Comments (8)
cbfinn
commented on July 29, 2024
For the omniglot experiments, I trained on 1-shot and evaluated on 5-shot.
To evaluate on 5-shot using a model trained on 1-shot, use the flags --train=False --update_batch_size=5 --train_update_batch_size=1
If you want to train a model on 5-shot, you can also reduce the meta-batch size so that it fits in memory.
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zfengwei
commented on July 29, 2024
OK, I see. Thank you very much! I reduced the meta_batch_size to 1 and num_updates to 1, but it still raised the same error. Also, I did these experiments several times and I got different results. It seems that there are some random factors, such as initialization of the network. How much of these random factors will have an effect on the results?
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cbfinn
commented on July 29, 2024
Generally, I have found the experiments to be fairly repeatable at
convergence (though learning speed at the beginning of training may vary).
What GPU are you using? You need a GPU with sufficient memory. I used a
Titan X, which has 12 GB.
…On Jul 18, 2017 6:57 PM, "zfengwei" ***@***.***> wrote:
OK, I see. Thank you very much! I reduced the meta_batch_size to 1 and
num_updates to 1, but it still raised the same error. Also, I did these
experiments several times and I got different results. It seems that there
are some random factors, such as initialization of the network. How much of
these random factors will have an effect on the results?
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zfengwei
commented on July 29, 2024
I also use a GPU with 12GB. It seems that this is the restriction of tensorflow. It cannot create a tensor larger than 2GB.
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cbfinn
commented on July 29, 2024
Ok. Is this the input tensor that the error is referring to? I don't think
that you should be hitting 2 GB with the input tensor with a meta-batch
size of 1. I would estimate that for 20-way classification, a meta-batch
size of 1, 5-shot, it should be 20*(5+15)*(80*80*3)*32 bits = 0.03 GB.
[The 5+15 is 5 images for calculating the gradient step and 15 images for
the post-update evaluation.]
Can you provide more information on the error?
…On Tue, Jul 18, 2017 at 8:57 PM, zfengwei ***@***.***> wrote:
I also use a GPU with 12GB. It seems that this is the restriction of
tensorflow. It cannot create a tensor larger than 2GB.
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cbfinn
commented on July 29, 2024
If you want to try decreasing the 15 (to as low as 1), you change this line:
https://github.com/cbfinn/maml/blob/master/main.py#L249
On Tue, Jul 18, 2017 at 9:09 PM, Chelsea Finn <[email protected]>
wrote:
… Ok. Is this the input tensor that the error is referring to? I don't think
that you should be hitting 2 GB with the input tensor with a meta-batch
size of 1. I would estimate that for 20-way classification, a meta-batch
size of 1, 5-shot, it should be 20*(5+15)*(80*80*3)*32 bits = 0.03 GB.
[The 5+15 is 5 images for calculating the gradient step and 15 images for
the post-update evaluation.]
Can you provide more information on the error?
On Tue, Jul 18, 2017 at 8:57 PM, zfengwei ***@***.***>
wrote:
> I also use a GPU with 12GB. It seems that this is the restriction of
> tensorflow. It cannot create a tensor larger than 2GB.
>
> —
> You are receiving this because you commented.
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zfengwei
commented on July 29, 2024
Thank you for your reply! I think the error is caused by this line:
https://github.com/cbfinn/maml/blob/master/data_generator.py#L86
I reduced the num_total_batches to 100000 and I can train the model on 5-shot.
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cbfinn
commented on July 29, 2024
Ah, okay. Thanks for letting me know!
…On Jul 19, 2017 12:35 AM, "zfengwei" ***@***.***> wrote:
Thank you for your reply! I think the error is caused by this line:
https://github.com/cbfinn/maml/blob/master/data_generator.py#L86
<http://url>
I reduced the num_total_batches to 100000 and I can train the model on
5-shot.
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