Will Tensorflow introduce pytorch feature of changing parameters while training??
Hi @Pratyush_Singh Welcome to The Tensorflow Forum ,
There isn’t currently a direct equivalent in TensorFlow to PyTorch’s functionality of directly modifying parameters during training. However, TensorFlow offers several approaches to achieve similar effects:
Trainable Flag: You can control whether a layer’s weights are updated during training using the trainable attribute. Setting it to False for a layer prevents its weights from being adjusted. This allows you to focus training on specific parts of the model.
Learning Rate Schedulers: TensorFlow provides learning rate schedulers that adjust the learning rate during training. This can indirectly influence how parameters change. For example, you can decrease the learning rate as training progresses to allow for finer adjustments and prevent overfitting.
Custom Training Loops: In TensorFlow’s eager execution mode, you can write custom training loops with more control. This allows you to access and potentially modify parameters directly within the loop. However, this approach requires careful implementation to ensure proper training behavior.
Callbacks: TensorFlow offers callbacks that execute at specific points during training. You can leverage callbacks to update hyperparameters or perform actions based on training progress, indirectly influencing parameter changes.
While TensorFlow doesn’t offer direct in-training parameter modification, these methods provide flexibility to achieve similar results. Consider the specific goal you’re trying to achieve and choose the approach that best suits your needs.
Thank You !