Real Vs Complex

class RealVsComplex(MonteCarlo)

Inherits from MonteCarlo. It generates the same files with the exception that the excel file is called ./log/rvnn_vs_cvnn_monte_carlo_summary.xlsx

Compares a complex model with it’s real equivalent.

Example usage:

# Assume you already have complex data 'x' with its labels 'y'... and a Cvnn model.

montecarlo = RealVsComplex(complex_model)
montecarlo.run(x, y)
__init__(self, complex_model, capacity_equivalent=True, equiv_technique='ratio')

Used to compare a single Complex Model given as a parameter. The Code will generate it’s real equivalent and compre both of them.

Parameters:
  • complex_model – tensorflow.keras.model
  • capacity_equivalent –

    An equivalent model can be equivalent in terms of layer neurons or trainable parameters (capacity equivalent according to this paper)

    • True, it creates a capacity-equivalent model in terms of trainable parameters
    • False, it will double all layer size (except the last one if classifier=True)
  • equiv_technique –

    Used to define the strategy of the capacity equivalent model. This parameter is ignored if capacity_equivalent=False

    • ’ratio’: neurons_real_valued_layer[i] = r * neurons_complex_valued_layer[i], ‘r’ constant for all ‘i’
    • ’alternate’: Method described in this paper where one alternates between multiplying by 2 or 1. Special case on the middle is treated as a compromise between the two.