caffe - 是否可以向 Caffe 添加新图层？

Question

我可以用 Caffe 添加一个新层（用于不同的损失）吗？我怎样才能使用 Matlab 或 Python 来做到这一点？

Answer 1

是的，您可以使用 pycaffe 添加自定义损失函数。这是 python 中的欧几里得损失层的示例（取自Caffe Github repo）。您需要在前向函数中提供损失函数，在后向方法中提供梯度：

import caffe
import numpy as np

class EuclideanLossLayer(caffe.Layer):
    """
    Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
    to demonstrate the class interface for developing layers in Python.
    """

    def setup(self, bottom, top):
        # check input pair
        if len(bottom) != 2:
            raise Exception("Need two inputs to compute distance.")

    def reshape(self, bottom, top):
        # check input dimensions match
        if bottom[0].count != bottom[1].count:
            raise Exception("Inputs must have the same dimension.")
        # difference is shape of inputs
        self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
        # loss output is scalar
        top[0].reshape(1)

    def forward(self, bottom, top):
        self.diff[...] = bottom[0].data - bottom[1].data
        top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.

    def backward(self, top, propagate_down, bottom):
        for i in range(2):
            if not propagate_down[i]:
                continue
            if i == 0:
                sign = 1
            else:
                sign = -1
            bottom[i].diff[...] = sign * self.diff / bottom[i].num

例如，将其另存为 pyloss.py。然后，您可以使用 prototxt 文件中的 python 层来加载它：

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
}

或者在你的 python 脚本中：

n.loss = L.Python(n.ipx, n.ipy,python_param=dict(module='pyloss',layer='EuclideanLossLayer'),
                     loss_weight=1)

在计算和实现梯度（后向函数）时要非常小心，因为它往往容易出错。