config.json

{
    "name": "METR-LA_DCRNN",
    "n_gpu": 1,

    "arch": {
        "type": "DCRNNModel",
        "args": {
			"batch_size": 64,
			"enc_input_dim": 2,
			"dec_input_dim": 1,
			"max_diffusion_step": 2,
			"num_nodes": 207,
			"num_rnn_layers": 2,
			"rnn_units": 64,
			"seq_len": 12,
			"output_dim": 1,
            "filter_type": "dual_random_walk"
		}
    },
    "dataloader": {
        "type": "Data",
        "args":{
			"train_batch_size": 64,
            "data_dir": "data/METR-LA/",
            "shuffle": true,
            "validation_split": 0.1,
			"val_batch_size": 64,
			"test_batch_size": 64,
            "num_workers": 2
        }
    },
    "optimizer": {
        "type": "Adam",
        "args":{
            "lr": 0.01,
            "weight_decay": 0,
			"eps": 1.0e-3,
            "amsgrad": true
        }
    },
    "loss": {
		"type": "masked_mae_loss",
		"args":{
			"null_val": 0.0
		}
	},
    "metrics": [
        "masked_mae_np", "masked_mape_np", "masked_rmse_np"
    ],
    "lr_scheduler": {
        "type": "MultiStepLR",
        "args": {
			"milestones": [20, 30, 40, 50],
            "gamma": 0.1
        }
    },
    "trainer": {
        "epochs": 1,
		"cl_decay_steps": 2000,
        "save_dir": "saved/",
        "save_period": 1,
        "verbosity": 2,
        "max_grad_norm": 5,
        "monitor": "min val_loss",
        "early_stop": 10,
        "tensorboard": true
    }
}