How to use non-linear multi-class SVM to predict class for new data?

[AlexHMJ]

I have modified "ch4 - Implementing Multiclass SVMs" code to use my own data set to train the classifier. The training process is good and so for the testing result. But I got some problems when I want to predict the new data that are without the labels.
I see three lines of code in "ch4 - Implementing Multiclass SVMs" which use those lines below to estimate the training acc :

prediction_output = tf.matmul(tf.multiply(y_target, b), pred_kernel)
prediction = tf.argmax(prediction_output - tf.expand_dims(tf.reduce_mean(prediction_output, 1), 1), 0)
accuracy = tf.reduce_mean(tf.cast(tf.equal(prediction, tf.argmax(y_target, 0)), tf.float32))

1. How do I use this trained SVM model to predict the new data (no label)?
2. It seems that I need the label for the data to run the prediction, but I think it is very weird why I need the y_target (label) to calculate the prediction result?
3. How can those three lines of code get correct prediction result?

Hope someone can help me to figure out what's going on.

[ArrowYL]

I am really confused too.so I post a question in issue #148 I am working on it.May be we can solve it from SVM theory.

[Nick]

Hi @AlexHMJ and @ArrowYL ,

Thanks for bringing this up. I'm quite busy in the next month, but I can check this out and see if I can extend it to the MNIST data in a few weeks. Let me know if you make any progress in the mean time.

[klchang]

# Predict one new sample
new_sample = np.array([6.5, 1.0]).reshape(-1,2)
pred = sess.run(prediction, feed_dict={x_data: rand_x, y_target: rand_y, prediction_grid: new_sample})
print("predicted: {}".format(pred[0]))

In my humble opinion, the naming 'y_target' in the prediction part is a little confusing, because its meanings in 'prediction_output' and 'accuracy' may be different: the former represents the target of training data, but the latter may represent the target of training data or that of test data.

[anbo1024]

I have encountered the same problem with a test accuracy of 100%. Is this problem solved by you?