A Neural Network implementation with Scala, Breeze & Spark

Deep Spark follows GPL v2 license.

DeepSpark supports following layered neural network implementation:

• Fully-connected Neural Network
• Fully-connected Rank-3 Tensor Network
• RBF Network
• Word Embedding Network

ScalaNetwork supports following training methodologies:

• Stochastic Gradient Descent w/ L1-, L2-regularization, Momentum.
• AdaGrad
• AdaDelta

ScalaNetwork supports following environments:

• Multi-Threaded Training Environment, which gets input from Spark RDD

Also you can add negative examples with Trainer.setNegativeSampler().

ScalaNetwork supports following activation functions:

• Linear
• Sigmoid
• HyperbolicTangent
• Rectifier
• Softplus
• HardSigmoid
• HardTanh
• Softmax
• LeakyReLU

And for RBF Layer,

• Gaussian RBF
• Inverse Quadratic RBF
• HardGaussian RBF (= Hard Inverse Quadratic RBF)

Here is some examples for basic usage.

Currently ScalaNetwork supports Scala version 2.10 ~ 2.11.

• Stable Release is 1.2.0

If you are using SBT, add a dependency as described below:

libraryDependencies += "com.github.nearbydelta" %% "deepspark" % "1.2.0"

If you are using Maven, add a dependency as described below:

<dependency>
  <groupId>com.github.nearbydelta</groupId>
  <artifactId>deepspark_{your.scala.version}</artifactId>
  <version>1.2.0</version>
</dependency>

• Simple XOR Network

• Simple Word Embedding Simulation