latest progress in deep learning--the anti-neural network. It mainly includes the idea of resisting the neural network and two specific Gan networks, the deep convolution countermeasure Generation Network (Dcgan) and the image translation (PIX2PIX) model. The knowledge points involved include generator G, discriminant D, deconvolution, u-net and so on. ... 10th
more time. This time our network learned more general, theoretically speaking, learning more general law than to learn to fit is always more difficult.This network will take an hour of training time, and we want to make sure that the resulting model is saved after training. Then you can go to have a cup of tea or do housework, washing clothes is also a good choice.net3.fit(X, y)importas picklewith open(‘ne
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 headline: Why should I focus on the interest f
1. Import various modulesThe basic form is:Import Module NameImport a module from a file2. Import data (take two types of classification issues as an example, Numclass = 2)Training Set DataAs you can see, data is a four-dimensional ndarrayTags for training sets3. Convert the imported data to the data format I keras acceptableThe label format required for Keras should be binary class matrices, so you need to convert the input label data to take advantage of the Keras enhanced to_categorical funct
The mnist examples of convolutional neural networks and the neural network examples in the previous blog post are mostly the same. But CNN has more layers, and the network model needs to be built on its own.The procedure is more complicated, I will be divided into several parts to describe.First, download and load the data:Importimport= Input_data.read_data_sets
of pre-training network:Ultimately, this solution is 2.13 RMSE on the leaderboard.Part 11 conclusionsNow maybe you have a dozen ideas to try and you can find the source code of the tutorial final program and start your attempt. The code also includes generating the commit file, running Python kfkd.py to find out how the command is exercised with this script.There's a whole bunch of obvious improvements you can make: try to optimize each ad hoc network
, and the map output of proposal in the entire Graph CONV5 output is calculated directly based on the relative position relationship of the proposal in the graph. So, for the 2000 proposal, we actually did the CONV1--->conv5 only once, then made the set map of the Conv5 Featuremap 2000 times, and then through Spp-layer, 2000 Spp-layer output vectors of the same length can be obtained, and the final 2000 proposal convolution neural network features are
ImageNet classification with deep convolutional neural Networks
Alexnet is the model structure used by Hinton and his students Alex Krizhevsky in the 12 Imagenet Challenge, which refreshes the chance of image classification from the deep Learning in the image of this piece began again and again more than State-of-art, even to the point of defeating mankind, look at the process of this article, found a lot of previous fragmented to see some of the optimization techniquesReference:
TensorFl
[CNN] interpretation of Light-Head R-CNN: Balancing accuracy and speed, light-headr-cnnSpeaker: Li Zeming | researcher at the face ++ Research InstituteEdit Qu XinProduced by QbitAI | public account QbitAI
On the evening of December 20, the quantum bit eat melon club and Face ++ paper interpretation series of the third phase of the lecture, this issue in the Face (Megvii) Research Institute to interpret the
Use tensorflow to build CNN and tensorflow to build cnn
Convolutional Neural Networks Convolutional Neural Network (CNN) transfers the data of an image to CNN. The original coating is composed of RGB, And then CNN thickened the th
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Source information
Using Keras to explore the filter for convolutional networks
Visualize All Filters
Deep Dream (Nightmare)
Fool the Neural network
The revolution has not been successful, comrades still need to work hard
Source informationThis address: http://blog.keras.io/how-convolutional-neural-networks-see-the-world.htmlThis article Francois CholletThe translation of this article was first published by
This article explains in detail the network architecture and workflow of Faster R-CNN, which leads the reader to understand the principle of target detection, and the author also provides the Luminoth realization for everyone's reference.
Luminoth implementation: GITHUB.COM/TRYOLABS/LUMINOTH/TREE/MASTER/LUMINOTH/MODELS/FASTERRCNN
Last year, we decided to dig deeper into Faster r-
Ren, Shaoqing, et al. "Faster r-cnn:towards Real-time object detection with region proposal networks." Advances in neural information processing Systems. 2015.After Rcnn[1],fast Rcnn[2], this article is another masterpiece of the Ross Girshick team, the leader of the target detection community in 2015. The detection speed of simple network target is 17fps, the accuracy of Pascal VOC is 59.9%, the complex network
the training and test samples are constructed according to these region proposal, noting that the size of these region proposal is different, and the sample category is 21 (including the background).Then there is the pre-training, which is trained with alexnet under the Imagenet data set. And then on our data set fine-tuning, the network structure is unchanged (except the last layer of output from 1000 to 21), the input is the front of the region pro
convolutional Neural Network is the first multi-layered neural network structure which has been successfully trained, and has strong fault tolerance, self-learning and parallel processing ability.First, the basic principle1.CNN algorithm Ideasconvolutional neural network can be regarded as a special case of Feedforward
AlexNet
contribution : ILSVRC2012 champion, showing the depth of CNN in the image task of the astonishing performance, the upsurge of CNN research, is now deep learning and the rapid development of AI important reason. The Imagenet competition provides a platform for the Hinton that has been studying neural networks, Alexnet was published by Hinton and his two students, and deep learning has been sile
Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE International Conference on computer Vision. 2015.Following the 2014 RCNN, Ross Girshick introduced fast rcnn in 15, with a sophisticated and compact process that significantly increased the speed of target detection. The source code is available on GitHub.The reason why fast r-cnn is proposed is mainly because R-CNN
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