inception v4 tensorflow

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"Turn" CNN convolutional Neural Network _ googlenet Inception (V1-V4)

http://blog.csdn.net/diamonjoy_zone/article/details/70576775Reference:1. inception[V1]: going deeper with convolutions2. inception[V2]: Batch normalization:accelerating deep Network Training by reducing Internal covariate Shift3. inception[V3]: Rethinking the Inception Architecture for computer Vision4.

From inception V1,v2,v3,v4,rexnext to Xception to MOBILENETS,SHUFFLENET,MOBILENETV2

dimension is relatively low 2,3, the loss of information using Relu is more serious. While the single dimension is higher 15, 30 o'clock, the loss of information is relatively small.Mobilenet v2 in order to ensure that the information is not a large loss, should be in the residual module to remove the last Relu. Therefore, it is also called a linear module unit.Mobilenet V2 Network structure:where T represents the expansion factor of the channel expansion factor,C represents the number of outpu

How to set the TensorFlow Inception-v3 model on Windows

There is INCEPTION-V3 model Python implementation on GitHub at:https://github.com/tensorflow/models/tree/master/inceptionThere is several shell scripts In/inception/inception/data folder. These scripts only can run on the Linux OS, especially on Ubuntu. So. How can we set up the IN

"Deep Learning Series" with Paddlepaddle and TensorFlow for Googlenet inceptionv2/v3/v4

, inception-resnet and the Impact of residual Connections on Learni Ng, the highlight of the paper is that: the googlenet Inception v4 network structure with better effect is proposed, and the structure of the network with residual error is more effective than V4 but the training speed is faster.googlenet

TensorFlow series: How to use inception ResNet v2 Network

First, the foreword recently in the Inception V3 and Inception ResNet v2 These two networks, these two network architectures I don't think I said more, Google produced. By fusing the feature map of different scales to replace the nxn convolution by 1xn convolution kernel nx1 convolution, the computational volume is effectively reduced, and the computational volume is reduced by using multiple 3x3 convolutio

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