def test_gradient(self): val = np.random.random((4, 2)) xth = KTH.variable(val) xtf = KTF.variable(val) expth = xth * KTH.exp(xth) exptf = xtf * KTF.exp(xtf) lossth = KTH.sum(expth) losstf = KTF.sum(exptf) zero_lossth = KTH.stop_gradient(lossth) zero_losstf = KTF.stop_gradient(losstf) gradth = KTH.gradients(lossth, [expth]) gradtf = KTF.gradients(losstf, [exptf]) zero_gradth = KTH.gradients(lossth + zero_lossth, [expth]) zero_gradtf = KTF.gradients(losstf + zero_losstf, [exptf]) zth = KTH.eval(gradth[0]) ztf = KTF.eval(gradtf[0]) zero_zth = KTH.eval(zero_gradth[0]) zero_ztf = KTF.eval(zero_gradtf[0]) assert zth.shape == ztf.shape assert zero_zth.shape == zero_ztf.shape assert_allclose(zth, ztf, atol=1e-05) assert_allclose(zero_zth, zero_ztf, atol=1e-05) assert_allclose(zero_zth, zth, atol=1e-05) assert_allclose(zero_ztf, ztf, atol=1e-05)
def test_gradient(self): val = np.random.random((4, 2)) xth = KTH.variable(val) xtf = KTF.variable(val) expth = xth * KTH.exp(xth) exptf = xtf * KTF.exp(xtf) lossth = KTH.sum(expth) losstf = KTF.sum(exptf) zero_lossth = KTH.stop_gradient(lossth) zero_losstf = KTF.stop_gradient(losstf) gradth = KTH.gradients(lossth, [expth]) gradtf = KTF.gradients(losstf, [exptf]) zero_gradth = KTH.gradients(lossth + zero_lossth, [expth]) zero_gradtf = KTF.gradients(losstf + zero_losstf, [exptf]) zth = KTH.eval(gradth[0]) ztf = KTF.eval(gradtf[0]) zero_zth = KTH.eval(zero_gradth[0]) zero_ztf = KTF.eval(zero_gradtf[0]) assert zth.shape == ztf.shape assert zero_zth.shape == zero_ztf.shape assert_allclose(zth, ztf, atol=1e-05) assert_allclose(zero_zth, zero_ztf, atol=1e-05) assert_allclose(zero_zth, zth, atol=1e-05) assert_allclose(zero_ztf, ztf, atol=1e-05)
def style_loss(style, combination, mask_path=None, nb_channels=None): assert K.ndim(style) == 3 assert K.ndim(combination) == 3 if mask_path is not None: style_mask = K.variable(load_mask(mask_path, nb_channels)) style = style * K.stop_gradient(style_mask) combination = combination * K.stop_gradient(style_mask) del style_mask S = gram_matrix(style) C = gram_matrix(combination) channels = 3 size = img_width * img_height return K.sum(K.square(S - C)) / (4.0 * (channels**2) * (size**2))