QA Official

Gray Image Normalization 2019-05-07
** > there are two normalization operations ** Normalize any value between 0-255: * oriImage = imread(‘ XXXX.jpg’); grayImage = rgb2gray(oriImage); figure; imshow(grayImage); originalMinValue = double(min(min(grayImage))); originalMaxValue = double(max(max(grayImage))); originalRange = originalMaxValue - originalMinValue; % Get a double image in the range 0 to +255 desiredMin = 0; desiredMax = 255; desiredRange = desiredMax - desiredMin; dblImageS255 = desiredRange * (double(grayImage) - originalMinValue) / originalRange + desiredMin;

Image Classification Using Keras 2019-05-07
Keras Deep Learning Framework can be used to understand what deep learning can be used to do. The following is an introduction to some basic image classification using Keras. Welcome to exchange. Reference: HTTPS:// Version I used: Python2.7, numpy1.13.1, Theano0.9.0, Keras2.0.6, h5py2.5.0, opencv2.4.13, WIN7 system. In order to classify images, data sets are needed first, and the downloaded image data sets need to be converted into numpy matrices that Keras

Import Excel file into database (POI+Excel+MySQL+jsp page import) First optimization 2019-05-07
This article is an improved version of my previous blog. Due to limited time, only a simple optimization has been made. Related Articles: Import excel into Database On April 1, 2018, add a new download address link: click Open Source Download Address I am very sorry that this link address has not been published in this article.I hope it's not too late. The last article was about such a data

Machine Learning (XII): Multi-label Classification 2019-05-07
MultiLabel means that a sample may belong to multiple classes at the same time, that is, there are multiple labels.For example, for an L-sized cotton-padded jacket, the sample has at least two labels-model number: L and type: winter clothing.Only two links are posted here for reference:To solve the problem of multi-label classificationLearning Problems of multi-label Data

Native ajax Sends post Requests 2019-05-07
1. Create an xmlhttpRequest object 2. Set callback monitoring 3. Open a connection Accept two parameters: 1.http method2.httpurl 4. Set the request header (get does not have this step) Notify the browser of the relevant settings for the requestor 5. Making requests Parameters: specific value sent readystate: 0-(uninitialized) send () method has not been called 1-(load) send () method has been called, request is being sent 2-(load complete) send ()

Normalize a group of data to (0,1) in MATLAB 2019-05-07
use function mapminmax 1 The default map range is [-1,1 ], so if [ 0,1 ] is required, provide the parameters in this format: MappedData = mapminmax(OriginalData, 0, 1); 2 is only normalized by rows. If it is a matrix, each row is normalized separately. If the whole matrix needs to be normalized, use the following method: FlattenedData = OriginalData(:)';The % expansion matrix is a column and then transposed to a row.

The Method of Running libsvm under weka 2019-05-07
The Method of Running libsvm under weka weka 3.6.9 failed to install using the " old method" below!" problemevaluating classificator rand" error thrown! Correct way: 1. first install WEKA3.7.9, install libsvm with tools-package manager, and then find libsvm.jar in wekafiles\packages\LibSVM\lib under the user directory. 2. Install WEKA3.6.9, and put libsvm.jar in the first step into the installation directory of WEKA3.6.9.() 3. amend the RunWeka.ini document of WEKA3.

Usage of Several Elements 2019-05-07
1, block level element display:block a, each block-level element starts from a new row, and the following elements also start from another row.(A block-level element has an exclusive row) b, element height, width, row height, and top and bottom margin can be set. c, element width is 100% of its own parent container (consistent with the width of the parent element) without setting, unless a width is set.

[ Keras ] Sequence Classification Using LSTM for Sequential Sequential Model Instances 2019-05-07
Sequence Classification Using LSTM from keras.models import Sequential from keras.layers import Dense, Dropout from keras.layers import Embedding from keras.layers import LSTM model = Sequential() model.add(Embedding(max_features, output_dim=256)) model.add(LSTM(128)) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']), y_train, batch_size=16, epochs=10) score = model.evaluate(x_test, y_test, batch_size=16)

in-depth introduction to Android Gradle building system (1: introduction) 2019-05-07
gradle是Android开发中引入的全新的构建系统,因为全新的构建系统主要是出于下面的目的: 1. 方便复用代码和资源 2. 构建多种版本的apk更