def _checkGradient(self, dataset=None, silent=False):
        if dataset:
            self.setData(dataset)
        res = []
        for seq in self.ds._provideSequences():
            self.module.resetDerivatives()
            self._calcDerivs(seq)
            e = 1e-6
            analyticalDerivs = self.module.derivs.copy()
            numericalDerivs = []
            for p in range(self.module.paramdim):
                storedoldval = self.module.params[p]
                self.module.params[p] += e
                righterror, dummy = self._calcDerivs(seq)
                self.module.params[p] -= 2 * e
                lefterror, dummy = self._calcDerivs(seq)
                approxderiv = (righterror - lefterror) / (2 * e)
                self.module.params[p] = storedoldval
                numericalDerivs.append(approxderiv)
            r = list(zip(analyticalDerivs, numericalDerivs))
            res.append(r)
            if not silent:
                print(r)
        return res
Numeric check of the computed gradient for debugging purposes.
dataset/ETHPy150Open pybrain/pybrain/pybrain/supervised/trainers/backprop.py/BackpropTrainer._checkGradient