QA Official

Common Constraints of Network Layer Weights 2019-05-06
MaxNorm Implicit Layer Weight Given Input Maximum Constraint ReferencesDropout: A Simple Way to Prevent Neural Networks from Overfitting Srivastava, Hinton, et al. 2014 NonNeg Ensure the weight in training is not negative (similar to nmf, or proning effect) UnitNorm Implicit Layer Weight-norm Rule

Deep Learning Beginner's Thesis Collection 2019-05-06
My writing is bad. This is my term paper in CS5312-deep learning course. Section II: List and highlight of papers you have studied.In this section, I separate the papers into 3 parts-NN networks, algorithms, hardware designs. 1.NN-networks Gradient-Based Learning Applied to Document Recognition. Yann Lecun, Yoshua Bengio. (1998) Neural networks used in this paper are called LeNet, which is well applied in the MNIST dataset. Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient-based learning technique.

Faster-RCNN(keras) Realize the Generation of Dog Identification-Labeling Box 2019-05-06
Preface: The article on Zhihu is very detailed about the use of trained models to generate annotation boxes, so let's make some usage records here.I found a source code written by Yann Henon on GitHub. I call it the initial version. The initial version has I used this version for initial learning and Faster-RCNN learning. The identification of pet dogs is also based on this project.However, it is very

Introduction and Understanding of WEKA and Mulan 2019-05-06 Weka: Weka's classifiers are all placed in the packet inside that starts with weka.classifiers.According to their functions, they are divided into different categories. See their methods for details.The core class of Weka inside is placed in the package inside with weka.core as the beginning. For Weka data, Instances inside exists.Then each piece of data is an instance of the interface Instrance (with and without s, which is well understood).

Java implementation of KNN classification algorithm 2019-05-06
Nearest Neighbor Classification Algorithm The idea of KNN algorithm is summarized as follows: under the condition that the data and labels in the training set are known, test data are input, and the characteristics of the test data and the corresponding characteristics in the training set are compared with each other to find the first K data in the training set that are most similar to them, then the category

Multi-label Classification 2019-05-06
Multilabel Classification Multilabel Classification Multi-label Classification Problem caffe corresponds to multiple label per sample? training a multi-label classification/regression model using caffe a single label image classification model using caffe fine-tune Multi-label Detection of googlenet Based on caffe Multi-label Training Based on Inception v3 Generate hdf5 File for Multi-label Training caffehdf5layerdata > 2G import caffe hdf5 data layer data generation caffe learning notes (11): HDF5Data type dataset generation for multitasking learning.

Non-local Neural Networks 2019-05-06
Reference Reference 2

The Simple Application ofKeras (Three Classification Problem)-Adapted from cifar10vgg16 2019-05-06
This is a three-class problem adapted from cifar10: from __future__ import print_function #此为在老版本的python中兼顾新特性的一种方法 import keras from keras.preprocessing.image import ImageDataGenerator from keras.preprocessing import image from keras.models import Sequential from keras.models import model_from_json from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D, BatchNormalization from keras

Types of Neural Networks 2019-05-06
KNN DNN SVM DL BP DBN RBF CNN RNN ANN Overview This paper mainly introduces the commonly used neural networks, their main uses, and the advantages and limitations of various neural networks. 1 BP neural network BP (Back Propagation) neural network is a neural network learning algorithm.It is a hierarchical neural network composed of an input layer, an intermediate layer and an output layer, and the intermediate layer can be expanded into multiple layers.

[ reprint ] Keras custom complex loss function 2019-05-06
Keras is a building block deep learning framework, which can be used to easily and intuitively build some common deep learning models.Before tensorflow came out, Keras was almost the most popular in-depth learning framework at that time, taking theano as the back end. Now Keras has supported four back ends at the same time: theano, tensorflow, cntk and mxnet (the first three official supports, mxnet has not yet been integrated