cnn network

Deploy a spark cluster with a Docker installation to train CNN (with Python instances) This blog is only for the author to record the use of notes, there are many details of the wrong place. Also hope that you crossing can forgive, welcome criticism correct. Blog Although the water, but also Bo master elbow grease also. If you want to reprint, please attach this article link , not very grateful!http://blog.csdn.net/cyh_24/article/

7014Image 7044dtype: int64X.shape == (2140, 9216); X.min == 0.000; X.max == 1.000y.shape == (2140, 30); y.min == -0.920; y.max == 0.996This result tells us that the feature points of many graphs are incomplete, such as the right lip angle, only 2,267 samples. We dropped all the images with less than 15 feature points, and this line did it:DF = Df.dropna () # Drop all rows this has missing values in themTrain our network with

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small, until the end can stop training. 5.4: A lot of people use a design learning rate principle is to monitor a ratio (each update gradient norm divided by the current weight norm), if this ratio around 10-3, if less than this value, learning will be very slow, if greater than this value, then the study is very unstable, which will lead to failure. 6: Using a validation set, you can know when to start lowering the learning rate, and when to stop training. 7: Some suggestions on the choice of

The CNN Formula derivation 1 prefaceBefore looking at this blog, please make sure that you have read my top two blog "Deep learning note 1 (convolutional neural Network)" and "BP algorithm and Formula derivation". and has read the paper "Notes on convolutional neural Networks" in the literature [1]. Because this is the interpretation of the literature [1] The derivation process of the formula in the first p

The CNN Formula derivation 1 prefaceBefore looking at this blog, please make sure that you have read my top two blog "Deep learning note 1 (convolutional neural Network)" and "BP algorithm and Formula derivation". and has read the paper "Notes on convolutional neural Networks" in the literature [1]. Because this is the interpretation of the literature [1] The derivation process of the formula in the first p

Cnn-convolutional Neural NetworksIn recent years in the field of machine vision is a very fire of acquiescence, first proposed by Yan LeCun.If you want to learn the details, see Li Feifei cs231n courseHow does it work?Give a picture, each circle is responsible for processing part of the picture.These circles form a filter.Filter identifies whether the specified pattern exists in the picture and in which region.There are 4 filter in the same color, the

word w_mn for the topic z_m the φ distribution sample.Thus the joint distribution of the entire model is as follows:To calculate the integral of the joint distribution, remove the partial hidden variables:The intermediate parameters θ and φ can be eliminated by indirectly calculating the transfer probability, so the transfer probability of the topic is as follows:So we can use the Gibbs sampling for each iteration of the iteration, the iterative process is: first generated by a uniform distribu

1 PrefaceIn my previous blog, I introduced some of the ways to run CNN on iOS. But, in general, we need a powerful machine to run the CNN, we just need to use the resulting results for the mobile side. Before the code modified using UFLDL in MATLAB ran the 3-layer CNN of hand recognition, here we consider porting Matlab to Xcode.Step 1:matlab Turn CThe first thin

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there is a total of n neurons in the network, p=0.5, then the equivalent of 2n sub-network training at the same time, is a model averaging method to improve generalization performance.Network Structure AnalysisUsually after the convolution layer should be a pooled layer, but alexnet only in the first convolutional layer, the second convolutional layer and the last convolutional layer behind the maximum poo

TINY-CNN is a CNN-based open Source library whose license is the BSD 3-clause. The author has also been maintaining the update, which is helpful for further mastering CNN, so the following is the compilation and use of tiny-cnn in Windows7 64bit vs2013.1. Download the source code from HTTPS://GITHUB.COM/NYANP/TINY-

Database Introduction Development tools Network framework Training results Training Essentials Activation function The role of dropout Training Code "Original" Liu_longpoReprint Please specify the source "CSDN" http://blog.csdn.net/llp1992Database IntroductionCIFAR-10 is a data set for universal object recognition, collected by Hinton's two great disciples Alex Krizhevsky, Ilya Sutskever.CIFAR-10 is

The original is: "The Impact of imbalanced Training Data for convolutional neural Networks" This blog is the paper's reading notes, there is inevitably a lot of details of the wrong place. Also hope that you crossing can forgive, welcome criticism correct. More related blog please poke: http://blog.csdn.net/cyh_24 If you want to reprint, please attach this article link: http://blog.csdn.net/cyh_24/article/details/49871387 Abstract This paper mainly studies the effec

This article mainly introduces about Pytorch + visdom CNN processing self-built image data set method, has a certain reference value, now share to everyone, have the need of friends can refer to Environment System: WIN10 Cpu:i7-6700hq gpu:gtx965m python:3.6 pytorch:0.3 Data download Source from Sasank chilamkurthy tutorial; Data: Download link. Download and then unzip to the project root directory: Data sets are used to classify ants and bees. There

/bottom[0]->num ();} Template__global__void Smoothl1backward (const int N, const dtype*inch, dtype*Out , Dtype sigma2) { F'(x) = Sigma * Sigma * x if |x| // =Sign (x) otherwise cuda_kernel_loop (index, N) {Dtype val=inch[index]; Dtype Abs_val=ABS (val);if(Abs_val sigma2) {Out[index]= Sigma2 *Val;} Else{Out[index]= (Dtype (0) Dtype (0)); } }} templatevoid Smoothl1losslayertop, const vectorbottom) { After forwards, Diff_ holds w_in * (B0-B1) int count=Diff_.count (); Smoothl1backward(Count, Diff_.

Call function print f (-2)Step 1 Define the input variablesA = Theano.tensor.scalar ()b =theano.tensor.matrix ()Simplified import theano.tensor as TStep 2 Define the relationship of the output variable to the input variableX1=t.matrix ()X2=t.matrix ()Y1=x1*x2Y2=t.dot (X1,X2) #矩阵乘法Step 3 declaring the functionF= theano.function ([x],y)The function input must be a list band []Example1 ImportTheano2 ImportTheano.tensor as T3 4A=T.matrix ()5b=T.matrix ()6c = A *b7D =T.dot (A, b)8f1=theano.

See above -collect high-quality callout data-Input and output data are normalized to prevent numerical problems, and the method is the principal component analysis of what.-Initialization of parameters is important. Too small, the parameters are not moving at all. General weight parameter 0.01 mean variance, 0 mean value of Gaussian distribution is omnipotent, not to try to bigger. The deviation parameter is all 0.-with SGD, Minibatch size 128. or smaller size, but the throughput becomes small

: Train multiple models, averaging the results when you test, and you can get a 2% boost. When training a single model, the results of checkpoints in the average different periods can also be improved. You can combine the parameters of the test with the parameters of the training when testing: 1. Whether CNN or Rnn,batch normalization useful, not necessarily result in a few points, convergence is much

is Faster r-cnn Doing well for pedestrian Detection?ECCV Liliang Zhang kaiming He　　Original link: http://arxiv.org/pdf/1607.07032v2.pdf　　Abstract: Pedestrian detection is argue said to be a specific subject, rather than general object detection. Although recent depth object detection methods such as: Fast/faster RCNN in general object detection, show a strong performance, but for pedestrian detection is not very successful. This paper studies the pro