regression model
2020.06.18 23:20
댓글 6
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WHRIA
2020.06.18 23:25
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WHRIA
2020.06.19 22:06
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WHRIA
2020.06.20 21:22
layer {
name: "pool5/7x7_s1"
type: "Pooling"
bottom: "conv5_3"
top: "pool5/7x7_s1"
pooling_param {
pool: AVE
kernel_size: 7
stride: 1
}
}
layer {
name: "whria_classifier"
type: "InnerProduct"
bottom: "pool5/7x7_s1"
top: "whria_classifier"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
inner_product_param {
num_output: 1
weight_filler{
type: "gaussian"
std:0.01
}
bias_filler{
type:"constant"
value:0
}
}
}
layer {
bottom: "whria_classifier"
bottom: "label"
top: "loss"
type: "EuclideanLoss"
name: "loss"
} -
WHRIA
2020.06.20 21:24
layer {
name: "whria_classifier"
type: "InnerProduct"
bottom: "pool5/7x7_s1"
top: "whria_classifier"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
inner_product_param {
num_output: 2
}
}
layer {
bottom: "whria_classifier"
bottom: "label"
top: "loss"
name: "loss"
type: "SoftmaxWithLoss"
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "whria_classifier"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
} -
WHRIA
2020.06.20 21:39
layer {
name: "pool5/7x7_s1"
type: "Pooling"
bottom: "conv5_3"
top: "pool5/7x7_s1"
pooling_param {
pool: AVE
kernel_size: 7
stride: 1
}
}
layer {
name: "whria_classifier1"
type: "InnerProduct"
bottom: "pool5/7x7_s1"
top: "whria_classifier1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
inner_product_param {
num_output: 16
}
}
layer {
name: "relu_whria1"
type: "ReLU"
bottom:"whria_classifier1"
top: "whria_classifier1"
}
layer {
name : "drop16"
type : "Dropout"
bottom : "whria_classifier1"
top: "whria_classifier1"
dropout_param {
dropout_ratio:0.5
}
}
layer {
name:"whria_classifier2"
type: "InnerProduct"
bottom:"whria_classifier1"
top:"whria_classifier2"
inner_product_param{
num_output: 1
weight_filler{
type: "gaussian"
std:0.01
}
bias_filler{
type:"constant"
value:0
}
}
}
layer {
bottom: "whria_classifier2"
bottom: "label"
top: "loss"
type: "EuclideanLoss"
name: "loss"
} -
WHRIA
2020.06.20 22:12
layer {
name: "whria_classifier1"
type: "InnerProduct"
bottom: "pool5/7x7_s1"
top: "whria_classifier1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
inner_product_param {
num_output: 32
}
}
layer {
name: "relu_whria1"
type: "ReLU"
bottom:"whria_classifier1"
top: "whria_classifier1"
}
layer {
name : "drop_whria1"
type : "Dropout"
bottom : "whria_classifier1"
top: "whria_classifier1"
dropout_param {
dropout_ratio:0.5
}
}
layer {
name: "whria_classifier2"
type: "InnerProduct"
bottom: "whria_classifier1"
top: "whria_classifier2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
inner_product_param {
num_output: 32
}
}
layer {
name: "relu_whria2"
type: "ReLU"
bottom:"whria_classifier2"
top: "whria_classifier2"
}
layer {
name : "drop_whria2"
type : "Dropout"
bottom : "whria_classifier2"
top: "whria_classifier2"
dropout_param {
dropout_ratio:0.5
}
}
layer {
name:"whria_classifier_final"
type: "InnerProduct"
bottom:"whria_classifier2"
top:"whria_classifier_final"
inner_product_param{
num_output: 1
weight_filler{
type: "gaussian"
std:0.01
}
bias_filler{
type:"constant"
value:0
}
}
}
layer {
bottom: "whria_classifier_final"
bottom: "label"
top: "loss"
type: "EuclideanLoss"
name: "loss"
}
번호 | 제목 | 글쓴이 | 날짜 | 조회 수 |
---|---|---|---|---|
169 | 정량화 논문들 | WHRIA | 2023.02.16 | 73 |
168 | center % crop | WHRIA | 2018.12.27 | 72 |
167 | psexec | WHRIA | 2016.07.28 | 71 |
166 | XP shutdown 시 강제종료 시키기 | WHRIA | 2016.08.16 | 71 |
165 | 싸이월드도 없어지는구나 | WHRIA | 2015.10.10 | 70 |
164 | bay trail ubuntu 20.04 [1] | WHRIA | 2020.12.24 | 70 |
163 | LG 글로벌 | WHRIA | 2019.01.31 | 69 |
162 | linux raid | WHRIA | 2019.11.30 | 69 |
161 | zmq async client server [1] | WHRIA | 2020.03.12 | 69 |
160 | color pallate | WHRIA | 2019.04.07 | 68 |
159 | raid monitor | WHRIA | 2019.12.15 | 68 |
158 | raid 6 rebuild | WHRIA | 2020.01.07 | 68 |
157 | youtube | WHRIA | 2016.09.30 | 65 |
156 | disk error | WHRIA | 2023.02.16 | 65 |
155 | swap memory | WHRIA | 2019.03.14 | 64 |
https://github.com/BVLC/caffe/issues/1396