caffe的batchnorm层解析
batch norm层
(48,)
>>> net.params['S_BatchNorm1'][0].data
array([ 819.11981201, 829.95129395, 794.78631592, 827.35858154,
850.00958252, 816.28717041, 827.91516113, 799.91137695,
828.44311523, 826.17230225, 830.20861816, 820.72338867,
874.72167969, 790.52697754, 830.56390381, 809.5446167 ,
826.6975708 , 826.20227051, 862.22235107, 834.30084229,
838.55450439, 823.67590332, 849.53387451, 826.62652588,
818.92474365, 853.89550781, 815.99163818, 838.94012451,
825.40240479, 838.93945312, 828.55383301, 759.22088623,
824.87506104, 829.83129883, 825.13092041, 918.137146 ,
887.98748779, 828.19226074, 843.36413574, 832.51928711,
818.15814209, 815.32025146, 852.34960938, 848.97436523,
810.09857178, 793.54125977, 834.05938721, 785.15637207], dtype=float32)
其实是mean的和
>>> net.params['S_BatchNorm1'][1].data
array([ 29.82979965, 17.90246964, 28.48996925, 16.73427582,
52.08065796, 23.63191414, 42.49412155, 32.62079239,
22.55072594, 12.61812878, 8.74132729, 43.2579155 ,
39.9254837 , 6.50472021, 16.77551651, 20.26323318,
7.65308332, 26.38513565, 10.02313232, 46.96594238,
31.32673836, 15.16614246, 32.49311829, 17.75225258,
13.66149616, 32.06713104, 30.29079819, 12.70900154,
9.03455734, 26.99464035, 37.29315948, 12.5563097 ,
43.37051392, 56.11575699, 31.56432915, 24.75470352,
17.89116859, 5.47338533, 45.2383728 , 24.48766518,
7.36466312, 13.79270363, 19.93126678, 25.57545853,
42.41948318, 28.65270233, 17.43808174, 14.86530399], dtype=float32)
>>> net.params['S_BatchNorm1'][2].data.shape
(1,)
>>> net.params['S_BatchNorm1'][2].data
array([ 999.98236084], dtype=float32)
次数
blobs[0]/blobs[2]才是mean
>>>
>>> net.params['S_BatchNorm1'][3].data.shape
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: Index out of range
[1]这个博客讲解的很详细 https://blog.csdn.net/lanran2/article/details/56278072
batch norm有三组参数
打印参数的流程为：
>>> import sys
>>> sys.path.insert(0,'./python')
>>> import caffe
>>> net = caffe.Net('./examples/mnist/lenet_train_test.prototxt','./examples/mnist/lenet_iter_10000.caffemodel',caffe.TEST)
(48,)
>>> net.params['S_BatchNorm1'][0].data
array([ 819.11981201, 829.95129395, 794.78631592, 827.35858154,
850.00958252, 816.28717041, 827.91516113, 799.91137695,
828.44311523, 826.17230225, 830.20861816, 820.72338867,
874.72167969, 790.52697754, 830.56390381, 809.5446167 ,
826.6975708 , 826.20227051, 862.22235107, 834.30084229,
838.55450439, 823.67590332, 849.53387451, 826.62652588,
818.92474365, 853.89550781, 815.99163818, 838.94012451,
825.40240479, 838.93945312, 828.55383301, 759.22088623,
824.87506104, 829.83129883, 825.13092041, 918.137146 ,
887.98748779, 828.19226074, 843.36413574, 832.51928711,
818.15814209, 815.32025146, 852.34960938, 848.97436523,
810.09857178, 793.54125977, 834.05938721, 785.15637207], dtype=float32)
其实是mean的和
>>> net.params['S_BatchNorm1'][1].data
array([ 29.82979965, 17.90246964, 28.48996925, 16.73427582,
52.08065796, 23.63191414, 42.49412155, 32.62079239,
22.55072594, 12.61812878, 8.74132729, 43.2579155 ,
39.9254837 , 6.50472021, 16.77551651, 20.26323318,
7.65308332, 26.38513565, 10.02313232, 46.96594238,
31.32673836, 15.16614246, 32.49311829, 17.75225258,
13.66149616, 32.06713104, 30.29079819, 12.70900154,
9.03455734, 26.99464035, 37.29315948, 12.5563097 ,
43.37051392, 56.11575699, 31.56432915, 24.75470352,
17.89116859, 5.47338533, 45.2383728 , 24.48766518,
7.36466312, 13.79270363, 19.93126678, 25.57545853,
42.41948318, 28.65270233, 17.43808174, 14.86530399], dtype=float32)
>>> net.params['S_BatchNorm1'][2].data.shape
(1,)
>>> net.params['S_BatchNorm1'][2].data
array([ 999.98236084], dtype=float32)
次数
blobs[0]/blobs[2]才是mean
>>>
>>> net.params['S_BatchNorm1'][3].data.shape
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: Index out of range
[1]这个博客讲解的很详细 https://blog.csdn.net/lanran2/article/details/56278072
