convolutional neural network tutorial

the composition of a convolutional neural network Image classification can be considered to be given a test picture as input Iϵrwxhxc Iϵrwxhxc, the output of this picture belongs to which category. The parameter W is the width of the image, H is the height, C is the number of channels, and C = 3 in the color image, and C = 1 in the grayscale image. The total num

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

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

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

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

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

Originally intended to begin the translation of the calculation of the part, the results of the last article just finished, mxnet upgraded the tutorial document (not hurt AH), updated the previous in the handwritten numeral recognition example of a detailed tutorial. Then this article on the Times, to the just updated this tutorial translated. Because the current

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