我在编写损失函数时，假设损失是针对双精度浮点数计算的，而不是针对张量。这里是我的函数：

def prediction_loss(a,b):    IGNORE=.0025    EPSILON=.00001      if IGNORE > abs(a) and IGNORE > abs(b) and np.sign(a)==np.sign(b):        return 0    scale=min(abs(a),abs(b))    distance=abs(a-b)    if abs(scale)<EPSILON:        scale=max(abs(a),abs(b))        if abs(scale)<EPSILON:            scale=1            distance**=2    return min(distance,distance/scale)

当我在model.compile中使用它时，我得到了以下错误：

OperatorNotAllowedInGraphError            Traceback (most recent call last)<ipython-input-44-92af3f50a682> in <module>()      9     keras.layers.Dense(1)     10 ])---> 11 model.compile(loss=prediction_loss, optimizer=keras.optimizers.SGD(lr=0.001, momentum=0.9, nesterov=True))~/anaconda3/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)    455     self._self_setattr_tracking = False  # pylint: disable=protected-access    456     try:--> 457       result = method(self, *args, **kwargs)    458     finally:    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access~/anaconda3/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, distribute, **kwargs)    371     372       # Creates the model loss and weighted metrics sub-graphs.--> 373       self._compile_weights_loss_and_weighted_metrics()    374     375       # Functions for train, test and predict will~/anaconda3/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)    455     self._self_setattr_tracking = False  # pylint: disable=protected-access    456     try:--> 457       result = method(self, *args, **kwargs)    458     finally:    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access~/anaconda3/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in _compile_weights_loss_and_weighted_metrics(self, sample_weights)   1651       #                   loss_weight_2 * output_2_loss_fn(...) +   1652       #                   layer losses.-> 1653       self.total_loss = self._prepare_total_loss(masks)   1654    1655   def _prepare_skip_target_masks(self):~/anaconda3/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in _prepare_total_loss(self, masks)   1711    1712           if hasattr(loss_fn, 'reduction'):-> 1713             per_sample_losses = loss_fn.call(y_true, y_pred)   1714             weighted_losses = losses_utils.compute_weighted_loss(   1715                 per_sample_losses,~/anaconda3/lib/python3.6/site-packages/tensorflow_core/python/keras/losses.py in call(self, y_true, y_pred)    219       y_pred, y_true = tf_losses_util.squeeze_or_expand_dimensions(    220           y_pred, y_true)--> 221     return self.fn(y_true, y_pred, **self._fn_kwargs)    222     223   def get_config(self):<ipython-input-43-4630edd6290a> in prediction_loss(a, b)     14     EPSILON=.00001     15 ---> 16     if IGNORE > abs(a) and IGNORE > abs(b) and np.sign(a)==np.sign(b):     17         return 0     18 ~/anaconda3/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py in __bool__(self)    763       `TypeError`.    764     """--> 765     self._disallow_bool_casting()    766     767   def __nonzero__(self):~/anaconda3/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py in _disallow_bool_casting(self)    532     else:    533       # Default: V1-style Graph execution.--> 534       self._disallow_in_graph_mode("using a `tf.Tensor` as a Python `bool`")    535     536   def _disallow_iteration(self):~/anaconda3/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py in _disallow_in_graph_mode(self, task)    521     raise errors.OperatorNotAllowedInGraphError(    522         "{} is not allowed in Graph execution. Use Eager execution or decorate"--> 523         " this function with @tf.function.".format(task))    524     525   def _disallow_bool_casting(self):OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed in Graph execution. Use Eager execution or decorate this function with @tf.function.

显然，TensorFlow将tf.Tensor作为参数a和b传递，而这些参数不能用于逻辑操作。我应该如何修改函数以使其正常工作？我希望忽略大小较小且符号相同的a和b。

回答：

是的，tf.tensor不能使用Python的bool类型。请使用keras.backend.switch()来处理条件语句。

请参考以下链接了解使用方法：

https://keras.io/backend/

该链接列出了所有可以用于适应您方程的函数，如greater, greater_than, equal等。

使用Keras后端功能修改您的语句“if IGNORE > abs(a) and IGNORE > abs(b) and np.sign(a)==np.sign(b):”，这应该可以解决您的问题。