caffe windows

Install Caffe under Mac OS X10.10[Email protected]Http://blog.csdn.net/surgewong in Linux learning Caffe "1" for some time, but also gradually to the framework of Caffe have a little understanding. There are many people who study Caffe under Linux, and the reference materials on the internet want to be more. Installati

An interface for the units of computation which can is composed into a Net. Layers must implement a Forward function, in which they take their input (bottom) blobs (if any) and compute the IR Output blobs (if any). They also implement a backward function, in which they compute the error gradients with respect to their input blobam P;s, given the error gradients with their output blobs. Follow our general convolution neural network model to understand: a network (net) c

Data through the data layer into the Caffe network: The data layer is at the bottom of the network, the data can be read from a high-efficiency database (such as: LevelDB, LMDB), can be read directly from memory, if the requirements for reading and writing efficiency is not high can also be read from the hard disk hdft files or ordinary picture files. The data comes from the database: Layer Type: Data Parameters that must be set: Source: The director

Caffe C + + usage TutorialCaffe Using Tutorialsby Shicai Yang (sorcerer under the Stars) on 2015/08/06Initializing the network#include "caffe/caffe.hpp"#include Load a trained modelchar *model = "H:\\Models\\Caffe\\bvlc_reference_caffenet.caffemodel"; net->CopyTrainedLayersFrom(model);Read image mean valuechar *mean_file = "H:\\Models\\

[Caffe] alexnet interpretation of the image classification model of deep learningOriginal address: http://blog.csdn.net/sunbaigui/article/details/39938097This article has been included in:Deep learning Knowledge BaseClassification:Deep Learning (+)Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced.On the Imagenet Image Classification Challenge, Alex proposed the Alexnet network structure model wo

(' input: ' data ' \ n ') F.write (' input_dim:1\n ') F.write (' input_dim:3\n ') F.write (' input_dim:28\n ') F.write (' input_dim:28\n ') F.write (str (Create_deploy ()))if__name__ = =' __main__ ': Write_deploy ()If you modify NET, you need to modify the data entry:layer { "data" "Input" "data" dim1dim3dim100dim100 } }}and add a Softmax, for the original Softmaxwithloss directly replaced on the line.Network testAfter training to get the model, the actual use is to use the model to predict

For Caffe networks that are well trainedInput: Color or grayscale imageDo minist under the handwriting recognition classification, can not be used directly, you need to remove the mean image, while the input image pixels normalized to 0-1 directly. #include #include #include #include #include #include #include #include #include using namespace Caffe; Nolint (build/namespaces)Using Std::string;/* Pair (label

[0,1,2,..., K-1] The label[i of the K-dimensional predictor of the sample I is multiplied by-1 is equivalent to the calculation principle of Hingelosslayer layer in//caffe and source code AnalysisBottom_diff[i * Dim +static_cast(Label[i]) *= -1; } for(inti =0; i for(intj =0; J //Calculation Caffe Hingelosslayer layer principle and source code analysis, deposited in Bottom_diff, namely Bottom[0]->muta

How to Train the Lenet network using Caffe + MNIST on Ubuntu 14.04 64-bit Machine How to Train the Lenet network using Caffe + MNIST on Ubuntu 14.04 64-bit Machine 1. Locate the terminal to the Caffe root directory; 2. Download and decompress the MNIST Database: $./data/mnist/get_mnist.sh 3. convert it to the Lmdb database format: $./examples/mnist/create_mni

This series of articles by the @yhl_leo produced, reproduced please indicate the source. Article Link: http://blog.csdn.net/yhl_leo/article/details/50961542 Spent a day, installed on the computer configuration of the Caffe deep learning framework, many of the online tutorials and guidance have expired, the middle of the time spent a bit, here the personal thought the simplest way to organize the following.version 1 Notebook: Think

Write in front: Before has been engaged in Keras, recently due to some needs, need to learn Caffe, this record Caffe installation record. The Cuda is already installed by defaultIf you are migrating from another deep learning platform to Caffe, follow this tutorial.First step: Git clone https://github.com/BVLC/caffe.git, then install the following pair of depende

Caffe is an efficient, deep learning framework. It can be executed either on the CPU or on the GPU.The following is an introduction to the Caffe configuration compilation process on Ubuntu without Cuda:1. Install the blas:$ sudo apt-get install Libatlas-base-dev2. Install dependencies: $ sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-devlibopencv-dev Libboost-all-dev libhdf5-se Rial-dev Proto

(Deploy,' W ') asF:f.write (' name: ' Lenet ' \ n ') F.write (' input: ' data ' \ n ') F.write (' input_dim:1\n ') F.write (' input_dim:3\n ') F.write (' input_dim:28\n ') F.write (' input_dim:28\n ') F.write (str (Create_deploy ()))if__name__ = =' __main__ ': Write_deploy ()Suppose you change net. Need to change data entry:layer { "data" "Input" "data" dim1dim3dim100dim100 } }}and add a Softmax. For the original Softmaxwithloss can be directly replaced.Network testGet the model after train

Reprint from https://www.cnblogs.com/denny402/p/5076285.html Caffe's operation provides three kinds of interfaces: C + + interface (command line), Python interface and MATLAB interface. This article first resolves the command line, followed by a description of the other two interfaces. Caffe C + + main program (CAFFE.CPP) is placed in the Tools folder under the root directory, and of course there are other functional files, such as: Convert_imagese

compiling Matcaffe Compile Matcaffe with the following command Make all Matcaffe1 1 After that, you can test Matcaffe with the following command: Make Mattest1 1 If you run the above command, you encounter the following error: Libstdc++.so.6 version ' glibcxx_3.4.15 ' not found, which means your MATLAB library does not match. You need to run the following command before starting MATLAB: Export ld_library_path=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64 Export ld_preload=/usr/lib/x86_64- Li

Solver is the core of Caffe, which coordinates the operation of the entire model. One of the parameters that the Caffe program runs must be the Solver configuration file. Running code is typically # Caffe Train--solver=*_slover.prototxt In deep learning, loss function is not convex, there is no analytic solution, we need to solve it by optimization method. The m

1. Procedure Entrance The entry of the program is in/caffe_root/tools/caffe.cpp, the class file where the main () function is located. In the/tools/directory, a framework tool is provided. However, several training-related tools, such as Train,test,finetune, have been integrated into the/caffe.cpp. What we want to know is that a standard training instruction:/build/tools/caffe train--solver=models/xx_caffenet/solver.prototxt, what happened when we hit

Label:Caffe of the Neural network framework (convert picture to LEVELDB format) This should be the older version of the Caffe, directly take it should not be used, but you can refer to the next Caffe in charge of the entire network input datalayer is read from the Leveldb, is a Google implementation of a very efficient KV database. Therefore, we must first turn the data into LEVELDB format for training the

Most of the documents are machine-turned, my English has not been four levels, so make a lookBuild ImagenetThis guide is designed to prepare you to train your own models based on your data. If you just want a imagenet training network, then note that because training requires a lot of electricity, we hate global warming, and we provide the model Zoo with caffenet models for training as described below.Data preparationThe guide specifies all paths and assumes that all commands are executed from t

1, the basic layer definition, Parameters 1, the basic layer definition, parameters How to use Caffe to define a network, first of all to understand the basic interface in Caffe, the following five types of layer are introduced Vision Layers The visual layer comes from header file header:./include/caffe/vision_layers.hpp the general input and output are images,