search

MousaviSajad / SleepEEGNet

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
200 stars 72 forks source link

the loss function is inconsistent with that in the paper #16

Open LindseyScharpman opened 3 years ago

LindseyScharpman commented 3 years ago

hi, I love what you do and open source work. and I have a question about the loss function. I read your paper, the paper said loss function is MFE and MSFE. but I found the code is:

   for i in range(logits.get_shape().as_list()[-1]): # [128, None, 7]
        class_fill_targets = tf.fill(tf.shape(targets), i) #[?,?]
        weights_i = tf.cast(tf.equal(targets, class_fill_targets), "float") #[?,?] 
        loss_is.append(tf.contrib.seq2seq.sequence_loss(logits, targets, weights_i, average_across_batch=False))

I googled this function. It compute cross-entropy loss. also weights_i parameter is my another question. for example, when i=7 the weight is 7. If I have something wrong, can you help me clarify it. thanks a lot!