问在我的梯度检查实现中，这些梯度差是可接受的吗？

EN

Epoch: 0
Cost: 2.8568426944476157
Numerical Grad           Computed Grad
-5.713070134419862e-11   -6.616929226765933e-11
-5.979710331310053e-11   -6.94999613415348e-11
-5.87722383797037e-11    -6.816769371198461e-11
-5.948114792212038e-11   -6.905587213168474e-11
-5.756886551189494e-11   -6.683542608243442e-11
-5.995452767971952e-11   -6.94999613415348e-11
-5.772401095738584e-11   -6.705747068735946e-11
-5.5480026579651e-11     -6.439293542825908e-11
-5.8138150324971285e-11  -6.727951529228449e-11
-5.76037967235867e-11    -6.683542608243442e-11

def gradient_check(self, layer):
    # get grads from layer
    grads = layer.backward_cache['dW']
    # flatten layer W
    shape = layer.W.shape
    W_flat = layer.W.flatten()

    epsilon = 0.001

    print('Numerical Grad', 'Computed Grad')
    # loop through first few W's
    for i in range(0, 10):
        W_initial = W_flat[i]
        W_plus = W_initial + epsilon
        W_minus = W_initial - epsilon

        W_flat[i] = W_plus
        layer.W = W_flat.reshape(shape)
        cost_plus = self.compute_cost(self.forward_propogate())

        W_flat[i] = W_minus
        layer.W = W_flat.reshape(shape)
        cost_minus = self.compute_cost(self.forward_propogate())

        computed_grad = (cost_plus - cost_minus) / (2 * epsilon)

        print(grads.flatten()[i], computed_grad)

        # reset layers W's
        W_flat[i] = W_initial
        layer.W = W_flat.reshape(shape)

    return layer