We have released Neural Network Libraries v1.31.0!
nnabla-rl v0.12.0, nnabla-nas v0.13.0 and nnabla-browser v0.2.0 are also released!
nnabla-rl v0.12.0 is released!
nnabla-rl v0.12.0 is released! In v0.12.0, we have added latest deep RL algorithms such as QRSAC and REDQ! We have also added optimal control algorithms iLQR and MPPI in this new release. From v0.12.0, visualization of training graphs and training curves with nnabla-browser is also supported.
Try nnabla-rl with:
$ pip install nnabla-rl
Check also the release note of nnabla-rl for details.
[XAI]Attention Branch Network (ABN) example
We have implemented colab interactive demo for Attention Branch Network, which extends a response-based visual explanation and accuracy improvement. It is realized by applying the focus area obtained from explanation to Attention mechanism.
|Attention Branch Network||Visualization|
Add tutorials for fairness in machine learning
We have added two colab demos to our fairness tutorial for bias mitigation algorithm. Prejudice Remover Regularizer mitigates bias on training, and Rejection Option-based classification mitigates bias on post-processing.
|Prejudice Remover Regularizer for Images||Model Bias Check and Mitigation|
|Rejection Option-based Classification for image||Bias mitigation|
Improve Diffusion Probabilistic Model (DPM) example
We have added faster sampling methods and conditional generation methods in Diffusion Model exmample. Please see the PRs below for more details.
– ODE-based sampler for faster inference
– class conditional and Cascaded DPM
- implement drop_last option for slice
- fix numpy indice error due to numpy version update
- add code to allow unlink variable
- Support compile nnabla-ext-cuda with cuTENSOR in non-docker environment
- Correct macro error in convolution.c
- add tensorboard version dependency of file format converter
- build android target with dynamic link
- Sync api level version from NNabla.
- Sync api_level version from NNabla for TopK
- Add TopK support to ONNX importer
- Add GlobalMaxPool, RandomNormal, RandomUniform support to ONNX importer
- Fix issues in ONNX importer
- restore the CL-initializer in legacy_nnp_graph.py to graph_def.py
- Fix algos. Properly apply grad clip and weight decay
- Correct variable to use during rnn training
- Check np_random instance and use correct randint alternative
- Fix pendulum-env render
- Fix ScreenRenderEnv to support gym 0.25.0
- Run PPO on single process when actor num is 1
- Add qrsac algorithm
- Add REDQ algorithm
- Update to support discrete tuple
- Add icra2018 qtopt
- Add goal_env module
- Add PPO tuple state support
- Add iLQR and LQR
- Add mppi
- Add ddp
- Add gmm and Update gaussian
- Fix wrong path to working directory when saving and loading files
- Remove the duplicate processes of saving learned weights in OFA searcher
- Fix KeyError: ‘comm’.
- [OFA] Add loss_weights arg in configs
- Fix OFA valid/test
- Fix imagenet datapath
- Fix wrong working directory for OFAResnet50 and OFAXception search spaces