max nargin decision problem in code about cnn-svm HOT 3 CLOSED

@mcu1988 Will you please describe the problem you see in the implementation? Thank you. :)

from cnn-svm.

from Deep Learning using Linear Support Vector Machines (2013)
Multiple SVM is described in this picture. Output of Multiple SVM is defined in (9). There is no need to convert output margins of each binary SVM to 1 and -1. When more than one svm outputs a positive margin value, these positive margin are converted to 1 by using output = tf.identity(tf.sign(output), name='prediction'). correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(y_input, 1)) may return a false value.

from cnn-svm.

The conversion to {+1, -1} is as per the SVM model design written by Vapnik and Cortes in their paper.

from cnn-svm.