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The trained model will be very large. For example, the original 2g model will be more than 4g after full training. Of course, this is the size after deleting checkpoints. For example, quantizing parameters or compressing the model can be used to reduce the size of the model. 1.2. Fine-tuning the model in the pre-training model. Model.train () and model.eval () We know that in pytorch, the model has two modes that can be set, one is the train mode and the other is the eval mode. The role of model.train() is to enable batch normalization and dropout. in train. Train on meaning to aim is a completely different meaning of train, and there should be no overlap between this meaning and the meaning of teach in the original sentence.
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Train loss is the loss on the training data, which measures the model's ability to fit on the training set. Val loss is the loss on the verification set, which measures the fitting ability on unseen data, which can also be said to be the generalization ability. The real effect of the model.
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For more details and authoritative references, refer to the official documentation on Wikipedia.
