scala-package/examples/src/main/scala/ml/dmlc/mxnetexamples/rnn/TestCharRnn.scala

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 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
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package ml.dmlc.mxnetexamples.rnn

import ml.dmlc.mxnet._
import org.kohsuke.args4j.{CmdLineParser, Option}
import org.slf4j.LoggerFactory
import scala.collection.JavaConverters._

/**
 * Follows the demo, to test the char rnn:
 * https://github.com/dmlc/mxnet/blob/master/example/rnn/char-rnn.ipynb
 * @author Depeng Liang
 */
object TestCharRnn {

  private val logger = LoggerFactory.getLogger(classOf[TrainCharRnn])

  def main(args: Array[String]): Unit = {
    val stcr = new TestCharRnn
    val parser: CmdLineParser = new CmdLineParser(stcr)
    try {
      parser.parseArgument(args.toList.asJava)
      assert(stcr.dataPath != null && stcr.modelPrefix != null && stcr.starterSentence != null)

      // The batch size for training
      val batchSize = 32
      // We can support various length input
      // For this problem, we cut each input sentence to length of 129
      // So we only need fix length bucket
      val buckets = List(129)
      // hidden unit in LSTM cell
      val numHidden = 512
      // embedding dimension, which is, map a char to a 256 dim vector
      val numEmbed = 256
      // number of lstm layer
      val numLstmLayer = 3

      // build char vocabluary from input
      val vocab = Utils.buildVocab(stcr.dataPath)

      // load from check-point
      val (_, argParams, _) = Model.loadCheckpoint(stcr.modelPrefix, 75)

      // build an inference model
      val model = new RnnModel.LSTMInferenceModel(numLstmLayer, vocab.size + 1,
                           numHidden = numHidden, numEmbed = numEmbed,
                           numLabel = vocab.size + 1, argParams = argParams, dropout = 0.2f)

      // generate a sequence of 1200 chars
      val seqLength = 1200
      val inputNdarray = NDArray.zeros(1)
      val revertVocab = Utils.makeRevertVocab(vocab)

      // Feel free to change the starter sentence
      var output = stcr.starterSentence
      val randomSample = true
      var newSentence = true
      val ignoreLength = output.length()

      for (i <- 0 until seqLength) {
        if (i <= ignoreLength - 1) Utils.makeInput(output(i), vocab, inputNdarray)
        else Utils.makeInput(output.takeRight(1)(0), vocab, inputNdarray)
        val prob = model.forward(inputNdarray, newSentence)
        newSentence = false
        val nextChar = Utils.makeOutput(prob, revertVocab, randomSample)
        if (nextChar == "") newSentence = true
        if (i >= ignoreLength) output = output ++ nextChar
      }

      // Let's see what we can learned from char in Obama's speech.
      logger.info(output)
    } catch {
      case ex: Exception => {
        logger.error(ex.getMessage, ex)
        parser.printUsage(System.err)
        sys.exit(1)
      }
    }
  }
}

class TestCharRnn {
  @Option(name = "--data-path", usage = "the input train data file")
  private val dataPath: String = "./data/obama.txt"
  @Option(name = "--model-prefix", usage = "the model prefix")
  private val modelPrefix: String = "./model/obama"
  @Option(name = "--starter-sentence", usage = "the starter sentence")
  private val starterSentence: String = "The joke"
}