Comments (1)
Final lr is approximately after 1/ gamma update steps have occurred. At this point, the clipping bounds are somewhat tight and cause the actual lr to fall close to the final lr after clipping.
In the initial updates though, the LR bounds are in the range of the initial lr so it allows for Adam type updates.
This means that if you use this optimizer on dataset for a task that SGD can't do well on (but Adam can), then this optimizer will get worse results than Adam alone. At least that's what I've experienced on Language modelling tasks.
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Related Issues (10)
- Make a PR to the main keras repo? HOT 4
- Unclear how to import and use tf.keras version
- Unexpected keyword argument passed to optimizer: amsbound
- Using SGDM with lr=0.1 leads to not learning HOT 10
- clip by value HOT 2
- AdaBound.iterations HOT 10
- any explanation of final_lr ? HOT 3
- suggestion: allow to train x2 or x3 bigger networks on same vram with TF backend HOT 13
- Can't set attribute HOT 5
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