Writing Custom Python Layer With Learnable Parameters in Caffe

Question

I know that this example is supposed to illustrate how to add trainable parameters in a Python layer using the add_blob() method.

However, I am still unable to understand how this can be used to set the dimensions of the blob based on user defined parameters.

There is a better example here on how to write a Python layer here. But here, the layer does not contain any trainable parameters.

Please explain how to write a custom Python layer with trainable parameters.

I'm not sure what you mean by "set dimensions of the blob". AFAIK, caffe sets the top blob size based on the bottom blob size and properties of the current layer; so, you don't need to set the dimensions. — Amir
– Amir, Commented Sep 13, 2016 at 4:53

Shai · Accepted Answer · 2016-09-13 06:07:55Z

0

When you add a parameters blob using add_blob(), you can reshape the added blob, either in setup() method (right when you add it), or in the layer's reshape() method.

answered Sep 13, 2016 at 6:07

Shai

115k39 gold badges259 silver badges398 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

Suhas Lohit Over a year ago

Could you comment on how to let the layer have hyper-parameters that are user-defined in the train_val.prototxt file ? Also, are you required to make any changes in the caffe.proto or the header files when writing a custom Python layer?

Collectives™ on Stack Overflow

Writing Custom Python Layer With Learnable Parameters in Caffe

1 Answer 1

1 Comment

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

1 Comment

Your Answer

Sign up or log in

Post as a guest

Linked

Related