how to train convolutional neural network

convolutional Neural NetworksReprinted from: http://blog.csdn.net/stdcoutzyx/article/details/41596663Since July this year, has been in the laboratory responsible for convolutional neural networks (convolutional neural

Learning Goals Understand multiple foundational papers of convolutional neural networks Analyze the dimensionality reduction of a volume in a very deep network Understand and Implement a residual network Build a deep neural

ExplainThis allows us to learn to predict a person ' s identity using a Softmax output unit, where the number of classes equals the Number of persons in the database plus 1 (for the final "not in Database" Class).Reasons for the above options error:1, plus 1 explanation error:Put someone's photo into the convolutional neural network, use the Softmax unit to outpu

mobilenets:efficient convolutional neural Networks for Mobile Vision applicationspaper Link:https://arxiv.org/pdf/1704.04861.pdf Abstract and prior work is a little, lazy. 1. Introductionintroduces an efficient network architecture and two hyper-parameters to build a very small, low latency (fast) model that can easily match the design requirements of mobile and

. The C5 is still labeled as a convolutional layer rather than a fully-connected layer, because if the input of LeNet-5 is larger and the others remain the same, then the dimension of the feature map will be larger than 1*1. The C5 layer has 48,120 training connections.The F6 layer has 84 units (The reason why this number is chosen is from the design of the output layer) and is fully connected to the C5 layer. There are 10,164 parameters that can be t

the same color link are the same, we can still use the gradient descent method to learn these weights, only need to make some small changes to the original algorithm, where the gradient of shared weights is the sum of the gradients of all shared parameters. We can not help asking why weight sharing? On the one hand, a repeating unit can recognize a feature regardless of its position in the visual domain. On the other hand, weight sharing allows us to perform feature extraction more efficiently

Recently has been looking at convolutional neural network, want to improve the improvement to make something new, read a lot of papers, wrote a review of Deep learning convolutional neural Network has some new understanding, and s

scientists have contributed significantly to the success of convolutional networks?There is no doubt that the neuro-cognitive machine (Neocognitron) proposed by Japanese scholar Kunihiko Fukushima has enlightening significance. Although the early forms of convolutional networks (Convnets) did not contain too many Neocognitron, the versions we used (with pooling layers) were affected.This is a demonstration

do this, we have to implement our own programming language and write our own compilers at the same time. As early as 1987/1988 years, Leon Bottou and I wrote a neural network simulator called SN, which is a LISP interpreter containing the numerical library (multidimensional arrays, neural network Graphics ...). ）。 We

Adit DeshpandeCS undergrad at UCLA (' 19)Blog Abouta Beginner ' s Guide to Understanding convolutional neural Networks Part 2IntroductionLink to Part 1In this post, we'll go to a lot more of the specifics of Convnets. Disclaimer: Now, I did realize that some of these topics is quite complex and could be made in whole posts by themselves. In a effort to remain concise yet retain comprehensiveness, I'll provi

"Recurrent convolutional neural Networks for Text classification" Paper Source: Lai, S., Xu, L., Liu, K., Zhao, J. (2015, January). Recurrent convolutional neural Networks for Text classification. In Aaai (vol. 333, pp. 2267-2273). Original link: http://blog.csdn.net/rxt2012kc/article/details/73742362 1. Abstract Te

holds.Each neuron also holds its own output value (double). The Nnconnection and Nnweight classes store some information separately.You may wonder why the weights and connections are defined separately? According to the above principle, each connection has a weight, why not directly put them in a class?The reason: weights are often shared by the connection.In fact, the weighted value of the shared connection is in the convolutional

This tutorial uses lasagne, a tool based on Theano to quickly build a neural network:1, the realization of several neural network construction2, Discussion data augmentation method3, discuss the importance of learning "potential"4, Pre-discussion training (pre-training)The above approach will help to improve our result

convolutional Neural Networks (convolutional neural Network): A type of classifier that uses neural networks to train parameters from data, extract features, pre-determine convolution k

"Convolutional neural Networks-evolutionary history" from Lenet to Alexnet This blog is "convolutional neural network-evolutionary history" of the first part of "from Lenet to Alexnet" If you want to reprint, please attach this article link: http://blog.csdn.net

"Paper Information""Fully convolutional Networks for Semantic Segmentation"CVPR Best PaperReference Link:http://blog.csdn.net/tangwei2014http://blog.csdn.net/u010025211/article/details/51209504Overview Key contributionsThis paper presents a end-to-end method of semantic segmentation, referred to as FCN.As shown, directly take segmentation's ground truth as the supervisory information, train an end-to-end

of the word vector effect is also possible.Channel (Channels): An image can take advantage of (R, G, B) as a different channel, while the input channel of the text is usually a different way of embedding (such as Word2vec or glove), In practice, the use of static word vectors and fine-tunning-word vectors as different channel methods are also used.One dimensional convolution (conv-1d): The image is a two-dimensional data, the word vector expression of the text is one-dimensional data, so in tex

This article first Huchi: HTTPS://JIZHI.IM/BLOG/POST/INTUITIVE_EXPLANATION_CNN What is convolutional neural network. And why it's important. convolutional Neural Networks (convolutional neu