__author__ = 'Nissanka' '''Calculate using analytical experssion for gradient''' from forwardMultiplyGate import * x = -2 y = 3 step_size = 0.01 out = forwardMultiplyGate(x, y); # before: -6 x_gradient = y; # by our complex mathematical derivation above y_gradient = x; x += step_size * x_gradient; # -2.03 y += step_size * y_gradient; # 2.98 out_new = forwardMultiplyGate(x, y); # -5.87. Higher output! Nice. print out_new
__author__ = 'Nissanka' '''Basic gradient''' from forwardMultiplyGate import * x = -2 y = 3 h = 0.0001 step_size = 0.01 out = forwardMultiplyGate(x, y) # before: -6 # compute derivative with respect to x xph = x + h # -1.9999 out2 = forwardMultiplyGate(xph, y) # -5.9997 x_derivative = (out2 - out) / h # 3.0 # compute derivative with respect to y yph = y + h # 3.0001 out3 = forwardMultiplyGate(x, yph) # -6.0002 y_derivative = (out3 - out) / h # -2.0 x = x + step_size * x_derivative # x becomes -1.97 y = y + step_size * y_derivative # y becomes 2.98 out_new = forwardMultiplyGate(x, y) # -5.87! exciting. print out_new
__author__ = 'Nissanka' '''Approximates partial derivative''' #import forwardMultiplyGate as fMG from forwardMultiplyGate import * x = -2 y = 3 out = forwardMultiplyGate(x, y) # -6 h = 0.0001 # compute derivative with respect to x xph = x + h # -1.9999 out2 = forwardMultiplyGate(xph, y) # -5.9997 x_derivative = (out2 - out) / h # 3.0 # compute derivative with respect to y yph = y + h # 3.0001 out3 = forwardMultiplyGate(x, yph) # -6.0002 y_derivative = (out3 - out) / h # -2.0 print x_derivative print y_derivative
