我目前正在处理一个全卷积神经网络（输入图像，输出图像），并尝试实现一个损失函数，该函数在对两张图像进行某些操作之前，先对它们进行快速傅里叶变换，代码如下所示
def fourierLoss2(y_actual,y_pred): actual_fft = tf.signal.rfft3d(y_actual) pred_fft = tf.signal.rfft3d(y_pred) lossV=tf.math.real(tf.math.reduce_mean(tf.math.square(actual_fft-pred_fft))) return lossVwith strategy.scope(): model = hd_unet_model(INPUT_SIZE) model.compile(optimizer=Adam(lr=0.1), loss= fourierLoss2, metrics=tf.keras.metrics.MeanSquaredError())两个张量（y_actual, y_pred）的类型为浮点数。但当我尝试训练模型时，遇到了以下错误
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function * return step_function(self, iterator) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/mirrored_strategy.py:585 _call_for_each_replica self._container_strategy(), fn, args, kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/mirrored_run.py:96 call_for_each_replica return _call_for_each_replica(strategy, fn, args, kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/mirrored_run.py:237 _call_for_each_replica coord.join(threads) /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/coordinator.py:389 join six.reraise(*self._exc_info_to_raise) /usr/local/lib/python3.6/dist-packages/six.py:703 reraise raise value /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/coordinator.py:297 stop_on_exception yield /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/mirrored_run.py:323 run self.main_result = self.main_fn(*self.main_args, **self.main_kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step ** outputs = model.train_step(data) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:757 train_step self.trainable_variables) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:2722 _minimize gradients = tape.gradient(loss, trainable_variables) /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/backprop.py:1073 gradient unconnected_gradients=unconnected_gradients) /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/imperative_grad.py:77 imperative_grad compat.as_str(unconnected_gradients.value)) /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/backprop.py:151 _gradient_function grad_fn = ops._gradient_registry.lookup(op_name) # pylint: disable=protected-access /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/registry.py:97 lookup "%s registry has no entry for: %s" % (self._name, name)) LookupError: gradient registry has no entry for: RFFT3D经过一些研究，我了解到问题在于操作 tf.signal.rfft3d 没有注册的梯度函数。有人知道如何解决这个问题吗？
回答：
我找到了解决这个问题的方法，不是使用 tf.signal.rfft3d，而是使用 tf.signal.fft3d，这个函数有梯度函数的条目，并且可以在损失函数中工作，缺点是现在我必须在进行傅里叶变换之前将浮点张量转换为复数类型
