64 lines
1.7 KiB
YAML
64 lines
1.7 KiB
YAML
basic:
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dataset: NYCBike-InFlow
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device: cuda:0
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mode: train
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model: MTGNN
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seed: 2023
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data:
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batch_size: 64
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column_wise: false
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days_per_week: 7
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horizon: 24
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input_dim: 1
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lag: 24
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normalizer: std
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num_nodes: 128
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steps_per_day: 48
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test_ratio: 0.2
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val_ratio: 0.2
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model:
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gcn_true: True # 是否使用图卷积网络 (bool)
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buildA_true: True # 是否动态构建邻接矩阵 (bool)
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subgraph_size: 20 # 子图大小 (int)
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num_nodes: 128 # 节点数量 (int)
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node_dim: 40 # 节点嵌入维度 (int)
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dilation_exponential: 1 # 膨胀卷积指数 (int)
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conv_channels: 32 # 卷积通道数 (int)
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residual_channels: 32 # 残差通道数 (int)
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skip_channels: 64 # 跳跃连接通道数 (int)
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end_channels: 128 # 输出层通道数 (int)
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seq_len: 24 # 输入序列长度 (int)
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in_dim: 1 # 输入特征维度 (int)
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out_len: 24 # 输出序列长度 (int)
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out_dim: 1 # 输出预测维度 (int)
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layers: 3 # 模型层数 (int)
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propalpha: 0.05 # 图传播参数alpha (float)
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tanhalpha: 3 # tanh激活参数alpha (float)
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layer_norm_affline: True # 层归一化是否使用affine变换 (bool)
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gcn_depth: 2 # 图卷积深度 (int)
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dropout: 0.3 # dropout率 (float)
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predefined_A: null # 预定义邻接矩阵 (optional, None)
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static_feat: null # 静态特征 (optional, None)
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train:
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batch_size: 64
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debug: false
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early_stop: true
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early_stop_patience: 15
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epochs: 100
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grad_norm: false
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log_step: 1000
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loss_func: mae
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lr_decay: true
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lr_decay_rate: 0.3
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lr_decay_step: 5,20,40,70
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lr_init: 0.003
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mae_thresh: None
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mape_thresh: 0.001
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max_grad_norm: 5
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output_dim: 1
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plot: false
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real_value: true
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weight_decay: 0 |