I have a keras model that takes in an image with (up to) 5 MNIST digits and outputs a length and then (up to) 5 digits. I see that model.evaluate() reports accuracies for each of the outputs but how do I determine how good the model is at predicting the numbers? Do I need to write that myself?
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$\begingroup$ Do you have multiple digits on the same input; i.e., a multi-label problem? $\endgroup$– EmreJan 20 '17 at 19:54
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$\begingroup$ I think the answer is yes -- my input is a 28x140 pixel image -- made by sequencing up to 5 28x28 images where each represents a hand-drawn digit. $\endgroup$– John AlbanoJan 21 '17 at 1:03
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It's going to take a bit of engineering - since you have a variable size output, you need to encode the length into the output in order to evaluate the accuracy of the model overall. If instead of outputting "up to 5 digits", you output an array of 5 predictions, where some non-digit (such as -1) operates as indicating that there is no digit present, you can better evaluate your network. If you retrain your network as such (where $X$ is the array of images and $Y$ is an array containing arrays of form $[1,4,3,-1,-1]$, for example), then model.evaluate($X_{test}$,$Y_{test}$) will work as expected.
If you don't want to re-train your network, you can write a simple function to take the output from model.predict($X_{test}$) and encode it into the corresponding format. This encode function will simply go from $[1,4,3]$ to $[1,4,3,-1,-1]$. You can then calculate the accuracy by sklearn.metrics.accuracy_score($encode$(model.predict($X_{test}$)),$Y_{test}$), where $encode$ is the aforementioned function.
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$\begingroup$ Thanks CMUEngineer. I actually stumbled upon your first suggestion on my own -- removing length and treating "blanks" as another label. The model still just outputs accuracies for each of the 5 outputs but I believe that multiplying them together is the overall accuracy now. $\endgroup$ Jan 23 '17 at 12:32
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$\begingroup$ Multiplying the accuracies together is a decent idea - but doesn't encode the ability of the network to accurately distinguish how many numbers there are in the image. Also multiplying the accuracy scores together would under-estimate the error. Think of the case where one of the five numbers is wrong for all the test cases: this would give you an accuracy of 0, although your network has still achieved something. I would suggest using the second approach - the encoding function is pretty trivial to write and would give you access to a better metric. $\endgroup$ Jan 23 '17 at 16:20
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$\begingroup$ I also wrote the code to actually compare the final numbers from the ground truth and the model -- and got the same accuracy (to 4 dec places) that multiplying the 5 digit-predictors got -- but I see your point that it might not always be the case -- thanks for the input. $\endgroup$ Jan 24 '17 at 14:55