自定义损失问题：急切执行函数的输入不能是 keras 符号张量但发现

Question

我使用tensorflow keras自定义损失的代码（使用额外的输入数据计算损失）如下：

inp = Input(shape=(inp_seq_len,feature_size))
w = Input(shape=(1))
# code ommitted

def custom_loss(w):
    def loss(y_true,y_pred):
        return -y_true*y_pred*w
    return loss

model = Model(
  inputs=[inp, w],
  outputs=[pred],
)
model.compile(loss=custom_loss(w), optimizer='adam')

在执行 model.fit() 时，出现错误： 急切执行函数的输入不能是 Keras 符号张量，而是找到：tf.Tensor 'input_19:0 shape=[None,1], dtype=float32

在https://github.com/tensorflow/tensorflow/issues/34944 之后，我做了： tf.config.experimental_run_functions_eagerly(True)

然后我得到错误： 无法将符号张量 (truediv_20:0) 转换为 numpy 数组

请注意，我已关注Inputs to eager execution function cannot be Keras symbolic tensors 更改我的代码。

我的原始代码是（从一个 inpA 中解压 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 loss

model = Model(
  inputs=[inpA],
  outputs=[pred],
)
model.compile(loss=custom_loss(w), optimizer='adam')

有/没有我得到的错误 tf.config.experimental_run_functions_eagerly（真） 同上。

我能做什么？

谢谢。

更新：使用以下帖子中的代码时出现此错误：

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, shuffle, distribution_strategy, 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,

Answer 1

这是一种将附加参数传递给自定义损失函数的解决方法，在您的情况下是权重数组。诀窍在于使用虚假输入，这些输入有助于以正确的方式构建和使用损失。不要忘记 keras 处理固定的批量维度

我在回归问题中提供了一个虚拟示例。这可以根据您的需要轻松修改

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)

# final fitted model to compute predictions
final_m = Model(inp, out)