Nikki Shah
Hello, I am new to Caffe and deep learning in general, and am hoping to find some answers here :)
I have installed SkimCaffe on my Ubuntu VM and am able to run classification models using Lenet. Now, I want to switch the convolution layer to winograd convolution layer, and perform comparative study between the two types of convolutions.
I have tried to add it as below, however this has not been successful. The winograd layer addition just zeros all entries in the matrix and gives wrong classifications (refer below). I believe I must be doing something wrong here. I would greatly appreciate if someone could guide me to the solution.
Basically, I want to add a Winograd Layer (winograd convolution) into Lenet, using winograd_layer.cpp.
Thank you for the help! Also, please let me know where I could ask this question, if this is not the right platform for it :)
LENET:
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mean_file: "/home/x/DIGITS/digits/jobs/20180302-235120-dbc4/mean.binaryproto"
}
data_param {
source: "/home/x/DIGITS/digits/jobs/20180302-235120-dbc4/train_db"
batch_size: 64
backend: LMDB
}
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mean_file: "/home/x/DIGITS/digits/jobs/20180302-235120-dbc4/mean.binaryproto"
}
data_param {
source: "/home/x/DIGITS/digits/jobs/20180302-235120-dbc4/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "scale"
type: "Power"
bottom: "data"
top: "scaled"
power_param {
scale: 0.0125000001863
}
}
layer {
name: "win1"
type: "Winograd"
bottom: "scaled"
top: "win1"
param {
lr_mult: 1.0
}
param {
lr_mult: 2.0
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "win1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "win2"
type: "Winograd"
bottom: "pool1"
top: "win2"
param {
lr_mult: 1.0
}
param {
lr_mult: 2.0
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "win2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1.0
}
param {
lr_mult: 2.0
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1.0
}
param {
lr_mult: 2.0
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "ip2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
CLASSIFICATION OUTPUT:
