### What are the types of cnn

What are the types of CNN

CNN refers to ConvolutionalNeuralNetwork, an important algorithm in the field of artificial intelligence. It has been used in various fields, such as computer vision, speech recognition and natural language processing. So, what kinds of CNN are there? This article will provide you with a detailed introduction.

1. Conventional Convolutional Neural Network

Conventional Convolutional Neural Network is a network consisting of several convolutional layers, pooling layers and fully connected layers. The convolutional layers are mainly used to extract features from the image, the pooling layer is used to reduce the size of the feature map, and the fully connected layer is used to classify the features. Conventional convolutional neural networks can be used in various applications such as image classification, target detection and image segmentation.

2. Residual Network

ResidualNeuralNetwork was proposed by KaimingHe et al. from Microsoft Research. Its main idea is to introduce the “residual block”, by letting the output of the network and the input to establish a direct mapping relationship, to solve the problem of gradient disappearance in some deep network. Residual networks can greatly improve the accuracy of deep neural networks, and have been widely used in various applications.

3. Interpretability Methods for Convolutional Neural Networks

Interpretability of convolutional neural networks has been one of the hotspots of research. In many practical applications, people need to know how the network makes decisions in order to better understand and interpret the results. Currently there are mainly two kinds of interpretability methods: one is based on gradient, such as Grad-CAM; the other is based on the internal features of the network, such as ActivationAtlas.These methods have been widely used in computer vision, medical image processing and other fields.

4. Convolutional Neural Networks in Target Detection

The application of convolutional neural networks in target detection is one of its important research areas. Target detection is an important problem in computer vision, and its main task is to locate and recognize objects in images. Currently, there are two main types of commonly used target detection methods: region-based methods and frame-based methods. In recent years, the development of deep learning technology has led to the widespread application of frame-based methods in target detection, such as YOLO and FasterR-CNN, which are convolutional neural network-based target detection methods.

5. Convolutional Neural Networks in Natural Language Processing

Besides in the field of computer vision, convolutional neural networks are also widely used in natural language processing. Convolutional neural networks can extract local features of text by performing convolutional operations on the text, and can be reduced in dimension by pooling layers. Convolutional neural networks have been widely used in tasks such as sentiment classification, text categorization and machine translation.

6. Summary

In summary, convolutional neural network is an important algorithm in the field of artificial intelligence, which has been widely used in various fields. In addition to conventional convolutional neural networks, there are residual networks, interpretable methods for convolutional neural networks, etc. In the field of computer vision, convolutional neural networks are widely used in target detection; and in natural language processing, the application of convolutional neural networks is also getting more and more attention.

### Which layers does a convolutional neural network consist of

Vision – Convolutional Layer Basics

If we design 6 convolutional kernels, it can be understood that: we consider that there are 6 underlying texture patterns on this image, i.e., we are able to depict an image using just 6 of the underlying patterns.

The role of the convolutional layer is to extract the features of a localized region. ConvolutionalNeuralNetwork (CNN or ConvNet) is a deep feedforward neural network with properties such as local connectivity and weight sharing. Convolutional neural networks are motivated by the biological mechanism of sensory field.

Each convolutional layer in a convolutional neural network consists of a number of convolutional units, and the parameters of each convolutional unit are optimally obtained by a back-propagation algorithm.

The connections between convolutional layers in a convolutional neural network are called sparseconnection, meaning that neurons in a convolutional layer are connected to only some, but not all, of their neighboring layers, as opposed to full connections in a feedforward neural network.

Convolutional Neural Networks in General

Convolutional neural networks are a class of feedforward neural networks that contain convolutional computation and have deep structure, and are one of the representative algorithms for deep learning. Convolutional neural networks are also known as “translation invariant artificial neural networks” because of their ability to learn representations and classify input information according to their hierarchical structure.

Convolutional Neural Networks (CNNs) are a class of feedforward neural networks that contain convolutional computation and have a deep structure, and are one of the representative algorithms of deeplearning.

ConvolutionalNeuralNetworks (CNN) is a feedforward neural network. Convolutional neural networks are proposed by the mechanism of biological ReceptiveField. Receptive field mainly refers to some properties of neurons in auditory system, proprioceptive system and visual system.

Structure of Convolutional Neural Networks

1, in other words, the most common convolutional neural network structure is as follows: INPUT-[[CONV-RELU]*N-POOL?]*M-[FC-RELU]*K-FC where * refers to the number of repetitions, and POOL? refers to an optional convergence layer.

2. Current convolutional neural networks are generally feed-forward neural networks consisting of a cross-stack of convolutional, convergence and fully connected layers, which are trained using a back-propagation algorithm. Convolutional neural networks have three structural properties: local connectivity, weight sharing, and convergence. These properties give the convolutional neural network some degree of translation, scaling, and rotation invariance.

3. ConvolutionalNeuralNetworks (CNNs) are feed-forward neural networks. Convolutional neural networks are proposed by the mechanism of biological ReceptiveField. Receptive field mainly refers to some properties of neurons in auditory system, proprioceptive system and visual system.

34-Convolutional Neural Networks (Conv)

Structural features: the basic composition of neural networks (neuralnetworks) include input layer, hidden layer, output layer. And the convolutional neural network is characterized by the hidden layer is divided into convolutional layer and pooling layer (poolinglayer, also known as downsampling layer).

ConvolutionalNeuralNetwork (CNN or ConvNet) is a deep feed-forward neural network with properties such as local connectivity and weight sharing. Convolutional neural networks are motivated by the biological mechanism of sensory field.

ConvolutionalNeuralNetworks (CNN) is a feed-forward neural network. Convolutional neural networks are proposed by the mechanism of biological ReceptiveField. Receptive field mainly refers to some properties of neurons in auditory system, proprioceptive system and visual system.

-Convolution step setting (StridedCOnvolution) convolution step is also when we carry out the convolution operation, the filter each time to move the step length, above we introduced the convolution operation step default are 1, that is to say, each time to move the filter we are moving to the right of a grid, or down a grid.

The basic structure of a convolutional neural network consists of the following components: an input layer, a convolutional layer, a pooling layer, an activation function layer, and a fully connected layer.

We use an odd number of kernels of height and width in a convolutional neural network, such as a 3×3, 5×5 convolutional kernel, and keep the input and output sizes the same by choosing padding of size k on both sides of the height (or width) of the kernel of size 2k+1, so that the step size is 1.

What is not the layer structure of a convolutional neural network

The main structures of a convolutional neural network are: the convolutional layer, the pooling layer, and the fully connected layer grouping. Convolutional layer convolutional kernel is a series of filters used to extract a certain kind of feature we use it to process an image, when the image features are similar to the features represented by the filters, the convolution operation can get a larger value.

the basic structure of cnn does not include: inverse pooling layer. introduction to the basic components of cnn: local receptive fields. In an image localized pixels are more strongly connected to each other, while pixels farther away are relatively weakly connected.

The basic structure of a convolutional neural network consists of the following parts: an input layer, a convolutional layer, a pooling layer, an activation function layer, and a fully connected layer.

Neural networks include convolutional layers, what other layers are included

1, ConvolutionalNeuralNetwork (CNN) is a kind of feed-forward neural network, its artificial neurons can respond to a portion of the coverage of the surrounding units, and has excellent performance for large-scale image processing.

2. The basic structure of a convolutional neural network consists of the following parts: an input layer, a convolutional layer, a pooling layer, an activation function layer and a fully connected layer.

3, the current convolutional neural network is generally a feed-forward neural network consisting of a cross-stack of convolutional, pooling and fully connected layers, trained using the back-propagation algorithm. Convolutional neural networks have three structural properties: local connectivity, weight sharing, and convergence.

### Principle of Convolutional Neural Network

Convolutional Neural Network (CNN) is a kind of feed-forward neural network, which is inspired by the natural visual cognitive mechanism of living beings. Nowadays, CNN has become one of the research hotspots in many scientific fields, especially in the field of pattern classification, because the network avoids the complex pre-processing of the image, and can be directly input to the original image, so it has been more widely used. It can be applied to image classification, target recognition, target detection, semantic segmentation and so on. The basic structure of convolutional neural network for image classification.

1. Definition

Convolutional Neural Networks (CNNs) are a class of feedforward neural networks that contain convolutional computation and have deep structure. It is one of the representative algorithms of deeplearning. Convolutional neural networks have the ability of representationlearning and can perform shift-invariantclassification of input information according to their hierarchical structure, so they are also called “Shift-invariantArtificialNeuralNetworks” (Shift-invariantArtificialNeuralNetworks). Invariant Artificial Neural Networks (SIANN)”.

2. Characteristics

Compared with the previously introduced neural networks, traditional neural networks have only linear connections, while CNNs include **convolution** operations, **pooling operations, and nonlinear activation function mapping (i.e., linear connections)** and so on.

3. Applications and Typical Networks

Classical CNN Networks:

Alex-Net

VGG-Nets

Resnet

Common Applications:

Deep Learning has been used with great success in computer image recognition. Using deep learning, we are able to recognize images with high accuracy, and to achieve this, we rely heavily on a branch of neural networks called convolutional networks

### What is convolution, convolutional neural network?

Convolution is as follows:

ConvolutionalNeuralNetworks (CNN) is a class of feedforward neural networks that contain convolutional computation and have deep structure, and is one of the representative algorithms of deeplearning. It is one of the representative algorithms of deep learning (deeplearning).

Research on convolutional neural networks began in the 1980s and 1990s, and time-delay networks and LeNet-5 were the first convolutional neural networks to appear; after the twenty-first century, with the introduction of the theory of deep learning and the improvement of numerical computation equipment, convolutional neural networks have been developed rapidly and have been applied to computer vision, natural language processing and other fields.

Properties

The connection between the convolutional layers in a convolutional neural network is called sparseconnection, i.e., compared with the full connection in a feed-forward neural network, the neurons in the convolutional layers are connected to only some, but not all, of their neighboring layers. Specifically, any pixel (neuron) in the feature map of layer l of the convolutional neural network is only a linear combination of pixels within the receptive field defined by the convolutional kernel in layer l-1.

The sparse connections of convolutional neural networks have the effect of regularization, which improves the stability and generalization ability of the network structure and avoids overfitting, and at the same time, the sparse connections reduce the total number of weight parameters, which is conducive to the fast learning of neural networks, and the reduction of the memory overhead during the computation.