readme

README.md

-# pytorch-DCRNN
-
-The original tensorflow implementation: [liyaguang/DCRNN](https://github.com/liyaguang/DCRNN),
-
-This repo is still under development.
-
-PyTorch implementation of Diffusion Convolutional Recurrent Neural Network in the following paper: \
-Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu, [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://arxiv.org/abs/1707.01926), ICLR 2018.
 
 
 ## Requirements

config.json

 {
-    "name": "METR-LA_DCRNN",
+    "name": "BAY_DCRNN",
     "n_gpu": 1,
+    "id": 5,
+
 
     "arch": {
         "type": "DCRNNModel",
         "args": {
 			"batch_size": 64,
-			"enc_input_dim": 2,
+			"enc_input_dim": 1,
 			"dec_input_dim": 1,
 			"max_diffusion_step": 2,
-			"num_nodes": 207,
+			"num_nodes": 32,
 			"num_rnn_layers": 2,
 			"rnn_units": 64,
 			"seq_len": 12,
@@ -21,7 +23,7 @@
         "type": "Data",
         "args":{
 			"train_batch_size": 64,
-            "data_dir": "data/METR-LA/",
+            "data_dir": "data/pems-bay-datasets-10/",
             "shuffle": true,
             "validation_split": 0.1,
 			"val_batch_size": 64,
@@ -32,7 +34,7 @@
     "optimizer": {
         "type": "Adam",
         "args":{
-            "lr": 0.01,
+            "lr": 0.005,
             "weight_decay": 0,
 			"eps": 1.0e-3,
             "amsgrad": true
@@ -45,17 +47,17 @@
 		}
 	},
     "metrics": [
-        "masked_mae_np", "masked_mape_np", "masked_rmse_np"
+        "masked_mae_np", "masked_mape_np", "masked_rmse_np", "masked_pcc"
     ],
     "lr_scheduler": {
         "type": "MultiStepLR",
         "args": {
-			"milestones": [20, 30, 40, 50],
+			"milestones": [15, 25, 35],
             "gamma": 0.1
         }
     },
     "trainer": {
-        "epochs": 1,
+        "epochs": 50,
 		"cl_decay_steps": 2000,
         "save_dir": "saved/",
         "save_period": 1,