Comments (1)
Hi @lijianyi1997, thanks for taking the time to look at our code. The difference in the unit implementation is in that we only compute the gates across the last dimension and copy it to every weight in the unit using tf.tile
.
variational_dropout weight level implementation: https://github.com/for-ai/TD/blob/master/models/utils/dropouts.py#L377
variational_dropout unit level implementation: https://github.com/for-ai/TD/blob/master/models/utils/dropouts.py#L390
from targeted-dropout.
Related Issues (13)
- why do you consider unit pruning and weight pruning in L1 norm and L2 norm?
- What's the difference between unit dropout and weight dropout?
- Targeted Units Dropout - Why dropout also on test? HOT 1
- Target Dropout not scaled by keep_prob HOT 5
- ResNet-32 architecture doesn't match ResNet paper
- Hello,I want to use targeted dropout in my model, can you provide the code of trageted dropout in pytorch?Expect for your reply。
- How replace dropout using your targeted dropout in my models? HOT 1
- How embed your targeted dropout in other model? HOT 6
- TypeError: bad operand type for unary -: 'TopKV2' HOT 1
- where can I call the function of targeted dropout? HOT 3
- Can your function only be used after training on the network? HOT 2
- ValueError: Please wrap optimizers with TowerOptimizer in order to use replicate_model_fn with multiple `devices` HOT 2
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from targeted-dropout.