如何计算the loss in a default
时间: 2024-04-05 09:30:17 浏览: 8
在信用风险管理中,如果借款人无法按时偿还借款,则会发生违约(default)。违约会导致债权人遭受损失,这个损失被称为“违约损失”(loss in a default)。
通常情况下,计算违约损失的公式如下:
违约损失 = 违约后的债权本金 - 违约后的债权实际收回金额
其中,违约后的债权本金是指违约时债权人拥有的债权本金,违约后的债权实际收回金额是指债权人实际收回的款项。如果违约后的债权实际收回金额小于违约后的债权本金,则违约损失为正数。如果实际收回金额大于或等于债权本金,则违约损失为零或负数。
相关问题
`tape` is required when a `Tensor` loss is passed.
This error message usually occurs when you are using TensorFlow's automatic differentiation to compute gradients for backpropagation, and you have passed a tensor as the loss argument to the `tf.GradientTape()` context manager. The `tf.GradientTape()` method requires a tensor to be watched in order to compute gradients, and the tensor you pass as the loss argument is not automatically watched by default.
To fix this error, you can explicitly tell the tape to watch the tensor by calling the `tape.watch()` method on the tensor before using it as the loss argument.
For example:
```
import tensorflow as tf
x = tf.constant(3.0)
y = tf.constant(4.0)
with tf.GradientTape() as tape:
tape.watch(x)
loss = x * y
grad = tape.gradient(loss, x)
print(grad.numpy()) # Output: 4.0
```
Here, we explicitly tell the tape to watch the `x` tensor by calling `tape.watch(x)`, so that it can compute the gradient of the loss with respect to `x`.
def compute_loss(self, model, inputs, return_outputs=False): """ How the loss is computed by Trainer. By default, all models return the loss in the first element. Subclass and override for custom behavior. """ if self.label_smoother is not None and "labels" in inputs: labels = inputs.pop("labels") else: labels = None outputs = model(**inputs) # Save past state if it exists # TODO: this needs to be fixed and made cleaner later. if self.args.past_index >= 0: self._past = outputs[self.args.past_index] if labels is not None: if unwrap_model(model)._get_name() in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values(): loss = self.label_smoother(outputs, labels, shift_labels=True) else: loss = self.label_smoother(outputs, labels) else: if isinstance(outputs, dict) and "loss" not in outputs: raise ValueError( "The model did not return a loss from the inputs, only the following keys: " f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}." ) # We don't use .loss here since the model may return tuples instead of ModelOutput. loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0] return (loss, outputs) if return_outputs else loss详细解释一下上面的代码
这段代码是在Trainer类中的compute_loss方法中实现的。该方法用于计算模型的损失,其默认行为是从模型的输出中获取第一个元素作为损失值。在这段代码中,首先判断是否使用了标签平滑器(label_smoother),如果使用了则将标签从输入中弹出,否则将标签置为None。接着,调用模型的forward方法获取输出,如果模型具有过去的状态,则将其保存在Trainer对象中。如果标签不为None,则使用标签平滑器计算损失值。最后,返回损失值和输出,或者仅返回损失值,具体取决于return_outputs参数的值。如果模型返回的是一个字典,并且其中不包含"loss"键,则会抛出ValueError异常。