forked from mindspore-Ecosystem/mindspore
75 lines
2.0 KiB
YAML
75 lines
2.0 KiB
YAML
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
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enable_modelarts: False
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# Url for modelarts
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data_url: ""
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train_url: ""
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checkpoint_url: ""
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# Path for local
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data_path: "/cache/data"
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output_path: "/cache/train"
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load_path: "/cache/checkpoint_path"
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device_target: "Ascend"
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need_modelarts_dataset_unzip: True
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modelarts_dataset_unzip_name: "amazon_beauty"
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# ==============================================================================
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# options
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dataset: "Beauty"
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datapath: "./scripts/data_mr"
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Ks: [5, 10, 20, 100]
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workers: 8
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ckptpath: "../ckpts"
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epsilon: 0.00000001 # 1e-8
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learning_rate: 0.001 # 1e-3
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l2: 0.03
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activation: "tanh"
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neighbor_dropout: [0.0, 0.2, 0.3]
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log_name: "test"
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num_epoch: 600
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input_dim: 64
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batch_pairs: 5000
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eval_interval: 20
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num_neg: 10
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raw_neighs: 40
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gnew_neighs: 20
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embedded_dimension: 64
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dist_reg: 0.003
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# export option
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device_id: 0
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ckpt_file: ""
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file_name: "bgcf"
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file_format: "AIR"
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row_neighs: 40
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---
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# Help description for each configuration
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dataset: "choose which dataset"
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datapath: "minddata path"
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Ks: "top K"
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workers: "number of process to generate data"
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ckptpath: "checkpoint path"
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epsilon: "optimizer parameter"
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learning_rate: "learning rate"
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l2: "l2 coefficient"
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activation: "activation function, choices in ['relu', 'tanh']."
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neighbor_dropout: "dropout ratio for different aggregation layer"
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log_name: "log name"
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num_epoch: "epoch sizes for training"
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input_dim: "user and item embedding dimension, choices in [64, 128]"
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batch_pairs: "batch size"
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eval_interval: "evaluation interval"
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num_neg: "negative sampling rate "
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raw_neighs: "num of sampling neighbors in raw graph"
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gnew_neighs: "num of sampling neighbors in sample graph"
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embedded_dimension: "output embedding dim"
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dist_reg: "distance loss coefficient"
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device_target: "device target, choices in ['Ascend', GPU]"
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# export option
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device_id: "Device id"
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ckpt_file: "Checkpoint file path."
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file_name: "output file name."
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file_format: "file format, choices in ['AIR', 'ONNX', 'MINDIR']"
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row_neighs: "num of sampling neighbors in raw graph" |