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sam sk
Jun 5, 2016, 3:56:45 AM6/5/16
to Caffe Users
Hi,
I am new to ML and Caffe. I am trying to code CNN_facepoint paper using regression.
I am training using approx 4000 images dataset and same hd5 file for testing.
My network is predicting same 10 values for different test images when I use deploy.prototxt
I am new to ML and Caffe. I am trying to code CNN_facepoint paper using regression.
I am training using approx 4000 images dataset and same hd5 file for testing.
My network is predicting same 10 values for different test images when I use deploy.prototxt
name: "facepoint"
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include: {
phase: TRAIN
}
hdf5_data_param {
source: "/home/smk/deep-learning/caffe/examples/facepoint/train_file_location.txt"
batch_size: 10
}
}
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include: {
phase: TEST
}
hdf5_data_param {
source: "/home/smk/deep-learning/caffe/examples/facepoint/train_file_location.txt"
batch_size: 10
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
pad: 0
kernel_size: 4
stride: 1
weight_filler {
type: "gaussian"
std: 0.0001
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "tanh1"
bottom: "conv1"
top: "tanh1"
type: "TanH"
}
layer {
name: "abs1"
type: "AbsVal"
bottom: "tanh1"
top: "abs1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "abs1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 40
pad: 0
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "tanh2"
bottom: "conv2"
top: "tanh2"
type: "TanH"
}
layer {
name: "abs2"
type: "AbsVal"
bottom: "tanh2"
top: "abs2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "abs2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 60
pad: 0
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "tanh3"
bottom: "conv3"
top: "tanh3"
type: "TanH"
}
layer {
name: "abs3"
type: "AbsVal"
bottom: "tanh3"
top: "abs3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "abs3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
##
layer {
name: "conv4"
type: "Convolution"
bottom: "pool3"
top: "conv4"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 80
pad: 0
kernel_size: 2
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "tanh4"
bottom: "conv4"
top: "tanh4"
type: "TanH"
}
layer {
name: "abs4"
type: "AbsVal"
bottom: "tanh4"
top: "abs4"
}
##
layer {
name: "ip1"
type: "InnerProduct"
bottom: "abs4"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 120
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "gaussian"
std: 0.1
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "ip2"
top: "ip2"
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
sam sk
Jun 7, 2016, 8:51:43 AM6/7/16
to Caffe Users
I also tried a smaller dataset of 50 images only so network may overfit. but still training loss is get stuck at 0.74xxx. Image size 39*39 grayscal. normalized to (0,1) labels also normalized (0,1)
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