python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.01_mk1_mn0_recur_net_recall_2d_01.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.01 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.01_mk1_mn0_recur_net_recall_2d_02.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.01 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.01_mk1_mn0_recur_net_recall_2d_03.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.01 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.01_mk1_mn0_recur_net_recall_2d_04.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.01 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.01_mk1_mn0_recur_net_recall_2d_05.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.01 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.1_mk1_mn0_recur_net_recall_2d_01.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.1 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.1_mk1_mn0_recur_net_recall_2d_02.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.1 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.1_mk1_mn0_recur_net_recall_2d_03.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.1 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.1_mk1_mn0_recur_net_recall_2d_04.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.1 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.1_mk1_mn0_recur_net_recall_2d_05.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.1 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recall_2d_01.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.5 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recall_2d_02.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.5 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recall_2d_03.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.5 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recall_2d_04.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.5 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.5_mk1_mn0_recur_net_recall_2d_05.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.5 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recall_2d_01.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 1 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recall_2d_02.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 1 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recall_2d_03.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 1 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recall_2d_04.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 1 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt1_mk1_mn0_recur_net_recall_2d_05.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 1 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.0_mk1_mn0_recur_net_recall_2d_01.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.0 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.0_mk1_mn0_recur_net_recall_2d_02.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.0 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.0_mk1_mn0_recur_net_recall_2d_03.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.0 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.0_mk1_mn0_recur_net_recall_2d_04.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.0 --width 128 
python train.py --checkpoint checkpoints/mazes/recur_net --epochs 50 --lr 0.001 --lr_decay step --lr_factor 0.1 --lr_schedule 100 --lr_throttle --max_iters 30 --min_k 1 --min_n 0 --model recur_net_recall_2d --optimizer adam --output results/mazes/recur_net --problem mazes --test_batch_size 25 --test_data 33 --test_iterations 20 30 40 50 60 70 80 90 100 150 200 300 400 500 750 1000 --test_mode max_conf --train_batch_size 50 --train_data 9 --train_log mi30_tmprogressive_wt0.0_mk1_mn0_recur_net_recall_2d_05.txt --train_mode progressive --val_period 20 --warmup_period 10 --weight_for_loss 0.0 --width 128 
