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can I adapt the code to multi-label classificaiton? #18
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Replacing softmax cross entropy loss with a sigmoid cross entropy loss
should do the job:)
…On Sun 23. Sep 2018 at 18:02 Xiaoyong Pan ***@***.***> wrote:
Hi,
I have a multi-label classification problem, where one node can have multi
labels, DO I need change the code for multi-class classification? thanks.
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thanks. I changed the the GCN model return F.sigmoid(x) |
Sounds correct!
…On Sun 23. Sep 2018 at 18:28 Xiaoyong Pan ***@***.***> wrote:
thanks.
I changed the the GCN model return F.sigmoid(x)
and replace the loss function F.nll_loss using loss_train =
F.binary_cross_entropy(output[idx_train], labels[idx_train]). is my change
correct?
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hello, have you finish the multi-label problem? |
I need to use GCN with TensorFlow, have you try multi-label problem with Tensorflow? So is it correct? |
@Chengmeng94 It seems to be correct. Make sure to adapt the accuracy function as well to handle a sigmoid output instead of a softmax output. currently it is just taking the max, as you would expect for a softmax function, so it is just evaluating a single label. This is typically done with some threshold. |
@anjanaskumar27 My response was to a mult-label situation, rather than a multi-class situation. I think the original code already works for multi-class situations. |
@Baukebrenninkmeijer sorry, my bad. I meant to ask procedure for multi label. The following are my changes: |
@anjanaskumar27 As far as I can see here, this should be correct except maybe for the metric. I'm not an expert on metrics for multilabel, but I expect just accuracy (which is what you get with the tf.equal) is not the most appropriate, although it still gives you some indication. |
@Chengmeng94 @tkipf @Baukebrenninkmeijer , to allow multi-label mulit-class classification: In addition to @Chengmeng94 changes (as follows), I added, Is this correct? |
@anjanaskumar27 tensorflow is not my strongsuit, but a quick google advised the following. Use correct_prediction = tf.equal(tf.round(tf.nn.sigmoid(self.outputs)), labels)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) |
@Baukebrenninkmeijer , thank you for the suggestion. I am new to tensorflow as well, thus finding it little tricky to modify things. |
Hi,
I have a multi-label classification problem, where one node can have multi labels, DO I need change the code for multi-class classification? thanks.
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