• Note: Newer version: https://github.com/gustavla/caffe/tree/gustav

  This offers the same functionality in a less invasive way, which means it can be kept up-to-date with the master branch much easier.

This is a branch of Caffe that allows training with weighted samples. The branch is experimental, so not every layer is updated to fit this new paradigm, nor are unit tests updated (yet). It might also easily break, since it has not be throughly tested.

The input data in Caffe is normally an associate array with the keys "data" and "label". The sample weights are input through this data structure by adding another key called "sample_weight". The weights should have the same shape as the labels.

Now, sample_weight can be accessed just like data and label, so we need to make sure our network's data layer loads them in:

layers { # Your data layer
    # ...
    top: "data"
    top: "label"
    top: "sample_weight"
}

Connect them to your loss layer:

layers { # Your loss layer
    name: "loss"
    type: SOFTMAX_LOSS
    bottom: "ip1"  # or whatever name it might have
    bottom: "label"
    bottom: "sample_weight"
    top: "loss"
}

Read sample_weight through:

• HDF5
• lmdb (untested)
• leveldb (untested)

The layers that have been made to appreciate sample_weight are:

• SOFTMAX_LOSS (label and sample_weight should be 1D)
• EUCLIDEAN_LOSS (label and sample_weight should be 2D)

New layers althogether include:

• SOFTMAX_CROSS_ENTROPY_LOSS (a multi-class generalization of SIGMOID_CROSS_ENTROPY_LOSS)

For now, sample_weight is required to be specified, both in the training and testing data (even though it is not used in the latter).

Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.
Consult the project website for all documentation.