8fc0e63ce7
* Libnd4j: Add broadcastable elementwise power derivative #7461 first step of Pow_bp operation implementation Signed-off-by: Oleg <oleg.semeniv@gmail.com> * Libnd4j: Add broadcastable elementwise power derivative #7461 some corrections of calculation steps Signed-off-by: Oleg <oleg.semeniv@gmail.com> * Libnd4j: Add broadcastable elementwise power derivative #7461 some bug fixes, the PowDerevative op made broadcastable, add the raw tests for op, need refactoring to use broadcast ops * Libnd4j: Add broadcastable elementwise power derivative #7461 fixed several bugs add broadcast support and tests, need to fix scalar+array and array+scalar Signed-off-by: Oleg <oleg.semeniv@gmail.com> * Libnd4j: Add broadcastable elementwise power derivative #7461 fixed bugs for scalar inputs, fixed multinomial tests, added tests Signed-off-by: Oleg <oleg.semeniv@gmail.com> * Libnd4j: Add broadcastable elementwise power derivative #7461 fised bugs for different shapes support, tests updated * Libnd4j: Add broadcastable elementwise power derivative #7461 applied all possible variants via tiled arrays, add support of broadcast for Pow and PowDerivative ops, covered by tests, before review have to be replaced tiled implementation by applyTrueBroadcast Signed-off-by: Oleg <oleg.semeniv@gmail.com> * Libnd4j: Add broadcastable elementwise power derivative #7461 replaced tile by broadcast implementation, fixed issue with negative x input, corrected tests, need additional testing Signed-off-by: Oleg <oleg.semeniv@gmail.com> * Libnd4j: Add broadcastable elementwise power derivative #7461 added and corrected test cases, corrected implementation need review Signed-off-by: Oleg <oleg.semeniv@gmail.com> * Libnd4j: Add broadcastable elementwise power derivative #7461 code clean up * Libnd4j: Add broadcastable elementwise power derivative #7461 code clean up, removed some tests, add tests with scalar Signed-off-by: Oleg <oleg.semeniv@gmail.com> * Libnd4j: Add broadcastable elementwise power derivative #7461 code improvement and clean up, split tests Signed-off-by: Oleg <oleg.semeniv@gmail.com> * Libnd4j: Add broadcastable elementwise power derivative #7461 some code clean up Signed-off-by: Oleg <oleg.semeniv@gmail.com> * Libnd4j: Add broadcastable elementwise power derivative replace __isnanf by internal realization Signed-off-by: Oleg <oleg.semeniv@gmail.com> * pow_bp wrapper * Fixed PowBp wrapper * Tests added * Test fixed * Fix return type * Disable powBp usage * Pow backprop changed Co-authored-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
Monorepo of Deeplearning4j
Welcome to the new monorepo of Deeplearning4j that contains the source code for all the following projects, in addition to the original repository of Deeplearning4j moved to deeplearning4j:
- https://github.com/eclipse/deeplearning4j/tree/master/libnd4j
- https://github.com/eclipse/deeplearning4j/tree/master/nd4j
- https://github.com/eclipse/deeplearning4j/tree/master/datavec
- https://github.com/eclipse/deeplearning4j/tree/master/arbiter
- https://github.com/eclipse/deeplearning4j/tree/master/nd4s
- https://github.com/eclipse/deeplearning4j/tree/master/gym-java-client
- https://github.com/eclipse/deeplearning4j/tree/master/rl4j
- https://github.com/eclipse/deeplearning4j/tree/master/scalnet
- https://github.com/eclipse/deeplearning4j/tree/master/pydl4j
- https://github.com/eclipse/deeplearning4j/tree/master/jumpy
- https://github.com/eclipse/deeplearning4j/tree/master/pydatavec
To build everything, we can use commands like
./change-cuda-versions.sh x.x
./change-scala-versions.sh 2.xx
./change-spark-versions.sh x
mvn clean install -Dmaven.test.skip -Dlibnd4j.cuda=x.x -Dlibnd4j.compute=xx
or
mvn -B -V -U clean install -pl '!jumpy,!pydatavec,!pydl4j' -Dlibnd4j.platform=linux-x86_64 -Dlibnd4j.chip=cuda -Dlibnd4j.cuda=9.2 -Dlibnd4j.compute=<your GPU CC> -Djavacpp.platform=linux-x86_64 -Dmaven.test.skip=true
An example of GPU "CC" or compute capability is 61 for Titan X Pascal.
Want some examples?
We have separate repository with various examples available: https://github.com/eclipse/deeplearning4j-examples
In the examples repo, you'll also find a tutorial series in Zeppelin: https://github.com/eclipse/deeplearning4j-examples/tree/master/tutorials