Package tv.floe.metronome.deeplearning.datasets.iterator.impl

Examples of tv.floe.metronome.deeplearning.datasets.iterator.impl.MnistHDFSDataSetIterator

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      txt_reader.setFile(splits[0].toString().split(":")[1], 0, len);   
   
       

     
    MnistHDFSDataSetIterator hdfs_fetcher = new MnistHDFSDataSetIterator( batchSize, totalNumExamples, txt_reader );
    DataSet hdfs_recordBatch = hdfs_fetcher.next();
   
    return hdfs_recordBatch;
  }
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          // ok, only in this situation do we lower the batch size
          this.batchSize = hdfs_recordBatch.getFirst().numRows();

          // re-setup the dataset iterator
          try {
            this.hdfs_fetcher = new MnistHDFSDataSetIterator( this.batchSize, this.totalTrainingDatasetSize, (TextRecordParser)lineParser );
          } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
          }
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    //System.out.println("Worker::setRecordParser()");
   
    try {
      // Q: is totalTrainingDatasetSize actually used anymore?
      this.hdfs_fetcher = new MnistHDFSDataSetIterator( this.batchSize, this.totalTrainingDatasetSize, (TextRecordParser)lineParser );
    } catch (IOException e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    }
   
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      txt_reader.setFile( path, 0, len);
   
         

     
    MnistHDFSDataSetIterator hdfs_fetcher = new MnistHDFSDataSetIterator( batchSize, 1, txt_reader );
    DataSet hdfs_recordBatch = hdfs_fetcher.next();
   
    Matrix hdfs_input = hdfs_recordBatch.getFirst();
    Matrix hdfs_labels = hdfs_recordBatch.getSecond();     
   
    //ModelTester.evaluateModel( recordBatch.getFirst(), recordBatch.getSecond(), dbn);
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Related Classes of tv.floe.metronome.deeplearning.datasets.iterator.impl.MnistHDFSDataSetIterator

  • tv.floe.metronome.deeplearning.datasets.fetchers.MnistHDFSDataFetcher
  • tv.floe.metronome.deeplearning.dbn.iterativereduce.Test_DBN_IR_MNIST
  • tv.floe.metronome.deeplearning.dbn.iterativereduce.WorkerNode
  • tv.floe.metronome.deeplearning.dbn.model.evaluation.ModelTester
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