def superloss_10(y_true, y_pred):
loss_sobel = sobelLoss_mse(y_true, y_pred)
dssim = DSSIMObjective(kernel_size=5)
loss_dssim = dssim(y_true, y_pred)
loss_mae = keras.losses.mae(y_true, y_pred)
loss_mse = keras.losses.mse(y_true, y_pred)
return 0.3 * loss_sobel + 0.3 * loss_dssim + 0.2 * loss_mse + 0.2 * loss_mae
def superloss_9(y_true, y_pred):
loss_sobel = sobelLoss_mse(y_true, y_pred)
dssim = DSSIMObjective()
loss_dssim = dssim(y_true, y_pred)
loss_mae = keras.losses.mae(y_true, y_pred)
loss_mse = keras.losses.mse(y_true, y_pred)
return 0.4 * loss_sobel + 0.4 * loss_dssim + 0.1 * loss_mse + 0.1 * loss_mae
def superloss_8(y_true, y_pred):
dssim = DSSIMObjective()
dstsim = DSTSIMObjective()
loss_mae = keras.losses.mae(y_true, y_pred)
loss_mse = keras.losses.mse(y_true, y_pred)
loss_dssim = dssim(y_true, y_pred)
loss_dstsim = dstsim(y_true, y_pred)
#loss_dstsim = K.print_tensor(loss_dstsim, message='loss_stsim')
#loss_dssim = K.print_tensor(loss_dssim, message='loss_ssim')
return 0.4 * loss_mse + 0.4 * loss_dstsim + 0.2 * loss_mae
def superloss_7(y_true, y_pred):
dssim = DSSIMObjective()
loss_mae = keras.losses.mae(y_true, y_pred)
loss_mse = keras.losses.mse(y_true, y_pred)
loss_dssim = dssim(y_true, y_pred)
return 0.5 * loss_dssim + 0.3 * loss_mae + 0.2 * loss_mse
def superloss_6(y_true, y_pred):
dssim = DSSIMObjective(kernel_size=5)
loss_mae = keras.losses.mae(y_true, y_pred)
loss_mse = keras.losses.mse(y_true, y_pred)
loss_dssim = dssim(y_true, y_pred)
return 0.4 * loss_dssim + 0.5 * loss_mae + 0.1 * loss_mse