layer{
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mean_file: "mean.binaryproto"
mirror: true
crop_size: 224
}
data_param {
source: "train_vgg_cnnf_leveldb"
batch_size: 128
backend: LEVELDB
}
}
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mean_file: "mean.binaryproto"
mirror: false
crop_size: 224
}
data_param {
source: "test_vgg_cnnf_leveldb"
batch_size: 100
backend: LEVELDB
}
}
layer {
bottom: "data"
top: "conv1"
name: "conv1"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 11
stride: 4
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "relu1"
type: "ReLU"
}
layer {
bottom: "conv1"
top: "norm1"
name: "norm1"
type: "LRN"
lrn_param {
local_size: 5
alpha: 0.0005
beta: 0.75
k: 2
}
}
layer {
bottom: "norm1"
top: "pool1"
name: "pool1"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
bottom: "pool1"
top: "conv2"
name: "conv2"
type: "Convolution"
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
}
}
layer {
bottom: "conv2"
top: "conv2"
name: "relu2"
type: "ReLU"
}
layer {
bottom: "conv2"
top: "norm2"
name: "norm2"
type: "LRN"
lrn_param {
local_size: 5
alpha: 0.0005
beta: 0.75
k: 2
}
}
layer {
bottom: "norm2"
top: "pool2"
name: "pool2"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
bottom: "pool2"
top: "conv3"
name: "conv3"
type: "Convolution"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv3"
top: "conv3"
name: "relu3"
type: "ReLU"
}
layer {
bottom: "conv3"
top: "conv4"
name: "conv4"
type: "Convolution"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv4"
top: "conv4"
name: "relu4"
type: "ReLU"
}
layer {
bottom: "conv4"
top: "conv5"
name: "conv5"
type: "Convolution"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv5"
top: "conv5"
name: "relu5"
type: "ReLU"
}
layer {
bottom: "conv5"
top: "pool5"
name: "pool5"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
bottom: "pool5"
top: "fc6"
name: "fc6"
type: "InnerProduct"
inner_product_param {
num_output: 4096
}
}
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
}
}
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 {
bottom: "fc7"
top: "fc8_32"
name: "fc8_32"
type: "InnerProduct"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 32
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "fc8_32"
top: "fc8-classification"
name: "fc8-classification"
type: "InnerProduct"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 81
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8-classification"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss" # Should this be replaced by sigmoid_cross_entropy_loss_layer??
bottom: "fc8-classification"
bottom: "label"
top: "loss"
}