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Java side:@RequestMapping ("/report")Public String report () {return "/credit/report/report";}@RequestMapping ("/reportout")@ResponseBodypublic void Reportout (@RequestParam (value = "filename") string fileName, @RequestParam (value = "Jasperpath") string Jasperpath, @RequestParam (value = "DocType") String doctype,httpservletrequest request,httpservletresponse response) Throws Jrexception, IOException, Servletexception, SQLException {SimpleDateFormat sd = new SimpleDateFormat ("Yyyymmddhhmmss")

In the process of making a report, the child table is essential, and today we have studied several ways to make a sub-tableOne, connect database create child tabletake MySQL for example :1) iReport Create parent tableThis is to create a new table, but remember that the data source to choose MySQL, the other database data source is the same.2) Once created, let's create a child table, find anywhere in the palette that you dragged into the parent table,

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 the remaining 2140 pictures as a training se

more time. This time our network learned more general, theoretically speaking, learning more general law than to learn to fit is always more difficult.This network will take an hour of training time, and we want to make sure that the resulting model is saved after training. Then you can go to have a cup of tea or do housework, washing clothes is also a good choice.net3.fit(X, y)importas picklewith open(‘net3.pickle‘‘wb‘as f: pickle.dump(net3, f, -1)$ python kfkd.py...Epoch | Train Loss | V

effective, but when deep enough to die, because weight update, is by a lot of weight multiplied, the smaller, a bit like the gradient disappears meaning (this sentence is I added) 8: If training rnn or LSTM, It is important to ensure that the norm of the gradient is constrained to 15 or 5 (provided that the gradient is first normalized), which is significant in RNN and lstm. 9: Check the gradient below, if it is your own calculation. 10: If you use LSTM to solve the problem of long-time depende

/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.

latest progress in deep learning--the anti-neural network. It mainly includes the idea of resisting the neural network and two specific Gan networks, the deep convolution countermeasure Generation Network (Dcgan) and the image translation (PIX2PIX) model. The knowledge points involved include generator G, discriminant D, deconvolution, u-net and so on. ... 10th Automatic Machine Learning Network-AUTOML This course provides an explanation of the latest advances in deep learning-automated machine

Note: My English proficiency is limited, translation is inappropriate, please the original English, do not like to spray, the other, the translation of this article is limited to academic exchanges, does not involve any copyright issues, if there is improper infringement or any other other than academic communication problems, please leave a message I, I immediately delete, thank you!!"Classification of benign and malignant breast tumors based on regional growth"SummaryBenign tumors are consider

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

://vision.stanford.edu/teaching/cs231n/syllabus.htmlnotes:http://cs231n.github.io/ Cloud : HTTP://PAN.BAIDU.COM/S/1PKSTIVP from Love Coco-love life3. Recent developments and practical tips for CNN (i)Ph. D., CAs Melody, who has just uploaded a talk slides in VALSE2016, on recent developments and practical tips on CNN, on CNN's Progress and Caffe's common skills [for a tutorial on Caffe use], see links http:

1. Import various modulesThe basic form is:Import Module NameImport a module from a file2. Import data (take two types of classification issues as an example, Numclass = 2)Training Set DataAs you can see, data is a four-dimensional ndarrayTags for training sets3. Convert the imported data to the data format I keras acceptableThe label format required for Keras should be binary class matrices, so you need to convert the input label data to take advantage of the Keras enhanced to_categorical funct

A number of well-known websites such as CNN have encountered error 503 errors recently, according to foreign media reports. Foreign media said, according to users, affected by the social news site, including Reddit, The New York Times, CNN, BuzzFeed and other well-known sites, their network management system has a big problem.▲CNN and many other well-known sites

The biggest problem with full-attached neural networks (Fully connected neural network) is that there are too many parameters for the full-connection layer. In addition to slowing down the calculation, it is easy to cause overfitting problems. Therefore, a more reasonable neural network structure is needed to effectively reduce the number of parameters in the neural network. convolutional Neural Networks (convolutional neural network,cnn) can do that.

To commemorate, today completed my first report, using the open source reporting tools Jasperreport and iReport. It took more than two days to make the first report from the Knowledge tool, and it was finally transferred today. I have just solved the problem of displaying the PDF in Chinese. Let's talk about some of the experiences we've explored these days: 1. Selection of tools The company is using the old report development tools, can be put into t

The mnist examples of convolutional neural networks and the neural network examples in the previous blog post are mostly the same. But CNN has more layers, and the network model needs to be built on its own.The procedure is more complicated, I will be divided into several parts to describe.First, download and load the data:Importimport= Input_data.read_data_sets ("mnist_data/" , One_hot=true) # Download and load mnist data x = Tf.placeholder (Tf.f

: 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