What are some effective pruning techniques in TensorFlow.js?
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What are some effective pruning techniques in TensorFlow.js?
Pruning in TensorFlow.js involves removing unnecessary weights from a model to reduce its size and improve performance. Techniques include weight pruning, structured pruning, and dynamic pruning.
Can you explain weight pruning in more detail?
Weight pruning focuses on removing individual weights that contribute least to the model's predictions. This can be done by setting a threshold and zeroing out weights below that threshold.
How does structured pruning differ from weight pruning?
Structured pruning removes entire neurons or filters instead of individual weights, which can lead to more efficient models while maintaining performance.
Are there any tools in TensorFlow.js to help with pruning?
Yes, TensorFlow.js provides APIs for model optimization, including pruning. You can use the tf.prune method to apply pruning techniques to your models.
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