small problems when testing

[csyanbin]

When I run the testing code after training as follow:
python main.py --datasource=miniimagenet --metatrain_iterations=60000 --meta_batch_size=4 --update_batch_size=1 --update_lr=0.01 --num_updates=5 --num_classes=5 --logdir=log/miniimagenet1shot/ --num_filters=32 --max_pool=True

python main.py --datasource=miniimagenet --metatrain_iterations=60000 --meta_batch_size=2 --update_batch_size=5 --update_lr=0.01 --num_updates=5 --num_classes=5 --logdir=logs/miniimagenet5shot/ --num_filters=32 --max_pool=True

I encountered several problems:
1). seems that in main.py Line 194-197 need indentation
L290 in main.py need to be commented for testing
2). I follow the default code and setting, but only get 46.90% and 61.03% for 1-shot and 5-shot rather than 48.70% and 63.11% reported in the paper on test set. Do I miss any training or testing tricks?

[cbfinn]

For 1, actually lines 175-178 can just be deleted. Thanks for catching that.
For 2:
Are you using the validation set or the test set? [Test set performance tends to be higher than the val performance. To test on the test set, set --train=False --test_set=True]
Are you testing for more than 5 steps? I think I tested for ~8-10 gradient steps during meta-test time (exact number is in the paper).

[cbfinn]

I also fixed the issue on line 290 (with an if statement)