I'm using a neural network to analyze item choices made by players in a computer game. In the game players can choose between 0 and 7 items. Right now I'm struggling with how I can evaluate my data.
Tensorflow provides a nice method for getting the k highest values.
tf.nn.top_k(input, k=1, sorted=True, name=None)
The input here would be the prediction by my neural network. The output I get by applying top_k to the prediction would then be compared to applying top_k to the correct_output (the one the neural net should have had) and by doing this multiple times and averaging I get the accuracy that I want to have. The problem I'm running into is that k should depend on the amount of 1's in the correct_output. I am lost as to how I can achieve this.
edit: correct output (if I understand how this works correctly) should already be a tensor. It is loaded from a .pickle file and at the beginning of the code it is prepared as follows: correct_output = tf.placeholder('float')
As to what it looks like: it is simply a list of given length of 1's and 0's
correct_outputalready a tensor (you should be feeding it in to your graph as a
tf.placeholder)? Can you include its definition? $\endgroup$
correct_output = tf.placeholder('float', shape=(None,7))- setting the shape just protects you a little from some kinds of errors in processing (the processing will fail with a meaningful error if you try to train against incorrect data). I'm pretty sure there is a standard loss function to use in your situation, I'll try to look it up later this weekend and write an answer if no-one else has done by then . . . $\endgroup$