版权声明:本文为jiarenyf原创文章,未经允许也可以转载,但是附上链接…… https://blog.csdn.net/u011762313/article/details/48851015 </div>
<div id="content_views" class="markdown_views">
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<ul>
name: "LeNet"
###for data and labels
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "labels"
include {
phase: TRAIN
}
hdf5_data_param {
source: "list_train.txt"
batch_size: 100
}
}
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "labels"
include {
phase: TEST
}
hdf5_data_param {
source: "list_test.txt"
batch_size: 100
}
}
layer {
name: "slicers"
type: "Slice"
bottom: "labels"
top: "label_1"
top: "label_2"
slice_param {
axis: 1
slice_point: 1
}
}
### for all
layer {
name: "conv_all"
type: "Convolution"
bottom: "data"
top: "conv_all"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu_all"
type: "ReLU"
bottom: "conv_all"
top: "conv_all"
}
layer {
name: "pool_all"
type: "Pooling"
bottom: "conv_all"
top: "pool_all"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
### for kind_1
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool_all"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy1"
type: "Accuracy"
bottom: "ip1"
bottom: "label_1"
top: "accuracy1"
include {
phase: TEST
}
}
layer {
name: "loss_1"
type: "SoftmaxWithLoss"
bottom: "ip1"
bottom: "label_1"
top: "loss_1"
}
###for kind_2
layer {
name: "ip2"
type: "InnerProduct"
bottom: "pool_all"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy2"
type: "Accuracy"
bottom: "ip2"
bottom: "label_2"
top: "accuracy2"
include {
phase: TEST
}
}
layer {
name: "loss_2"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label_2"
top: "loss_2"
}
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- 注:如何生成hdf5文件,详见:生成hdf5文件用于多标签训练
- 注:Hdf5Data详见:HDF5 Input
- 注:Slice详见:Slicing
- 最终网络结构如下图:
- 注:Caffe学习:使用pycaffe绘制网络结构
版权声明:本文为jiarenyf原创文章,未经允许也可以转载,但是附上链接…… https://blog.csdn.net/u011762313/article/details/48851015 </div>
<div id="content_views" class="markdown_views">
<!-- flowchart 箭头图标 勿删 -->
<svg xmlns="http://www.w3.org/2000/svg" style="display: none;"><path stroke-linecap="round" d="M5,0 0,2.5 5,5z" id="raphael-marker-block" style="-webkit-tap-highlight-color: rgba(0, 0, 0, 0);"></path></svg>
<ul>