Set of hyperparameters.

def slicenet_params1():
  """Set of hyperparameters."""
  hparams = common_hparams.basic_params1()
  hparams.batch_size = 1024
  hparams.hidden_size = 768
  hparams.dropout = 0.5
  hparams.symbol_dropout = 0.2
  hparams.label_smoothing = 0.1
  hparams.clip_grad_norm = 2.0
  hparams.num_hidden_layers = 4
  hparams.kernel_height = 3
  hparams.kernel_width = 1
  hparams.norm_type = "layer"
  hparams.learning_rate_decay_scheme = "exp"
  hparams.learning_rate = 0.05
  hparams.learning_rate_warmup_steps = 3000
  hparams.initializer_gain = 1.0
  hparams.weight_decay = 3.0
  hparams.num_sampled_classes = 0
  hparams.sampling_method = "argmax"
  hparams.optimizer_adam_epsilon = 1e-6
  hparams.optimizer_adam_beta1 = 0.85
  hparams.optimizer_adam_beta2 = 0.997
  hparams.add_hparam("large_kernel_size", 15)  # New ones are added like this.
  hparams.add_hparam("separability", -2)
  # A dilation scheme, one of _DILATION_SCHEMES.
  hparams.add_hparam("dilation_scheme", "1.1.1.1")
  # A kernel scheme, one of _KERNEL_SCHEMES; overrides large_kernel_size.
  hparams.add_hparam("kernel_scheme", "3.7.15.31")
  hparams.add_hparam("audio_compression", 8)
  # attention-related flags
  hparams.add_hparam("attention_type", "simple")
  hparams.add_hparam("num_heads", 8)
  hparams.add_hparam("attention_key_channels", 0)
  hparams.add_hparam("attention_value_channels", 0)
  hparams.add_hparam("sim_loss_mult", 0.0)  # Try 10.0 for experiments.
  hparams.add_hparam("attention_dropout", 0.2)
  hparams.shared_embedding_and_softmax_weights = True
  return hparams