I'm tring to lea siamese network model for cifar10 data.
I built paired data using cifar10 data and trained with revised cifar10 model definition (original: https://github.com/ycyoon/caffe/blob/master/examples/cifar10/cifar10_full_train_test.prototxt) for siamese network. I just appended feat layers for 2D embedding and changed weight filler from gausian to xavier.
Here's the revised model definition
name: "cifar10_siamese_train_test"
layer {
name: "pair_data"
type: "Data"
top: "pair_data"
top: "sim"
include {
phase: TRAIN
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/siamese/mnist_siamese_train_leveldb/"
batch_size: 64
}
}
layer {
name: "pair_data"
type: "Data"
top: "pair_data"
top: "sim"
include {
phase: TEST
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/siamese/mnist_siamese_test_leveldb/"
batch_size: 100
}
}
layer {
name: "slice_pair"
type: "Slice"
bottom: "pair_data"
top: "data"
top: "data_p"
slice_param {
slice_dim: 1
slice_point: 1
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
pad: 0
keel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
keel_size: 3
stride: 2
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "pool1"
top: "pool1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "pool1"
top: "norm1"
l_param {
local_size: 3
alpha: 5e-05
beta: 0.75
norm_region: WITHIN_CHANNEL
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "norm1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 32
pad: 2
keel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: AVE
keel_size: 3
stride: 2
}
}
layer {
name: "norm2"
type: "LRN"
bottom: "pool2"
top: "norm2"
l_param {
local_size: 3
alpha: 5e-05
beta: 0.75
norm_region: WITHIN_CHANNEL
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
top: "conv3"
convolution_param {
num_output: 64
pad: 2
keel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3"
top: "pool3"
pooling_param {
pool: AVE
keel_size: 3
stride: 2
}
}
layer {
name: "ip1"
type: "IerProduct"
bottom: "pool3"
top: "ip1"
param {
lr_mult: 1
decay_mult: 250
}
param {
lr_mult: 2
decay_mult: 0
}
ier_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "feat"
type: "IerProduct"
bottom: "ip1"
top: "feat"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
ier_product_param {
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "conv1_p"
type: "Convolution"
bottom: "data_p"
top: "conv1_p"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
pad: 0
keel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1_p"
type: "Pooling"
bottom: "conv1_p"
top: "pool1_p"
pooling_param {
pool: MAX
keel_size: 3
stride: 2
}
}
layer {
name: "relu1_p"
type: "ReLU"
bottom: "pool1_p"
top: "pool1_p"
}
layer {
name: "norm1_p"
type: "LRN"
bottom: "pool1_p"
top: "norm1_p"
l_param {
local_size: 3
alpha: 5e-05
beta: 0.75
norm_region: WITHIN_CHANNEL
}
}
layer {
name: "conv2_p"
type: "Convolution"
bottom: "norm1_p"
top: "conv2_p"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 32
pad: 2
keel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu2_p"
type: "ReLU"
bottom: "conv2_p"
top: "conv2_p"
}
layer {
name: "pool2_p"
type: "Pooling"
bottom: "conv2_p"
top: "pool2_p"
pooling_param {
pool: AVE
keel_size: 3
stride: 2
}
}
layer {
name: "norm2_p"
type: "LRN"
bottom: "pool2_p"
top: "norm2_p"
l_param {
local_size: 3
alpha: 5e-05
beta: 0.75
norm_region: WITHIN_CHANNEL
}
}
layer {
name: "conv3_p"
type: "Convolution"
bottom: "norm2_p"
top: "conv3_p"
convolution_param {
num_output: 64
pad: 2
keel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu3_p"
type: "ReLU"
bottom: "conv3_p"
top: "conv3_p"
}
layer {
name: "pool3_p"
type: "Pooling"
bottom: "conv3_p"
top: "pool3_p"
pooling_param {
pool: AVE
keel_size: 3
stride: 2
}
}
layer {
name: "ip1_p"
type: "IerProduct"
bottom: "pool3_p"
top: "ip1_p"
param {
lr_mult: 1
decay_mult: 250
}
param {
lr_mult: 2
decay_mult: 0
}
ier_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "feat_p"
type: "IerProduct"
bottom: "ip1_p"
top: "feat_p"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
ier_product_param {
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "loss"
type: "ContrastiveLoss"
bottom: "feat"
bottom: "feat_p"
bottom: "sim"
top: "loss"
contrastive_loss_param {
margin: 1
}
}
However, when I tried to get embeding points from test examples, all outputs are converged to one same point. (for example, 0.38288754 -0.08905841)
I changed the cifar10 data to mnist data, however it also gives the same result.
Is there anything wrong with the definition file?