generate train script for users
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parent
069ca21618
commit
47a2666530
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@ -1,6 +1,6 @@
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use_gpu: True
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seed: 10
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device: 1
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device: 0
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early_stop:
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patience: 60
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federate:
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@ -62,7 +62,7 @@ train:
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max_grad_norm: 5
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real_value: True
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criterion:
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type: RMSE
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type: L1Loss
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trainer:
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type: trafficflowtrainer
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log_dir: ./
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@ -64,7 +64,7 @@ train:
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max_grad_norm: 5
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real_value: True
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criterion:
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type: RMSE
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type: L1Loss
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trainer:
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type: trafficflowtrainer
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log_dir: ./
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@ -62,7 +62,7 @@ train:
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max_grad_norm: 5
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real_value: True
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criterion:
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type: RMSE
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type: L1Loss
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trainer:
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type: trafficflowtrainer
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log_dir: ./
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@ -60,7 +60,7 @@ train:
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grad_norm: True
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real_value: True
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criterion:
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type: RMSE
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type: L1loss
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trainer:
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type: trafficflowtrainer
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log_dir: ./
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@ -0,0 +1,73 @@
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use_gpu: True
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seed: 10
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device: 0
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early_stop:
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patience: 60
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federate:
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mode: standalone
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total_round_num: 70
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client_num: 10
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data:
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root: data/trafficflow/PeMS03
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type: trafficflow
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splitter: trafficflowprediction
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num_nodes: 358
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lag: 12
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horizon: 12
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val_ratio: 0.2
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test_ratio: 0.2
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tod: False
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normalizer: std
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column_wise: False
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default_graph: True
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add_time_in_day: True
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add_day_in_week: True
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steps_per_day: 288
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days_per_week: 7
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dataloader:
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type: trafficflow
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batch_size: 64
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drop_last: True
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model:
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type: FedDGCN
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task: TrafficFlowPrediction
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dropout: 0.1
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horizon: 12
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num_nodes: 0
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input_dim: 1
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output_dim: 1
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embed_dim: 10
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rnn_units: 64
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num_layers: 1
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cheb_order: 2
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use_day: True
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use_week: True
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train:
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batch_or_epoch: 'epoch'
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local_update_steps: 1
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optimizer:
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type: 'Adam'
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lr: 0.003
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weight_decay: 0.0
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batch_size: 64
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epochs: 300
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lr_init: 0.003
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weight_decay: 0
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lr_decay: False
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lr_decay_rate: 0.3
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lr_decay_step: 5,20,40,70
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early_stop: False
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early_stop_patience: 15
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grad_norm: True
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max_grad_norm: 5
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real_value: True
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criterion:
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type: L1Loss
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trainer:
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type: trafficflowtrainer
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log_dir: ./
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grad:
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grad_clip: 5.0
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eval:
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metrics: ['avg_loss']
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@ -0,0 +1,74 @@
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use_gpu: True
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seed: 10
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device: 0
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early_stop:
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patience: 60
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federate:
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mode: standalone
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total_round_num: 70
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client_num: 10
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#personalization:
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# local_param: ['D_i_W_emb', "T_i_D_emb", "encoder1", "encoder2", "node_embeddings1", "node_embeddings2"]
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data:
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root: data/trafficflow/PeMS04
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type: trafficflow
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splitter: trafficflowprediction
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num_nodes: 307
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lag: 12
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horizon: 12
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val_ratio: 0.2
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test_ratio: 0.2
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tod: False
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normalizer: std
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column_wise: False
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default_graph: True
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add_time_in_day: True
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add_day_in_week: True
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steps_per_day: 288
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days_per_week: 7
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dataloader:
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type: trafficflow
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batch_size: 64
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drop_last: True
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model:
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type: FedDGCN
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task: TrafficFlowPrediction
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dropout: 0.1
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horizon: 12
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num_nodes: 0
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input_dim: 1
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output_dim: 1
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embed_dim: 10
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rnn_units: 64
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num_layers: 1
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cheb_order: 2
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use_day: True
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use_week: True
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train:
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batch_or_epoch: 'epoch'
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local_update_steps: 1
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optimizer:
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type: 'Adam'
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lr: 0.003
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weight_decay: 0.0
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batch_size: 64
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epochs: 300
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lr_init: 0.003
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weight_decay: 0
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lr_decay: False
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lr_decay_rate: 0.3
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lr_decay_step: 5,20,40,70
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early_stop: False
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early_stop_patience: 15
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grad_norm: True
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max_grad_norm: 5
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real_value: True
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criterion:
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type: L1Loss
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trainer:
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type: trafficflowtrainer
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log_dir: ./
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grad:
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grad_clip: 5.0
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eval:
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metrics: ['avg_loss']
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@ -0,0 +1,71 @@
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use_gpu: True
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seed: 10
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device: 0
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early_stop:
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patience: 60
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federate:
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mode: standalone
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total_round_num: 70
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client_num: 10
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data:
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root: data/trafficflow/PeMS08
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type: trafficflow
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splitter: trafficflowprediction
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num_nodes: 170
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lag: 12
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horizon: 12
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val_ratio: 0.2
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test_ratio: 0.2
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tod: False
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normalizer: std
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column_wise: False
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default_graph: True
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add_time_in_day: True
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add_day_in_week: True
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steps_per_day: 288
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days_per_week: 7
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dataloader:
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type: trafficflow
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batch_size: 64
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drop_last: True
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model:
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type: FedDGCN
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task: TrafficFlowPrediction
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dropout: 0.1
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horizon: 12
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num_nodes: 0
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input_dim: 1
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output_dim: 1
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embed_dim: 10
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rnn_units: 64
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num_layers: 1
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cheb_order: 2
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use_day: True
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use_week: True
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train:
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batch_or_epoch: 'epoch'
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local_update_steps: 1
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optimizer:
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type: 'Adam'
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lr: 0.01
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weight_decay: 0.0
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batch_size: 64
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epochs: 300
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lr_init: 0.003
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lr_decay: False
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lr_decay_rate: 0.3
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lr_decay_step: 5,20,40,70
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early_stop: False
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early_stop_patience: 15
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grad_norm: True
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real_value: True
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criterion:
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type: L1loss
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trainer:
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type: trafficflowtrainer
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log_dir: ./
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grad:
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grad_clip: 5.0
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eval:
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metrics: ['avg_loss']
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