Description: The term ‘epochs per step’ in the context of machine learning frameworks, such as TensorFlow, refers to the number of complete training cycles executed in each training step. In machine learning, an ‘epoch’ represents a full iteration over the training dataset, where the model adjusts its parameters based on the errors made. By setting a specific number of epochs per step, developers can control how frequently the model’s weights are updated, which can influence convergence and model performance. This approach allows for greater flexibility in training, as multiple parameter updates can be performed before completing a full epoch. This is particularly useful in scenarios where the dataset is large or when optimizing training time is desired. The choice of the number of epochs per step is crucial, as a value that is too low may result in inefficient training, while a value that is too high may lead to overfitting. Therefore, finding an appropriate balance is essential to maximize model performance without compromising its generalization ability.
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