Model.py 文件源码

def finite_diff_gradients(self, f, delta=1e-6):
        """
        f is called without parameters, the changes in the parameters happen as a side effect
        """
        gradients = dict()
        fx = f()
        for p in self.parameters_to_optimise:
            original = self.parameters[p].get_value()
            grad = np.zeros_like(original)
            if np.prod(original.shape) > 1:
                for index, _ in np.ndenumerate(original):
                    xh = original.copy()
                    xh[index] += delta
                    self.parameters[p].set_value(xh)
                    grad[index] = (f() - fx) / delta
                    self.parameters[p].set_value(original)
            else:
                xh = original.copy()
                xh += delta
                self.parameters[p].set_value(xh)
                grad = (f() - fx) / delta
                self.parameters[p].set_value(original)
            gradients[p] = grad
        return gradients