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vgg-16 with HingeLoss loss function
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#VGGNet16 | |
layer { | |
name: "train-data" | |
type: "Data" | |
top: "data" | |
top: "label" | |
transform_param { | |
mirror: true | |
crop_size: 224 | |
} | |
data_param { | |
batch_size: 32 | |
} | |
include { stage: "train" } | |
} | |
layer { | |
name: "val-data" | |
type: "Data" | |
top: "data" | |
top: "label" | |
transform_param { | |
mirror: false | |
crop_size: 224 | |
} | |
data_param { | |
batch_size: 16 | |
} | |
include { stage: "val" } | |
} | |
layer { | |
bottom: "data" | |
top: "conv1_1" | |
name: "conv1_1" | |
type: "Convolution" | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv1_1" | |
top: "conv1_1" | |
name: "relu1_1" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv1_1" | |
top: "conv1_2" | |
name: "conv1_2" | |
type: "Convolution" | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv1_2" | |
top: "conv1_2" | |
name: "relu1_2" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv1_2" | |
top: "pool1" | |
name: "pool1" | |
type: "Pooling" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
bottom: "pool1" | |
top: "conv2_1" | |
name: "conv2_1" | |
type: "Convolution" | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv2_1" | |
top: "conv2_1" | |
name: "relu2_1" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv2_1" | |
top: "conv2_2" | |
name: "conv2_2" | |
type: "Convolution" | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv2_2" | |
top: "conv2_2" | |
name: "relu2_2" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv2_2" | |
top: "pool2" | |
name: "pool2" | |
type: "Pooling" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
bottom: "pool2" | |
top: "conv3_1" | |
name: "conv3_1" | |
type: "Convolution" | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv3_1" | |
top: "conv3_1" | |
name: "relu3_1" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv3_1" | |
top: "conv3_2" | |
name: "conv3_2" | |
type: "Convolution" | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv3_2" | |
top: "conv3_2" | |
name: "relu3_2" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv3_2" | |
top: "conv3_3" | |
name: "conv3_3" | |
type: "Convolution" | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv3_3" | |
top: "conv3_3" | |
name: "relu3_3" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv3_3" | |
top: "pool3" | |
name: "pool3" | |
type: "Pooling" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
bottom: "pool3" | |
top: "conv4_1" | |
name: "conv4_1" | |
type: "Convolution" | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv4_1" | |
top: "conv4_1" | |
name: "relu4_1" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv4_1" | |
top: "conv4_2" | |
name: "conv4_2" | |
type: "Convolution" | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv4_2" | |
top: "conv4_2" | |
name: "relu4_2" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv4_2" | |
top: "conv4_3" | |
name: "conv4_3" | |
type: "Convolution" | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv4_3" | |
top: "conv4_3" | |
name: "relu4_3" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv4_3" | |
top: "pool4" | |
name: "pool4" | |
type: "Pooling" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
bottom: "pool4" | |
top: "conv5_1" | |
name: "conv5_1" | |
type: "Convolution" | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv5_1" | |
top: "conv5_1" | |
name: "relu5_1" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv5_1" | |
top: "conv5_2" | |
name: "conv5_2" | |
type: "Convolution" | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv5_2" | |
top: "conv5_2" | |
name: "relu5_2" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv5_2" | |
top: "conv5_3" | |
name: "conv5_3" | |
type: "Convolution" | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "conv5_3" | |
top: "conv5_3" | |
name: "relu5_3" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "conv5_3" | |
top: "pool5" | |
name: "pool5" | |
type: "Pooling" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
bottom: "pool5" | |
top: "fc6" | |
name: "fc6" | |
type: "InnerProduct" | |
inner_product_param { | |
num_output: 4096 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "fc6" | |
top: "fc6" | |
name: "relu6" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "fc6" | |
top: "fc6" | |
name: "drop6" | |
type: "Dropout" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
bottom: "fc6" | |
top: "fc7" | |
name: "fc7" | |
type: "InnerProduct" | |
inner_product_param { | |
num_output: 4096 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0.1 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
bottom: "fc7" | |
top: "fc7" | |
name: "relu7" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "fc7" | |
top: "fc7" | |
name: "drop7" | |
type: "Dropout" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc8" | |
bottom: "fc7" | |
top: "fc8" | |
type: "InnerProduct" | |
inner_product_param { | |
#num_output: 1000 | |
weight_filler { | |
type: "xavier" | |
std: 0.0005 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
} | |
layer { | |
name: "accuracy" | |
type: "Accuracy" | |
bottom: "fc8" | |
bottom: "label" | |
top: "accuracy" | |
include { stage: "val" } | |
} | |
layer { | |
name: "loss" | |
type: "HingeLoss" | |
bottom: "fc8" | |
bottom: "label" | |
top: "loss" | |
exclude { stage: "deploy" } | |
hinge_loss_param{ | |
norm: L1 | |
#norm: L2 | |
# OR U HAVE TO USE L2 instead of norm: L1 | |
} | |
#loss_param{ | |
#ignore_label: 9 | |
#normalize: 1 | |
#normalization: VALID | |
#} | |
} | |
layer { | |
name: "softmax" | |
type: "Softmax" | |
bottom: "fc8" | |
top: "softmax" | |
include { stage: "deploy" } | |
} |
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