JarCastingdl4jgrapher
Classes to generate Graphviz DOT files from DL4J MultiLayerNetworks and ComputationGraphs
For prototyping for example, with VS Code as your IDE, you can use João Pinto's excellent Graphviz (dot) language support extension (https://github.com/joaompinto/vscode-graphviz) to preview the generated DOT file alongside the dl4j code. You can also use Graphviz tools (https://graphviz.org/) to generate files in formats such as pdf, png, svg etc. from the DOT file for display / publication purposes.
Installation
SBT
In build.sbt:
libraryDependencies += "com.drissoft" %% "dl4jgrapher" % "0.1.0"
Scala Examples
MultiLayerNetwork
AlexNet
import org.deeplearning4j.zoo.model.AlexNet
import com.drissoft.dl4jgrapher._
val h = 224
val w = 224
val c = 3
val inputType = new InputType.InputTypeConvolutional(h, w, c)
// Build AlexNet
val net = AlexNet
.builder()
.numClasses(10)
.build()
.init()
val input = Nd4j.rand(1, c, h, w)
val grapher = new MultiLayerNetworkGrapher(net).getGrapher(input, inputType)
// Output the DOT file
grapher.writeDotFile(java.nio.file.Paths.get("alexnet.dot"))
Graphviz
<Graphviz Dir>\bin\dot.exe -Tsvg alexnet.dot -o alexnet.svg
AlexNet Output
ComputationGraph
ResNet50
import org.deeplearning4j.zoo.model.ResNet50
import com.drissoft.dl4jgrapher._
val h = 224
val w = 200
val c = 3
val inputTypes = List(new InputType.InputTypeConvolutional(h, w, c))
// Build ResNet50
val net = ResNet50
.builder()
.numClasses(10)
.build()
.init()
val input = Nd4j.rand(1, c, h, w)
val grapher = new ComputationGraphGrapher(net).getGrapher(Array(input), inputTypes)
// Output the DOT file
grapher.writeDotFile(java.nio.file.Paths.get("resnet.dot"))
ResNet50 Output
InceptionResNetV1 Output
