/
default_config.yaml
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/
default_config.yaml
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---
problem: qap # PB_DIR = experiments-gnn/$problem
name: expe_norm # results will be stored in PB_DIR/$name
cpu: No
#root_dir: 'experiments-gnn' # not used...
#test_enabled: Yes
#use_dgl: No
#path_dataset: data # Path where datasets are stored, default data/
data:
train: # Train/Val data generation parameters
num_examples_train: 20000
num_examples_val: 1000
n_vertices: 50
sparsify: None #Only works for not fgnns. Put to None if you don't want sparsifying
generative_model: Regular #Seed # so far ErdosRenyi, Regular or BarabasiAlbert
noise_model: ErdosRenyi
edge_density: 0.2 #0.05 #0.015 #0.025
vertex_proba: 1. # Parameter of the binomial distribution of vertices
noise: 0.1 #0.3 #0.32 #0.2 #0.2 0.4 0.6 0.8 0.9
test: #Test data generation parameters not used yet...
num_examples_test: 1000
n_vertices: 50
#sparsify: None #Only works for not fgnns. Put to None if you don't want sparsifying
#custom: No #If No, keeps the data_generation from train, just a failsafe so people consciously have to activate custom test
generative_model: Regular #Seed # so far ErdosRenyi, Regular or BarabasiAlbert
noise_model: ErdosRenyi
edge_density: 0.2 #0.0125
vertex_proba: 1. # Parameter of the binomial distribution of vertices
noise: 0.1
path_model: '/home/mlelarge/experiments-gnn/qap/expe_norm/node_embedding_Regular_100_0.05/07-27-23-14-45/qap_expe_norm/prges07j/checkpoints/epoch=9-step=6250.ckpt'
#path_model: '/home/mlelarge/experiments-gnn/qap/expe_norm/node_embedding_RegularSeed_100_0.05/07-25-23-11-30/qap_expe_norm/mvki2vap/checkpoints/epoch=9-step=6250.ckpt' #'/home/mlelarge/experiments-gnn/qap/expe_norm/node_embedding_Regular_100_0.05/07-19-23-11-54/qap_expe_norm/qye55q7e/checkpoints/epoch=7-step=5000.ckpt' #'/home/mlelarge/experiments-gnn/qap/expe_norm/node_embedding_rec_Regular_100_0.05/01-12-23-14-18/qap_expe_norm/262h3uh7/checkpoints/epoch=4-step=3125.ckpt'
train: # Training parameters
epochs: 100
batch_size: 256 #32 #10 #8 #32 #16 #64
lr: !!float 1e-3 #1e-3
scheduler_step: 3
scheduler_decay: 0.5
lr_stop: !!float 1e-5
log_freq: 50
anew: Yes
start_model: '/home/mlelarge/experiments-gnn/qap/qap_res/gatedgcn_8_ErdosRenyi_64_0.09375/02-11-22-20-55/model_best.pth.tar' #'/home/mlelarge/experiments-gnn/qap/qap_res/fgnn_4_ErdosRenyi_64_0.09375/02-11-22-09-31/model_best.pth.tar'
arch: # Architecture and model
original_features_num: 2 # 2 for fgnn 1 for mgnn
node_emb:
type: node_embedding
block_init: block_emb
block_inside: block
num_blocks: 4
in_features: 32
out_features: 32
depth_of_mlp: 3
num_heads: 16
#arch_gnn: fgnn #fgnn, gcn, gatedgcn
#arch_load: siamese #siamese or simple(to be done)
#embedding: node #node or edge, rs_node
#num_blocks: 4 #4
#dim_features: 64 #64
#depth_of_mlp: 3
#input_embed: No # No
observers:
wandb: Yes