Our old data generator used rather lengthy headers for specifying training data and labels. We removed them, but the generator code still references their variables.
The right thing to do is create a synthetic training set by adding noise to a computable function. This would fix the undefined variable problems, and it would also enable backprop/backprop to work properly when the input_dimension and possible_outputs parameters are changed.
Our old data generator used rather lengthy headers for specifying training data and labels. We removed them, but the generator code still references their variables.
The right thing to do is create a synthetic training set by adding noise to a computable function. This would fix the undefined variable problems, and it would also enable
backprop/backpropto work properly when theinput_dimensionandpossible_outputsparameters are changed.