/NN_Test

This project contains some neural network code

Primary LanguageC++

NN_Test

This project contains some neural network code(Note: depend on opencv):

  • neural network code(C++)
    • Perceptron
    • BP(Back Propagation)
    • CNN(Convolutional Neural Networks)
    • Linear Regression(gradient descent、least squares)
    • Naive Bayes Classifier(sex classification)
    • Logistic Regression(gradient descent, Batch/Mini Batch)
    • KNN(K-Nearest Neighbor, classification)
    • PCA(Principal Components Analysis)
    • single hidden layer(two categories)
    • K-Means
    • Decision Tree(CART)
  • mathematical formula's implement with C++
    • Linear Algebra
      • transpose
      • determinant
      • adjoint matrix
      • inverse matrix
      • norm(vector、matrix)
      • eigenvalues/eigenvectors(real symmetric matrix)
      • SVD(Singular Value Decomposition)
      • pseudoinverse
      • trace
    • Probability
      • mean、variance、standard deviation
      • covariance matrix
    • Activation Function
      • logistic sigmoid function
      • softplus function
      • ReLU(rectified linear unit)
      • Leaky ReLUs(leaky rectified linear units)
      • ELUs(Exponential linear units)
      • softmax function
      • tanh(Hyperbolic tangent)
    • Loss Function
      • MSE(Mean Square Error)
      • Cross-Entropy
    • Optimization
      • BGD(Batch Gradient Descent)
      • SGD(Stochastic Gradient Descent)
      • MBGD(Mini-batch Gradient Descent)
      • SGD with Momentum
      • AdaGrad(Adaptive Gradient)
      • RMSProp(Root Mean Square Propagation)
      • Adadelta(an adaptive learning rate method)
      • Adam(Adaptive Moment Estimation)
      • AdaMax(a variant of Adam based on the infinity norm)
      • NAG(Nesterov Accelerated Gradient)
      • Nadam(Nesterov-accelerated Adaptive Moment Estimation)
    • Layers
      • Dropout
      • LRN(Local Response Normalization)
      • BN(Batch Normalization)
    • common algorithm
      • Brute Force(string match)
  • open source library's usage(C++)
    • tiny-cnn GitHub
    • tiny-dnn GitHub
    • libsvm GitHub
    • OpenCV GitHub
      • Logistic Regression
      • Support Vector Machines(SVM)
      • KNN(K-Nearest Neighbor)
      • Decision Tree
      • PCA(Principal Components Analysis)
      • k-means
  • Python code(support windows and linux platform)
    • Installation(Anaconda) 
       conda create -n NN_Test python=3.9
 conda activate NN_Test
 git clone https://github.com/fengbingchun/NN_Test
 cd NN_Test/demo/Python
 pip install -r requirements.txt
    • KNN(K-Nearest Neighbor)
    • Decision Tree(CART)
    • PCA(Principal Components Analysis)
    • k-means
  • Database convert to Images or Images conver to Database
    • MNIST
    • CIFAR10/CIFAR100
    • ORL Faces Database

The project support platform:

  • windows10 64 bits: It can be directly build with VS2022 in windows10 64bits.
  • Linux:
    • DatasetToImage support cmake build(file position: prj/linux_cmake_DatasetToImage)
    • Math support cmake build(file position: prj/linux_cmake_Math)
    • tiny-cnn support cmake build(file position: prj/linux_cmake_tiny-cnn)
    • tiny-dnn support cmake build(file position: prj/linux_cmake_tiny-dnn)
    • NN support cmake build(file position: prj/linux_cmake_NN)

Windows VS Screenshot:

Blog: fengbingchun