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
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.
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.
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 train.py. 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
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).
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 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.
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
KNN DNN SVM DL BP DBN RBF CNN RNN ANN
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.
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