Description
set -x
cifar100
GPU=1
DATASET=cifar100
MODEL=resnet50
EPOCH=20
BATCH=128
LR=0.1
WD=0.0002
AWD=0.0
ALR=0.005
CUTOUT=16
TEMPERATE=0.5
which python
python train_search_paper.py --unrolled --report_freq 1 --num_workers 0 --epoch ${EPOCH} --batch_size ${BATCH} --learning_rate ${LR} --dataset ${DATASET} --model_name ${MODEL} --gpu ${GPU} --arch_weight_decay ${AWD} --arch_learning_rate ${ALR} --weight_decay ${WD} --cutout --cutout_length ${CUTOUT} --temperature ${TEMPERATE}
Hello, I used the reset50 network to search for the augmentation policy. During searching, I noticed that the accuracy for training and validation is very low.
04/27 03:59:04 PM valid 187 2.398671e+00 38.285406 70.545213
04/27 03:59:04 PM valid 188 2.397762e+00 38.289517 70.568783
04/27 03:59:04 PM valid 189 2.397031e+00 38.297697 70.575658
04/27 03:59:04 PM valid 190 2.395328e+00 38.326243 70.590641
04/27 03:59:04 PM valid 191 2.396694e+00 38.309733 70.576986
04/27 03:59:04 PM valid 192 2.396227e+00 38.337921 70.575615
04/27 03:59:05 PM valid 193 2.396536e+00 38.321521 70.574259
04/27 03:59:05 PM valid 194 2.396318e+00 38.325321 70.584936
04/27 03:59:05 PM valid 195 2.395298e+00 38.336000 70.588000
04/27 03:59:05 PM valid_acc 38.336000
Is this Ok?
Activity