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experiment_log
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### fcn baseline with softmax inference
./train.py -c 1 -n softmax_pascal -d pascal -g 0
./train.py -c 1 -n softmax_context -d context -g 0
### one-hot
# see performance drop from onehot embeddings (perfect) to 21D Word2Vec embeddings
# TODO: finetune learning rate
./train.py -c 2 -n 21D -lr 1e-5 -g 0
./train.py -c 2 -n 21D_one_hot -lr 1e-5 -oh -g 0
### joint embedding model
# get best embedding size (writeup assumes 20D is best)
./train.py -c 4 -n 5D -e 5 -g 0 # TODO: finetune learning rate
./train.py -c 4 -n 10D -e 10 -g 0 # TODO: finetune learning rate
./train.py -c 4 -n 20D -e 20 -g 0 # TODO: finetune learning rate
./train.py -c 4 -n 50D -e 50 -g 0 # TODO: finetune learning rate
# sgd vs adam
./train.py -c 4 -n 20D_context -o sgd -g 0
# loss function
./train.py -c 4 -n 20D_pascal -loss mse -g 0
# dataset baselines
./train.py -c 4 -n 20D_context -d context -g 0
### zeroshot
./train.py -n "8_2_10" -c 14 -g 2
./train.py -n "8_2_10" -c 15 -g 0 -m "test_fcn"
./train.py -n "8_2_10" -c 15 -g 0 -m "test_all"
./train.py -n "8_2_10" -c 15 -g 0 -m "test_fcn" -fu
./train.py -n "16_2_2" -c 16 -g 1
./train.py -n "16_2_2" -c 17 -g 1 -m "test_fcn"
./train.py -n "16_2_2" -c 17 -g 1 -m "test_all"
./train.py -n "16_2_2" -c 17 -g 1 -m "test_fcn" -fu
./train.py -n "31_2_2" -c 18 -g 2
./train.py -n "31_2_2" -c 19 -g 2 -m "test_fcn"
./train.py -n "31_2_2" -c 19 -g 2 -m "test_all"
./train.py -n "31_2_2" -c 19 -g 2 -m "test_fcn" -fu
column -s, -t < val_log.csv | less -#2 -N -S
# one hot with inference (mse)
# cross dataset zeroshot
base minimum for monday:
./train.py -c 4 -n 20D -g 0
./train.py -c 14 -n "8_2_10" -g 2
./train.py -c 16 -n "16_2_2" -g 3