tensorflow convolutional neural network

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

1, IntroductionDL solves VO problem: End-to-end vo with RCNN2. Network structureA.CNN based Feature ExtractionThe paper uses the Kitti data set.The CNN section has 9 convolutional layers, with the exception of CONV6, the other convolutional layers are connected to 1 layers of relu, and there are 17 layers.B, RNN based sequential modellingRNN is different from CNN

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

ImageNet classification with deep convolutional neural Networks reading notes(after deciding to read a paper each time, the notes are recorded on the blog.) ）This article, published in NIPS2012, was Hinton and his students, in response to doubts about deep learning, used deep learning for imagenet, the largest database of image recognition, and eventually achieved very surprising results, The result is much

. Pretreatment: Mean removal;whitening (ZCA) Enhanced generalization capability: Data augmentation;weight regularization; adding noise to the network, including dropout,dropconnect,stochastic pooling. Dropout: The output of some neurons in the fully connected layer is randomly set to 0 at the full connection layer only. Dropconnect: Also only used on the full-connection layer, Random binary mask on weights. Stochastic Pooli

0-Background The so-called style conversion is based on a content image and a style image, merging the two, creating a new image that combines both contents and style.The required dependencies are as follows: Import OS import sys import scipy.io import scipy.misc import Matplotlib.pyplot as Plt from Matplotlib.pyplot import imshow from PIL import Image from nst_utils import * import NumPy as NP import te Nsorflow as TF %matplotlib inline 1-transfer Learning Migration learning is the applicat

The 1th chapter introduces the course of deep learning, mainly introduces the application category of deep learning, the demand of talents and the main algorithms. This paper introduces the course chapters, the course arrangement, the applicable crowd, the prerequisites and the degree to be achieved after the completion of the study, so that students have a basic understanding of the course. The 2nd chapter of Neural

alexnet Summary Notes Thesis: "Imagenet classification with Deep convolutional neural" 1 Network Structure The network uses the logic regression objective function to obtain the parameter optimization, this network structure as shown in Figure 1, a total of 8 layer

TensorFlow realize Classic Depth Learning Network (4): TensorFlow realize ResNet ResNet (Residual neural network)-He Keming residual, a team of Microsoft Paper Networks, has successfully trained 152-layer neural networks using re

I. Documentation names and authorsconvolutional neural Networks at Constrained time COST,CVPR two. Reading timeJune 30, 2015Three. Purpose of the documentThe author hopes to improve the accuracy of CNN by modifying the model depth and the parameters of the convolution template, while maintaining the computational complexity. Through a lot of experiments, the author finds the importance of different parameters in the

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

affected.This is a demonstration of the mutual connection between the middle layer and the layers of the neuro-cognitive machine. Fukushima K. (1980) in the neuro-cognitive machine article, the self-organizing neural network model of pattern recognition mechanism is not affected by the change of position.Can you recall the "epiphany" moments or breakthroughs that occurred in the early days of

set, the KL distance is the indicator that describes the diversity, thus reducing the amount of computation. Traditional deep learning will need to do before the training of data enhancement, each sample is equal; This article contains some data enhancement not only does not play a good role, but brings the noise, it needs to do some processing, but also some of the data does not need to be enhanced, which reduces noise and saves calculation. Qa Q: Why did the active learning not b

Wang, Min, Baoyuan Liu, and Hassan Foroosh. "Factorized convolutional neural Networks." ArXiv preprint (2016). This paper focuses on the optimization of the convolution layer in the deep network, which has three unique features:-Can be trained directly . You do not need to train the original model first, then use the sparse, compressed bits and so on to compress.

of the "object" in the "the position with the maximum score Use a cost function this can explicitly model multiple objects present in the image. Because there may be many objects in the graph, the multi-class classification loss is not applicable. The author sees this task as multiple two classification questions, loss function and classification score as followsTrainingMuti-scale TestExperimentClassification MAP on VOC test: +3.1% compared with [56] MAP on VOC test: +7.

. We use the cublas. lib and curand. Lib libraries. One is matrix calculation and the other is random number generation. I applied for all the memory I needed at one time. After the program started running, there was no data exchange between the CPU and GPU. This proved to be very effective. The program performance is about dozens of times faster than the original C language version (if the network is relatively large, it can reach a speed-up ratio of

Minimalist notes Deepid-net:object detection with deformable part Based convolutional Neural Networks Paper Address Http://www.ee.cuhk.edu.hk/~xgwang/papers/ouyangZWpami16.pdf This is the CUHK Wang Xiaogang group 2017 years of a tpami, the first hair in the CVPR2015, increased after the experiment to cast the journal, so the contrast experiment are some alexnet,googlenet and other early

ImageNet classification with deep convolutional neural Networks reading notes(2013-07-06 22:16:36) reprint Tags: deep_learning imagenet Hinton Category: machine learning (after deciding to read a paper each time, the notes are recorded on the blog.) )This article, published in NIPS2012, is Hinton and his students are using deep learning in response to doubts about deep learn

Https://zhuanlan.zhihu.com/p/24720659?utm_source=tuicoolutm_medium=referral Author: YjangoLink: https://zhuanlan.zhihu.com/p/24720659Source: KnowCopyright belongs to the author. Commercial reprint please contact the author to obtain authorization, non-commercial reprint please indicate the source. Everyone seems to be called recurrent neural networks is a circular neural