Chiyuan Zhang
Chiyuan Zhang
Yes, this is technically possible for could backend only. Though I doubt it will be a seriously issue because nowadays cpu memory is very large. If you have huge models,...
For "cpu backend only", sorry I am on a phone and the auto correction is so crazy that it does not know cpu.
All the layers mentioned here are supported. Checkout the I Julia notebook of pretrained image net model for example of the correspondence.
Sorry I'm currently traveling and do not have a computer. So I'll try to be brief. There is a link to the notebook in the tutorial section of the doc....
Yes caffe has two blobs for convolution. They are not bottoms, bottoms are input blobs, what we were talking about are parameter blobs. I'm not sure I like the idea...
@waTeim filters and bias are parameters of a layer. For example, in an InnerProductLayer, top = parameter \* bottom. There are three kinds of blobs: input (bottom), output (top), and...
Is the x object a mocha net or a caffe net? In mocha layer state, there is a field called blobs which hold reference to output blobs, but you don't...
Yes, the easiest way I can imagine is to create an initializer that simply copy the content of an existing array to the target blob being initialized. Something roughly like...
Yes, maybe small modifications - I'm not sure whether the data in `caffe.BlobProto` will retain after you close the protobuffer file. You might need to copy the data into a...
@waTeim Yes, `num_output` is exactly `output_dim`, and similar as before, the `filler`s correspond to `initializer`s in Mocha.