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mnist_siamese_train_test.prototxt 4.8 КБ
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Greg Heinrich Отправлено 14.03.2016 16:54 8659c76
layer {
name: "scale"
type: "Power"
bottom: "data"
top: "scale"
power_param {
scale: 0.0125000001863
}
}
layer {
name: "slice_triplet"
type: "Slice"
bottom: "scale"
top: "data_left"
top: "data_right"
top: "data_discard"
slice_param {
slice_dim: 1
}
}
layer {
name: "conv1_left"
type: "Convolution"
bottom: "data_left"
top: "conv1_left"
param {
name: "conv1_w"
lr_mult: 1.0
}
param {
name: "conv1_b"
lr_mult: 2.0
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1_left"
type: "Pooling"
bottom: "conv1_left"
top: "pool1_left"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2_left"
type: "Convolution"
bottom: "pool1_left"
top: "conv2_left"
param {
name: "conv2_w"
lr_mult: 1.0
}
param {
name: "conv2_b"
lr_mult: 2.0
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2_left"
type: "Pooling"
bottom: "conv2_left"
top: "pool2_left"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1_left"
type: "InnerProduct"
bottom: "pool2_left"
top: "ip1_left"
param {
name: "ip1_w"
lr_mult: 1.0
}
param {
name: "ip1_b"
lr_mult: 2.0
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1_left"
top: "ip1_left"
}
layer {
name: "ip2_left"
type: "InnerProduct"
bottom: "ip1_left"
top: "ip2_left"
param {
name: "ip2_w"
lr_mult: 1.0
}
param {
name: "ip2_b"
lr_mult: 2.0
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "feat_left"
type: "InnerProduct"
bottom: "ip2_left"
top: "feat_left"
param {
name: "feat_w"
lr_mult: 1.0
}
param {
name: "feat_b"
lr_mult: 2.0
}
inner_product_param {
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "conv1_right"
type: "Convolution"
bottom: "data_right"
top: "conv1_right"
param {
name: "conv1_w"
lr_mult: 1.0
}
param {
name: "conv1_b"
lr_mult: 2.0
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1_right"
type: "Pooling"
bottom: "conv1_right"
top: "pool1_right"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2_right"
type: "Convolution"
bottom: "pool1_right"
top: "conv2_right"
param {
name: "conv2_w"
lr_mult: 1.0
}
param {
name: "conv2_b"
lr_mult: 2.0
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2_right"
type: "Pooling"
bottom: "conv2_right"
top: "pool2_right"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1_right"
type: "InnerProduct"
bottom: "pool2_right"
top: "ip1_right"
param {
name: "ip1_w"
lr_mult: 1.0
}
param {
name: "ip1_b"
lr_mult: 2.0
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1_right"
type: "ReLU"
bottom: "ip1_right"
top: "ip1_right"
}
layer {
name: "ip2_right"
type: "InnerProduct"
bottom: "ip1_right"
top: "ip2_right"
param {
name: "ip2_w"
lr_mult: 1.0
}
param {
name: "ip2_b"
lr_mult: 2.0
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "feat_right"
type: "InnerProduct"
bottom: "ip2_right"
top: "feat_right"
param {
name: "feat_w"
lr_mult: 1.0
}
param {
name: "feat_b"
lr_mult: 2.0
}
inner_product_param {
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "discard"
type: "Silence"
bottom: "data_discard"
}
layer {
name: "loss"
type: "ContrastiveLoss"
bottom: "feat_left"
bottom: "feat_right"
bottom: "label"
top: "loss"
contrastive_loss_param {
margin: 1.0
}
exclude { stage: "deploy" }
}

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