convolutional neural network explained

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

Introduction: Yesterday and everyone talked about convolutional neural network, today to bring you a paper: Pca+cnn=pcanet. Now let me take you to understand this article.Paper:pcanet:A simple deeplearning Baseline for Image classificationPaper Address: https://core.ac.uk/download/pdf/25018742.pdfArticle code: Https://github.com/Ldpe2G/PCANet1 SummaryThis Part

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

useful when combined with a number of different random subsets of other neurons. The first two fully connected layers use dropout. Without dropout, our network would show a lot of overfitting. The dropout increases the number of iterations required for convergence by roughly one-fold.4. Image preprocessing① size NormalizationTo 256x256 all the pictures to the size of the scale, as for why not directly normalized to 224 (227), please refer to the abov

convolutional Neural Networks (convolutional neural Network): A type of classifier that uses neural networks to train parameters from data, extract features, pre-determine convolution kernel size, initialize randomly, and after fe

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

are several forms of activation functions in convolutional neural networks: A is a fixed parameter in the formula. In the formula, each batch training sample is randomly sampled from the distribution of the mean value, which is taken in the test. From the above convolution neural network, we can see that gradient

Wholeimage to do training, do not carry patchwise sampling. The experiment proves that the direct use of the whole map has been very effectiveand efficient.A full 0 initialization is done for the Classscore convolution layer. Stochastic initialization has no advantage in performance and convergence."Experimental Design"1, compare 3 kinds of cnn:alexnet with good performance, VGG16, googlenet experiment, choose VGG162, compare fcn-32s-fixed, Fcn-32s, Fcn-16s, fcn-8s, prove the best dense predict

value. And pick out the hardest, most informative samples to mark. Using the samples just labeled to train the deep learning Network, get a network n Put the remaining unlabeled images with N over again, get the predicted values, pick the hardest ones, use the manual to label it We will continue to train this network by taking the samples that have j

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

Origin: The human visual cortex of the MeowIn the 1958, a group of wonderful neuroscientists inserted electrodes into the cat's brain to observe the activity of the visual cortex. and infer that the biological vision system starts from a small part of the object, After layers of abstraction, it is finally put together into a processing center to reduce the suspicious nature of object judgment. This approach runs counter to BP's network.The BP network

convolution layer of the error-sensitive items, because the reverse propagation when the output is smaller than the input, so the gradient at the time of transmission and traditional BP algorithm, So how to get the error-sensitive item of convolutional layer is the problem to consider. The third problem is to consider the pooling layer below the convolution layer, this is because we want to get the pooling layer error sensitivity, relying on the conv

visual comprehension of convolutional neural networks The first to suggest a visual understanding of convolutional neural Networks is Matthew D. Zeiler in the visualizing and understanding convolutional Networks. The following two blog posts can help you understand this a

very interesting. He said, what is convolution? For example, the constant bending of a wire, assuming that the heating function is f (t), and that the heat dissipation function is g (t), the temperature at this moment is the convolution of f (t) and g (t). In a given environment, the sound source function of the sound body is f (t), and the reflection effect function of the sound source is g (t), then the receiving voice is the convolution of f (t) and g (t) in this environment. Without conside