In our latest release, version 0.10.1.145, we have added support for a new Dual Recurrent/Reinforcement Learning Trainer that also supports the newly released NVIDIA CUDA 10.1.168 (Update 1) with cuDNN 7.6. With the dual RNN/RL trainer you can train the same model first in an recurrent style learning and then train the same model with a second training pass that uses a reinforcement learning style of learning. In addition, this release includes dramatic optimizations that greatly improve the responsiveness of the application when opening and closing projects.
The following new features have been added to this release.
- Dramatically improved application start-up time.
- Dramatically improved application responsiveness to project actions (open, close, etc.)
- Dramatically improved application responsiveness when changing project properties.
- Added option to save image in all Image Viewer dialogs.
- Added dual RNN/RL training model support with stages to distinguish between each during training.
- New setting allows changing the maximum debug data/diff displayed.
- Editor now automatically shows RNN or RL stage (if used).
- Added new Finance Gym with simulated price stream.
- Added ability to debug data of specific items on data or data link.
- Added __half sized memory support to CONVOLUTION, POOLING, TANH, SIGMOID, RELU and INPUT layers.
- Moved minimum compute from 3.5 to 5.3 (Maxwell) for half memory support.
The following bugs have been fixed in this release.
- LSTM layer output now shows values in debug data window instead of all zeros.
- Dummy Data Layer shape now supported in the editor.
- Google Dream Evaluator now shows octaves properly.
- Image Evaluator now shows debug layers properly.
- Snapshot Update, Snapshot Load Method and Image Load Method now saved.
- Custom trainers now properly take snapshots.
- Large images in image viewer are no longer skewed and support scrolling.
Check out our Tutorials for easy step-by-step instructions that will get you started quickly with several complex model types! For cool example videos, including an ATARI Pong video and Cart-Pole balancing video, check out our Examples page.