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update configs

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Xuanyi Dong
2019-03-31 23:00:37 +08:00
parent 4bac459bf9
commit 322d5462a3
5 changed files with 31 additions and 1 deletions
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1
configs/nas-cifar-cos-cut.config
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@@ -5,6 +5,7 @@
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0003],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],

14
configs/nas-cifar-cos-cutB128.config Normal file
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@@ -0,0 +1,14 @@
{
"type" : ["str", "cosine"],
"batch_size": ["int", 128],
"epochs" : ["int", 600],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0003],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"cutout" : ["int", 16],
"drop_path_prob" : ["float", 0.2]
}

14
configs/nas-cifar-cos-cutB64.config Normal file
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@@ -0,0 +1,14 @@
{
"type" : ["str", "cosine"],
"batch_size": ["int", 64],
"epochs" : ["int", 600],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0003],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"cutout" : ["int", 16],
"drop_path_prob" : ["float", 0.2]
}

1
configs/nas-cifar-cos-nocut.config
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@@ -5,6 +5,7 @@
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0003],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],

2
exps-cnn/train_utils.py
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@@ -54,7 +54,7 @@ def main_procedure(config, dataset, data_path, args, genotype, init_channels, la
optimizer = torch.optim.SGD(model.parameters(), config.LR, momentum=config.momentum, weight_decay=config.decay)
#optimizer = torch.optim.SGD(model.parameters(), config.LR, momentum=config.momentum, weight_decay=config.decay, nestero=True)
if config.type == 'cosine':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(config.epochs))
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(config.epochs), eta_min=float(config.LR_MIN))
else:
raise ValueError('Can not find the schedular type : {:}'.format(config.type))