python train.py --checkpoint checkpoints/chess/ffsept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model feedforward_net_2d --optimizer sgd --output results/chess/ffsept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_mode default --train_batch_size 300 --train_data 600000 --train_log mi30_ff_net_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/ffsept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model feedforward_net_2d --optimizer sgd --output results/chess/ffsept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_mode default --train_batch_size 300 --train_data 600000 --train_log mi30_ff_net_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/ffsept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model feedforward_net_2d --optimizer sgd --output results/chess/ffsept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_mode default --train_batch_size 300 --train_data 600000 --train_log mi30_ff_net_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512

python train.py --checkpoint checkpoints/chess/ffoct2_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model feedforward_net_recallx_2d --optimizer sgd --output results/chess/ffoct2_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_mode default --train_batch_size 300 --train_data 600000 --train_log mi30_ff_recallx_net_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/ffoct2_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model feedforward_net_recallx_2d --optimizer sgd --output results/chess/ffoct2_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_mode default --train_batch_size 300 --train_data 600000 --train_log mi30_ff_recallx_net_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/ffoct2_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model feedforward_net_recallx_2d --optimizer sgd --output results/chess/ffoct2_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_mode default --train_batch_size 300 --train_data 600000 --train_log mi30_ff_recallx_net_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512

python train.py --checkpoint checkpoints/chess/ffoct2_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model feedforward_net_recallx_2d --optimizer sgd --output results/chess/ffoct2_main_schoop --problem chess --test_batch_size 100 --test_data 700000 --test_mode default --train_batch_size 100 --train_data 600000 --train_log mi30_ff_recallx_net_2d_w05_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512
python train.py --checkpoint checkpoints/chess/ffoct2_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model feedforward_net_recallx_2d --optimizer sgd --output results/chess/ffoct2_main_schoop --problem chess --test_batch_size 100 --test_data 700000 --test_mode default --train_batch_size 100 --train_data 600000 --train_log mi30_ff_recallx_net_2d_w05_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512
python train.py --checkpoint checkpoints/chess/ffoct2_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model feedforward_net_recallx_2d --optimizer sgd --output results/chess/ffoct2_main_schoop --problem chess --test_batch_size 100 --test_data 700000 --test_mode default --train_batch_size 100 --train_data 600000 --train_log mi30_ff_recallx_net_2d_w05_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512

# >22 hrs
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_2d_02.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_2d_03.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_2d_04.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512

# >30 hrs
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_recall_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_recall_2d_02.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_recall_2d_03.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_recall_2d_04.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512

# >36 hrs
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_recallx_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_recallx_2d_02.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_recallx_2d_03.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0_mk1_mn0_recur_net_recallx_2d_04.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0 --width 512

# >32 hrs
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_2d_02.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_2d_03.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_2d_04.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512

# >48 hrs
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recall_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recall_2d_02.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recall_2d_03.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recall_2d_04.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512

# >40 hrs
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recallx_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recallx_2d_02.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recallx_2d_03.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recallx_2d_04.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 0.5 --width 512

# >24 hrs
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_2d_02.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_2d_03.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_2d_04.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512

# >32 hrs
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recall_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recall_2d_02.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recall_2d_03.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recall_2d_04.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512

# >30 hrs
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recallx_2d_01.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recallx_2d_02.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recallx_2d_03.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512
python train.py --checkpoint checkpoints/chess/sept10_main_schoop --epochs 120 --lr 0.01 --lr_decay step --lr_factor 0.1 --lr_schedule 100 110 --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recallx_2d --optimizer sgd --output results/chess/sept10_main_schoop --problem chess --test_batch_size 300 --test_data 700000 --test_iterations 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 --test_mode max_conf --train_batch_size 300 --train_data 600000 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recallx_2d_04.txt --train_mode progressive --val_period 50 --warmup_period 3 --weight_for_loss 1 --width 512
