CODEWITHSUNDEEP
matlabneural-networkregressioncaffeconv-neural-network
Deven
Deven

Reputation: 617

Single Label Regression(Finetuning) With Input Artificial Features In Caffe

I have,say, n images and for each of them I have additional 2 artificial(made-up) features, and image-labels are single dimensional integers.

I want to fine-tune Image-net on my dataset, but I do not know how to handle these 2 additional features as input, how should I feed the data to caffe? Please help!

EDIT: The 2 features can be any 2 numbers (1 dimensional) say two numbers representing what class an image falls into, and how many images fall into that class.

Say, I have 'cat.jpg', then the features are say, 5 and 2000, where 5 is the feature 1 representing the class and 2000 is the total images in that class. In short, the 2 features can be any two integers.

Upvotes: 0

Views: 148

Answers (1)

Shai
Shai

Reputation: 114826

I think the most straight forward way for you is to use "HDF5Data" input layer, where you can store both the input images, the additional two "features" and the expected output value (for regression).

You can see an example here for creating HDF5 data in python. A Matlab example can be found here.

Your HDF5 should have 4 "datasets": one is the input images (or the image descriptors of dim 4096). n dimensional array of images/descriptors.
Another dataset is "feat_1" an n by 1 array, and "feat_2" and n by 1 array.
Finally you should have another input "target" an n by 1 array of the expected output you wish to learn.

Once you have an HDF5 file ready with these datasets in it, you should have

layer {
  type: "HDF5Data"
  top: "data" # name of dataset with images/imagenet features
  top: "feat_1"
  top: "feat_2"
  top: "target"
  hdf5_data_param {
    source: "/path/to/list/file.txt"
  }
}

As you can see a single "HDF5Data" layer can produce several "top"s.

Upvotes: 1

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