我使用 TensorFlow Keras 自定义损失（使用额外的输入数据计算损失）的代码如下：
inp = Input(shape=(inp_seq_len,feature_size))w = Input(shape=(1))# 代码已省略def custom_loss(w): def loss(y_true,y_pred): return -y_true*y_pred*w return lossmodel = Model( inputs=[inp, w], outputs=[pred],)model.compile(loss=custom_loss(w), optimizer='adam')在执行 model.fit() 时，我遇到了错误：无法将 Keras 符号张量作为急切执行函数的输入，但发现了：tf.Tensor ‘input_19:0 shape=[None,1], dtype=float32
根据 https://github.com/tensorflow/tensorflow/issues/34944 的建议，我执行了：tf.config.experimental_run_functions_eagerly(True)
然后我得到了错误：无法将符号张量（truediv_20:0）转换为 numpy 数组
请注意，我按照 无法将 Keras 符号张量作为急切执行函数的输入 更改了我的代码。
我的原始代码是（从一个 inpA 中解包 inp 和 w）：
inpA = Input(shape=(inp_seq_len,feature_size+1))inp = Lambda(lambda x: x[:,:,:feature_size])(inpA)w = Flatten()(Lambda(lambda x: x[:,-1,feature_size])(inpA))def custom_loss(w): def loss(y_true,y_pred): return -y_true*y_pred*w return lossmodel = Model( inputs=[inpA], outputs=[pred],)model.compile(loss=custom_loss(w), optimizer='adam')无论是否使用 tf.config.experimental_run_functions_eagerly(True)，我得到的错误与上面相同。
我该怎么办？
谢谢。
更新：使用下方帖子的代码时，遇到了以下错误：
WARNING:tensorflow:Output dense_3 missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to dense_3.---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-5-1f23bd570780> in <module>() 19 m.add_loss( mse( true, out, sample_weight ) ) 20 m.compile(loss=None, optimizer='adam')---> 21 history = m.fit([X, y, W], y, epochs=10) 22 23 # final fitted model to compute predictions/nfs/anaconda2/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training.pyc in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs) 817 max_queue_size=max_queue_size, 818 workers=workers,--> 819 use_multiprocessing=use_multiprocessing) 820 821 def evaluate(self,/nfs/anaconda2/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.pyc in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs) 233 max_queue_size=max_queue_size, 234 workers=workers,--> 235 use_multiprocessing=use_multiprocessing) 236 237 total_samples = _get_total_number_of_samples(training_data_adapter)/nfs/anaconda2/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.pyc in _process_training_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, steps_per_epoch, validation_split, validation_data, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing) 591 max_queue_size=max_queue_size, 592 workers=workers,--> 593 use_multiprocessing=use_multiprocessing) 594 val_adapter = None 595 if validation_data:/nfs/anaconda2/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.pyc in _process_inputs(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing) 644 standardize_function = None 645 x, y, sample_weights = standardize(--> 646 x, y, sample_weight=sample_weights) 647 elif adapter_cls is data_adapter.ListsOfScalarsDataAdapter: 648 standardize_function = standardize/nfs/anaconda2/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training.pyc in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset) 2381 is_dataset=is_dataset, 2382 class_weight=class_weight,-> 2383 batch_size=batch_size) 2384 2385 def _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs,/nfs/anaconda2/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training.pyc in _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs, is_dataset, class_weight, batch_size) 2467 shapes=None, 2468 check_batch_axis=False, # Don't enforce the batch size.-> 2469 exception_prefix='target') 2470 2471 # Generate sample-wise weight values given the `sample_weight` and/nfs/anaconda2/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_utils.pyc in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix) 494 raise ValueError( 495 'Error when checking model ' + exception_prefix + ': '--> 496 'expected no data, but got:', data) 497 return [] 498 if data is None:ValueError: ('Error when checking model target: expected no data, but got:', array([3.39102071e-01, 1.23122638e-01, 7.54209531e-01, 8.10110230e-01,回答：
这是一个向自定义损失函数传递额外参数的解决方法，在你的案例中是一个权重数组。诀窍在于使用伪输入，这些输入有助于正确构建和使用损失函数。请记住，Keras 处理的是固定批次维度
我提供了一个回归问题的虚拟示例。这可以根据你的需求轻松修改
def mse(y_true, y_pred, sample_weight): error = y_true-y_pred return K.mean(K.sqrt(error)*sample_weight)X = np.random.uniform(0,1, (1000,10))y = np.random.uniform(0,1, 1000)W = np.random.uniform(1,2, 1000)inp = Input((10,))true = Input((1,))sample_weight = Input((1,))x = Dense(32, activation='relu')(inp)out = Dense(1)(x)m = Model([inp,true, sample_weight], out)m.add_loss( mse( true, out, sample_weight ) )m.compile(loss=None, optimizer='adam')history = m.fit([X, y, W], y=None, epochs=10)# 最终拟合模型用于计算预测final_m = Model(inp, out)