No batchnorm layer in your source?

[SarthakYadav]

I am trying to use Resnet50. But your src has no files corresponding to batch_norm_layers, therefore the binary has no concept of "batch_norm". How can one build it with batch_norm support? The latest caffe code for the same obviously throws a lot of errors

[kevinlin311tw]

Since this caffe is an old version, it doesn't support batch normalization. One of the possible solutions is adding our loss functions to a newer caffe. Then, you can modify the last few layers of the deep network, and learn binary codes. 2017-07-20 6:49 GMT-07:00 Sarthak Yadav <[email protected]>:

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I am trying to use Resnet50. But your src has no files corresponding to batch_norm_layers, therefore the binary has no concept of "batch_norm". How can one build it with batch_norm support? The latest caffe code for the same obviously throws a lot of errors — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#19>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AJgtMNwUo6PZOdWNevUHCGtvb9bBGj6Pks5sP1rSgaJpZM4OeIIG> .

-- Best regards, 林可昀 Kevin Lin

[SarthakYadav]

I actually added the source code for the loss function implementations. They wouldn't compile with new Caffe versions

[kevinlin311tw]

Sorry for the late reply. I am too busy with current projects.

If you want to generate binary codes quickly and avoid additional implementations, you can just train the model with latent layer + sigmoid activation function + softmax loss (this is exactly our workshop paper). It already learns high-quality and somehow evenly-distributed binary codes.