# Making sense of blocks python package output

I've modified the Blocks tutorial, to train an MLP neural net with a dataset provided for an assignment in my ML course.

I'd like to evaluate the accuracy of the network with varying parameterization, but I am not sure how to obtain the accuracy in first place.

Inspecting the main_loop object useing dir() & vars(), I'm not coming across anything other than the test_cost_with_regularization.

It is possible to record means of different Theano variables via the monitor classes, so perhaps the answer lies there within?

Final Output

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TRAINING HAS BEEN FINISHED:
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Training status: