
class_weights = tf.convert_to_tensor(parameter.class_weights, dtype=floatx())
# weight targets with class weights and create pattern with which loss can be multiplied
class_weights_pattern = tf.multiply(target, class_weights)
class_weights_pattern = tf.reduce_sum(class_weights_pattern, reduction_indices=len(class_weights_pattern.get_shape())-1)#, keep_dims=True)
if not from_logits:
# scale preds so that the class probas of each sample sum to 1
output /= tf.reduce_sum(output,
reduction_indices=len(output.get_shape()) - 1,
keep_dims=True)
# manual computation of crossentropy
epsilon = _to_tensor(_EPSILON, output.dtype.base_dtype)
output = tf.clip_by_value(output, epsilon, 1. - epsilon)
loss = - tf.reduce_sum(target * tf.log(output), reduction_indices=len(output.get_shape()) - 1)
return tf.multiply(loss, class_weights_pattern)
else:
loss = tf.nn.softmax_cross_entropy_with_logits(labels=target, logits=output)
return tf.multiply(loss, class_weights_pattern)
我最后只改变了损失乘以class_weights模式 . class_weights_pattern包含每个像素对应的类权重，因此应该对正常的categorical_crossentropy损失进行加权 . 但是，如果我使用修改后的损失训练我的模型，结果会比仅使用keras categorical_crossentropy损失更糟糕 . 即使我将所有类权重设置为1，因此我的class_weighted_categorical_crossentropy损失应该与来自keras的categorical_crossentropy损失完全相同，结果更糟 . 我已经用一些样本图像打印了这两种损失，损失完全相同 .
有谁能够帮我？为什么不起作用？提前致谢！