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Thanks for the invitation. Let me give the conclusion first: Based on my experience of writing pytorch code for two or three years, a better order is to write model first, then dataset, and finally train. Before discussing the specific order of code components, let's first analyze the purpose and logic behind each component. Do they mean the same? 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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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. 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.
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