def get_default_hparams():
    """Get a tf.HParams object with the default values for the hyperparameters.

      name: string
        name of the compression specification. Used for adding summaries and ops
        under a common tensorflow name_scope.
      alpha_decrement_value: float
        a positive real number by which alpha is decremented at each update.
      begin_compression_step: integer
        the global step at which to begin compression.
      end_compression_step: integer
        the global step at which to terminate compression. Defaults to -1
        implying that compression continues till the training stops.
      use_tpu: False
        indicates whether to use TPU.
      compression_option: integer
        indicates what type of factorization (if any) is used.
      rank: integer
        indicates what type of factorization (if any) is used.
      update_option: integer
        indicates how the update logic is being run. More specifically:
        0 - run the update logic in TF; needed when using GPU/TPU.
        1 - run the update logic in regular python as opposed to TF.
        2 - run the update logic in TF and in regular python.

    Returns:
      tf.HParams object initialized to default values.

    """
    return HParams(
        name='model_compression',
        alpha_decrement_value=0.01,
        begin_compression_step=0,
        end_compression_step=-1,
        compression_frequency=10,
        use_tpu=False,
        compression_option=0,
        rank=100,
        update_option=0,
        run_update_interval_check=1,
        block_size=1,
        pruning_fraction=0.0,
        begin_pruning_step=0,
        end_pruning_step=-1,
        weight_sparsity_map=[''],
        block_dims_map=[''],
        threshold_decay=0.0,
        pruning_frequency=10,
        nbins=256,
        block_height=1,
        block_width=1,
        block_pooling_function='AVG',
        initial_sparsity=0.0,
        target_sparsity=0.5,
        sparsity_function_begin_step=0,
        sparsity_function_end_step=100,
        sparsity_function_exponent=3.0,
        gradient_decay_rate=0.99,
        prune_option='weight')