caffe

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name: 'LinearRegressionExample'
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# define a simple network for linear regression on dummy data
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# that computes the loss by a PythonLayer.
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layer {
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  type: 'DummyData'
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  name: 'x'
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  top: 'x'
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  dummy_data_param {
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    shape: { dim: 10 dim: 3 dim: 2 }
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    data_filler: { type: 'gaussian' }
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  }
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}
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layer {
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  type: 'DummyData'
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  name: 'y'
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  top: 'y'
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  dummy_data_param {
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    shape: { dim: 10 dim: 3 dim: 2 }
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    data_filler: { type: 'gaussian' }
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  }
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}
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# include InnerProduct layers for parameters
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# so the net will need backward
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layer {
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  type: 'InnerProduct'
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  name: 'ipx'
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  top: 'ipx'
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  bottom: 'x'
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  inner_product_param {
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    num_output: 10
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    weight_filler { type: 'xavier' }
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  }
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}
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layer {
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  type: 'InnerProduct'
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  name: 'ipy'
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  top: 'ipy'
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  bottom: 'y'
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  inner_product_param {
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    num_output: 10
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    weight_filler { type: 'xavier' }
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  }
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}
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layer {
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  type: 'Python'
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  name: 'loss'
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  top: 'loss'
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  bottom: 'ipx'
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  bottom: 'ipy'
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  python_param {
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    # the module name -- usually the filename -- that needs to be in $PYTHONPATH
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    module: 'pyloss'
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    # the layer name -- the class name in the module
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    layer: 'EuclideanLossLayer'
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  }
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  # set loss weight so Caffe knows this is a loss layer.
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  # since PythonLayer inherits directly from Layer, this isn't automatically
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  # known to Caffe
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  loss_weight: 1
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}
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