Abstract: Systems, methods, and non-transitory computer-readable storage media for an adjustable neural network. Systems measure feature applicability for an octave of a Convolutional Neural Network (CNN) at a standard scale, resulting in (1) at least one drop-off point where the octave no longer resonates with pre-defined features; and (2) a common drop-off between the CNN and at least one other CNN trained on at least one other separate domain. The system can then measure octave resonance for a plurality of CNNs trained on large data sets with a distribution of octaves for features, and measure a pattern of octaves learned in the CNN, resulting in a measurement pattern. The system can then compare that measurement pattern to the pre-defined features, resulting in a level of adaptability of the CNN, and modify the CNN based on the level of adaptability of the CNN, resulting in a modified CNN.