Some questions about the code of the svm prediction part

[anbo1024]

This is the predicted output part of the svm network：
output = tf.identity(tf.sign(output), name='prediction')
correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(y_input, 1))

After the output is processed by the function tf.sign()，a two-category tag that is processed into (1,-1).This is not predictable for multi-class handwritten data sets.
I want to ask you this question.

[AFAgarap]

@anbo1024 it can be used in a one-versus-all manner, i.e. in a one-hot encoded vector, instead of using 0 for the classes that a sample does not belong, we use -1 : [0, 1, 0, 0] --> [-1, 1, -1, 1]

[anbo1024]

@AFAgarap I still have some doubts.
1、You will [0, 1, 0, 0] --> [-1, 1, -1, 1]，Softmax regards the maximum subscript as the final classification result. How do you determine the final classification result with this encoding method?
2、I think that in CNN, whether using SVM or Softmax, the network output has been classified. SVM and Softmax calculate loss in different ways.

output = tf.identity(tf.sign(output), name='prediction')
output = tf.identity((output), name='prediction')
correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(y_input, 1))

I think tf. sign and tf. nn. softmax functions can be removed.The following Correct_prediction function already contains the maximum operation.
I hope I can get your advice. Thank you very much.

[AFAgarap]

Well, in terms of having a one-hot encoded vector with a -1 in place of 0, e.g. [-1, 1, -1, -1], which one is greater, -1 or 1? 1, yes? So, it's still fine to be there. But you're right, it can be discarded for the purposes of getting the training accuracy.

[anbo1024]

@AFAgarap I understand what you mean. Now, I use SVM as a loss function on my own dataset, but the performance is very poor, and Softmax performs very well. Why is this? Is the data still to be preprocessed?

[AFAgarap]

In Yichuan Tang's paper, Deep Learning using Linear Support Vector Machines, he used PCA and added Gaussian noise for MNIST. But that was on a feed-forward neural network with 2 layers having 512 units each.

[AFAgarap]

The paper is here

[anbo1024]

Yes, I have read this article. Good results have been achieved in your code. Did you preprocess it?

[AFAgarap]

No, I didn't