v0.12.0: · sekwiatkowski/komputation@800d713 · GitHub

- Simplified the specification of networks
- The input dimensions over the continuations of the network are computed automatically.
- Removed the Layer suffix from instruction factory functions
- Overloaded the instruction factory function to simplify the specification of initialization strategies
- Renamed Direction.Forward/Backward to Direction.LeftToRight/RightToLeft
- Shortened "ActivationFunction" to "Activation" and "ActivationLayer" to "Activation"
- Generalized BaseCudaEntrywiseActivationLayer to BaseCudaEntrywiseLayer
- The specification of the minimum length is required in the lookup instruction and optional in the input instruction.
- TREC categories are indexed based on all available training data.
- Renamed "forward" layer to "continuation" and shortened "combination layer" to "combination"
- Moved the architecture-specific interfaces from the general package to the respective architecture-specific packages
- Improved the names used in SparseAccumulator and SparseUpdate
- The series is passed on to the method of the ResultExtractionStrategy interface.
- Introduced CpuCombinationSeries to implement the addition of the weighted previous state and the weighted current input.
- Added the Cpu prefix to Series and ParameterizedSeries in preparation of the CUDA implementation of recurrent neural networks
- Optimized the performance RNN implementation by adding the bias to the input rather than adding at each step
- Fixed the specification of the number of rows in CpuLogisticLoss
- Renamed the "Negation" demo to "Not"
- Stopped experimenting with dynamic parallelism
- CudaIdentity now implements CudaActivation.
- Introduced a base class for higher-order layers
- Differentiated the CUDA continuation base class into one class for layers that change the number of columns and one class for layers that don't.
- Reused the code for the computation of launch configurations in CudaHashing and CudaGroupSum
- Fixed the sparse updated in CudaLookup
- Added a "copy" helper function that encapsulates System.arraycopy for copies
- Added a setter to InputMemory that caches all possible data
- Clarified references to the hash table in CUDA optimizers
- CUDA layers pass a pointer to the length of the input data and the maximum length within the batch.
- Unified the activation instruction factory functions over the two architectures
- Moved the concatenation layer to a separate package
- Added an instruction for weightings with shared parameters that is separate from the instruction for the weighting layer that uses a dedicated parameter
- The two weighting instructions inherit from the new BaseWeighting class.
- Added instructions for the tree series types: Series, ParameterizedSeries and CombinationSeries
- Refactored the CPU RNN factory function based on the instructions
- Continuation instructions implement HasOutputDimensions and CanSetInputDimensions, while entry point instructions only implement HasOutputDimensions.
- Inlined some CUDA C helper functions
- Moved the division by 2 in the squared loss function from the host to the device
- Added the missing scaling of gradients in some of the optimization kernels
- Refactored the for loops used to update entries in optimization kernels
- Temporarily removed the CUDA forward layer tests
- Updated the links in the README
- Upgraded to Kotlin 1.2.10