Set of hyperparameters.

def mtf_resnet_base():
  """Set of hyperparameters."""
  hparams = common_hparams.basic_params1()
  hparams.no_data_parallelism = True
  hparams.use_fixed_batch_size = True
  hparams.batch_size = 32
  hparams.max_length = 3072
  hparams.hidden_size = 256
  hparams.label_smoothing = 0.0
  # 8-way model-parallelism
  hparams.add_hparam("mesh_shape", "batch:8")
  hparams.add_hparam("layout", "batch:batch")
  hparams.add_hparam("filter_size", 1024)

  hparams.add_hparam("num_layers", 6)
  # Share weights between input and target embeddings
  hparams.shared_embedding = True

  hparams.shared_embedding_and_softmax_weights = True
  hparams.optimizer = "Adafactor"
  hparams.learning_rate_schedule = "rsqrt_decay"
  hparams.learning_rate_warmup_steps = 10000
  hparams.add_hparam("d_kv", 32)

  # Image related hparams
  hparams.add_hparam("img_len", 32)
  hparams.add_hparam("num_channels", 3)
  hparams.add_hparam("row_blocks", 1)
  hparams.add_hparam("col_blocks", 1)
  hparams.add_hparam("rows_size", 32)
  hparams.add_hparam("cols_size", 32)

  # Model-specific parameters
  hparams.add_hparam("layer_sizes", [3, 4, 6, 3])
  hparams.add_hparam("filter_sizes", [64, 64, 128, 256, 512])
  hparams.add_hparam("is_cifar", False)

  # Variable init
  hparams.initializer = "normal_unit_scaling"
  hparams.initializer_gain = 2.

  # TODO(nikip): Change optimization scheme?
  hparams.learning_rate = 0.1
  return hparams