Comments (1)
Congratulations on the launch! In general our philosophy with Haiku is to keep the library fairly minimal and focussed just on providing NN modules (with an API that is quite familiar to Sonnet users) and making it really easy for you to build your own modules.
I think it's really great that frameworks like Elegy can make use of Haiku and provide additional functionality that many users want (e.g. losses, training loops, evaluation etc).
from dm-haiku.
Related Issues (20)
- More fine-grained mixed-precision configuration HOT 2
- Suggestion: alias `Transformed`(WithState) apply to __call__ HOT 2
- Is there a way to load parameters from Flax model? HOT 2
- Support model examples HOT 7
- Change to jax.interpreters.xla for JAX==0.4.14 HOT 3
- Warning: hk.LayerNorm when used in transformer decoder causes violation of autoregressive property HOT 1
- Reservoir Computing with Haiku
- Efficiency difference in using jax.lax.fori_loop vs looping over identical layers? HOT 2
- Please publish requirements.txt fix to pip
- How to use `apply` with additional parameters? HOT 1
- hk.Conv2DTranspose takes FOREVER to initialize and compile HOT 1
- 0.4.16 timeline HOT 2
- How to export haiku network parameters into Pytorch network?
- Modules got silently "reused" with `hk.vmap` HOT 2
- Wrong gradients in a Haiku network
- Direct Feedback Alignment
- Issue with wheels including docs and examples folder
- `haiku.experimental.flax` is not part of newest pip release HOT 1
- Train multiple hk.nets.MLP with one optimizer HOT 2
- TypeError: 'type' object is not subscriptable HOT 4
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from dm-haiku.