code: def text_cnn_base():
    
    hparams = common_hparams.basic_params1()
    hparams.batch_size = 4096
    hparams.max_length = 256
    hparams.clip_grad_norm = 0.  # i.e. no gradient clipping
    hparams.optimizer_adam_epsilon = 1e-9
    hparams.learning_rate_schedule = "legacy"
    hparams.learning_rate_decay_scheme = "noam"
    hparams.learning_rate = 0.1
    hparams.learning_rate_warmup_steps = 4000
    hparams.initializer_gain = 1.0
    hparams.num_hidden_layers = 6
    hparams.initializer = "uniform_unit_scaling"
    hparams.weight_decay = 0.0
    hparams.optimizer_adam_beta1 = 0.9
    hparams.optimizer_adam_beta2 = 0.98
    hparams.num_sampled_classes = 0
    hparams.label_smoothing = 0.1
    hparams.shared_embedding_and_softmax_weights = True
    hparams.symbol_modality_num_shards = 16
  
    # Add new ones like this.
    hparams.add_hparam("filter_sizes", [2, 3, 4, 5])
    hparams.add_hparam("num_filters", 128)
    hparams.add_hparam("output_dropout", 0.4)
    return hparams
docs: Set of hyperparameters.