def error(self, net, input): layer = net.layers[0] winner_output = np.zeros_like(input) output = net.sim(input) winners = np.argmax(output, axis=1) e = layer.np["w"][winners] - input return net.errorf(e)
def deriv(self, x, y): """ Derivative of transfer function SatLin """ d = np.zeros_like(x) d[(x > self.out_min) & (x < self.out_max) ] = 1 return d
def deriv(self, x, y): """ Derivative of transfer function SatLins """ d = np.zeros_like(x) d[(x > -1) & (x < 1) ] = 1 return d
def __call__(self, dist): r = np.zeros_like(dist) min = np.argmin(dist) r[min] = 1.0 return r
def deriv(self, x, y): """ Derivative of transfer function HardLims """ return np.zeros_like(x)