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