Neural network 如何在Google Net上计算Inception模块的感受野？
图中附有谷歌网的一个初始模块。 我们如何计算这个初始模块的感受野？ 我们可以只计算一个卷积分支吗 编辑： 我有一个计算感受野大小的程序Neural network 如何在Google Net上计算Inception模块的感受野？,neural-network,computer-vision,deep-learning,caffe,conv-neural-network,Neural Network,Computer Vision,Deep Learning,Caffe,Conv Neural Network,图中附有谷歌网的一个初始模块。 我们如何计算这个初始模块的感受野？ 我们可以只计算一个卷积分支吗 编辑： 我有一个计算感受野大小的程序 import math convnet = [[7,2,3],[1,1,0],[3,2,0],[1,1,0],[1,1,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[3,2,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[3,2,0],[1,1
import math
convnet = [[7,2,3],[1,1,0],[3,2,0],[1,1,0],[1,1,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[3,2,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[3,2,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[5,3,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[5,3,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[3,2,1],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[1,1,0],[1,1,0],[3,1,1],[1,1,0],[7,1,1]]
layer_names = ["conv1/7x7_s2","conv1/relu_7x7","pool1/3x3_s2","pool1/norm1","conv2/3x3_reduce","conv2/relu_3x3_reduce","conv2/3x3","conv2/relu_3x3","pool2/3x3_s2","inception_3a/3x3_reduce","inception_3a/relu_3x3_reduce","inception_3a/3x3","inception_3a/relu_3x3","inception_3b/3x3_reduce","inception_3b/relu_3x3_reduce","inception_3b/3x3","inception_3b/relu_3x3","pool3/3x3_s2","inception_4a/3x3_reduce","inception_4a/relu_3x3_reduce","inception_4a/3x3","inception_4a/relu_3x3","loss1/ave_pool","inception_4b/3x3_reduce","inception_4b/relu_3x3_reduce","inception_4b/3x3","inception_4b/relu_3x3","inception_4c/3x3_reduce","inception_4c/relu_3x3_reduce","inception_4c/3x3","inception_4c/relu_3x3","inception_4d/3x3_reduce","inception_4d/relu_3x3_reduce","inception_4d/3x3","inception_4d/relu_3x3","loss2/ave_pool","inception_4e/3x3_reduce","inception_4e/relu_3x3_reduce","inception_4e/3x3","inception_4e/relu_3x3","pool4/3x3_s2","inception_5a/3x3_reduce","inception_5a/relu_3x3_reduce","inception_5a/3x3","inception_5a/relu_3x3","inception_5b/3x3_reduce","inception_5b/relu_3x3_reduce","inception_5b/3x3","inception_5b/relu_3x3","pool5/7x7_s1"]
imsize = 720
def outFromIn(isz, layernum, net = convnet):
if layernum>len(net): layernum=len(net)
totstride = 1
insize = isz
#for layerparams in net:
for layer in range(layernum):
fsize, stride, pad = net[layer]
outsize = (insize - fsize + 2*pad) / stride + 1
insize = outsize
totstride = totstride * stride
return outsize, totstride
def inFromOut( layernum, net = convnet):
if layernum>len(net): layernum=len(net)
outsize = 1
#for layerparams in net:
for layer in reversed(range(layernum)):
fsize, stride, pad = net[layer]
outsize = ((outsize -1)* stride) + fsize
RFsize = outsize
return RFsize
if __name__ == '__main__':
print "layer output sizes given image = %dx%d" % (imsize, imsize)
for i in range(len(convnet)):
p = outFromIn(imsize,i+1)
rf = inFromOut(i+1)
print "Layer Name = %s, Output size = %3d, Stride = % 3d, RF size = %3d" % (layer_names[i], p[0], p[1], rf)
layer output sizes given image = 224x224
Layer Name = conv1/7x7_s2, Output size = 112, Stride = 2, RF size = 7
Layer Name = conv1/relu_7x7, Output size = 112, Stride = 2, RF size = 7
Layer Name = pool1/3x3_s2, Output size = 55, Stride = 4, RF size = 11
Layer Name = pool1/norm1, Output size = 55, Stride = 4, RF size = 11
Layer Name = conv2/3x3_reduce, Output size = 55, Stride = 4, RF size = 11
Layer Name = conv2/relu_3x3_reduce, Output size = 55, Stride = 4, RF size = 11
Layer Name = conv2/3x3, Output size = 55, Stride = 4, RF size = 19
Layer Name = conv2/relu_3x3, Output size = 55, Stride = 4, RF size = 19
Layer Name = pool2/3x3_s2, Output size = 27, Stride = 8, RF size = 27
Layer Name = inception_3a/3x3_reduce, Output size = 27, Stride = 8, RF size = 27
Layer Name = inception_3a/relu_3x3_reduce, Output size = 27, Stride = 8, RF size = 27
Layer Name = inception_3a/3x3, Output size = 27, Stride = 8, RF size = 43
Layer Name = inception_3a/relu_3x3, Output size = 27, Stride = 8, RF size = 43
Layer Name = inception_3b/3x3_reduce, Output size = 27, Stride = 8, RF size = 43
Layer Name = inception_3b/relu_3x3_reduce, Output size = 27, Stride = 8, RF size = 43
Layer Name = inception_3b/3x3, Output size = 27, Stride = 8, RF size = 59
Layer Name = inception_3b/relu_3x3, Output size = 27, Stride = 8, RF size = 59
Layer Name = pool3/3x3_s2, Output size = 13, Stride = 16, RF size = 75
Layer Name = inception_4a/3x3_reduce, Output size = 13, Stride = 16, RF size = 75
Layer Name = inception_4a/relu_3x3_reduce, Output size = 13, Stride = 16, RF size = 75
Layer Name = inception_4a/3x3, Output size = 13, Stride = 16, RF size = 107
Layer Name = inception_4a/relu_3x3, Output size = 13, Stride = 16, RF size = 107
Layer Name = inception_4b/3x3_reduce, Output size = 13, Stride = 16, RF size = 107
Layer Name = inception_4b/relu_3x3_reduce, Output size = 13, Stride = 16, RF size = 107
Layer Name = inception_4b/3x3, Output size = 13, Stride = 16, RF size = 139
Layer Name = inception_4b/relu_3x3, Output size = 13, Stride = 16, RF size = 139
Layer Name = inception_4c/3x3_reduce, Output size = 13, Stride = 16, RF size = 139
Layer Name = inception_4c/relu_3x3_reduce, Output size = 13, Stride = 16, RF size = 139
Layer Name = inception_4c/3x3, Output size = 13, Stride = 16, RF size = 171
Layer Name = inception_4c/relu_3x3, Output size = 13, Stride = 16, RF size = 171
Layer Name = inception_4d/3x3_reduce, Output size = 13, Stride = 16, RF size = 171
Layer Name = inception_4d/relu_3x3_reduce, Output size = 13, Stride = 16, RF size = 171
Layer Name = inception_4d/3x3, Output size = 13, Stride = 16, RF size = 203
Layer Name = inception_4d/relu_3x3, Output size = 13, Stride = 16, RF size = 203
Layer Name = inception_4e/3x3_reduce, Output size = 13, Stride = 16, RF size = 203
Layer Name = inception_4e/relu_3x3_reduce, Output size = 13, Stride = 16, RF size = 203
Layer Name = inception_4e/3x3, Output size = 13, Stride = 16, RF size = 235
Layer Name = inception_4e/relu_3x3, Output size = 13, Stride = 16, RF size = 235
Layer Name = pool4/3x3_s2, Output size = 7, Stride = 32, RF size = 267
Layer Name = inception_5a/3x3_reduce, Output size = 7, Stride = 32, RF size = 267
Layer Name = inception_5a/relu_3x3_reduce, Output size = 7, Stride = 32, RF size = 267
Layer Name = inception_5a/3x3, Output size = 7, Stride = 32, RF size = 331
Layer Name = inception_5a/relu_3x3, Output size = 7, Stride = 32, RF size = 331
Layer Name = inception_5b/3x3_reduce, Output size = 7, Stride = 32, RF size = 331
Layer Name = inception_5b/relu_3x3_reduce, Output size = 7, Stride = 32, RF size = 331
Layer Name = inception_5b/3x3, Output size = 7, Stride = 32, RF size = 395
Layer Name = inception_5b/relu_3x3, Output size = 7, Stride = 32, RF size = 395
Layer Name = pool5/7x7_s1, Output size = 3, Stride = 32, RF size = 587
你应该计算每条路径（你有四条），然后取最大感受野。我添加了编辑。GoogleNet的感受野计算有什么问题。