aws machine learning image recognition

Official website: Github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/toco You can also easily customize your image recognition application after you have mastered it. The first step. Preparing data Data are: http://download.tensorflow.org/example_images/flower_photos.tgz This is a collection of pictures about the flower classification, after downloading the decompression,

with the Sofamax output of multiple convolutional networks , multiple models are fused together to output results. The results are shown in table 6. 4.5 COMPARISON with the state of the ARTwith the current compare the state of the ART model. Compared with the previous 12,13 network Vgg Advantage is obvious. With googlenet comparison single model good point,7 Network fusion is inferior to googlenet. 5 ConclusionIn this paper , the deep convolution neural network has good results in effect and

[i]) if (classifierresu Lt! = Datinglabels[i]): ErrOrcount + = 1.0 print "The total error rate is:%f"% (Errorcount/float (numtestvecs)) Print error count def img2vector (filename): Returnvect = zeros ((1,1024)) FR = open ( FileName) For I in range (+): LINESTR = Fr.readline () F or J in range (+): RETURNVECT[0,32*I+J] = Int (linestr[j]) RETURN RET Urnvectdef handwritingclasstest (): hwlabels = [] trainingfilelist = Listdir (' trainingDigits ') #load the training

a handwritten recognition system using K-Nearest neighbor algorithm The system constructed here can only recognize digital 0~9.The numbers that need to be identified are already using graphics processing software, processed to have the same color and size: a Black-and-white image with a width high of 32 pixels x32 pixels. Example: Handwriting recognition system

Tags: tin mac reg ATI Learning-Bayesian att complexity testIn fact, it only took a little time to study the book today,If the model has too many parameters, and the training data is not enough, there will be overfitting.Overfitting can be solved by regularization, the Bayesian method can also avoid the appearance of overfitting, in fact, in the Bayesian model, the effective parameters of the model is automatically determined by the size of the trainin

to the derivative of the scalar y-to-column vector x,The y is biased for the elements of each x without transpose.DY/DX = [Dy/dx (IJ)]Important Conclusions:y = U ' XV =σσu (i) x (IJ) v (j) then Dy/dx = = UV 'y = U ' X ' XU then dy/dx = 2XUU 'y = (xu-v) ' (xu-v) then dy/dx = d (U ' X ' xu-2v ' XU + V ' V)/dx = 2XUU '-2VU ' + 0 = 2 (xu-v) U '9. Derivative of matrix Y to matrix x:Each element of Y is derivative of x, and then it is lined together to form a super matrix.Mathematical knowledge of pa

probability estimate. Merging the two best model in Figure 3 and Figure 4 to achieve a better value, the fusion of seven model will become worse.Ten. Reference[1]. Simonyan K, Zisserman A. Very deep convolutional Networks for large-scale Image recognition[j]. ARXIV Preprint arxiv:1409.1556, 2014.[2]. Krizhevsky, A., Sutskever, I., and Hinton, G. E. ImageNet classification with deep convolutional neural net

1 Preparing data: Converting an image to a test vectorThere are two kinds of data sets, the training data set and the test data set, respectively, there are 2000, 900.We will convert a 32*32 binary image matrix to a vector of 1 x 1024 so that the classifier used in the first two sections can process the digital image information.Code:

in the previous section.We want the additional layer to learn the identity mapping, which is still very difficult to train because it is a non-linear layer .However, if we are learning the residual mapping, that is, the total zero residuals, it is obviously much easier . Thought is similar to SVM, but you can't think of it!!! Iv. Implementation Shortcut connectionsThought has, concrete how to achieve it?Can't help: He Dashen too awesom

industry for image classification with KNN,SVM,BP neural networks. Gain deep learning experience. Explore Google's machine learning framework TensorFlow. Below is the detailed implementation details. System Design In this project, 5 algorithms for experiments are KNN, SVM, BP Neural Network, CNN and Migration

1, Xiao Wei's practice road Http://blog.csdn.net/xiaowei_cqu 2, Morning Chenyusi far (Shi Yuhua Beihang University) Http://blog.csdn.net/chenyusiyuan 3, Rachel Zhang (Zhang Ruiqing) 's blog Http://blog.csdn.net/abcjennifer 4. ZOUXY09 (Shaoyi) http://blog.csdn.net/zouxy09 (deep learning, image segmentation, Kinect development Learning, compression sensing) 5, Love

is: Understanding-Bayesian model.http://www.merl.com/people/brand/Merl (Mitsubishi Electric Laboratory) specializes in "Style machine".http://research.microsoft.com/~ablake/A.Blake, a highly prestigious CV, graduated from Cambridge University in 1977 with a bachelor's degree in mathematics and electronic science from 31 College. After that, he set up a research group in Mit,edinburgh,oxford and became Professor of Oxford until 1999, when he entered t

[Ai refining] machine learning 051-bag of Vision Model + extreme random forest to build an image classifier (Python library and version number used in this article: Python 3.6, numpy 1.14, scikit-learn 0.19, matplotlib 2.2) Bag of visual words (bovw) comes from bag of words (BOW) in natural language processing, for more information, see my blog [ai refining]

"Furnace-Refining AI" machine learning 046-image edge detection method(Python libraries and version numbers used in this article: Python 3.6, Numpy 1.14, Scikit-learn 0.19, matplotlib 2.2)Image in the detection of various shapes in the field of computer vision is one of the most common technology, especially in the

determined For k-initialized cluster centers, The final clustering center is computed using the K-means algorithm. For the specific process of the K-means algorithm can refer to the blog post easy to learn the machine learning algorithm--kmeans,k-means++ algorithm of the specific process will be added Later.II. Preparation of the practiceIn practice, Python is used as the development languag

illumination of the JWST aperture. It is a 0 fill in the array, twice times the aperture width, resulting in a nquist sampled psf (image). We produce PSFS based on the second-to five-order global Zernike polynomial and do not include any error in each segment. All PSFs are normalized to a maximum value of 1 before entering CNN. We use Minibatch training with PSFS minibatch size. CNN's Learning parameters a

First, install the virtual machine (this article takes VMware Workstation 12 as an example)1. On-line all virtual machines and download.2. Find the download file to install3. Always Next4. Next is to choose the installation of the directory, of course, if your computer C disk enough, installed in the C drive can also, but it is recommended to install in non-system disk, so as not to affect the speed of the computer. Select OK to click Next.5. After th

different value of a feature at different times of the sample) as a description of its statistical characteristics. Each row has a mean value, so the mean value of each row should be the same, so that all features can be considered to have similar distributions . This attribute we call high dispersal (highly dispersive). But for a good feature description, this attribute is not necessary. However, it can prevent the degradation of features , that is, to prevent the extraction of the same charac

Small task: Achieve picture classification1. Picture materialPython bulk compress jpg images: PiL library resizehttp://blog.csdn.net/u012234115/article/details/502484092. Environment ConstructionInstallation version of Python under Windows comparison 2.7 vs 3.6Https://pypi.python.org/pypiInstallation of the PIL Library under WindowsHttps://pypi.python.org/pypiInstallation of the PIL Library under Windowshttp://zjfsharp.iteye.com/blog/2311523Installation and upgrade of PIP under Windowshttp://blo