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# 卷积神经网络与计算机视觉

## Regularization for Neural Networks - Dropout

Dropout 是 DNN 的一种正则化技术。

## ConvNets Architecture Overview

• A simple ConvNet for CIFAR-10 classification could have the architecture [INPUT - CONV - RELU - POOL - FC]. In more detail:
• INPUT [32x32x3] will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.
• CONV layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and the region they are connected to in the input volume. This may result in volume such as [32x32x12].
• RELU layer will apply an elementwise activation function, such as the max(0,x). This leaves the size of the volume unchanged ([32x32x12]).
• POOL layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as [16x16x12].
• FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size [1x1x10], where each of the 10 numbers correspond to a class score, such as among the 10 categories of CIFAR-10.

## Modern ConvNets

• AlexNet. Winner of ImageNet 2012. It significantly outperformed the second runner-up (top 5 error of 16% compared to runner-up with 26% error).
• ZF-Net. Winner of ImageNet 2013. It was an improvement on AlexNet by tweaking the architecture hyperparameters.
• GoogLeNet. Winner of ImageNet 2014. Its main contribution was the development of an Inception Module that dramatically reduced the number of parameters in the network (4M, compared to AlexNet with 60M).
• VGGNet. Runner-up of ImageNet 2014. Its main contribution was in showing that the depth of the network is a critical component for good performance. A downside of the VGGNet is that it is more expensive to evaluate and uses a lot more memory and parameters (140M).
• ResNet. Runner-up of ImageNet 2015. It features special skip connections and a heavy use of batch normalization.
• Inception-V4. Inception+ResNet.

## NCFM – Marine Fish Classification

github 项目开源地址：github repo

Using Keras + TensorFlow to solve NCFM-Leadboard Top 5%.