Convolutional neural network CNN learning 1
Ten years of sharpening a sword, the frost blade has never been tried.
Introduction:Convolutional neural network CNN learning.
CNN Chinese video learning link:Convolutional neural network working principle Video – Chinese version
CNN English learning link:Convolutional neural network working principle video
1、 Definition
2、 CNN inspiration?
For different objects, human vision is also through this layer by layer classification, to carry out the following cognition.
Human visionIdentification examples
At the bottom level, the features are basically similar, that is, the higher the edges, the more features (wheels, eyes, trunk, etc.) of such objects can be extracted. At the top level, different high-level features are finally combined into corresponding images, so that people can accurately distinguish different objects. Therefore, it imitates the characteristics of human brain, constructs a multi-layer neural network, recognizes the primary image features at the lower level, and composes a higher level features with a number of lower level features. Finally, through the combination of multiple levels, it makes the classification at the top level.
3、 What does convolution neural network solve?
Generally speaking, it is to preserve the image features, reduce the dimension of parameters and simplify the complex parameters.
Image pixel RGB
As we all know, the image is composed of pixels, and each pixel is composed of color.Now any picture is more than 1000 × 1000 pixels, and each pixel has three RGB parameters to represent the color information.
If we process a 1000 × 1000 pixel image, we need to process 3 million parameters! 1000×1000×3=3,000,000
It is very resource consuming to process such a large amount of data. The first problem solved by CNN is “convolutional neural network”Simplify complex problemsFirstly, a large number of parameters are reduced to a small number of parameters, and then processed.
More importantly, in most scenarios, dimensionality reduction does not affect the results. For example, if a 1000 pixel image is reduced to 200 pixels, it doesn’t affect whether the naked eye recognizes a cat or a dog in the image, and so does the machine.
The traditional way of picture digitization
Simple digital image can not retain image features, such as the figure above, if there is a circle is 1, no circle is 0, then different positions of the circle will produce completely different data expression. But from the visual point of view, the content (essence) of the image has not changed, just the position has changed. So when we move the object in the image, the parameters obtained in the traditional way will be very different! This is not in line with the requirements of image processing. And CNN To solve this problem, he used a vision like way [imitating the principle of human brain vision, constructing a multi-layer neural network, recognizing the primary image features at the lower level, combining several lower level features to form a higher level feature, and finally making classification at the top level through the combination of multiple levels] to retain the image features, when the image is flipped, rotated or changed position, It can also effectively identify similar images.
4、 Architecture of convolutional neural network
A typical CNN consists of three parts: convolution layer, pooling layer and fully connected layer
Typical CNN components
5、 Data input layer
Decorrelation and whitening effect
6、 Convolution computing layer
There are two important operations in convolution layer, one is local correlation, and each neuron is regarded as a filter; the other is window sliding, and the filter calculates the local data.
The operation process of convolution layer is shown in the following figure. Scan the whole picture with a convolution core
Convolution layer dynamic operation graph
The operation process of convolution layer can be regarded as using a filter (convolution kernel) to filter each small area of the image, so as to get the eigenvalues of these small areas, that is, the convolution layer extracts the local features of the image through the convolution kernel filtering.
Convolution layer operation graph
Dynamic graph of convolution layer calculation process
7、 Incentive layer
The incentive level isThe output of the convolution layer is mapped nonlinearly. The excitation function used by CNN is generally relu (the rectified linear unit / modified linear unit).
8、 Pooling layer
Pooling layer is in the middle of continuous convolution layer, which is used to compress the amount of data and parameters and reduce over fitting. The most important role is to keep the characteristics unchanged, compress the image and reduce the data dimension.
The methods of pooling layer are Max pooling and average pooling, but the actual method is Max pooling. Max pooling idea: for each 2 * 2 window, select the maximum number as the value of the corresponding element of the output matrix. For example, if the maximum number in the first 2 * 2 window of the input matrix is 6, then the first element of the output matrix is 6, and so on, keep the feature unchanged and reduce the dimension.
Max pooling diagram
Dynamic pooling diagram
We can see that in the dynamic pooling diagram,Original pictureIt’s 20 × 20, and we do down sampling,Sampling windowFinally, it is downsampled into a 2 × 2 size imageFeature map。
9、 Fully connected layer
10、 CNN application scenarios
Image classification / retrieval, target location detection, target segmentation, face recognition, bone recognition, etc.
CNN face recognition
Finally
CNN Chinese video learning link:Convolutional neural network working principle Video – Chinese version
CNN English learning link:Convolutional neural network working principle video
Ten years to sharpen a sword
Frost blade never tried