ValueError：未知損失函數：custom_loss_function。 請確保將此對象傳遞給 `custom_objects` 參數

Question

我正在嘗試使用此自定義損失函數訓練我的模型： 1

其中 S(pn;ω) 是預測值（y_pred），MOSn 是目標（y_true），所以我是這樣寫的：

import keras.backend as K
def custom_loss_function(y_true,y_pred):
  for i in range(1,n+1):
    l= K.abs(y_pred-y_true)
    l = K.mean(l, axis=-1)  
  return l

然后我建立了我的模型：

#Model definition
from keras import models
from keras import layers
def build_model():
  model = models.Sequential()
  model.add(layers.Conv2D(32, (5, 5), activation='relu', input_shape=(32, 32, 1)))
  model.add(layers.MaxPooling2D((2, 2)))
  model.add(layers.Conv2D(32, (5, 5), activation='relu'))
  model.add(layers.MaxPooling2D((10, 10)))
  model.add(layers.Dense(250, activation='relu'))
  model.add(layers.Dense(250, activation='relu'))
  model.add(layers.Dense(1))
  model.compile(optimizer='rmsprop', loss='custom_loss_function', metrics=['mae'])
  return model
  
model = build_model()

但是當我運行訓練過程時：

num_epochs = 20
history = model.fit(train_data, train_labels, epochs=num_epochs, batch_size=None, verbose=0)

我收到此錯誤：

ValueError                                Traceback (most recent call last)
<ipython-input-16-eb44c7fa4ec7> in <module>()
      1 num_epochs = 20
----> 2 history = model.fit(train_data, train_labels, epochs=num_epochs, batch_size=None, verbose=0)

9 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    992           except Exception as e:  # pylint:disable=broad-except
    993             if hasattr(e, "ag_error_metadata"):
--> 994               raise e.ag_error_metadata.to_exception(e)
    995             else:
    996               raise

ValueError: in user code:

    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:853 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:842 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:835 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:789 train_step
        y, y_pred, sample_weight, regularization_losses=self.losses)
    /usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:184 __call__
        self.build(y_pred)
    /usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:133 build
        self._losses = tf.nest.map_structure(self._get_loss_object, self._losses)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py:869 map_structure
        structure[0], [func(*x) for x in entries],
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py:869 <listcomp>
        structure[0], [func(*x) for x in entries],
    /usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:273 _get_loss_object
        loss = losses_mod.get(loss)
    /usr/local/lib/python3.7/dist-packages/keras/losses.py:2136 get
        return deserialize(identifier)
    /usr/local/lib/python3.7/dist-packages/keras/losses.py:2095 deserialize
        printable_module_name='loss function')
    /usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py:709 deserialize_keras_object
        .format(printable_module_name, object_name))

    ValueError: Unknown loss function: custom_loss_function. Please ensure this object is passed to the `custom_objects` argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.

我閱讀了有關“自定義對象”參數的詳細信息並嘗試應用它，但仍然無法弄清楚，我究竟如何將我的自定義函數傳遞給“自定義對象”參數？

Answer 1

在 python 中，您可以將函數作為自包含對象或Callable傳遞。 在這種情況下，您可以傳遞不帶單引號的參數loss=custom_loss_function 。 這是有關Tensorflow 中可調用對象和自定義損失函數的更多信息。