def normal_loss_1 (y_true, y_pred):
    y_true_flat = K.flatten(y_true)
    y_pred_flat = K.flatten(y_pred)
    #d = y_pred_flat * y_true_flat
    d = np.dot(y_pred_flat,y_true_flat)
    y_sum = K.sum(d)
    n_pixels = 256 * 256
    normal_output = y_sum/n_pixels
    normal_output = tf.reduce_mean(normal_output)
    return -normal_output