Description: The output tensor is the final result produced by a machine learning model after processing the input data. In the context of machine learning frameworks, a tensor is a data structure that can be seen as a generalization of matrices and vectors, allowing for the representation of data in multiple dimensions. The output tensor can have different shapes and dimensions depending on the type of task the model is performing, such as classification, regression, or data generation. For example, in an image classification model, the output tensor could be a vector containing the probabilities that the image belongs to each of the possible classes. The interpretation of this tensor is crucial, as it provides the necessary information to make decisions based on the model’s results. Additionally, the output tensor is fundamental for evaluating the model’s performance, as it is compared with the actual labels to calculate metrics such as accuracy or loss. In summary, the output tensor is an essential component in the machine learning workflow, as it represents the culmination of data processing and model inference.
