iGAN_predict.py 文件源码

def invert_bfgs(gen_model, invert_model, ftr_model, im, z_predict=None, npx=64):
    _f, z = invert_model
    nz = gen_model.nz
    if z_predict is None:
        z_predict = np_rng.uniform(-1., 1., size=(1, nz))
    else:
        z_predict = floatX(z_predict)
    z_predict = np.arctanh(z_predict)
    im_t = gen_model.transform(im)
    ftr = ftr_model(im_t)

    prob = optimize.minimize(f_bfgs, z_predict, args=(_f, im_t, ftr),
                             tol=1e-6, jac=True, method='L-BFGS-B', options={'maxiter':200})
    print('n_iters = %3d, f = %.3f' % (prob.nit, prob.fun))
    z_opt = prob.x
    z_opt_n = floatX(z_opt[np.newaxis, :])
    [f_opt, g, gx] = _f(z_opt_n, im_t, ftr)
    gx = gen_model.inverse_transform(gx, npx=npx)
    z_opt = np.tanh(z_opt)
    return gx, z_opt,f_opt