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 | webapp [1] | WHRIA | 2020.05.10 | 86 |
168 | android / capture and crop [5] | WHRIA | 2020.05.10 | 49 |
167 | 식약청 메뉴 | WHRIA | 2020.05.14 | 78 |
166 | CE | WHRIA | 2020.05.17 | 52 |
165 | encfs | WHRIA | 2020.05.17 | 36004 |
164 | single file encrypt decrypt | WHRIA | 2020.05.17 | 1778 |
163 | 영어로 숫자 표현 | WHRIA | 2020.05.28 | 4464 |
162 | BERT | WHRIA | 2020.06.10 | 3276 |
161 | MS bot framework [3] | WHRIA | 2020.06.13 | 114 |
» | regression model [6] | WHRIA | 2020.06.18 | 177 |
159 | 바이두 | WHRIA | 2020.06.20 | 671 |
158 | reddit downloader | WHRIA | 2020.06.21 | 221 |
157 | resnest50 caffe | WHRIA | 2020.06.21 | 168 |
156 | caffe hdf5 error | WHRIA | 2020.06.22 | 189 |
155 | 할일 | WHRIA | 2020.06.24 | 1523 |
https://github.com/BVLC/caffe/issues/1396