Update readme

tasks/image_classification/README.md

@@ -23,7 +23,7 @@ torchrun --standalone --nnodes=1 --nproc_per_node=8 -m tasks.image_classificatio
 
 You can run the same setup on a single GPU with:
 ```
-python -m tasks.image_classification.train tasks.image_classification.train --d_model 4096 --d_input 1024 --synapse_depth 12 --heads 16 --n_synch_out 150 --n_synch_action 150 --neuron_select_type random --iterations 75 --memory_length 25 --deep_memory --memory_hidden_dims 64 --dropout 0.05 --no-do_normalisation --positional_embedding_type none --backbone_type resnet152-4 --batch_size 60 --batch_size_test 64 --lr 5e-4 --training_iterations 500001 --warmup_steps 10000 --use_scheduler --scheduler_type cosine --weight_decay 0.0 --log_dir logs-lambda/imagenet-distributed-4april/d=4096--i=1024--h=16--ns=150-random--iters=75x25--h=64--drop=0.05--pos=none--back=152x4--seed=42 --dataset imagenet --save_every 2000 --track_every 5000 --seed 42 --n_test_batches 50 --use_amp --device 0
+python -m tasks.image_classification.train --d_model 4096 --d_input 1024 --synapse_depth 12 --heads 16 --n_synch_out 150 --n_synch_action 150 --neuron_select_type random --iterations 75 --memory_length 25 --deep_memory --memory_hidden_dims 64 --dropout 0.05 --no-do_normalisation --positional_embedding_type none --backbone_type resnet152-4 --batch_size 60 --batch_size_test 64 --lr 5e-4 --training_iterations 500001 --warmup_steps 10000 --use_scheduler --scheduler_type cosine --weight_decay 0.0 --log_dir logs-lambda/imagenet-distributed-4april/d=4096--i=1024--h=16--ns=150-random--iters=75x25--h=64--drop=0.05--pos=none--back=152x4--seed=42 --dataset imagenet --save_every 2000 --track_every 5000 --seed 42 --n_test_batches 50 --use_amp --device 0
 ```
 
 ## Checkpoint