def _callback(pi): Y_hat = tespo.exe(pred, [pi, X]) out = {} out['MSE'] = np.mean(Y_hat == y) out['Loss'] = tespo.exe(loss, [pi, X, y]) opt = {'freq': 10} return out, opt
def _callback(pi): Y_hat = tespo.exe(pred, [pi, X]) out = {} out['MSE'] = np.mean(Y_hat==y) out['Loss'] = tespo.exe(loss, [pi, X,y]) opt = {'freq':10} return out, opt
def test_default(self): p1, res = tespo.optimize( fun=loss, p0=p0, jac=grad, callback='default', args=(X, y), method='BFGS', options = {'maxiter': 20, 'disp': 0}, ) loss_0 = tespo.exe(loss, [p0, X, y]) loss_1 = tespo.exe(loss, [p1, X, y]) assert loss_0 > loss_1