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METR_LA_AutoSTG.yaml
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METR_LA_AutoSTG.yaml
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---
sys:
seed: 1
data:
path: METR-LA
num_sensors: 207
in_length: 12
out_length: 12
in_channels: 2
out_channels: 1
batch_size_per_gpu: 48
train_prop: 0.7
valid_prop: 0.1
model:
name: METR_LA_AutoSTG
node_hiddens: [32,32]
edge_hiddens: [32,32]
hidden_channels: 32
skip_channels: 256
end_channels: 512
layer_names: [STCell, AvgPooling, STCell, STCell, AvgPooling, STCell, STCell]
num_mixed_ops: 6
candidate_op_profiles: [
[Zero, {
}],
[Identity, {
}],
[Conv, {
type_name: fc,
kernel_size: [1,1],
stride: [1,1],
padding: [0,0],
dilation: [1,1],
use_bn: True,
dropout: 0
}],
[MetaConv, {
type_name: tc,
node_in_hidden: 32,
meta_hiddens: [8,4],
kernel_size: [1,3],
stride: [1,1],
padding: [0,1],
dilation: [1,1],
use_bn: True,
dropout: 0
}],
[MetaGraphConv, {
edge_in_hidden: 32,
meta_hiddens: [8,4],
num_graphs: 4,
order: 2,
use_bn: True,
dropout: 0.3
}],
]
trainer:
arch_lr: 0.01
arch_lr_decay_milestones: [50,60,70,80]
arch_lr_decay_ratio: 0.1
arch_decay: 0.00001
arch_clip_gradient: 5
weight_lr: 0.01
weight_lr_decay_milestones: [50,60,70,80]
weight_lr_decay_ratio: 0.1
weight_decay: 0.00001
weight_clip_gradient: 5
num_search_iterations: 64
num_search_arch_samples: 8
num_train_iterations: 500
criterion: 'masked_mae'
metric_names: ['mae', 'rmse']
metric_indexes: [2,5,11]
print_frequency: 50