Caffe Practical Notes __deep

Caffe Brief Introduction:

Caffe doesn't have a Windows version yet, so I need to log on to a Linux server

Caffe main processing picture/picture sequence

data format read by Caffe

Read from a dedicated database (Lmdb, LEVELDB)

Read pictures directly

Read from memory (takes up a lot of memory)

Read from the HDF5 file

Read from the sliding window (slide once as a small picture in a larger image)

The most common is the first two ways. The default is to read from the Lmdb database format, so you need to first convert the picture file into a lmdb format file. Reading a picture directly can cause the mean value to be reduced. If you do not consider the reduction of the average value, you can read the picture directly.

Caffe Operation 1----Preparing data

STEP1: Get a list of files

The image above originates from the Fer library, in which a single image represents an expression, and the write code automatically generates TXT documents

STEP2: Converting to IMDB format

Command principle:

This includes the above four parameters, the pink parameter can choose not to set, which----means that you can not adjust

Actual operation:

Open the terminal under the/caffe path under the root directory home, enter the sudo command, enter the password, and enter the maximum permissions:

Enter command: (note space when entering)

In Caffe, the author provides us with such a file: convert_imageset.cpp, stored in the Tools folder under the root directory. After compiling, the resulting executable file is placed under buile/tools/, which is used to convert the picture file into a IMDB file that can be used directly in the Caffe frame.

The screen shows:

View results:

A folder I named was added to the target directory

The folder contains two files, Data.mdb store the data; Lock.mdb Store the label

Basic Knowledge Supplement

Folders and directories under Linux

/home	root directory, maximum partition time
/bin	Store system commands
/user	Largest directories, storing applications and files
/etc	Storing configuration files
/dev	Device Special files
/mnt	For temporary mounting of hard disks, discs
/src	Put source files like CPP

How to generate train.txt files and how to adjust the format of a picture when you turn IMDB formatting, see: http://www.cnblogs.com/denny402/p/5082341.html

STEP3: Calculating mean value

Minus the mean, you can improve the accuracy. Of course, you can also choose not to reduce the mean value.

Extension must be Binaryproto

Actual code:

This includes two parameters, one is the file address where the IMDB data is placed, and the other is the address and file name of the save mean file

Get Binarypro mean file

Extended reading: http://www.cnblogs.com/denny402/p/5102328.html

Caffe Operation 2----Build network structure

The whole process of running can be divided into three phases: the training phase, the verification phase and the testing phase. The network structure is different at different stages and is stored in the Prototxt file. In order to facilitate, the training phase and the verification phase of the network structure in a file, the test phase of the network structure in a separate file:

A layer represents a layer of

Layer can be copied by nesting

1) Data layer

Name can be arbitrarily taken

Type is brought by the system and cannot be named by itself

Top indicates that the data is sent up, and the bottom indicates that the data is received from below, and that the bottom determines the flow of data and the link between the layers.

Phase:train says this layer is only called during training.

Mirror means flip

Cropsize indicates that a picture becomes 40*40*10, which increases the number of samples.

BatchSize is a multiple of 2, representing batch processing

Http://www.cnblogs.com/denny402/p/5070928.html

2) convolution layer

Decay=0 means no decay

Number of num_output convolution cores/nodes

Kernal_size is usually 3*3 5*5 7*7 not too big

Padding after the volume of the plot smaller, so fixed fill 0, if = 2, indicating the surrounding padding around 2 pixel

Gaussian indicates that W and B are initialized with the Gaussian method

Http://www.cnblogs.com/denny402/p/5071126.html

3) Activation layer

Common Relu,sigmod

Http://www.cnblogs.com/denny402/p/5072507.html

4) Pool Layer

Caffe only supports Max and average,stochastic is random meaning

Stride cannot write 1, otherwise there will be no pooling function

Http://www.cnblogs.com/denny402/p/5071126.html

5) Full Connection layer

Xvaier initialization method is good, the default value is 0

Type types are unchanged

Http://www.cnblogs.com/denny402/p/5072746.html

6) Other layers

The loss layer is added behind the full connecting layer.

If you want to test, add a layer of Softmax layer

The Deploy.prototxt file is used for the test phase, and the test data does not have a tagged value, so the data entry layer differs from the other two stages.

Caffe automatically copies grayscale to 3-channel graphs

Caffe Operation 3----configuration Parameters Solver.prototxt

Set global parameters here

After each training completes one interval, verifies once whether >449

Basic Learning rate determines convergence and does not converge

Step: Change every how many times

Stepshot: Save one parameter per training

Http://www.cnblogs.com/denny402/p/5074049.html

Caffe Operation 4----training Model

This step will generate the Caffe model file, the parameter training completed

Cases:

Build/tools/caffe Train-solver/home/bnu/fer/solver.prototxt

Http://www.cnblogs.com/denny402/p/5076285.html

Caffe Operation 5----test Model

Classification.bin is an executable with its own body

Http://www.cnblogs.com/denny402/p/5111018.html

Original address: Http://www.cnblogs.com/weiqinglan/p/5969016.html?utm_source=itdadao&utm_medium=referral