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@mfigurnov
Created September 9, 2015 12:45
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CIFAR-10 Network in Network with fix for cuDNN
net: "train_val.prototxt"
test_iter: 100
test_interval: 500
base_lr: 0.1
momentum: 0.9
weight_decay: 0.0001
lr_policy: "step"
gamma: 0.1
stepsize: 100000
display: 100
max_iter: 120000
snapshot: 10000
snapshot_prefix: "cifar10_nin"
solver_mode: GPU
name: "CIFAR10_full"
layer {
name: "cifar"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
hdf5_data_param {
source: "../cifar-train.txt"
batch_size: 128
shuffle: false
}
}
layer {
name: "cifar"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TEST
}
hdf5_data_param {
source: "../cifar-test.txt"
batch_size: 100
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 192
pad: 2
kernel_size: 5
weight_filler {
type: "gaussian"
std: 0.05
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "cccp1"
type: "Convolution"
bottom: "conv1"
top: "cccp1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 160
kernel_size: 1
group: 1
weight_filler {
type: "gaussian"
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu_cccp1"
type: "ReLU"
bottom: "cccp1"
top: "cccp1"
}
layer {
name: "cccp2"
type: "Convolution"
bottom: "cccp1"
top: "cccp2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 1
group: 1
weight_filler {
type: "gaussian"
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu_cccp2"
type: "ReLU"
bottom: "cccp2"
top: "cccp2"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "cccp2"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
engine: CAFFE
}
}
layer {
name: "drop3"
type: "Dropout"
bottom: "pool1"
top: "pool1"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 192
pad: 2
kernel_size: 5
weight_filler {
type: "gaussian"
std: 0.05
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "cccp3"
type: "Convolution"
bottom: "conv2"
top: "cccp3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 192
kernel_size: 1
group: 1
weight_filler {
type: "gaussian"
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu_cccp3"
type: "ReLU"
bottom: "cccp3"
top: "cccp3"
}
layer {
name: "cccp4"
type: "Convolution"
bottom: "cccp3"
top: "cccp4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 192
kernel_size: 1
group: 1
weight_filler {
type: "gaussian"
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu_cccp4"
type: "ReLU"
bottom: "cccp4"
top: "cccp4"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "cccp4"
top: "pool2"
pooling_param {
pool: AVE
kernel_size: 3
stride: 2
}
}
layer {
name: "drop6"
type: "Dropout"
bottom: "pool2"
top: "pool2"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.05
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "cccp5"
type: "Convolution"
bottom: "conv3"
top: "cccp5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 192
kernel_size: 1
group: 1
weight_filler {
type: "gaussian"
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu_cccp5"
type: "ReLU"
bottom: "cccp5"
top: "cccp5"
}
layer {
name: "cccp6"
type: "Convolution"
bottom: "cccp5"
top: "cccp6"
param {
lr_mult: 0.1
decay_mult: 1
}
param {
lr_mult: 0.1
decay_mult: 0
}
convolution_param {
num_output: 10
kernel_size: 1
group: 1
weight_filler {
type: "gaussian"
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu_cccp6"
type: "ReLU"
bottom: "cccp6"
top: "cccp6"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "cccp6"
top: "pool3"
pooling_param {
pool: AVE
kernel_size: 8
stride: 1
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "pool3"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "pool3"
bottom: "label"
top: "loss"
}
@ih4cku
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ih4cku commented Nov 6, 2015

Why does layer pool2 use average pooling?

layer {
  name: "pool2"
  type: "Pooling"
  bottom: "cccp4"
  top: "pool2"
  pooling_param {
    pool: AVE # shouldn't this be MAX?
    kernel_size: 3
    stride: 2
  }
}

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