def classify(units_by_layer, xk, theta, factivation):
phi, s = MLPLearning.forward_propagation(units_by_layer, xk, theta,
factivation)
return s[-1].index(max(s[-1]))
def regression(units_by_layer, xk, theta, factivation):
phi, s = MLPLearning.forward_propagation(units_by_layer, xk, theta,
factivation)
return s[-1]
def get_output_vector(units_by_layer, xk, theta, factivation):
phi, s = MLPLearning.forward_propagation(units_by_layer, xk, theta,
factivation)
return s[len(units_by_layer) - 1]
def get_output_vector(units_by_layer,xk,theta,factivation): phi,s = MLPLearning.forward_propagation(units_by_layer,xk,theta,factivation) return s[len(units_by_layer)-1]
def classify(units_by_layer,xk,theta,factivation): phi,s = MLPLearning.forward_propagation(units_by_layer,xk,theta,factivation) return s[-1].index(max(s[-1]))
def regression(units_by_layer,xk,theta,factivation): phi,s = MLPLearning.forward_propagation(units_by_layer,xk,theta,factivation) return s[-1]
