Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
name: "fkp_net"
layers {
name: "data"
type: MEMORY_DATA
top: "data"
top: "label"
memory_data_param {
batch_size: 1783 #batch size, so how many prediction youu want to do at once. Best is "1", but higher number get better performance
channels: 1
height: 96
width: 96
}
}
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
blobs_lr: 1
blobs_lr: 2
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "relu1"
type: RELU
bottom: "conv1"
top: "conv1"
}
layers {
name: "pool1"
type: POOLING
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "dropout1"
type: "Dropout"
bottom: "pool1"
top: "pool1"
dropout_param {
dropout_ratio: 0.1
}
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "pool1"
top: "conv2"
blobs_lr: 1
blobs_lr: 2
convolution_param {
num_output: 48
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "pool2"
type: POOLING
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "dropout2"
type: "Dropout"
bottom: "pool2"
top: "pool2"
dropout_param {
dropout_ratio: 0.3
}
}
layers {
name: "conv3"
type: CONVOLUTION
bottom: "pool2"
top: "conv3"
blobs_lr: 1
blobs_lr: 2
convolution_param {
num_output: 64
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "relu3"
type: RELU
bottom: "conv3"
top: "conv3"
}
layer {
name: "dropout3"
type: "Dropout"
bottom: "conv3"
top: "conv3"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc5"
type: INNER_PRODUCT
bottom: "conv3"
top: "fc5"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "drop4"
type: "Dropout"
bottom: "fc5"
top: "fc5"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc6"
type: INNER_PRODUCT
bottom: "fc5"
top: "fc6"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 30
weight_filler {
type: "xavier"
variance_norm: AVERAGE
}
bias_filler {
type: "constant"
value: 0
}
}
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment