如何使用截断的 SVD 减少全连接(“InnerProduct")层

本文介绍了如何使用截断的 SVD 减少全连接(“InnerProduct")层的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

在论文中 Girshick, R Fast-RCNN(ICCV 2015)，3.1 Truncated SVD for fast detection"部分，作者建议使用SVD 技巧来减少全连接层的大小和计算时间.

In the paper Girshick, R Fast-RCNN (ICCV 2015), section "3.1 Truncated SVD for faster detection", the author proposes to use SVD trick to reduce the size and computation time of a fully connected layer.

给定一个训练模型(deploy.prototxt 和 weights.caffemodel)，我如何使用这个技巧来替换一个全连接层截断的?

Given a trained model (deploy.prototxt and weights.caffemodel), how can I use this trick to replace a fully connected layer with a truncated one?

推荐答案

一些线性代数背景
奇异值分解 (SVD) 是任何矩阵 W 的分解成三个矩阵:

Some linear-algebra background
Singular Value Decomposition (SVD) is a decomposition of any matrix W into three matrices:

W = U S V*

其中U 和V 是正交矩阵，S 是对角线，元素在对角线上的大小递减.SVD 的一个有趣特性是它允许使用较低秩矩阵轻松逼近 W:假设您将 S 截断为只有它的 k> 前导元素(而不是对角线上的所有元素)然后

Where U and V are ortho-normal matrices, and S is diagonal with elements in decreasing magnitude on the diagonal.One of the interesting properties of SVD is that it allows to easily approximate W with a lower rank matrix: Suppose you truncate S to have only its k leading elements (instead of all elements on the diagonal) then

W_app = U S_trunc V*

是W的秩k近似值.

使用 SVD 逼近全连接层
假设我们有一个带有全连接层的模型 deploy_full.prototxt

# ... some layers here
layer {
  name: "fc_orig"
  type: "InnerProduct"
  bottom: "in"
  top: "out"
  inner_product_param {
    num_output: 1000
    # more params...
  }
  # some more...
}
# more layers...

此外，我们有 trained_weights_full.caffemodel - deploy_full.prototxt 模型的训练参数.

Furthermore, we have trained_weights_full.caffemodel - trained parameters for deploy_full.prototxt model.

将 deploy_full.protoxt 复制到 deploy_svd.protoxt 并在您选择的编辑器中打开它.用这两层替换全连接层:

Copy deploy_full.protoxt to deploy_svd.protoxt and open it in editor of your choice. Replace the fully connected layer with these two layers:

layer {
  name: "fc_svd_U"
  type: "InnerProduct"
  bottom: "in" # same input
  top: "svd_interim"
  inner_product_param {
    num_output: 20  # approximate with k = 20 rank matrix
    bias_term: false
    # more params...
  }
  # some more...
}
# NO activation layer here!
layer {
  name: "fc_svd_V"
  type: "InnerProduct"
  bottom: "svd_interim"
  top: "out"   # same output
  inner_product_param {
    num_output: 1000  # original number of outputs
    # more params...
  }
  # some more...
}

在 python 中，有点网络手术:

import caffe
import numpy as np

orig_net = caffe.Net('deploy_full.prototxt', 'trained_weights_full.caffemodel', caffe.TEST)
svd_net = caffe.Net('deploy_svd.prototxt', 'trained_weights_full.caffemodel', caffe.TEST)
# get the original weight matrix
W = np.array( orig_net.params['fc_orig'][0].data )
# SVD decomposition
k = 20 # same as num_ouput of fc_svd_U
U, s, V = np.linalg.svd(W)
S = np.zeros((U.shape[0], k), dtype='f4')
S[:k,:k] = s[:k]  # taking only leading k singular values
# assign weight to svd net
svd_net.params['fc_svd_U'][0].data[...] = np.dot(U,S)
svd_net.params['fc_svd_V'][0].data[...] = V[:k,:]
svd_net.params['fc_svd_V'][1].data[...] = orig_net.params['fc_orig'][1].data # same bias
# save the new weights
svd_net.save('trained_weights_svd.caffemodel')

现在我们有 deploy_svd.prototxt 和 trained_weights_svd.caffemodel 以更少的乘法和权重来近似原始网络.

Now we have deploy_svd.prototxt with trained_weights_svd.caffemodel that approximate the original net with far less multiplications, and weights.

这篇关于如何使用截断的 SVD 减少全连接(“InnerProduct")层的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！

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