convolutional neural network python code

AlexNet: (ILSVRC Top 5 test error rate of 15.4%) the first successful display of the convolutional neural network potential network structure. key point: with a large amount of data and long-time training to get the final model, the results are very significant (get 2012 classification first) using two GPU, divide

1. Target positioning 1.1 Introduction to classification, positioning and testing -Image classificationImage classification, is to give you a picture, you determine the target category, such as cars, cats and so on.-Classification with localizationPositioning classification, not only to determine the target category, but also to output the position of the target object, such as the box up.-DetectionDetection, there may be multiple objects in the picture, you need to find them out. 1.2 Position

convolutional Neural Networks (convolution neural network, CNN) have achieved great success in the field of digital image processing, which has sparked a frenzy of deep learning in the field of natural language processing (Natural Language processing, NLP). Since 2015, papers on deep learning in the field of NLP have e

Kalchbrenner ' s PaperKal's article cited a high number of citations, he proposed a network model called DCNN (Dynamic convolutional neural Networks), in the previous (Kim's Paper) experimental results Section also verified the effectiveness of this model. The subtleties of this model lie in the way of pooling, using a method 动态Pooling called.Is the model of th

biased term, followed by a nonlinear function. If you use $h ^{k}$ to represent the feature map of the $k $ layer, the corresponding filter is determined by the $W ^{k}$ and bias $b _{k}$, then the feature map $h ^{k}$ can be computed from the next (using Tanh for nonlinear functions):$h _{ij}^{k}=tanh (w^{k}*x) _{ij}+b_{k}$In order to get a richer representation of the data, each hidden layer is usually composed of multiple feature graphs: $\{h^{\text{(k)}},k=0,... k\}$. The weight $W $ is rep

Refer to:Https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0(The fall of Rnn/lstm)"hierarchical neural attention encoder", shown in the figure below:Hierarchical neural Attention EncoderA better-to-look-into-the-past is-to-use attention modules-summarize all past encoded vectors into a context vector Ct.Notice There is a hierarchy of attention modules here, very similar to the hierarchy of

content feature extraxtor or style feature extractor effect is not the same. We find that matching the "style representations up" higher layers in the network preserves local images creasingly large scale, leading to a smoother and more continuous visual experience. Accordingly, Conv (1-5) _1 was chosen as style layer The following figure shows the different effects of different conv layer as content layer: different initialization methods In the exp

homepage: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Home.html5. Chris Olah, who received the Peter Thiel Scholarship, has several blogs about understanding and visualizing neural Networks: Calculus on Computational graphs:backpropagation,understanding LSTM Networks, visualizing Mnist:an exploration of dimensionality reduction,understanding convolutionsAddress: http://colah.github.io/archive.html6. Why should I focus on interest feedsPublished the h

Weilin huang--"TIP2015" text-attentional convolutional neural Network for Scene Text Detection)Directory Author and RELATED LINKS Method Summary Innovation points and contributions Method details Experimental results Question Discussion Author and RELATED LINKS Summary and Harvest Point Author Supplemental Information

introduces the Yolo algorithm process, which is a review of the previous sections. Shows the network structure, including two anchor boxes. For each grid call, get 2 predicted bounding boxes. Get rid of Low Probability predictions. For each class (pedestrian, car, motorcycle) use non-Max suppression to generate final predictions. 10. region proposals The sliding window algorithm previously introduced scans each area of the original image, even ar

layer after two-dimensional convolution results Unlike the simple Max-pooling method after the first layer, the pooling of the subsequent convolution layer is a dynamic pooling method , which derives from the reference [1]. Properties of Structure II Keep the word order information; More general, in fact structure I is a special case of Structure II (cancellation of the specified weight parameters); Experimental section1. Model Training and parameters