Caffe

This fork allows to use more MemoryData layers.

Example

Example of net configuration, that uses two memory data layers. Memory data layer is strict about the dimensionality of label output. In this example, label can have arbitrary dimensions.

layer {
  name: "data"
  type: "MemoryData"
  top: "data"
  top: "data_foo"
  include {
    phase: TRAIN
  }
  memory_data_param {
    batch_size: 8
    channels: 3
    height: 227
    width: 227
  }
}
layer {
  name: "label"
  type: "MemoryData"
  top: "label"
  top: "label_foo"
  include {
    phase: TRAIN
  }
  memory_data_param {
    batch_size: 8
    channels: 1844
    height: 1
    width: 1
  }
}
layer {
  name: "silence1"
  type: "Silence"
  bottom: "label_foo"
  include {
    phase: TRAIN
  }
}
layer {
  name: "silence2"
  type: "Silence"
  bottom: "data_foo"
  include {
    phase: TRAIN
  }
}

Unnecessary output (*_foo) is silenced with Silence layers. MemoryData layer has always two outputs, so we cannot omit ony of them.

In python, the usage is like this:

solver=caffe.SGDSolver(....)
solver.net.set_input_arrays(data, label_foo, 0)
solver.net.set_input_arrays(label, label_foo, 1)
solver.step(....)

set_input_arrays method has additional parameter, index of memory data layer, into which the arrays will be connected