How can a pytorch CNN classifier training process be lifted from a local minimum?

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There are several methods that can be used in PyTorch to lift a CNN classifier training process from a local minimum:

1. Change the learning rate: Adjusting the step size (learning rate) can help move the optimizer out of a local minimum and towards a better solution.

2. Regularization: Incorporating regularizers into the loss function such as L1 or L2 regularization, dropout or early stopping can prevent the optimizer from overfitting and getting stuck in a local minimum.

3. Modify the optimizer: Alternating the optimizer or changing the hyperparameters of the optimizer can sometimes help to get out of a local minimum.

4. Increase the batch size: Using a larger batch size can sometimes help to improve the generalization of the model and escape local minimum.

5. Use a different network architecture: Using a different network architecture such as altering the number of layers, increasing the number of filters, or adding more units to a particular layer can also help to get out of a local minimum.