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overfeat, Can you help me solve this problem #10

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HTaCheng opened this issue Oct 21, 2020 · 5 comments
Closed

overfeat, Can you help me solve this problem #10

HTaCheng opened this issue Oct 21, 2020 · 5 comments

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@HTaCheng
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HTaCheng commented Oct 21, 2020

选区_001

@craymichael
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Do you continue to have this problem out of debug mode? There is a strong possibility that this is due to Overfeat giving results that put nans or infs in the similarity matrix. For example, there is issue #1 (comment). It is likely this is on the Overfeat side of things.

@HTaCheng
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Thank you very much for your reply.
Yes, this problem exists whether it is debug mode or not. I have also seen the previous issue, but still can't find a solution.

@craymichael
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Hi, I was able to reproduce the issue - I had forgotten what the error messages "looked" like. This is definitely the same issue as I referenced above. Unfortunately, this is an overfeat issue, and it doesn't look like they will fix it, and I am unsure what the problem is as the code used to work fine. See sermanet/OverFeat#39. This is something I will not fix - my recommendation is that you use one of the TensorFlow slim models I describe, or find an implementation of overfeat to replace the sermanet implementation in the code. I apologize that this isn't working, and I will likely archive this project soon as the code is old and isn't planned on being maintained for new versions of libraries.

@HTaCheng
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HTaCheng commented Nov 21, 2020

How do I get the output of this network layer structure? If I want to change the network model such as resnet-50, how can I change this?

DEFAULT_FEATURE_LAYER = {
'inception_v1': 'InceptionV1/Logits/AvgPool_0a_7x7/AvgPool:0',
'inception_v2': 'InceptionV2/Logits/AvgPool_1a_7x7/AvgPool:0',
'inception_v3': 'InceptionV3/Logits/AvgPool_1a_8x8/AvgPool:0',
'inception_v4': 'InceptionV4/Logits/AvgPool_1a/AvgPool:0',
'nasnet_large': 'final_layer/Mean:0',
'resnet_v2_152': 'resnet_v2_152/pool5:0',
'overfeat_0': 19,
'overfeat_1': 20
}
网络结构014

@craymichael
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Hi, only resnet_v2_152 is available for use. You can specify this on the command-line (run main script with --help for all options). If you really want ResNet-50, you will need to modify the code, assuming TF Slim has the model and weights you want. Add the name of the model and its metadata to the DEFAULT_FEATURE_LAYER, MODEL_PARAMS_NAME, and MODEL_CKPT_PATHS dictionaries - follow the other model definitions as guidance.

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