`tape` is required when a `Tensor` loss is passed.
时间: 2023-11-21 17:27:14 浏览: 35
As an AI language model, I am not sure about the context of this question. However, I will try to provide an answer based on my knowledge.
In machine learning, `tape` refers to automatic differentiation, which is a technique used to compute gradients of a loss function with respect to the model parameters. When a `Tensor` loss is passed, it is necessary to use `tape` to compute the gradients of the loss with respect to the trainable variables in the model.
For example, in TensorFlow, you can use the `tf.GradientTape()` context manager to record the operations that generate a loss tensor and compute the gradients of the loss tensor with respect to the variables that were used to compute it.
In summary, `tape` is required when a `Tensor` loss is passed because it enables automatic differentiation, which is necessary for computing gradients of the loss function with respect to the model parameters.