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License: The Unlicense
Keras-like APIs for JAX framework
License: The Unlicense
Description
Add regularization layers such as Inverted Dropout and Batch Norm
Description
Implement JAX vmap structure for batching
Solution
Use JAX vmap for batched gradient descent implementation.
Additional context
Refer to https://jax.readthedocs.io/en/latest/index.html
Description
Implement various weight initialization schemes
Solution
Use jax.nn module for implementation notes.
Description
PyTorch support inefficient. Takes too much time.
Describe the solution you'd like
Reimplement the library in JAX
Additional context
Refer to https://github.com/google/jax
Description
Update README and add experiments.
Solution
Create experiments or examples package to work
Description
Constant accuracy and large losses while using CNN APIs
Expected behaviour
Accuracy should increase while loss should decrease
Description
Softmax classification algorithm
Description
Add optimization algorithms
Solution
Use JAX experimental module
Description of feature request
Multiclass / Multinomial classification problems not supported.
Solution proposed
Implement softmax function
Additional context
Refer to docs.fast.ai, PyTorch and Keras documentation for implementation details
Description
Implement a databunch-like API for loading image data.
Solution
Use PyTorch and TensorFlow data loading libraries
Additional context
Refer to jax.readthedocs.io for more info.
Description
JAX imports displays NotImplementedError
Expected behavior
To use JAX-numpy in accordance with the original NumPy package.
Proposed Solution
Replace JAX computation engine with PyTorch package.
Is your feature request related to a problem? Please describe.
After training large models, we shall be able to save and reload them for further experiments.
Describe the solution you'd like
Models shall be able to save (serialize) and load (deserialize) items as follows :
Additional context
Refer to flax serialization logic for more info. It involves serializing and deserializing JAX PyTrees to MessagePack format.
Description
Stax-like layers API refactoring
Solution
Write every layer with apply_fun and init_fun
Feature request description
Regression algorithms missing.
Solution proposed
Implement loss functions like MSE, RMSE, etc.
Additional context
Refer to docs.fast.ai, PyTorch and Keras docs for implementation
Description
Refactor the forward prop code to accommodate different layers in the future.
Solution
Write forward prop code in layers API
Additional context
Refer to https://jax.readthedocs.io/en/latest/index.html
Bug description
APIs too congested in dnet.py script
Additional context
Needs API cleanup from ground up
Description
Implement CNN layers and relevant APIs
Solution
Use LAX APIs
Additional context
Refer to jax.readthedocs.io for more info.
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