def cost_func(parameters):
candidate_activations = []
for activation in activations:
candidate_activations.append(self._get_output_node_activation_from_activations(parameters, activation))
return -sum(CascadeNet._real_correlations(candidate_activations, errors)) \
+ regularizer_coef*numpy.sum(numpy.abs(candidate_activations))
def cost_func(parameters):
candidate_activations = []
for activation in activations:
candidate_activations.append(
self._get_output_node_activation_from_activations(
parameters, activation))
return -sum(CascadeNet._real_correlations(candidate_activations, errors)) \
+ regularizer_coef*numpy.sum(numpy.abs(candidate_activations))
