from torch import optim
时间: 2023-09-24 16:14:29 浏览: 39
from torch package provides various optimization algorithms that can be used to optimize neural network weights during training. Some of the popular optimization algorithms provided by the torch.optim package are:
1. SGD (Stochastic Gradient Descent): This is a basic optimization algorithm that updates the weights based on the gradient of the loss function with respect to the weights.
2. Adam: This is an adaptive optimization algorithm that uses a combination of gradient information and moving averages of the parameters to update the weights.
3. Adagrad: This is an adaptive optimization algorithm that adapts the learning rate of each weight based on the historical gradients for that weight.
4. RMSprop: This is an adaptive optimization algorithm that uses a moving average of the squared gradients to adapt the learning rate for each weight.
5. AdamW: This is an extension of the Adam optimizer that adds weight decay regularization to the optimization process.
These optimization algorithms can be used with different learning rate schedules, weight decay values, and other hyperparameters to fine-tune the training process for a specific neural network architecture and dataset.