caffe machine learning

Deep Learning notes ------ configure and install caffe-cpu only mode in linux, and install caffecpuonly This article is suitable for beginners to learn the simplest way to configure caffe. This is the most silly configuration method I have summarized after reading the methods of various great gods on the Internet. I am also suffering from attacks and growth in th

net; lower the learning rate (divided by 100), The reason for this is that compared to the random initialization of the weight parameters, the pre training parameters are relatively good, do not need to update too fast, you can modify the maximum number of iterations and snapshot. 3.4. Training Compared with the training of random initialization of parameters and training on the basis of training, a-weight parameter is added when invoking the command

Original address: https://www.zhihu.com/question/27982282 Gein Chen's answer many thanks —————————————————————————————————————————— 1. The first step of learning the program, first let the program run, see the results, so that there will be an intuitive feeling.Caffe's official Online Caffe | The Deep learning Framework provides a lot of examples, and you can eas

separate channels (for example, their size is 1 x local_size x local_size). Each input value is divided by (1+ (α/n) ∑ix2i) Beta (1+ (\alpha/n) \sum_{i}x_{i}^{2}) ^{\beta}, where n n is the size of the local area, and the area centered on the value is summed (0 padding is added if required). 4.im2col Im2col is a useful tool for transforming an image into a column vector, and you don't need to know it in most cases. Im2col the entire block layout to a matrix and converts the

(batch_size) is nWith the loss function, you can iteratively solve loss and gradients to optimize the problem. In the neural network, the forward pass is used to solve the loss, and the backward pass is used to solve the gradient.In Caffe, the stochastic Gradient descent (SGD) is used by default to optimize the solution. The following methods are also gradient-based optimization methods (like SGD), so this article only describes SGD. Other methods, i

Cpp_classification Classification of CPP interface file feature_extraction feature Demo folder MATLAB matlab corresponds to the interface The interface of Python python Models model file path, some training good model can refer to Caffe official website model zoo:http://caffe.berkeleyvision.org/model_zoo.html Tools Some tool SRC all source code storage location The files in the docs/tutorial are well worth reading, with a thorough understanding of th

forward:0.49879Ms. I102510:20:01.517771 8487 caffe.cpp:412] Conv1 backward:0.647739Ms. I102510:20:01.517773 8487 caffe.cpp:409] pool1 forward:0.165693Ms. I102510:20:01.517779 8487 caffe.cpp:412] pool1 backward:0.648113Ms. I102510:20:01.517783 8487 caffe.cpp:409] Conv2 forward:0.398481Ms. I102510:20:01.517786 8487 caffe.cpp:412] Conv2 backward:3.08044Ms. I102510:20:01.517791 8487 caffe.cpp:409] pool2 forward:0.0440877Ms. I102510:20:01.517794 8487 caffe.cpp:412] pool2 backward:0.206023Ms. I102510

This is the fourth example in the official Caffe document notebook examples, link address: http://nbviewer.jupyter.org/github/bvlc/caffe/blob/master/examples/03- Fine-tuning.ipynb This example is used to fine-tune flickr_style data on a trained network. Fine-tune your data with a trained Caffe network. The advantage of this approach is that with the training netw

. build_release/lib/libcaffe.so.1.0.0Clang:warning:argument unused during compilation:'-pthread'[-wunused-command-line-argument]LD: Framework not found VecLibclang:error:linker command failed with exit code1(Use-v to see invocation) Make: * * * [. build_release/lib/libcaffe.so.1.0.0] Error1Solution:On the command line, enter: Xcode-select--installStep three, compile Pycaffe make PycaffeQuestion one:Cxx/ld-o python/caffe/_caffe.so Python/

Python to do deep learning caffe design CombatEssay background: In a lot of times, many of the early friends will ask me: I am from other languages transferred to the development of the program, there are some basic information to learn from us, your frame feel too big, I hope to have a gradual tutorial or video to learn just fine. For learning difficulties do no

", "Data/ilsvrc12/synset_words.txt", "Examples/images/cat.jpg"], "Program": "${workspaceroot}/build/examples/cpp_classification/classification.bin", "Stopatentry":false, "CWD": "/home/kellygod/caffe/", "Environment": [], "Externalconsole":true, "Mimode": "GdB", "Setupcommands": [ { "description": "Enable pretty-p

Caffe of Deep Learning (i) using C + + interface to extract features and classify them with SVM Reprint please dms contact Bo Master, do not reprint without consent. Recently because of the teacher's request to touch a little depth of learning and caffe things, one task is to use the ResNet network to extract the char

Caffe Data blob shape is n*c*h*w, with the number of channels in front. While the python image is processed, shape is h*w*c, and the number of channels is behind. So you need to switch. Picture Display and saveBecause there is no graphical interface, very convenient jupyter notebook can not be used, had to save pictures to view. Caffe Python Interface

a few points to note: Conversion of data formatsThe Caffe Data blob shape is n*c*h*w. Number of channels in front. While the python image is processed, shape is h*w*c. Number of channels in the rear.So we need to switch. Picture Display and saveBecause there is no graphical interface, very convenient jupyter notebook can not be used, just good to save the picture view. Caffe Pyt

bottom, down to top. The default is LR. Example: Drawing a lenet model # sudo python python/draw_net.py examples/mnist/lenet_train_test.prototxt netimage/lenet.png--rankdir=TB 　　　　　　 3. Summary The graph drawn with Netscope is simple and easy to understand the network model quickly, but lacks the detail information in the layer.The structure diagram drawn with draw_net.py preserves the parameter information, and the details are richer, but the structure is not very clear, and this is particula

[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 Ima

With the rapid development of in-depth learning, there have been a lot of in-depth learning framework. These frameworks each have their own advantages and disadvantages, and Caffe as a big brother, personally think its advantage is convenient, concise. In the creation of some structural comparison of the traditional network model, with the help of

URL here# For example:# My_image_url = "Https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_ Sarawak%2c_borneo%2c_malaysia.jpg "!wget-o image.jpg $my _image_url# transform it and copy it into the netImage = Caffe.io.load_image (' image.jpg ')net.blobs[' data '].data[...] = transformer.preprocess (' data ', image)# Perform classificationNet.forward ()# Obtain the output probabilitiesOutput_prob = net.blobs[' prob '].data[0]# sort top Five predictions fro

to computational capacity. For example, for an image of a 96X96 pixel, suppose we have learned 400 features defined on the 8x8 input, each feature and image convolution will get one (96−8 + 1) * (96−8 + 1) = 7921 dimensional convolution feature, because there are 400 features, so each A sample (example) will get a convolution feature vector of 892 * 400 = 3,168,400 dimensions. Learning a classifier with more than 3 million feature inputs is inconveni

I0721 10:38:17.342094 4692 net.cpp:125] Top shape:256 4096 1 1 (1048576) I0721 10:38:17.342157 4692 net.cpp:151] fc7 needs backward computation. I0721 10:38:17.342175 4692 net.cpp:74] Creating Layer RELU7 I0721 10:38:17.342185 4692 net.cpp:84] Relu7 I0721 10:38:17.342198 4692 net.cpp:98] Relu7-FC7 (In-place) I0721 10:38:17.342208 4692 net.cpp:125] Top shape:256 4096 1 1 (1048576) I0721 10:38:17.342217 4692 net.cpp:151] relu7 needs backward computation. I0721 10:38:17.34