The nn module in MindSpore provides essential tools for building neural networks, including:
C . Optimizers: such as Momentum and Adam, which are used to adjust the weights of the model during training.
D . Loss functions: such as MSELoss (Mean Squared Error Loss) and SoftmaxCrossEntropyWithLogits, which are used to compute the difference between predicted and actual values.
The other options are incorrect because:
A . Hyperparameter search modes (like GridSearch and RandomSearch) are typically found in model training and tuning modules, but not in the nn module.
B . Model evaluation indicators like F1 Score and AUC are also handled by specific evaluation functions or libraries outside the nn module.
HCIA AI
Reference:
AI Development Framework: Detailed coverage of MindSpore's nn module, its optimizers, and loss functions.
Introduction to Huawei AI Platforms: Explains various MindSpore features, including network construction and training.