code: def shakeshake_small():
    
    hparams = common_hparams.basic_params1()
    hparams.batch_size = 128
    hparams.hidden_size = 32
    hparams.layer_prepostprocess_dropout = 0.0
    hparams.dropout = 0
    hparams.label_smoothing = 0.0
    hparams.clip_grad_norm = 0.0  # No clipping for now, one can also try 2.0.
    hparams.num_hidden_layers = 26
    hparams.learning_rate_decay_scheme = "cosine"
    # Model should be run for 700000 steps with batch size 128 (~1800 epochs)
    hparams.learning_rate_cosine_cycle_steps = 700000
    hparams.learning_rate = 0.2
    hparams.learning_rate_warmup_steps = 100  # That's basically unused.
    hparams.initializer = "uniform_unit_scaling"
    hparams.initializer_gain = 1.0
    hparams.weight_decay = 1e-4
    hparams.optimizer = "Momentum"
    hparams.optimizer_momentum_momentum = 0.9
    hparams.add_hparam("shake_shake_num_branches", 2)
    hparams.add_hparam("shake_shake_concat", int(False))
    return hparams
docs: Parameters for CIFAR-10. Gets to about 96% accuracy@700K steps, 1 GPU.