Caffe: Train your network with your own data mnist

Software to draw white on black:kolourpaint.

Assume that all "1" images are placed under the folder named 1. (0-9 similar).. Gets the name of each number file after the label on the manual table. Then synthesize the train. Txt

1. Get the name of all the images in the folder:

Find./1-name ' *.png ' >1.txt

In this case, the image name in the 1.txt file includes the road strength information, to remove the previous path information.

$ sudo sed-i "s/.\/1\///g" 1.txt//(\ = Escaped, so here are double quotes instead of single quotes)

2. Add a label after each name within 1.txt

1. txt:

1101.png 1

1102.png 1

..... So

3. Convert picture data to Lmdb format data

Caffe/examples build a file to save training files for: sd_mnist

3.1 Sd_mnist Create a sd_create_lmdb.sh to convert the picture format:

sudo vim sd_create_lmdb.sh, content as follows:

#!/usr/bin/env SH
# Create the imagenet lmdb inputs
# n.b. Set the path to the Imagenet train + Val Data dirs

Example=examples/sd_mnist (! Note: This is the directory you created under examples)
Data=data/sd_mnist (! Note: You create a new directory under the Data folder with two picture sets (training and test training set) and two txt above).
Tools=build/tools

train_data_root=data/sd_mnist/train/(! Note: The path to the training picture set)
val_data_root=data/sd_mnist/test/(! Note: Just test the picture set path)

# Set Resize=true to RESIZE the images to 256x256. Leave as False if images has
# already been resized using another tool.
Resize=true
if $RESIZE; Then
Resize_height=28
Resize_width=28
Else
Resize_height=0
Resize_width=0
Fi

if [!-d "$TRAIN _data_root"]; Then
echo "Error:train_data_root is not a path to a directory: $TRAIN _data_root"
echo "Set the Train_data_root variable in create_imagenet.sh to the path" \
"Where the ImageNet training data is stored."
Exit 1
Fi

if [!-d "$VAL _data_root"]; Then
echo "Error:val_data_root is not a path to a directory: $VAL _data_root"
echo "Set the Val_data_root variable in create_imagenet.sh to the path" \
"Where the ImageNet validation data is stored."
Exit 1
Fi

echo "Creating train Lmdb ..."

Glog_logtostderr=1 $TOOLS/convert_imageset \
--resize_height= $RESIZE _height \
--resize_width= $RESIZE _width \
--shuffle \
$TRAIN _data_root \
$DATA/train.txt \ (! Pay attention to road strength)
$EXAMPLE/mnist_train_lmdb

echo "Creating Test Lmdb ..."

Glog_logtostderr=1 $TOOLS/convert_imageset \
--resize_height= $RESIZE _height \
--resize_width= $RESIZE _width \
--shuffle \
$VAL _data_root \
$DATA/test.txt \ (! Pay attention to road strength)
$EXAMPLE/mnist_test_lmdb

echo "Done."

-----------------------------------------------------------------------

3.2 Running sh example/sd_mnist/sd_create_lmdb.sh

If successful, the information returned by the terminal, the image is of a size rather than 0kb. And there will be two files under Examples/sd_mnist: Mnist_train_lmdb,mnist_test_lmdb they are data.mdb and Lock.mdb.

4, training Our data set: The following files are copied from caffe\examples\mnist to caffe\examples\sd_mnist down for modification. The main is to modify the path information, the entire network remains unchanged.

4.1 The first SH file is Train_lenet,sh

#!/usr/bin/env SH
Set-e

./build/tools/caffe train--solver=examples/sd_mnist/lenet_solver.prototxt [email protected]

4.2, copy the Lenet_solver.prototxt file, and modify:

# The Train/test net protocol buffer definition
Net: "Examples/sd_mnist / Lenet_train_test.prototxt "
# Test_iter Specifies how many forward passes the test should carry out.
# of MNIST, we have test batch size and test iterations,
# covering the full testing images.
test_iter:100
# Carry out testing every training iterations.
test_interval:500
# The base learning rate, momentum and the weight decay of the network.
base_lr:0.01
momentum:0.9
weight_decay:0.0005
# The Learning rate policy
Lr_policy: "INV"
Gamma: 0.0001
power:0.75
# Display every iterations
display:100
# The maximum number of iterations
Max_iter : 10000
# Snapshot Intermediate results
snapshot:5000
snapshot_prefix: " Examples/sd_mnist/lenet "
# Solver mode:cpu or GPU
Solver_mode:cpu

4.3. Lenet_train_test.prototxt Copy from the Mnist folder to the current folder

modifying paths

Name: "LeNet"
Layer {
Name: "Mnist"
Type: "Data"
Top: "Data"
Top: "Label"
Include {
Phase:train
}
Transform_param {
scale:0.00390625
}
Data_param {
Source: "Examples/sd_mnist/mnist_train_lmdb"
Batch_size:64
Backend:lmdb
}
}
Layer {
Name: "Mnist"
Type: "Data"
Top: "Data"
Top: "Label"
Include {
Phase:test
}
Transform_param {
scale:0.00390625
}
Data_param {
Source: "Examples/sd_mnist/mnist_test_lmdb"
batch_size:100
Backend:lmdb
}
}
Layer {
Name: "Conv1"
Type: "Convolution"
Bottom: "Data"
Top: "Conv1"
param {
Lr_mult:1
}
param {
Lr_mult:2
}
Convolution_param {
Num_output:20
Kernel_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
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:50
Kernel_size:5
Stride:1
Weight_filler {
Type: "Xavier"
}
Bias_filler {
Type: "Constant"
}
}
}
Layer {
Name: "Pool2"
Type: "Pooling"
Bottom: "Conv2"
Top: "Pool2"
Pooling_param {
Pool:max
Kernel_size:2
Stride:2
}
}
Layer {
Name: "Ip1"
Type: "Innerproduct"
Bottom: "Pool2"
Top: "Ip1"
param {
Lr_mult:1
}
param {
Lr_mult:2
}
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
}
param {
Lr_mult:2
}
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"
}

4.4 lenet.prototxt Copy from Mnist folder to current folder without modification

4.5 Running SH example/sd_mnist/train_lenet.sh

No error, come out accuracy loss these, illustrate success!!

Reference: http://blog.csdn.net/xiaoxiao_huitailang/article/details/51361036

Caffe: Train your network with your own data mnist