Convolutional neural networks (CNNs) are an important network structure in the field of deep learning, which are widely used in image recognition, object detection, semantic segmentation and other tasks. In convolutional networks, the reasonable setting of parameters plays a crucial role in the performance of the network. In this paper, we will interpret and analyze the parameters in the convolutional network, and give examples of the corresponding development techniques.
1. Convolutional kernel
A convolutional kernel is a small matrix in a convolutional network that is used to perform convolution operations with input data. The size, stride, and padding of the convolution kernel are the key parameters that affect the convolution effect.
Convolution kernel size: The common convolution kernel size is 1xxx5, etc., and choosing the appropriate convolution kernel size can balance the hierarchical relationship between feature extraction and feature abstraction.
Step size: The step size determines the step size at which the convolution kernel slides over the input data, and larger steps can speed up the calculation, but some detail may be lost.
Padding: Padding is the addition of extra values to the boundaries of the input data to control the size of the output data. Proper padding can increase the receptive field of the network, but it will increase the amount of computation and the number of parameters.
Examples of development technologies:
python reproduction**.
2. Pooling layer
The pooling layer is used to downsample the output of the convolutional layer, reducing the dimensionality of the data while retaining important features. Pooling operations can be divided into max pooling and average pooling.
Examples of development technologies:
python reproduction**.
3. Activation function
Activation functions are used to introduce nonlinear features that allow the network to better learn and simulate complex features. Common activation functions include relu, sigmoid, tanh, etc.
Examples of development technologies:
python reproduction**.
4. Batch normalization
Batch normalization is a technique used to speed up training and improve model stability. By normalizing the activation values for each batch, the network can be helped to learn the features better.
Examples of development technologies:
python reproduction**.
In practical applications, it is necessary to select appropriate parameter configurations according to the task requirements and data characteristics. At the same time, the optimal parameter combination can be found through experiments and parameter tuning to improve the performance and generalization ability of the network.