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 |
---|---|---|---|---|
1686 | ubuntu cuda nvidia-smi | WHRIA | 2020.08.29 | 850 |
1685 | concat network [3] | WHRIA | 2020.08.27 | 147 |
1684 | GPT2 [1] | WHRIA | 2020.08.03 | 250 |
1683 | scopus [1] | WHRIA | 2020.08.02 | 344 |
1682 | melafind | WHRIA | 2020.08.01 | 771 |
1681 | nvidia dali [1] | WHRIA | 2020.08.01 | 265 |
1680 | pytorch optimize | WHRIA | 2020.08.01 | 126 |
1679 | startup , 미국 | WHRIA | 2020.07.30 | 134 |
1678 | consort , stard | WHRIA | 2020.07.30 | 81 |
1677 | model split [1] | WHRIA | 2020.07.30 | 142 |
1676 | FTC [1] | WHRIA | 2020.07.28 | 238 |
1675 | 암호화 | WHRIA | 2020.07.26 | 1000 |
1674 | raid 6 | WHRIA | 2020.07.23 | 43 |
1673 | asyncio | WHRIA | 2020.07.23 | 286 |
1672 | amp distributed pytorch [1] | WHRIA | 2020.07.14 | 51 |
https://github.com/BVLC/caffe/issues/1396