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#!/bin/sh
export CUDA_VISIBLE_DEVICES=0
# Your tcmalloc .so path
export LD_PRELOAD="/usr/lib/libtcmalloc.so"
train_dir="./group_2-2-2"
# Our train/val split dataset
# Run 'python download_cifar100.py' before training
data_dir="./cifar100/train_val_split"
python train.py --train_dir $train_dir \
--data_dir $data_dir \
--batch_size 90 \
--test_interval 500 \
--test_iter 50 \
--num_residual_units 2 \
--k 8 \
--ngroups1 2 \
--ngroups2 2 \
--ngroups3 2 \
--l2_weight 0.0001 \
--gamma1 1.0 \
--gamma2 1.0 \
--gamma3 10.0 \
--dropout_keep_prob 0.5 \
--initial_lr 0.1 \
--lr_step_epoch 240.0,300.0 \
--lr_decay 0.1 \
--bn_no_scale True \
--weighted_group_loss True \
--max_steps 200000 \
--checkpoint_interval 5000 \
--group_summary_interval 5000 \
--gpu_fraction 0.96 \
--display 100 \
#--checkpoint "./group_2-2-2/model.ckpt-149999" \
#--finetune True \
#--basemodel "./baseline/model.ckpt-449999" \
#--load_last_layers True \
# Deep split(2-2-2)
# Dropout with prob 0.5
# Softmax reparametrization & Training from scratch