Number of epochs and weight updates in deep models Does training any model from scratch require more or less updates compared to fine-tuning a pre-trained model?
For cancer disease classification, I have built a network from scratch, with batch size 20, number of epochs 15, and learning rate 0.001. I have also tried a pre-trained model with fine-tuning. In this case, the batch size is 7, the number of epochs is 10, and the learning rate is 0.0001. Since the batch size of the built model is larger than the batch size of the fine-tuned model, the number of iterations per epoch is smaller, and also the total number of iterations (all epochs) is smaller.
Weight updates occur after each iteration, and thus the number of updates with the built model is less than the number of updates with the fine-tuned model. The classification accuracy of the built model is slightly less than the accuracy of the pre-trained model. Could you please help me interpreting the reasons?

There is no such rule. From an optimization point of view, both are weight initializations. Whether one is better than the other depends on how your data compares to the data that the pre-trained model is trained on.
