Playing with Java Deep Learning (DJL), tutorial-01 / published by https://github.com/dacr/code-examples-manager #15d7706b-fdc7-4aab-a788-8a2262c5d189/976d4e4a0ff1bb9d54a769b16990ebed15d99c47

djl-tutorial-01.sc

// summary : Playing with Java Deep Learning (DJL), tutorial-01
// keywords : djl, machine-learning, tutorial, ai, @testable
// publish : gist
// authors : David Crosson
// license : Apache License Version 2.0 (https://www.apache.org/licenses/LICENSE-2.0.txt)
// id : 15d7706b-fdc7-4aab-a788-8a2262c5d189
// created-on : 2021-03-05T09:23:01Z
// managed-by : https://github.com/dacr/code-examples-manager
// run-with : scala-cli $file

// ---------------------
//> using scala "3.7.2"
//> using dep "org.slf4j:slf4j-api:2.0.17"
//> using dep "org.slf4j:slf4j-simple:2.0.17"
//> using dep "ai.djl:api:0.33.0"
// ---------------------


// inspired from https://docs.djl.ai/jupyter/tutorial/01_create_your_first_network.html

import ai.djl._
import ai.djl.nn._
import ai.djl.nn.core._
import ai.djl.training._


val application = Application.CV.IMAGE_CLASSIFICATION
val inputSize = 28*28
val outputSize = 10

val block = new SequentialBlock()

block.add(Blocks.batchFlattenBlock(inputSize))
block.add(Linear.builder().setUnits(128).build())
block.add(arr => Activation.relu(arr))
block.add(Linear.builder().setUnits(64).build())
block.add(arr => Activation.relu(arr))
block.add(Linear.builder().setUnits(outputSize).build())

println(block)