Caffe LENET或Imagenet模型中的参数数量

Question

如何计算模型中的参数数量，例如用于mnist的LENET，或用于imagent模型的ConvNet等。在caffe中是否有任何特定的函数来返回或保存模型中的参数数量。问候

Answer 1

以下是计算Caffe模型中参数数量的python代码片段：

import caffe
caffe.set_mode_cpu()
import numpy as np
from numpy import prod, sum
from pprint import pprint

def print_net_parameters (deploy_file):
    print "Net: " + deploy_file
    net = caffe.Net(deploy_file, caffe.TEST)
    print "Layer-wise parameters: "
    pprint([(k, v[0].data.shape) for k, v in net.params.items()])
    print "Total number of parameters: " + str(sum([prod(v[0].data.shape) for k, v in net.params.items()]))

deploy_file = "/home/ubuntu/deploy.prototxt"
print_net_parameters(deploy_file)

# Sample output:
# Net: /home/ubuntu/deploy.prototxt
# Layer-wise parameters: 
#[('conv1', (96, 3, 11, 11)),
# ('conv2', (256, 48, 5, 5)),
# ('conv3', (384, 256, 3, 3)),
# ('conv4', (384, 192, 3, 3)),
# ('conv5', (256, 192, 3, 3)),
# ('fc6', (4096, 9216)),
# ('fc7', (4096, 4096)),
# ('fc8', (819, 4096))]
# Total number of parameters: 60213280

https://gist.github.com/kaushikpavani/a6a32bd87fdfe5529f0e908ed743f779

Answer 2

我可以通过Matlab接口提供一种明确的方法(确保首先安装matcaffe )。基本上，您从每个网络层提取一组参数并对它们进行计数。在Matlab中：

% load the network
net_model = <path to your *deploy.prototxt file>
net_weights = <path to your *.caffemodel file>
phase = 'test';
test_net = caffe.Net(net_model, net_weights, phase);

% get the list of layers
layers_list = test_net.layer_names;
% for those layers which have parameters, count them
counter = 0;
for j = 1:length(layers_list),
    if ~isempty(test_net.layers(layers_list{j}).params)
    feat = test_net.layers(layers_list{j}).params(1).get_data();
    counter = counter + numel(feat)
    end
end

最后，'counter‘包含参数的数量。