function parseArguments()

in sentiment/train.js [87:158]

• 72 lines of code
• 1 McCabe index (conditional complexity)

function parseArguments() {
  const parser = new ArgumentParser(
      {description: 'Train a model for IMDB sentiment analysis'});
  parser.addArgument('modelType', {
    type: 'string',
    optionStrings: [
       'multihot', 'flatten', 'cnn', 'simpleRNN', 'lstm', 'bidirectionalLSTM'],
    help: 'Model type'
  });
  parser.addArgument('--numWords', {
    type: 'int',
    defaultValue: 10000,
    help: 'Number of words in the vocabulary'
  });
  parser.addArgument('--maxLen', {
    type: 'int',
    defaultValue: 100,
    help: 'Maximum sentence length in number of words. ' +
        'Shorter sentences will be padded; longers ones will be truncated.'
  });
  parser.addArgument('--embeddingSize', {
    type: 'int',
    defaultValue: 128,
    help: 'Number of word embedding dimensions'
  });
  parser.addArgument(
      '--gpu', {action: 'storeTrue', help: 'Use GPU for training'});
  parser.addArgument('--optimizer', {
    type: 'string',
    defaultValue: 'adam',
    help: 'Optimizer to be used for model training'
  });
  parser.addArgument(
      '--epochs',
      {type: 'int', defaultValue: 10, help: 'Number of training epochs'});
  parser.addArgument(
      '--batchSize',
      {type: 'int', defaultValue: 128, help: 'Batch size for training'});
  parser.addArgument('--validationSplit', {
    type: 'float',
    defaultValue: 0.2,
    help: 'Validation split for training'
  });
  parser.addArgument('--modelSaveDir', {
    type: 'string',
    defaultValue: 'dist/resources',
    help: 'Optional path for model saving.'
  });
  parser.addArgument('--embeddingFilesPrefix', {
    type: 'string',
    defaultValue: '',
    help: 'Optional path prefix for saving embedding files that ' +
    'can be loaded in the Embedding Projector ' +
    '(https://projector.tensorflow.org/). For example, if this flag '  +
    'is configured to the value /tmp/embed, then the embedding vectors ' +
    'file will be written to /tmp/embed_vectors.tsv and the labels ' +
    'file will be written to /tmp/embed_label.tsv'
  });
  parser.addArgument('--logDir', {
    type: 'string',
    help: 'Optional tensorboard log directory, to which the loss and ' +
    'accuracy will be logged during model training.'
  });
  parser.addArgument('--logUpdateFreq', {
    type: 'string',
    defaultValue: 'batch',
    optionStrings: ['batch', 'epoch'],
    help: 'Frequency at which the loss and accuracy will be logged to ' +
    'tensorboard.'
  });
  return parser.parseArgs();
}