Comments (3)
Sorry but I did not get your point. Could you paste some code or pseudocode?
from deeplearning.scala.
object A {
val a: Matrix = Matrix(3, 3)
val b: Matrix = Matrix(3, 3)
val c: Matrix = a * c
}
A.a.append(myVector)
Above similar to https://github.com/lihaoyi/scala.rx on vectors which a time dimetion.
object A {
val a: Table = Table ("Col A", "Col B")
val b: Table = Table ("Col A", "Col C")
val c: Table = a * c
}
A.a.append(myRow)
A.a.latest("Col A")
Above inspired by http://flix.github.io/
from deeplearning.scala.
I guess you mean you want to let the same DSL syntax have different kernels. In order to achieve the goal, some implicit abstract factories are required to create DSL's ASTs, like what they did in http://okmij.org/ftp/tagless-final/ . The current DeepLearning.scala's codebase does not use this approach.
from deeplearning.scala.
Related Issues (20)
- Support custom implicit parameters introduced from hyperparameter plugins HOT 1
- Release DeepLearning.scala 2.0.0-RC0
- Create a compiler plugin that supports magic import $url.`http://hostname.com/path/to/File.scala` HOT 1
- Separate INDArray.implicits to plugins HOT 1
- Release DeepLearning.scala 2.0
- Big DEMO for DeepLearning.scala 2.0
- Implement missing operators
- Tests for diamond dependencies
- Add a plugin for per-operation logging
- Separate versioning
- fresh build fails on 3.0.x HOT 2
- Wrong result in nd4j's CUDA backend HOT 2
- Change Delta from DeviceBuffers to DeviceBuffers => Do[Unit] HOT 1
- Travis CI crashes due to clReleaseEvent HOT 1
- Add an operation for creating checkpoints
- Create a DSL for eager execution
- README.md: ND4J or Compute.scala? HOT 1
- where can i find hyperparameters.INDArrayWeight HOT 2
- what kind of reverse mode ad does this project use
- Scala 2.13.x support
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from deeplearning.scala.