# UnderstandingAndVisualizingCNN

**Repository Path**: changlin_nanshan/UnderstandingAndVisualizingCNN

## Basic Information

- **Project Name**: UnderstandingAndVisualizingCNN
- **Description**: Several a*proaches for understanding and visualizing Convolutional Networks have been developed in the literature.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-07-20
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# UnderstandingAndVisualizingCNN
  Several approaches for understanding and visualizing Convolutional Networks have been developed in the literature,partly as a response the common criticism that the learned features in a Neural Network are not interpretable. In this section we briefly survey some of these approaches and related work.It's about Visualizing what ConvNets learn.

<!--TOC-->
 - [Visualizing the activations and first-layer weights](#Visualizing the activations and first-layer weights)
 - [Retrieving images that maximally activate a neuron](#Retrieving images that maximally activate a neuron)
 - [Embedding the codes with t-SNE](#Embedding the codes with t-SNE)
 - [Occluding parts of the image](#Occluding parts of the image)
<!--/TOC-->

## Visualizing the activations and first-layer weights

**Layer Activations.** The most straight-forward visualization technique is to show the activations of the
network during the forward pass. For ReLU networks, the activations usually start out looking relatively
blobby(满是点滴状的) and dense, but as the training progresses the activations ususlly become more
sparse and localized. One dangerous pitfall(缺陷，陷阱) that can be easily noticed with this visualization is that
some activation maps may be all zero for many different inputs, which can indicate dead filters, and 
can be a symptom of high learning rates.
<hr>

![](https://github.com/liwenjian12/UnderstandingAndVisualizingCNN/blob/master/pictures/act1.jpeg)

![](https://github.com/liwenjian12/UnderstandingAndVisualizingCNN/blob/master/pictures/act2.jpeg)

Typical-looking activations on the first CONV layer (left), and the 5th CONV layer (right) of a trained AlexNet looking at a picture of a cat. Every box shows an activation map corresponding to some filter. Notice that the activations are sparse (most values are zero, in this visualization shown in black) and mostly local. 
<hr>

**Conv/FC Filters.** The second common strategy is to visualize the weights. These are usually 
most interpretable on the first CONV layer which is looking directly at the raw pixel data, but 
it is possible to also show the filter weights deeper in the network. The weights are useful to
visualize because well-trained networks usually display nice and smooth filters without any noisy
patterns.Noisy patterns can be an indicator of a network that hasn't been trained for long enough,
or possibly a very low regularization strength that may have led to overfitting.
<hr>
![](https://github.com/liwenjian12/UnderstandingAndVisualizingCNN/blob/master/pictures/filters.PNG)

Typical-looking filters on the first CONV layer (left), and the 2nd CONV layer (right) of a trained AlexNet. Notice that the first-layer weights are very nice and smooth, indicating nicely converged network. The color/grayscale features are clustered because the AlexNet contains two separate streams of processing, and an apparent consequence of this architecture is that one stream develops high-frequency grayscale features and the other low-frequency color features. The 2nd CONV layer weights are not as interpretable, but it is apparent that they are still smooth, well-formed, and absent of noisy patterns. 
<hr>

## Retrieving images that maximally activate a neuron

## Embedding the codes with t-SNE

## Occluding parts of the image